mirror of
https://github.com/storytold/storyteller-ml.git
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Add StyleTTS2 inference
This commit is contained in:
Justin John
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log_dir: "Models/LJSpeech"
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first_stage_path: "first_stage.pth"
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save_freq: 2
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log_interval: 10
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device: "cuda"
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epochs_1st: 200 # number of epochs for first stage training (pre-training)
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epochs_2nd: 100 # number of peochs for second stage training (joint training)
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batch_size: 16
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max_len: 400 # maximum number of frames
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pretrained_model: ""
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second_stage_load_pretrained: true # set to true if the pre-trained model is for 2nd stage
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load_only_params: false # set to true if do not want to load epoch numbers and optimizer parameters
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F0_path: "Utils/JDC/bst.t7"
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ASR_config: "Utils/ASR/config.yml"
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ASR_path: "Utils/ASR/epoch_00080.pth"
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PLBERT_dir: 'Utils/PLBERT/'
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data_params:
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train_data: "Data/train_list.txt"
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val_data: "Data/val_list.txt"
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root_path: "/local/LJSpeech-1.1/wavs"
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OOD_data: "Data/OOD_texts.txt"
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min_length: 50 # sample until texts with this size are obtained for OOD texts
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preprocess_params:
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sr: 24000
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spect_params:
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n_fft: 2048
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win_length: 1200
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hop_length: 300
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model_params:
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multispeaker: false
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dim_in: 64
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hidden_dim: 512
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max_conv_dim: 512
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n_layer: 3
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n_mels: 80
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n_token: 178 # number of phoneme tokens
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max_dur: 50 # maximum duration of a single phoneme
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style_dim: 128 # style vector size
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dropout: 0.2
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# config for decoder
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decoder:
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type: 'istftnet' # either hifigan or istftnet
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resblock_kernel_sizes: [3,7,11]
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upsample_rates : [10, 6]
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upsample_initial_channel: 512
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resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
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upsample_kernel_sizes: [20, 12]
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gen_istft_n_fft: 20
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gen_istft_hop_size: 5
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# speech language model config
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slm:
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model: 'microsoft/wavlm-base-plus'
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sr: 16000 # sampling rate of SLM
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hidden: 768 # hidden size of SLM
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nlayers: 13 # number of layers of SLM
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initial_channel: 64 # initial channels of SLM discriminator head
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# style diffusion model config
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diffusion:
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embedding_mask_proba: 0.1
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# transformer config
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transformer:
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num_layers: 3
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num_heads: 8
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head_features: 64
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multiplier: 2
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# diffusion distribution config
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dist:
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sigma_data: 0.2 # placeholder for estimate_sigma_data set to false
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estimate_sigma_data: true # estimate sigma_data from the current batch if set to true
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mean: -3.0
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std: 1.0
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loss_params:
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lambda_mel: 5. # mel reconstruction loss
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lambda_gen: 1. # generator loss
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lambda_slm: 1. # slm feature matching loss
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lambda_mono: 1. # monotonic alignment loss (1st stage, TMA)
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lambda_s2s: 1. # sequence-to-sequence loss (1st stage, TMA)
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TMA_epoch: 50 # TMA starting epoch (1st stage)
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lambda_F0: 1. # F0 reconstruction loss (2nd stage)
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lambda_norm: 1. # norm reconstruction loss (2nd stage)
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lambda_dur: 1. # duration loss (2nd stage)
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lambda_ce: 20. # duration predictor probability output CE loss (2nd stage)
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lambda_sty: 1. # style reconstruction loss (2nd stage)
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lambda_diff: 1. # score matching loss (2nd stage)
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diff_epoch: 20 # style diffusion starting epoch (2nd stage)
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joint_epoch: 50 # joint training starting epoch (2nd stage)
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optimizer_params:
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lr: 0.0001 # general learning rate
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bert_lr: 0.00001 # learning rate for PLBERT
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ft_lr: 0.00001 # learning rate for acoustic modules
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slmadv_params:
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min_len: 400 # minimum length of samples
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max_len: 500 # maximum length of samples
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batch_percentage: 0.5 # to prevent out of memory, only use half of the original batch size
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iter: 10 # update the discriminator every this iterations of generator update
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thresh: 5 # gradient norm above which the gradient is scaled
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scale: 0.01 # gradient scaling factor for predictors from SLM discriminators
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sig: 1.5 # sigma for differentiable duration modeling
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@@ -0,0 +1,111 @@
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log_dir: "Models/LJSpeech"
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save_freq: 5
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log_interval: 10
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device: "cuda"
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epochs: 50 # number of finetuning epoch (1 hour of data)
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batch_size: 8
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max_len: 400 # maximum number of frames
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pretrained_model: "Models/LibriTTS/epochs_2nd_00020.pth"
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second_stage_load_pretrained: true # set to true if the pre-trained model is for 2nd stage
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load_only_params: true # set to true if do not want to load epoch numbers and optimizer parameters
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F0_path: "Utils/JDC/bst.t7"
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ASR_config: "Utils/ASR/config.yml"
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ASR_path: "Utils/ASR/epoch_00080.pth"
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PLBERT_dir: 'Utils/PLBERT/'
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data_params:
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train_data: "Data/train_list.txt"
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val_data: "Data/val_list.txt"
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root_path: "/local/LJSpeech-1.1/wavs"
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OOD_data: "Data/OOD_texts.txt"
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min_length: 50 # sample until texts with this size are obtained for OOD texts
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preprocess_params:
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sr: 24000
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spect_params:
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n_fft: 2048
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win_length: 1200
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hop_length: 300
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model_params:
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multispeaker: true
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dim_in: 64
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hidden_dim: 512
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max_conv_dim: 512
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n_layer: 3
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n_mels: 80
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n_token: 178 # number of phoneme tokens
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max_dur: 50 # maximum duration of a single phoneme
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style_dim: 128 # style vector size
|
||||
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dropout: 0.2
|
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# config for decoder
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decoder:
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type: 'hifigan' # either hifigan or istftnet
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resblock_kernel_sizes: [3,7,11]
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upsample_rates : [10,5,3,2]
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upsample_initial_channel: 512
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resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
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upsample_kernel_sizes: [20,10,6,4]
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# speech language model config
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slm:
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model: 'microsoft/wavlm-base-plus'
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sr: 16000 # sampling rate of SLM
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hidden: 768 # hidden size of SLM
|
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nlayers: 13 # number of layers of SLM
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initial_channel: 64 # initial channels of SLM discriminator head
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# style diffusion model config
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diffusion:
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embedding_mask_proba: 0.1
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# transformer config
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transformer:
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num_layers: 3
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num_heads: 8
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head_features: 64
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multiplier: 2
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# diffusion distribution config
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dist:
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sigma_data: 0.2 # placeholder for estimate_sigma_data set to false
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estimate_sigma_data: true # estimate sigma_data from the current batch if set to true
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mean: -3.0
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std: 1.0
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loss_params:
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lambda_mel: 5. # mel reconstruction loss
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lambda_gen: 1. # generator loss
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lambda_slm: 1. # slm feature matching loss
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lambda_mono: 1. # monotonic alignment loss (TMA)
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lambda_s2s: 1. # sequence-to-sequence loss (TMA)
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lambda_F0: 1. # F0 reconstruction loss
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lambda_norm: 1. # norm reconstruction loss
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lambda_dur: 1. # duration loss
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lambda_ce: 20. # duration predictor probability output CE loss
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lambda_sty: 1. # style reconstruction loss
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lambda_diff: 1. # score matching loss
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diff_epoch: 10 # style diffusion starting epoch
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joint_epoch: 30 # joint training starting epoch
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optimizer_params:
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lr: 0.0001 # general learning rate
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bert_lr: 0.00001 # learning rate for PLBERT
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ft_lr: 0.0001 # learning rate for acoustic modules
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slmadv_params:
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min_len: 400 # minimum length of samples
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max_len: 500 # maximum length of samples
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batch_percentage: 0.5 # to prevent out of memory, only use half of the original batch size
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iter: 10 # update the discriminator every this iterations of generator update
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thresh: 5 # gradient norm above which the gradient is scaled
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scale: 0.01 # gradient scaling factor for predictors from SLM discriminators
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sig: 1.5 # sigma for differentiable duration modeling
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@@ -0,0 +1,113 @@
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log_dir: "Models/LibriTTS"
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first_stage_path: "first_stage.pth"
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save_freq: 1
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log_interval: 10
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device: "cuda"
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epochs_1st: 50 # number of epochs for first stage training (pre-training)
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epochs_2nd: 30 # number of peochs for second stage training (joint training)
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batch_size: 16
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max_len: 300 # maximum number of frames
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pretrained_model: ""
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second_stage_load_pretrained: true # set to true if the pre-trained model is for 2nd stage
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load_only_params: false # set to true if do not want to load epoch numbers and optimizer parameters
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F0_path: "Utils/JDC/bst.t7"
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ASR_config: "Utils/ASR/config.yml"
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ASR_path: "Utils/ASR/epoch_00080.pth"
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PLBERT_dir: 'Utils/PLBERT/'
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data_params:
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train_data: "Data/train_list.txt"
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val_data: "Data/val_list.txt"
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root_path: ""
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OOD_data: "Data/OOD_texts.txt"
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min_length: 50 # sample until texts with this size are obtained for OOD texts
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||||
|
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preprocess_params:
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sr: 24000
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spect_params:
|
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n_fft: 2048
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win_length: 1200
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hop_length: 300
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model_params:
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multispeaker: true
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dim_in: 64
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hidden_dim: 512
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max_conv_dim: 512
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n_layer: 3
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n_mels: 80
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||||
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n_token: 178 # number of phoneme tokens
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||||
max_dur: 50 # maximum duration of a single phoneme
|
||||
style_dim: 128 # style vector size
|
||||
|
||||
dropout: 0.2
|
||||
|
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# config for decoder
|
||||
decoder:
|
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type: 'hifigan' # either hifigan or istftnet
|
||||
resblock_kernel_sizes: [3,7,11]
|
||||
upsample_rates : [10,5,3,2]
|
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upsample_initial_channel: 512
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resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
|
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upsample_kernel_sizes: [20,10,6,4]
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|
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# speech language model config
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slm:
|
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model: 'microsoft/wavlm-base-plus'
|
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sr: 16000 # sampling rate of SLM
|
||||
hidden: 768 # hidden size of SLM
|
||||
nlayers: 13 # number of layers of SLM
|
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initial_channel: 64 # initial channels of SLM discriminator head
|
||||
|
||||
# style diffusion model config
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diffusion:
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embedding_mask_proba: 0.1
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# transformer config
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transformer:
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num_layers: 3
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num_heads: 8
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head_features: 64
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multiplier: 2
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# diffusion distribution config
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dist:
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sigma_data: 0.2 # placeholder for estimate_sigma_data set to false
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estimate_sigma_data: true # estimate sigma_data from the current batch if set to true
|
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mean: -3.0
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std: 1.0
|
||||
|
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loss_params:
|
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lambda_mel: 5. # mel reconstruction loss
|
||||
lambda_gen: 1. # generator loss
|
||||
lambda_slm: 1. # slm feature matching loss
|
||||
|
||||
lambda_mono: 1. # monotonic alignment loss (1st stage, TMA)
|
||||
lambda_s2s: 1. # sequence-to-sequence loss (1st stage, TMA)
|
||||
TMA_epoch: 5 # TMA starting epoch (1st stage)
|
||||
|
||||
lambda_F0: 1. # F0 reconstruction loss (2nd stage)
|
||||
lambda_norm: 1. # norm reconstruction loss (2nd stage)
|
||||
lambda_dur: 1. # duration loss (2nd stage)
|
||||
lambda_ce: 20. # duration predictor probability output CE loss (2nd stage)
|
||||
lambda_sty: 1. # style reconstruction loss (2nd stage)
|
||||
lambda_diff: 1. # score matching loss (2nd stage)
|
||||
|
||||
diff_epoch: 10 # style diffusion starting epoch (2nd stage)
|
||||
joint_epoch: 15 # joint training starting epoch (2nd stage)
|
||||
|
||||
optimizer_params:
|
||||
lr: 0.0001 # general learning rate
|
||||
bert_lr: 0.00001 # learning rate for PLBERT
|
||||
ft_lr: 0.00001 # learning rate for acoustic modules
|
||||
|
||||
slmadv_params:
|
||||
min_len: 400 # minimum length of samples
|
||||
max_len: 500 # maximum length of samples
|
||||
batch_percentage: 0.5 # to prevent out of memory, only use half of the original batch size
|
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iter: 20 # update the discriminator every this iterations of generator update
|
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thresh: 5 # gradient norm above which the gradient is scaled
|
||||
scale: 0.01 # gradient scaling factor for predictors from SLM discriminators
|
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sig: 1.5 # sigma for differentiable duration modeling
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,100 @@
|
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LJ022-0023.wav|ðɪ ˌoʊvɚwˈɛlmɪŋ mədʒˈɔːɹᵻɾi ʌv pˈiːpəl ɪn ðɪs kˈʌntɹi nˈoʊ hˌaʊ tə sˈɪft ðə wˈiːt fɹʌmðə tʃˈæf ɪn wʌt ðeɪ hˈɪɹ ænd wʌt ðeɪ ɹˈiːd .|0
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LJ043-0030.wav|ɪf sˈʌmbɑːdi dˈɪd ðˈæt tə mˌiː , ɐ lˈaʊsi tɹˈɪk lˈaɪk ðˈæt , tə tˈeɪk maɪ wˈaɪf ɐwˈeɪ , ænd ˈɔːl ðə fˈɜːnɪtʃɚ , aɪ wʊd biː mˈæd æz hˈɛl , tˈuː .|0
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LJ005-0201.wav|ˌæzˌɪz ʃˈoʊn baɪ ðə ɹᵻpˈoːɹt ʌvðə kəmˈɪʃənɚz tʊ ɪŋkwˈaɪɚɹ ˌɪntʊ ðə stˈeɪt ʌvðə mjuːnˈɪsɪpəl kˌɔːɹpɚɹˈeɪʃənz ɪn ˈeɪtiːn θˈɜːɾi fˈaɪv .|0
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LJ001-0110.wav|ˈiːvən ðə kˈæslɑːn tˈaɪp wɛn ɛnlˈɑːɹdʒd ʃˈoʊz ɡɹˈeɪt ʃˈɔːɹtkʌmɪŋz ɪn ðɪs ɹᵻspˈɛkt :|0
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LJ003-0345.wav|ˈɔːl ðə kəmˈɪɾi kʊd dˈuː ɪn ðɪs ɹᵻspˈɛkt wʌz tə θɹˈoʊ ðə ɹᵻspˌɑːnsəbˈɪlɪɾi ˌɔn ˈʌðɚz .|0
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LJ007-0154.wav|ðiːz pˈʌndʒənt ænd wˈɛl ɡɹˈaʊndᵻd stɹˈɪktʃɚz ɐplˈaɪd wɪð stˈɪl ɡɹˈeɪɾɚ fˈoːɹs tə ðɪ ʌŋkənvˈɪktᵻd pɹˈɪzənɚ , ðə mˈæn hˌuː kˈeɪm tə ðə pɹˈɪzən ˈɪnəsənt , ænd stˈɪl ʌŋkəntˈæmᵻnˌeɪɾᵻd ,|0
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LJ018-0098.wav|ænd ɹˈɛkəɡnˌaɪzd æz wˈʌn ʌvðə fɹˈiːkwɛntɚz ʌvðə bˈoʊɡəs lˈɔː stˈeɪʃənɚz . hɪz ɚɹˈɛst lˈɛd tə ðæt ʌv ˈʌðɚz .|0
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LJ047-0044.wav|ˈɑːswəld wʌz , haʊˈɛvɚ , wˈɪlɪŋ tə dɪskˈʌs hɪz kˈɑːntækts wɪð sˈoʊviət ɐθˈɔːɹɪɾiz . hiː dᵻnˈaɪd hˌævɪŋ ˌɛni ɪnvˈɑːlvmənt wɪð sˈoʊviət ɪntˈɛlɪdʒəns ˈeɪdʒənsiz|0
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LJ031-0038.wav|ðə fˈɜːst fɪzˈɪʃən tə sˈiː ðə pɹˈɛzɪdənt æt pˈɑːɹklənd hˈɑːspɪɾəl wʌz dˈɑːktɚ . tʃˈɑːɹlz dʒˈeɪ . kˈæɹɪkˌoʊ , ɐ ɹˈɛzᵻdənt ɪn dʒˈɛnɚɹəl sˈɜːdʒɚɹi .|0
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LJ048-0194.wav|dˈʊɹɹɪŋ ðə mˈɔːɹnɪŋ ʌv noʊvˈɛmbɚ twˈɛnti tˈuː pɹˈaɪɚ tə ðə mˈoʊɾɚkˌeɪd .|0
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LJ049-0026.wav|ˌɔn əkˈeɪʒən ðə sˈiːkɹᵻt sˈɜːvɪs hɐzbɪn pɚmˈɪɾᵻd tə hæv ɐn ˈeɪdʒənt ɹˈaɪdɪŋ ɪnðə pˈæsɪndʒɚ kəmpˈɑːɹtmənt wɪððə pɹˈɛzɪdənt .|0
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LJ004-0152.wav|ɔːlðˈoʊ æt mˈɪstɚ . bˈʌkstənz vˈɪzɪt ɐ nˈuː dʒˈeɪl wʌz ɪn pɹˈɑːsɛs ʌv ɪɹˈɛkʃən , ðə fˈɜːst stˈɛp təwˈɔːɹdz ɹᵻfˈɔːɹm sˈɪns hˈaʊɚdz vˌɪzɪtˈeɪʃən ɪn sˈɛvəntˌiːn sˈɛvənti fˈoːɹ .|0
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LJ008-0278.wav|ɔːɹ ðˈɛɹz mˌaɪt biː wˈʌn ʌv mˈɛni , ænd ɪt mˌaɪt biː kənsˈɪdɚd nˈɛsᵻsɚɹi tə dˈɑːlɚ mˌeɪk ɐn ɛɡzˈæmpəl.dˈɑːlɚ|0
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LJ043-0002.wav|ðə wˈɔːɹəŋ kəmˈɪʃən ɹᵻpˈoːɹt . baɪ ðə pɹˈɛzɪdənts kəmˈɪʃən ɔnðɪ ɐsˌæsᵻnˈeɪʃən ʌv pɹˈɛzɪdənt kˈɛnədi . tʃˈæptɚ sˈɛvən . lˈiː hˈɑːɹvi ˈɑːswəld :|0
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LJ009-0114.wav|mˈɪstɚ . wˈeɪkfiːld wˈaɪndz ˈʌp hɪz ɡɹˈæfɪk bˌʌt sˈʌmwʌt sɛnsˈeɪʃənəl ɐkˈaʊnt baɪ dᵻskɹˈaɪbɪŋ ɐnˈʌðɚ ɹᵻlˈɪdʒəs sˈɜːvɪs , wˌɪtʃ mˈeɪ ɐpɹˈoʊpɹɪˌeɪtli biː ɪnsˈɜːɾᵻd hˈɪɹ .|0
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LJ028-0506.wav|ɐ mˈɑːdɚn ˈɑːɹɾɪst wʊdhɐv dˈɪfɪkˌʌlti ɪn dˌuːɪŋ sˈʌtʃ ˈækjʊɹət wˈɜːk .|0
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LJ050-0168.wav|wɪððə pɚtˈɪkjʊlɚ pˈɜːpəsᵻz ʌvðɪ ˈeɪdʒənsi ɪnvˈɑːlvd . ðə kəmˈɪʃən ɹˈɛkəɡnˌaɪzᵻz ðæt ðɪs ɪz ɐ kˌɑːntɹəvˈɜːʃəl ˈɛɹiə|0
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LJ039-0223.wav|ˈɑːswəldz mɚɹˈiːn tɹˈeɪnɪŋ ɪn mˈɑːɹksmənʃˌɪp , hɪz ˈʌðɚ ɹˈaɪfəl ɛkspˈiəɹɪəns ænd hɪz ɪstˈæblɪʃt fəmˌɪliˈæɹɪɾi wɪð ðɪs pɚtˈɪkjʊlɚ wˈɛpən|0
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LJ029-0032.wav|ɐkˈoːɹdɪŋ tʊ oʊdˈɑːnəl , kwˈoʊt , wiː hæd ɐ mˈoʊɾɚkˌeɪd wɛɹˈɛvɚ kplˈʌsplʌs wˌɪtʃ hɐdbɪn bˌɪn hˈeɪstili sˈʌmənd fɚðə ðə pˈɜːpəs wiː wˈɛnt , ˈɛnd kwˈoʊt .|0
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LJ031-0070.wav|dˈɑːktɚ . klˈɑːɹk , hˌuː mˈoʊst klˈoʊsli əbzˈɜːvd ðə hˈɛd wˈuːnd ,|0
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LJ034-0198.wav|jˈuːɪnz , hˌuː wʌz ɔnðə saʊθwˈɛst kˈɔːɹnɚɹ ʌv ˈɛlm ænd hjˈuːstən stɹˈiːts tˈɛstᵻfˌaɪd ðæt hiː kʊd nˌɑːt dᵻskɹˈaɪb ðə mˈæn hiː sˈɔː ɪnðə wˈɪndoʊ .|0
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LJ026-0068.wav|ˈɛnɚdʒi ˈɛntɚz ðə plˈænt , tʊ ɐ smˈɔːl ɛkstˈɛnt ,|0
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LJ039-0075.wav|wˈʌns juː nˈoʊ ðæt juː mˈʌst pˌʊt ðə kɹˈɔshɛɹz ɔnðə tˈɑːɹɡɪt ænd ðæt ɪz ˈɔːl ðæt ɪz nˈɛsᵻsɚɹi .|0
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LJ004-0096.wav|ðə fˈeɪɾəl kˈɑːnsɪkwənsᵻz wˈɛɹɑːf mˌaɪt biː pɹɪvˈɛntᵻd ɪf ðə dʒˈʌstɪsᵻz ʌvðə pˈiːs wɜː djˈuːli ˈɔːθɚɹˌaɪzd|0
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LJ005-0014.wav|spˈiːkɪŋ ˌɔn ɐ dᵻbˈeɪt ˌɔn pɹˈɪzən mˈæɾɚz , hiː dᵻklˈɛɹd ðˈæt|0
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LJ012-0161.wav|hiː wʌz ɹᵻpˈoːɹɾᵻd tə hæv fˈɔːlən ɐwˈeɪ tʊ ɐ ʃˈædoʊ .|0
|
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LJ018-0239.wav|hɪz dˌɪsɐpˈɪɹəns ɡˈeɪv kˈʌlɚ ænd sˈʌbstəns tʊ ˈiːvəl ɹᵻpˈoːɹts ɔːlɹˌɛdi ɪn sˌɜːkjʊlˈeɪʃən ðætðə wɪl ænd kənvˈeɪəns əbˌʌv ɹᵻfˈɜːd tuː|0
|
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LJ019-0257.wav|hˈɪɹ ðə tɹˈɛd wˈiːl wʌz ɪn jˈuːs , ðɛɹ sˈɛljʊlɚ kɹˈæŋks , ɔːɹ hˈɑːɹd lˈeɪbɚ məʃˈiːnz .|0
|
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LJ028-0008.wav|juː tˈæp dʒˈɛntli wɪð jʊɹ hˈiːl əpˌɑːn ðə ʃˈoʊldɚɹ ʌvðə dɹˈoʊmdɚɹi tʊ ˈɜːdʒ hɜːɹ ˈɔn .|0
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LJ024-0083.wav|ðɪs plˈæn ʌv mˈaɪn ɪz nˈoʊ ɐtˈæk ɔnðə kˈoːɹt ;|0
|
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LJ042-0129.wav|nˈoʊ nˈaɪt klˈʌbz ɔːɹ bˈoʊlɪŋ ˈælɪz , nˈoʊ plˈeɪsᵻz ʌv ɹˌɛkɹiːˈeɪʃən ɛksˈɛpt ðə tɹˈeɪd jˈuːniən dˈænsᵻz . aɪ hæv hæd ɪnˈʌf .|0
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LJ036-0103.wav|ðə pəlˈiːs ˈæskt hˌɪm wˈɛðɚ hiː kʊd pˈɪk ˈaʊt hɪz pˈæsɪndʒɚ fɹʌmðə lˈaɪnʌp .|0
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LJ046-0058.wav|dˈʊɹɹɪŋ hɪz pɹˈɛzɪdənsi , fɹˈæŋklɪn dˈiː . ɹˈoʊzəvˌɛlt mˌeɪd ˈɔːlmoʊst fˈoːɹ hˈʌndɹɪd dʒˈɜːniz ænd tɹˈævəld mˈoːɹ ðɐn θɹˈiː hˈʌndɹɪd fˈɪfti θˈaʊzənd mˈaɪlz .|0
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LJ014-0076.wav|hiː wʌz sˈiːn ˈæftɚwɚdz smˈoʊkɪŋ ænd tˈɔːkɪŋ wɪð hɪz hˈoʊsts ɪn ðɛɹ bˈæk pˈɑːɹlɚ , ænd nˈɛvɚ sˈiːn ɐɡˈɛn ɐlˈaɪv .|0
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LJ002-0043.wav|lˈɔŋ nˈæɹoʊ ɹˈuːmz wˈʌn θˈɜːɾi sˈɪks fˈiːt , sˈɪks twˈɛnti θɹˈiː fˈiːt , ænd ðɪ ˈeɪtθ ˈeɪtiːn ,|0
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LJ009-0076.wav|wiː kˈʌm tə ðə sˈɜːmən .|0
|
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LJ017-0131.wav|ˈiːvən wɛn ðə hˈaɪ ʃˈɛɹɪf hæd tˈoʊld hˌɪm ðɛɹwˌʌz nˈoʊ pˌɑːsəbˈɪlɪɾi əvɚ ɹᵻpɹˈiːv , ænd wɪðˌɪn ɐ fjˈuː ˈaʊɚz ʌv ˌɛksɪkjˈuːʃən .|0
|
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LJ046-0184.wav|bˌʌt ðɛɹ ɪz ɐ sˈɪstəm fɚðɪ ɪmˈiːdɪət nˌoʊɾɪfɪkˈeɪʃən ʌvðə sˈiːkɹᵻt sˈɜːvɪs baɪ ðə kənfˈaɪnɪŋ ˌɪnstɪtˈuːʃən wɛn ɐ sˈʌbdʒɛkt ɪz ɹᵻlˈiːst ɔːɹ ɛskˈeɪps .|0
|
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LJ014-0263.wav|wˌɛn ˈʌðɚ plˈɛʒɚz pˈɔːld hiː tˈʊk ɐ θˈiəɾɚ , ænd pˈoʊzd æz ɐ mjuːnˈɪfɪsənt pˈeɪtɹən ʌvðə dɹəmˈæɾɪk ˈɑːɹt .|0
|
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LJ042-0096.wav|ˈoʊld ɛkstʃˈeɪndʒ ɹˈeɪt ɪn ɐdˈɪʃən tə hɪz fˈæktɚɹi sˈælɚɹi ʌv ɐpɹˈɑːksɪmətli ˈiːkwəl ɐmˈaʊnt|0
|
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LJ049-0050.wav|hˈɪl hæd bˈoʊθ fˈiːt ɔnðə kˈɑːɹ ænd wʌz klˈaɪmɪŋ ɐbˈoːɹd tʊ ɐsˈɪst pɹˈɛzɪdənt ænd mˈɪsɪz . kˈɛnədi .|0
|
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LJ019-0186.wav|sˈiːɪŋ ðæt sˈɪns ðɪ ɪstˈæblɪʃmənt ʌvðə sˈɛntɹəl kɹˈɪmɪnəl kˈoːɹt , nˈuːɡeɪt ɹᵻsˈiːvd pɹˈɪzənɚz fɔːɹ tɹˈaɪəl fɹʌm sˈɛvɹəl kˈaʊntiz ,|0
|
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LJ028-0307.wav|ðˈɛn lˈɛt twˈɛnti dˈeɪz pˈæs , ænd æt ðɪ ˈɛnd ʌv ðæt tˈaɪm stˈeɪʃən nˌɪɹ ðə tʃˈældæsəŋ ɡˈeɪts ɐ bˈɑːdi ʌv fˈoːɹ θˈaʊzənd .|0
|
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LJ012-0235.wav|wˌaɪl ðeɪ wɜːɹ ɪn ɐ stˈeɪt ʌv ɪnsˌɛnsəbˈɪlɪɾi ðə mˈɜːdɚ wʌz kəmˈɪɾᵻd .|0
|
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LJ034-0053.wav|ɹˈiːtʃt ðə sˈeɪm kəŋklˈuːʒən æz lætˈoʊnə ðætðə pɹˈɪnts fˈaʊnd ɔnðə kˈɑːɹtənz wɜː ðoʊz ʌv lˈiː hˈɑːɹvi ˈɑːswəld .|0
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LJ014-0030.wav|ðiːz wɜː dˈæmnətˌoːɹi fˈækts wˌɪtʃ wˈɛl səpˈoːɹɾᵻd ðə pɹˌɑːsɪkjˈuːʃən .|0
|
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LJ015-0203.wav|bˌʌt wɜː ðə pɹɪkˈɔːʃənz tˈuː mˈɪnɪt , ðə vˈɪdʒɪləns tˈuː klˈoʊs təbi ᵻlˈuːdᵻd ɔːɹ ˌoʊvɚkˈʌm ?|0
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LJ028-0093.wav|bˌʌt hɪz skɹˈaɪb ɹˈoʊt ɪɾ ɪnðə mˈænɚ kˈʌstəmˌɛɹi fɚðə skɹˈaɪbz ʌv ðoʊz dˈeɪz tə ɹˈaɪt ʌv ðɛɹ ɹˈɔɪəl mˈæstɚz .|0
|
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LJ002-0018.wav|ðɪ ɪnˈædɪkwəsi ʌvðə dʒˈeɪl wʌz nˈoʊɾɪst ænd ɹᵻpˈoːɹɾᵻd əpˌɑːn ɐɡˈɛn ænd ɐɡˈɛn baɪ ðə ɡɹˈænd dʒˈʊɹɹiz ʌvðə sˈɪɾi ʌv lˈʌndən ,|0
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LJ028-0275.wav|æt lˈæst , ɪnðə twˈɛntiəθ mˈʌnθ ,|0
|
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LJ012-0042.wav|wˌɪtʃ hiː kˈɛpt kənsˈiːld ɪn ɐ hˈaɪdɪŋ plˈeɪs wɪð ɐ tɹˈæp dˈoːɹ dʒˈʌst ˌʌndɚ hɪz bˈɛd .|0
|
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LJ011-0096.wav|hiː mˈæɹid ɐ lˈeɪdi ˈɔːlsoʊ bᵻlˈɔŋɪŋ tə ðə səsˈaɪəɾi ʌv fɹˈɛndz , hˌuː bɹˈɔːt hˌɪm ɐ lˈɑːɹdʒ fˈɔːɹtʃʊn , wˈɪtʃ , ænd hɪz ˈoʊn mˈʌni , hiː pˌʊt ˌɪntʊ ɐ sˈɪɾi fˈɜːm ,|0
|
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LJ036-0077.wav|ɹˈɑːdʒɚ dˈiː . kɹˈeɪɡ , ɐ dˈɛpjuːɾi ʃˈɛɹɪf ʌv dˈæləs kˈaʊnti ,|0
|
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LJ016-0318.wav|ˈʌðɚɹ əfˈɪʃəlz , ɡɹˈeɪt lˈɔɪɚz , ɡˈʌvɚnɚz ʌv pɹˈɪzənz , ænd tʃˈæplɪnz səpˈoːɹɾᵻd ðɪs vjˈuː .|0
|
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LJ013-0164.wav|hˌuː kˈeɪm fɹʌm hɪz ɹˈuːm ɹˈɛdi dɹˈɛst , ɐ səspˈɪʃəs sˈɜːkəmstˌæns , æz hiː wʌz ˈɔːlweɪz lˈeɪt ɪnðə mˈɔːɹnɪŋ .|0
|
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LJ027-0141.wav|ɪz klˈoʊsli ɹᵻpɹədˈuːst ɪnðə lˈaɪf hˈɪstɚɹi ʌv ɛɡzˈɪstɪŋ dˈɪɹ . ɔːɹ , ɪn ˈʌðɚ wˈɜːdz ,|0
|
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LJ028-0335.wav|ɐkˈoːɹdɪŋli ðeɪ kəmˈɪɾᵻd tə hˌɪm ðə kəmˈænd ʌv ðɛɹ hˈoʊl ˈɑːɹmi , ænd pˌʊt ðə kˈiːz ʌv ðɛɹ sˈɪɾi ˌɪntʊ hɪz hˈændz .|0
|
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LJ031-0202.wav|mˈɪsɪz . kˈɛnədi tʃˈoʊz ðə hˈɑːspɪɾəl ɪn bəθˈɛzdə fɚðɪ ˈɔːtɑːpsi bɪkˈʌz ðə pɹˈɛzɪdənt hæd sˈɜːvd ɪnðə nˈeɪvi .|0
|
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LJ021-0145.wav|fɹʌm ðoʊz wˈɪlɪŋ tə dʒˈɔɪn ɪn ɪstˈæblɪʃɪŋ ðɪs hˈoʊpt fɔːɹ pˈiəɹɪəd ʌv pˈiːs ,|0
|
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LJ016-0288.wav|dˈɑːlɚ mˈuːlɚ , mˈuːlɚ , hiːz ðə mˈæn , dˈɑːlɚ tˈɪl ɐ daɪvˈɜːʒən wʌz kɹiːˈeɪɾᵻd baɪ ðɪ ɐpˈɪɹəns ʌvðə ɡˈæloʊz , wˌɪtʃ wʌz ɹᵻsˈiːvd wɪð kəntˈɪnjuːəs jˈɛlz .|0
|
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LJ028-0081.wav|jˈɪɹz lˈeɪɾɚ , wˌɛn ðɪ ˌɑːɹkiːˈɑːlədʒˌɪsts kʊd ɹˈɛdili dɪstˈɪŋɡwɪʃ ðə fˈɔls fɹʌmðə tɹˈuː ,|0
|
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LJ018-0081.wav|hɪz dᵻfˈɛns bˌiːɪŋ ðæt hiː hæd ɪntˈɛndᵻd tə kəmˈɪt sˈuːɪsˌaɪd , bˌʌt ðˈæt , ɔnðɪ ɐpˈɪɹəns ʌv ðɪs ˈɑːfɪsɚ hˌuː hæd ɹˈɔŋd hˌɪm ,|0
|
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LJ021-0066.wav|təɡˌɛðɚ wɪð ɐ ɡɹˈeɪt ˈɪŋkɹiːs ɪnðə pˈeɪɹoʊlz , ðɛɹ hɐz kˈʌm ɐ səbstˈænʃəl ɹˈaɪz ɪnðə tˈoʊɾəl ʌv ɪndˈʌstɹɪəl pɹˈɑːfɪts|0
|
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LJ009-0238.wav|ˈæftɚ ðɪs ðə ʃˈɛɹɪfs sˈɛnt fɔːɹ ɐnˈʌðɚ ɹˈoʊp , bˌʌt ðə spɛktˈeɪɾɚz ˌɪntəfˈɪɹd , ænd ðə mˈæn wʌz kˈæɹid bˈæk tə dʒˈeɪl .|0
|
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LJ005-0079.wav|ænd ɪmpɹˈuːv ðə mˈɔːɹəlz ʌvðə pɹˈɪzənɚz , ænd ʃˌæl ɪnʃˈʊɹ ðə pɹˈɑːpɚ mˈɛʒɚɹ ʌv pˈʌnɪʃmənt tə kənvˈɪktᵻd əfˈɛndɚz .|0
|
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LJ035-0019.wav|dɹˈoʊv tə ðə nɔːɹθwˈɛst kˈɔːɹnɚɹ ʌv ˈɛlm ænd hjˈuːstən , ænd pˈɑːɹkt ɐpɹˈɑːksɪmətli tˈɛn fˈiːt fɹʌmðə tɹˈæfɪk sˈɪɡnəl .|0
|
||||
LJ036-0174.wav|ðɪs ɪz ðɪ ɐpɹˈɑːksɪmət tˈaɪm hiː ˈɛntɚd ðə ɹˈuːmɪŋhˌaʊs , ɐkˈoːɹdɪŋ tʊ ˈɜːliːn ɹˈɑːbɚts , ðə hˈaʊskiːpɚ ðˈɛɹ .|0
|
||||
LJ046-0146.wav|ðə kɹaɪtˈiəɹɪə ɪn ɪfˈɛkt pɹˈaɪɚ tə noʊvˈɛmbɚ twˈɛnti tˈuː , nˈaɪntiːn sˈɪksti θɹˈiː , fɔːɹ dɪtˈɜːmɪnɪŋ wˈɛðɚ tʊ ɐksˈɛpt mətˈɪɹiəl fɚðə pˌiːˌɑːɹɹˈɛs dʒˈɛnɚɹəl fˈaɪlz|0
|
||||
LJ017-0044.wav|ænd ðə dˈiːpɪst æŋzˈaɪəɾi wʌz fˈɛlt ðætðə kɹˈaɪm , ɪf kɹˈaɪm ðˈɛɹ hɐdbɪn , ʃˌʊd biː bɹˈɔːt hˈoʊm tʊ ɪts pˈɜːpɪtɹˌeɪɾɚ .|0
|
||||
LJ017-0070.wav|bˌʌt hɪz spˈoːɹɾɪŋ ˌɑːpɚɹˈeɪʃənz dɪdnˌɑːt pɹˈɑːspɚ , ænd hiː bɪkˌeɪm ɐ nˈiːdi mˈæn , ˈɔːlweɪz dɹˈɪvən tə dˈɛspɚɹət stɹˈeɪts fɔːɹ kˈæʃ .|0
|
||||
LJ014-0020.wav|hiː wʌz sˈuːn ˈæftɚwɚdz ɚɹˈɛstᵻd ˌɔn səspˈɪʃən , ænd ɐ sˈɜːtʃ ʌv hɪz lˈɑːdʒɪŋz bɹˈɔːt tə lˈaɪt sˈɛvɹəl ɡˈɑːɹmənts sˈætʃɚɹˌeɪɾᵻd wɪð blˈʌd ;|0
|
||||
LJ016-0020.wav|hiː nˈɛvɚ ɹˈiːtʃt ðə sˈɪstɚn , bˌʌt fˈɛl bˈæk ˌɪntʊ ðə jˈɑːɹd , ˈɪndʒɚɹɪŋ hɪz lˈɛɡz sᵻvˈɪɹli .|0
|
||||
LJ045-0230.wav|wˌɛn hiː wʌz fˈaɪnəli ˌæpɹihˈɛndᵻd ɪnðə tˈɛksəs θˈiəɾɚ . ɔːlðˈoʊ ɪɾ ɪz nˌɑːt fˈʊli kɚɹˈɑːbɚɹˌeɪɾᵻd baɪ ˈʌðɚz hˌuː wɜː pɹˈɛzənt ,|0
|
||||
LJ035-0129.wav|ænd ʃiː mˈʌstɐv ɹˈʌn dˌaʊn ðə stˈɛɹz ɐhˈɛd ʌv ˈɑːswəld ænd wʊd pɹˈɑːbəbli hæv sˈiːn ɔːɹ hˈɜːd hˌɪm .|0
|
||||
LJ008-0307.wav|ˈæftɚwɚdz ɛkspɹˈɛs ɐ wˈɪʃ tə mˈɜːdɚ ðə ɹᵻkˈoːɹdɚ fɔːɹ hˌævɪŋ kˈɛpt ðˌɛm sˌoʊ lˈɔŋ ɪn səspˈɛns .|0
|
||||
LJ008-0294.wav|nˌɪɹli ɪndˈɛfɪnətli dᵻfˈɜːd .|0
|
||||
LJ047-0148.wav|ˌɔn ɑːktˈoʊbɚ twˈɛnti fˈaɪv ,|0
|
||||
LJ008-0111.wav|ðeɪ ˈɛntɚd ɐ dˈɑːlɚ stˈoʊŋ kˈoʊld ɹˈuːm , dˈɑːlɚɹ ænd wɜː pɹˈɛzəntli dʒˈɔɪnd baɪ ðə pɹˈɪzənɚ .|0
|
||||
LJ034-0042.wav|ðæt hiː kʊd ˈoʊnli tˈɛstᵻfˌaɪ wɪð sˈɜːtənti ðætðə pɹˈɪnt wʌz lˈɛs ðɐn θɹˈiː dˈeɪz ˈoʊld .|0
|
||||
LJ037-0234.wav|mˈɪsɪz . mˈɛɹi bɹˈɑːk , ðə wˈaɪf əvə mɪkˈænɪk hˌuː wˈɜːkt æt ðə stˈeɪʃən , wʌz ðɛɹ æt ðə tˈaɪm ænd ʃiː sˈɔː ɐ wˈaɪt mˈeɪl ,|0
|
||||
LJ040-0002.wav|tʃˈæptɚ sˈɛvən . lˈiː hˈɑːɹvi ˈɑːswəld : bˈækɡɹaʊnd ænd pˈɑːsᵻbəl mˈoʊɾɪvz , pˈɑːɹt wˌʌn .|0
|
||||
LJ045-0140.wav|ðɪ ˈɑːɹɡjuːmənts hiː jˈuːzd tə dʒˈʌstᵻfˌaɪ hɪz jˈuːs ʌvðɪ ˈeɪliəs sədʒˈɛst ðæt ˈɑːswəld mˌeɪhɐv kˈʌm tə θˈɪŋk ðætðə hˈoʊl wˈɜːld wʌz bᵻkˈʌmɪŋ ɪnvˈɑːlvd|0
|
||||
LJ012-0035.wav|ðə nˈʌmbɚ ænd nˈeɪmz ˌɔn wˈɑːtʃᵻz , wɜː kˈɛɹfəli ɹᵻmˈuːvd ɔːɹ əblˈɪɾɚɹˌeɪɾᵻd ˈæftɚ ðə ɡˈʊdz pˈæst ˌaʊɾəv hɪz hˈændz .|0
|
||||
LJ012-0250.wav|ɔnðə sˈɛvənθ dʒuːlˈaɪ , ˈeɪtiːn θˈɜːɾi sˈɛvən ,|0
|
||||
LJ016-0179.wav|kəntɹˈæktᵻd wɪð ʃˈɛɹɪfs ænd kənvˈiːnɚz tə wˈɜːk baɪ ðə dʒˈɑːb .|0
|
||||
LJ016-0138.wav|æɾə dˈɪstəns fɹʌmðə pɹˈɪzən .|0
|
||||
LJ027-0052.wav|ðiːz pɹˈɪnsɪpəlz ʌv həmˈɑːlədʒi ɑːɹ ᵻsˈɛnʃəl tʊ ɐ kɚɹˈɛkt ɪntˌɜːpɹɪtˈeɪʃən ʌvðə fˈækts ʌv mɔːɹfˈɑːlədʒi .|0
|
||||
LJ031-0134.wav|ˌɔn wˈʌn əkˈeɪʒən mˈɪsɪz . dʒˈɑːnsən , ɐkˈʌmpənid baɪ tˈuː sˈiːkɹᵻt sˈɜːvɪs ˈeɪdʒənts , lˈɛft ðə ɹˈuːm tə sˈiː mˈɪsɪz . kˈɛnədi ænd mˈɪsɪz . kˈɑːnæli .|0
|
||||
LJ019-0273.wav|wˌɪtʃ sˌɜː dʒˈɑːʃjuːə dʒˈɛb tˈoʊld ðə kəmˈɪɾi hiː kənsˈɪdɚd ðə pɹˈɑːpɚɹ ˈɛlɪmənts ʌv pˈiːnəl dˈɪsɪplˌɪn .|0
|
||||
LJ014-0110.wav|æt ðə fˈɜːst ðə bˈɑːksᵻz wɜːɹ ɪmpˈaʊndᵻd , ˈoʊpənd , ænd fˈaʊnd tə kəntˈeɪn mˈɛnɪəv oʊkˈɑːnɚz ɪfˈɛkts .|0
|
||||
LJ034-0160.wav|ˌɔn bɹˈɛnənz sˈʌbsᵻkwənt sˈɜːʔn̩ aɪdˈɛntɪfɪkˈeɪʃən ʌv lˈiː hˈɑːɹvi ˈɑːswəld æz ðə mˈæn hiː sˈɔː fˈaɪɚ ðə ɹˈaɪfəl .|0
|
||||
LJ038-0199.wav|ᵻlˈɛvən . ɪf aɪɐm ɐlˈaɪv ænd tˈeɪkən pɹˈɪzənɚ ,|0
|
||||
LJ014-0010.wav|jˈɛt hiː kʊd nˌɑːt ˌoʊvɚkˈʌm ðə stɹˈeɪndʒ fˌæsᵻnˈeɪʃən ɪt hˈæd fɔːɹ hˌɪm , ænd ɹᵻmˈeɪnd baɪ ðə sˈaɪd ʌvðə kˈɔːɹps tˈɪl ðə stɹˈɛtʃɚ kˈeɪm .|0
|
||||
LJ033-0047.wav|aɪ nˈoʊɾɪst wɛn aɪ wɛnt ˈaʊt ðætðə lˈaɪt wʌz ˈɔn , ˈɛnd kwˈoʊt ,|0
|
||||
LJ040-0027.wav|hiː wʌz nˈɛvɚ sˈæɾɪsfˌaɪd wɪð ˈɛnɪθˌɪŋ .|0
|
||||
LJ048-0228.wav|ænd ˈʌðɚz hˌuː wɜː pɹˈɛzənt sˈeɪ ðæt nˈoʊ ˈeɪdʒənt wʌz ɪnˈiːbɹɪˌeɪɾᵻd ɔːɹ ˈæktᵻd ɪmpɹˈɑːpɚli .|0
|
||||
LJ003-0111.wav|hiː wʌz ɪŋ kˈɑːnsɪkwəns pˌʊt ˌaʊɾəv ðə pɹətˈɛkʃən ʌv ðɛɹ ɪntˈɜːnəl lˈɔː , ˈɛnd kwˈoʊt . ðɛɹ kˈoʊd wʌzɐ sˈʌbdʒɛkt ʌv sˌʌm kjˌʊɹɹɪˈɔsɪɾi .|0
|
||||
LJ008-0258.wav|lˈɛt mˌiː ɹᵻtɹˈeɪs maɪ stˈɛps , ænd spˈiːk mˈoːɹ ɪn diːtˈeɪl ʌvðə tɹˈiːtmənt ʌvðə kəndˈɛmd ɪn ðoʊz blˈʌdθɜːsti ænd bɹˈuːɾəli ɪndˈɪfɹənt dˈeɪz ,|0
|
||||
LJ029-0022.wav|ðɪ ɚɹˈɪdʒɪnəl plˈæŋ kˈɔːld fɚðə pɹˈɛzɪdənt tə spˈɛnd ˈoʊnli wˈʌn dˈeɪ ɪnðə stˈeɪt , mˌeɪkɪŋ wˈɜːlwɪnd vˈɪzɪts tə dˈæləs , fˈɔːɹt wˈɜːθ , sˌæn æntˈoʊnɪˌoʊ , ænd hjˈuːstən .|0
|
||||
LJ004-0045.wav|mˈɪstɚ . stˈɜːdʒᵻz bˈoːɹn , sˌɜː dʒˈeɪmz mˈækɪntˌɑːʃ , sˌɜː dʒˈeɪmz skˈɑːɹlɪt , ænd wˈɪljəm wˈɪlbɚfˌoːɹs .|0
|
||||
@@ -0,0 +1,31 @@
|
||||
# Use ubuntu as the base image from the hub
|
||||
# then have to build this as a image.
|
||||
FROM nvidia/cuda:12.2.0-devel-ubuntu20.04
|
||||
# Optional: Set environment variables
|
||||
ENV MY_ENV_VARIABLE=value
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
# Optional: Run commands to set up the image
|
||||
RUN apt-get update && apt-get install -y curl
|
||||
|
||||
# Preconfigure package selections (as an example)
|
||||
RUN echo "package-name package-name/some-option select option-value" | debconf-set-selections && \
|
||||
apt-get install -y software-properties-common
|
||||
RUN apt install software-properties-common -y
|
||||
RUN add-apt-repository ppa:deadsnakes/ppa
|
||||
RUN apt install python3.10 -y
|
||||
RUN apt-get install -y git
|
||||
RUN sudo apt install ffmpeg
|
||||
RUN sudo apt-get install espeak-ng
|
||||
# Clone the repository
|
||||
|
||||
# RUN pwd
|
||||
RUN pip3 install requirements.txt
|
||||
|
||||
# Navigate to the cloned directory
|
||||
WORKDIR TestingDockerIO
|
||||
COPY styletts2 .
|
||||
|
||||
|
||||
|
||||
# # Allow parameters to be passed
|
||||
ENTRYPOINT ["python3", "fakeyou_infer.py"]
|
||||
@@ -0,0 +1,25 @@
|
||||
LICENSE FOR STYLETTS2 DEMO PAGE: GPL (I KNOW I DON'T LIKE GPL BUT I HAVE TO BECAUSE OF PHONEMIZER REQUIREMENT)
|
||||
|
||||
|
||||
styletts 2 license:
|
||||
|
||||
Copyright (c) 2023 Aaron (Yinghao) Li
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1,92 @@
|
||||
from math import pi
|
||||
from random import randint
|
||||
from typing import Any, Optional, Sequence, Tuple, Union
|
||||
|
||||
import torch
|
||||
from einops import rearrange
|
||||
from torch import Tensor, nn
|
||||
from tqdm import tqdm
|
||||
|
||||
from .utils import *
|
||||
from .sampler import *
|
||||
|
||||
"""
|
||||
Diffusion Classes (generic for 1d data)
|
||||
"""
|
||||
|
||||
|
||||
class Model1d(nn.Module):
|
||||
def __init__(self, unet_type: str = "base", **kwargs):
|
||||
super().__init__()
|
||||
diffusion_kwargs, kwargs = groupby("diffusion_", kwargs)
|
||||
self.unet = None
|
||||
self.diffusion = None
|
||||
|
||||
def forward(self, x: Tensor, **kwargs) -> Tensor:
|
||||
return self.diffusion(x, **kwargs)
|
||||
|
||||
def sample(self, *args, **kwargs) -> Tensor:
|
||||
return self.diffusion.sample(*args, **kwargs)
|
||||
|
||||
|
||||
"""
|
||||
Audio Diffusion Classes (specific for 1d audio data)
|
||||
"""
|
||||
|
||||
|
||||
def get_default_model_kwargs():
|
||||
return dict(
|
||||
channels=128,
|
||||
patch_size=16,
|
||||
multipliers=[1, 2, 4, 4, 4, 4, 4],
|
||||
factors=[4, 4, 4, 2, 2, 2],
|
||||
num_blocks=[2, 2, 2, 2, 2, 2],
|
||||
attentions=[0, 0, 0, 1, 1, 1, 1],
|
||||
attention_heads=8,
|
||||
attention_features=64,
|
||||
attention_multiplier=2,
|
||||
attention_use_rel_pos=False,
|
||||
diffusion_type="v",
|
||||
diffusion_sigma_distribution=UniformDistribution(),
|
||||
)
|
||||
|
||||
|
||||
def get_default_sampling_kwargs():
|
||||
return dict(sigma_schedule=LinearSchedule(), sampler=VSampler(), clamp=True)
|
||||
|
||||
|
||||
class AudioDiffusionModel(Model1d):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**{**get_default_model_kwargs(), **kwargs})
|
||||
|
||||
def sample(self, *args, **kwargs):
|
||||
return super().sample(*args, **{**get_default_sampling_kwargs(), **kwargs})
|
||||
|
||||
|
||||
class AudioDiffusionConditional(Model1d):
|
||||
def __init__(
|
||||
self,
|
||||
embedding_features: int,
|
||||
embedding_max_length: int,
|
||||
embedding_mask_proba: float = 0.1,
|
||||
**kwargs,
|
||||
):
|
||||
self.embedding_mask_proba = embedding_mask_proba
|
||||
default_kwargs = dict(
|
||||
**get_default_model_kwargs(),
|
||||
unet_type="cfg",
|
||||
context_embedding_features=embedding_features,
|
||||
context_embedding_max_length=embedding_max_length,
|
||||
)
|
||||
super().__init__(**{**default_kwargs, **kwargs})
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
default_kwargs = dict(embedding_mask_proba=self.embedding_mask_proba)
|
||||
return super().forward(*args, **{**default_kwargs, **kwargs})
|
||||
|
||||
def sample(self, *args, **kwargs):
|
||||
default_kwargs = dict(
|
||||
**get_default_sampling_kwargs(),
|
||||
embedding_scale=5.0,
|
||||
)
|
||||
return super().sample(*args, **{**default_kwargs, **kwargs})
|
||||
@@ -0,0 +1,700 @@
|
||||
from math import floor, log, pi
|
||||
from typing import Any, List, Optional, Sequence, Tuple, Union
|
||||
|
||||
from .utils import *
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from einops import rearrange, reduce, repeat
|
||||
from einops.layers.torch import Rearrange
|
||||
from einops_exts import rearrange_many
|
||||
from torch import Tensor, einsum
|
||||
|
||||
|
||||
"""
|
||||
Utils
|
||||
"""
|
||||
|
||||
|
||||
class AdaLayerNorm(nn.Module):
|
||||
def __init__(self, style_dim, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.fc = nn.Linear(style_dim, channels * 2)
|
||||
|
||||
def forward(self, x, s):
|
||||
x = x.transpose(-1, -2)
|
||||
x = x.transpose(1, -1)
|
||||
|
||||
h = self.fc(s)
|
||||
h = h.view(h.size(0), h.size(1), 1)
|
||||
gamma, beta = torch.chunk(h, chunks=2, dim=1)
|
||||
gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
|
||||
|
||||
x = F.layer_norm(x, (self.channels,), eps=self.eps)
|
||||
x = (1 + gamma) * x + beta
|
||||
return x.transpose(1, -1).transpose(-1, -2)
|
||||
|
||||
|
||||
class StyleTransformer1d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
num_layers: int,
|
||||
channels: int,
|
||||
num_heads: int,
|
||||
head_features: int,
|
||||
multiplier: int,
|
||||
use_context_time: bool = True,
|
||||
use_rel_pos: bool = False,
|
||||
context_features_multiplier: int = 1,
|
||||
rel_pos_num_buckets: Optional[int] = None,
|
||||
rel_pos_max_distance: Optional[int] = None,
|
||||
context_features: Optional[int] = None,
|
||||
context_embedding_features: Optional[int] = None,
|
||||
embedding_max_length: int = 512,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
StyleTransformerBlock(
|
||||
features=channels + context_embedding_features,
|
||||
head_features=head_features,
|
||||
num_heads=num_heads,
|
||||
multiplier=multiplier,
|
||||
style_dim=context_features,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
for i in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
Rearrange("b t c -> b c t"),
|
||||
nn.Conv1d(
|
||||
in_channels=channels + context_embedding_features,
|
||||
out_channels=channels,
|
||||
kernel_size=1,
|
||||
),
|
||||
)
|
||||
|
||||
use_context_features = exists(context_features)
|
||||
self.use_context_features = use_context_features
|
||||
self.use_context_time = use_context_time
|
||||
|
||||
if use_context_time or use_context_features:
|
||||
context_mapping_features = channels + context_embedding_features
|
||||
|
||||
self.to_mapping = nn.Sequential(
|
||||
nn.Linear(context_mapping_features, context_mapping_features),
|
||||
nn.GELU(),
|
||||
nn.Linear(context_mapping_features, context_mapping_features),
|
||||
nn.GELU(),
|
||||
)
|
||||
|
||||
if use_context_time:
|
||||
assert exists(context_mapping_features)
|
||||
self.to_time = nn.Sequential(
|
||||
TimePositionalEmbedding(
|
||||
dim=channels, out_features=context_mapping_features
|
||||
),
|
||||
nn.GELU(),
|
||||
)
|
||||
|
||||
if use_context_features:
|
||||
assert exists(context_features) and exists(context_mapping_features)
|
||||
self.to_features = nn.Sequential(
|
||||
nn.Linear(
|
||||
in_features=context_features, out_features=context_mapping_features
|
||||
),
|
||||
nn.GELU(),
|
||||
)
|
||||
|
||||
self.fixed_embedding = FixedEmbedding(
|
||||
max_length=embedding_max_length, features=context_embedding_features
|
||||
)
|
||||
|
||||
def get_mapping(
|
||||
self, time: Optional[Tensor] = None, features: Optional[Tensor] = None
|
||||
) -> Optional[Tensor]:
|
||||
"""Combines context time features and features into mapping"""
|
||||
items, mapping = [], None
|
||||
# Compute time features
|
||||
if self.use_context_time:
|
||||
assert_message = "use_context_time=True but no time features provided"
|
||||
assert exists(time), assert_message
|
||||
items += [self.to_time(time)]
|
||||
# Compute features
|
||||
if self.use_context_features:
|
||||
assert_message = "context_features exists but no features provided"
|
||||
assert exists(features), assert_message
|
||||
items += [self.to_features(features)]
|
||||
|
||||
# Compute joint mapping
|
||||
if self.use_context_time or self.use_context_features:
|
||||
mapping = reduce(torch.stack(items), "n b m -> b m", "sum")
|
||||
mapping = self.to_mapping(mapping)
|
||||
|
||||
return mapping
|
||||
|
||||
def run(self, x, time, embedding, features):
|
||||
mapping = self.get_mapping(time, features)
|
||||
x = torch.cat([x.expand(-1, embedding.size(1), -1), embedding], axis=-1)
|
||||
mapping = mapping.unsqueeze(1).expand(-1, embedding.size(1), -1)
|
||||
|
||||
for block in self.blocks:
|
||||
x = x + mapping
|
||||
x = block(x, features)
|
||||
|
||||
x = x.mean(axis=1).unsqueeze(1)
|
||||
x = self.to_out(x)
|
||||
x = x.transpose(-1, -2)
|
||||
|
||||
return x
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
time: Tensor,
|
||||
embedding_mask_proba: float = 0.0,
|
||||
embedding: Optional[Tensor] = None,
|
||||
features: Optional[Tensor] = None,
|
||||
embedding_scale: float = 1.0,
|
||||
) -> Tensor:
|
||||
b, device = embedding.shape[0], embedding.device
|
||||
fixed_embedding = self.fixed_embedding(embedding)
|
||||
if embedding_mask_proba > 0.0:
|
||||
# Randomly mask embedding
|
||||
batch_mask = rand_bool(
|
||||
shape=(b, 1, 1), proba=embedding_mask_proba, device=device
|
||||
)
|
||||
embedding = torch.where(batch_mask, fixed_embedding, embedding)
|
||||
|
||||
if embedding_scale != 1.0:
|
||||
# Compute both normal and fixed embedding outputs
|
||||
out = self.run(x, time, embedding=embedding, features=features)
|
||||
out_masked = self.run(x, time, embedding=fixed_embedding, features=features)
|
||||
# Scale conditional output using classifier-free guidance
|
||||
return out_masked + (out - out_masked) * embedding_scale
|
||||
else:
|
||||
return self.run(x, time, embedding=embedding, features=features)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class StyleTransformerBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
features: int,
|
||||
num_heads: int,
|
||||
head_features: int,
|
||||
style_dim: int,
|
||||
multiplier: int,
|
||||
use_rel_pos: bool,
|
||||
rel_pos_num_buckets: Optional[int] = None,
|
||||
rel_pos_max_distance: Optional[int] = None,
|
||||
context_features: Optional[int] = None,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.use_cross_attention = exists(context_features) and context_features > 0
|
||||
|
||||
self.attention = StyleAttention(
|
||||
features=features,
|
||||
style_dim=style_dim,
|
||||
num_heads=num_heads,
|
||||
head_features=head_features,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
|
||||
if self.use_cross_attention:
|
||||
self.cross_attention = StyleAttention(
|
||||
features=features,
|
||||
style_dim=style_dim,
|
||||
num_heads=num_heads,
|
||||
head_features=head_features,
|
||||
context_features=context_features,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
|
||||
self.feed_forward = FeedForward(features=features, multiplier=multiplier)
|
||||
|
||||
def forward(
|
||||
self, x: Tensor, s: Tensor, *, context: Optional[Tensor] = None
|
||||
) -> Tensor:
|
||||
x = self.attention(x, s) + x
|
||||
if self.use_cross_attention:
|
||||
x = self.cross_attention(x, s, context=context) + x
|
||||
x = self.feed_forward(x) + x
|
||||
return x
|
||||
|
||||
|
||||
class StyleAttention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
features: int,
|
||||
*,
|
||||
style_dim: int,
|
||||
head_features: int,
|
||||
num_heads: int,
|
||||
context_features: Optional[int] = None,
|
||||
use_rel_pos: bool,
|
||||
rel_pos_num_buckets: Optional[int] = None,
|
||||
rel_pos_max_distance: Optional[int] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.context_features = context_features
|
||||
mid_features = head_features * num_heads
|
||||
context_features = default(context_features, features)
|
||||
|
||||
self.norm = AdaLayerNorm(style_dim, features)
|
||||
self.norm_context = AdaLayerNorm(style_dim, context_features)
|
||||
self.to_q = nn.Linear(
|
||||
in_features=features, out_features=mid_features, bias=False
|
||||
)
|
||||
self.to_kv = nn.Linear(
|
||||
in_features=context_features, out_features=mid_features * 2, bias=False
|
||||
)
|
||||
self.attention = AttentionBase(
|
||||
features,
|
||||
num_heads=num_heads,
|
||||
head_features=head_features,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self, x: Tensor, s: Tensor, *, context: Optional[Tensor] = None
|
||||
) -> Tensor:
|
||||
assert_message = "You must provide a context when using context_features"
|
||||
assert not self.context_features or exists(context), assert_message
|
||||
# Use context if provided
|
||||
context = default(context, x)
|
||||
# Normalize then compute q from input and k,v from context
|
||||
x, context = self.norm(x, s), self.norm_context(context, s)
|
||||
|
||||
q, k, v = (self.to_q(x), *torch.chunk(self.to_kv(context), chunks=2, dim=-1))
|
||||
# Compute and return attention
|
||||
return self.attention(q, k, v)
|
||||
|
||||
|
||||
class Transformer1d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
num_layers: int,
|
||||
channels: int,
|
||||
num_heads: int,
|
||||
head_features: int,
|
||||
multiplier: int,
|
||||
use_context_time: bool = True,
|
||||
use_rel_pos: bool = False,
|
||||
context_features_multiplier: int = 1,
|
||||
rel_pos_num_buckets: Optional[int] = None,
|
||||
rel_pos_max_distance: Optional[int] = None,
|
||||
context_features: Optional[int] = None,
|
||||
context_embedding_features: Optional[int] = None,
|
||||
embedding_max_length: int = 512,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
TransformerBlock(
|
||||
features=channels + context_embedding_features,
|
||||
head_features=head_features,
|
||||
num_heads=num_heads,
|
||||
multiplier=multiplier,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
for i in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
Rearrange("b t c -> b c t"),
|
||||
nn.Conv1d(
|
||||
in_channels=channels + context_embedding_features,
|
||||
out_channels=channels,
|
||||
kernel_size=1,
|
||||
),
|
||||
)
|
||||
|
||||
use_context_features = exists(context_features)
|
||||
self.use_context_features = use_context_features
|
||||
self.use_context_time = use_context_time
|
||||
|
||||
if use_context_time or use_context_features:
|
||||
context_mapping_features = channels + context_embedding_features
|
||||
|
||||
self.to_mapping = nn.Sequential(
|
||||
nn.Linear(context_mapping_features, context_mapping_features),
|
||||
nn.GELU(),
|
||||
nn.Linear(context_mapping_features, context_mapping_features),
|
||||
nn.GELU(),
|
||||
)
|
||||
|
||||
if use_context_time:
|
||||
assert exists(context_mapping_features)
|
||||
self.to_time = nn.Sequential(
|
||||
TimePositionalEmbedding(
|
||||
dim=channels, out_features=context_mapping_features
|
||||
),
|
||||
nn.GELU(),
|
||||
)
|
||||
|
||||
if use_context_features:
|
||||
assert exists(context_features) and exists(context_mapping_features)
|
||||
self.to_features = nn.Sequential(
|
||||
nn.Linear(
|
||||
in_features=context_features, out_features=context_mapping_features
|
||||
),
|
||||
nn.GELU(),
|
||||
)
|
||||
|
||||
self.fixed_embedding = FixedEmbedding(
|
||||
max_length=embedding_max_length, features=context_embedding_features
|
||||
)
|
||||
|
||||
def get_mapping(
|
||||
self, time: Optional[Tensor] = None, features: Optional[Tensor] = None
|
||||
) -> Optional[Tensor]:
|
||||
"""Combines context time features and features into mapping"""
|
||||
items, mapping = [], None
|
||||
# Compute time features
|
||||
if self.use_context_time:
|
||||
assert_message = "use_context_time=True but no time features provided"
|
||||
assert exists(time), assert_message
|
||||
items += [self.to_time(time)]
|
||||
# Compute features
|
||||
if self.use_context_features:
|
||||
assert_message = "context_features exists but no features provided"
|
||||
assert exists(features), assert_message
|
||||
items += [self.to_features(features)]
|
||||
|
||||
# Compute joint mapping
|
||||
if self.use_context_time or self.use_context_features:
|
||||
mapping = reduce(torch.stack(items), "n b m -> b m", "sum")
|
||||
mapping = self.to_mapping(mapping)
|
||||
|
||||
return mapping
|
||||
|
||||
def run(self, x, time, embedding, features):
|
||||
mapping = self.get_mapping(time, features)
|
||||
x = torch.cat([x.expand(-1, embedding.size(1), -1), embedding], axis=-1)
|
||||
mapping = mapping.unsqueeze(1).expand(-1, embedding.size(1), -1)
|
||||
|
||||
for block in self.blocks:
|
||||
x = x + mapping
|
||||
x = block(x)
|
||||
|
||||
x = x.mean(axis=1).unsqueeze(1)
|
||||
x = self.to_out(x)
|
||||
x = x.transpose(-1, -2)
|
||||
|
||||
return x
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
time: Tensor,
|
||||
embedding_mask_proba: float = 0.0,
|
||||
embedding: Optional[Tensor] = None,
|
||||
features: Optional[Tensor] = None,
|
||||
embedding_scale: float = 1.0,
|
||||
) -> Tensor:
|
||||
b, device = embedding.shape[0], embedding.device
|
||||
fixed_embedding = self.fixed_embedding(embedding)
|
||||
if embedding_mask_proba > 0.0:
|
||||
# Randomly mask embedding
|
||||
batch_mask = rand_bool(
|
||||
shape=(b, 1, 1), proba=embedding_mask_proba, device=device
|
||||
)
|
||||
embedding = torch.where(batch_mask, fixed_embedding, embedding)
|
||||
|
||||
if embedding_scale != 1.0:
|
||||
# Compute both normal and fixed embedding outputs
|
||||
out = self.run(x, time, embedding=embedding, features=features)
|
||||
out_masked = self.run(x, time, embedding=fixed_embedding, features=features)
|
||||
# Scale conditional output using classifier-free guidance
|
||||
return out_masked + (out - out_masked) * embedding_scale
|
||||
else:
|
||||
return self.run(x, time, embedding=embedding, features=features)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
"""
|
||||
Attention Components
|
||||
"""
|
||||
|
||||
|
||||
class RelativePositionBias(nn.Module):
|
||||
def __init__(self, num_buckets: int, max_distance: int, num_heads: int):
|
||||
super().__init__()
|
||||
self.num_buckets = num_buckets
|
||||
self.max_distance = max_distance
|
||||
self.num_heads = num_heads
|
||||
self.relative_attention_bias = nn.Embedding(num_buckets, num_heads)
|
||||
|
||||
@staticmethod
|
||||
def _relative_position_bucket(
|
||||
relative_position: Tensor, num_buckets: int, max_distance: int
|
||||
):
|
||||
num_buckets //= 2
|
||||
ret = (relative_position >= 0).to(torch.long) * num_buckets
|
||||
n = torch.abs(relative_position)
|
||||
|
||||
max_exact = num_buckets // 2
|
||||
is_small = n < max_exact
|
||||
|
||||
val_if_large = (
|
||||
max_exact
|
||||
+ (
|
||||
torch.log(n.float() / max_exact)
|
||||
/ log(max_distance / max_exact)
|
||||
* (num_buckets - max_exact)
|
||||
).long()
|
||||
)
|
||||
val_if_large = torch.min(
|
||||
val_if_large, torch.full_like(val_if_large, num_buckets - 1)
|
||||
)
|
||||
|
||||
ret += torch.where(is_small, n, val_if_large)
|
||||
return ret
|
||||
|
||||
def forward(self, num_queries: int, num_keys: int) -> Tensor:
|
||||
i, j, device = num_queries, num_keys, self.relative_attention_bias.weight.device
|
||||
q_pos = torch.arange(j - i, j, dtype=torch.long, device=device)
|
||||
k_pos = torch.arange(j, dtype=torch.long, device=device)
|
||||
rel_pos = rearrange(k_pos, "j -> 1 j") - rearrange(q_pos, "i -> i 1")
|
||||
|
||||
relative_position_bucket = self._relative_position_bucket(
|
||||
rel_pos, num_buckets=self.num_buckets, max_distance=self.max_distance
|
||||
)
|
||||
|
||||
bias = self.relative_attention_bias(relative_position_bucket)
|
||||
bias = rearrange(bias, "m n h -> 1 h m n")
|
||||
return bias
|
||||
|
||||
|
||||
def FeedForward(features: int, multiplier: int) -> nn.Module:
|
||||
mid_features = features * multiplier
|
||||
return nn.Sequential(
|
||||
nn.Linear(in_features=features, out_features=mid_features),
|
||||
nn.GELU(),
|
||||
nn.Linear(in_features=mid_features, out_features=features),
|
||||
)
|
||||
|
||||
|
||||
class AttentionBase(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
features: int,
|
||||
*,
|
||||
head_features: int,
|
||||
num_heads: int,
|
||||
use_rel_pos: bool,
|
||||
out_features: Optional[int] = None,
|
||||
rel_pos_num_buckets: Optional[int] = None,
|
||||
rel_pos_max_distance: Optional[int] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.scale = head_features**-0.5
|
||||
self.num_heads = num_heads
|
||||
self.use_rel_pos = use_rel_pos
|
||||
mid_features = head_features * num_heads
|
||||
|
||||
if use_rel_pos:
|
||||
assert exists(rel_pos_num_buckets) and exists(rel_pos_max_distance)
|
||||
self.rel_pos = RelativePositionBias(
|
||||
num_buckets=rel_pos_num_buckets,
|
||||
max_distance=rel_pos_max_distance,
|
||||
num_heads=num_heads,
|
||||
)
|
||||
if out_features is None:
|
||||
out_features = features
|
||||
|
||||
self.to_out = nn.Linear(in_features=mid_features, out_features=out_features)
|
||||
|
||||
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
|
||||
# Split heads
|
||||
q, k, v = rearrange_many((q, k, v), "b n (h d) -> b h n d", h=self.num_heads)
|
||||
# Compute similarity matrix
|
||||
sim = einsum("... n d, ... m d -> ... n m", q, k)
|
||||
sim = (sim + self.rel_pos(*sim.shape[-2:])) if self.use_rel_pos else sim
|
||||
sim = sim * self.scale
|
||||
# Get attention matrix with softmax
|
||||
attn = sim.softmax(dim=-1)
|
||||
# Compute values
|
||||
out = einsum("... n m, ... m d -> ... n d", attn, v)
|
||||
out = rearrange(out, "b h n d -> b n (h d)")
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
features: int,
|
||||
*,
|
||||
head_features: int,
|
||||
num_heads: int,
|
||||
out_features: Optional[int] = None,
|
||||
context_features: Optional[int] = None,
|
||||
use_rel_pos: bool,
|
||||
rel_pos_num_buckets: Optional[int] = None,
|
||||
rel_pos_max_distance: Optional[int] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.context_features = context_features
|
||||
mid_features = head_features * num_heads
|
||||
context_features = default(context_features, features)
|
||||
|
||||
self.norm = nn.LayerNorm(features)
|
||||
self.norm_context = nn.LayerNorm(context_features)
|
||||
self.to_q = nn.Linear(
|
||||
in_features=features, out_features=mid_features, bias=False
|
||||
)
|
||||
self.to_kv = nn.Linear(
|
||||
in_features=context_features, out_features=mid_features * 2, bias=False
|
||||
)
|
||||
|
||||
self.attention = AttentionBase(
|
||||
features,
|
||||
out_features=out_features,
|
||||
num_heads=num_heads,
|
||||
head_features=head_features,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
|
||||
def forward(self, x: Tensor, *, context: Optional[Tensor] = None) -> Tensor:
|
||||
assert_message = "You must provide a context when using context_features"
|
||||
assert not self.context_features or exists(context), assert_message
|
||||
# Use context if provided
|
||||
context = default(context, x)
|
||||
# Normalize then compute q from input and k,v from context
|
||||
x, context = self.norm(x), self.norm_context(context)
|
||||
q, k, v = (self.to_q(x), *torch.chunk(self.to_kv(context), chunks=2, dim=-1))
|
||||
# Compute and return attention
|
||||
return self.attention(q, k, v)
|
||||
|
||||
|
||||
"""
|
||||
Transformer Blocks
|
||||
"""
|
||||
|
||||
|
||||
class TransformerBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
features: int,
|
||||
num_heads: int,
|
||||
head_features: int,
|
||||
multiplier: int,
|
||||
use_rel_pos: bool,
|
||||
rel_pos_num_buckets: Optional[int] = None,
|
||||
rel_pos_max_distance: Optional[int] = None,
|
||||
context_features: Optional[int] = None,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.use_cross_attention = exists(context_features) and context_features > 0
|
||||
|
||||
self.attention = Attention(
|
||||
features=features,
|
||||
num_heads=num_heads,
|
||||
head_features=head_features,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
|
||||
if self.use_cross_attention:
|
||||
self.cross_attention = Attention(
|
||||
features=features,
|
||||
num_heads=num_heads,
|
||||
head_features=head_features,
|
||||
context_features=context_features,
|
||||
use_rel_pos=use_rel_pos,
|
||||
rel_pos_num_buckets=rel_pos_num_buckets,
|
||||
rel_pos_max_distance=rel_pos_max_distance,
|
||||
)
|
||||
|
||||
self.feed_forward = FeedForward(features=features, multiplier=multiplier)
|
||||
|
||||
def forward(self, x: Tensor, *, context: Optional[Tensor] = None) -> Tensor:
|
||||
x = self.attention(x) + x
|
||||
if self.use_cross_attention:
|
||||
x = self.cross_attention(x, context=context) + x
|
||||
x = self.feed_forward(x) + x
|
||||
return x
|
||||
|
||||
|
||||
"""
|
||||
Time Embeddings
|
||||
"""
|
||||
|
||||
|
||||
class SinusoidalEmbedding(nn.Module):
|
||||
def __init__(self, dim: int):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
device, half_dim = x.device, self.dim // 2
|
||||
emb = torch.tensor(log(10000) / (half_dim - 1), device=device)
|
||||
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
|
||||
emb = rearrange(x, "i -> i 1") * rearrange(emb, "j -> 1 j")
|
||||
return torch.cat((emb.sin(), emb.cos()), dim=-1)
|
||||
|
||||
|
||||
class LearnedPositionalEmbedding(nn.Module):
|
||||
"""Used for continuous time"""
|
||||
|
||||
def __init__(self, dim: int):
|
||||
super().__init__()
|
||||
assert (dim % 2) == 0
|
||||
half_dim = dim // 2
|
||||
self.weights = nn.Parameter(torch.randn(half_dim))
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
x = rearrange(x, "b -> b 1")
|
||||
freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * pi
|
||||
fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
|
||||
fouriered = torch.cat((x, fouriered), dim=-1)
|
||||
return fouriered
|
||||
|
||||
|
||||
def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
|
||||
return nn.Sequential(
|
||||
LearnedPositionalEmbedding(dim),
|
||||
nn.Linear(in_features=dim + 1, out_features=out_features),
|
||||
)
|
||||
|
||||
|
||||
class FixedEmbedding(nn.Module):
|
||||
def __init__(self, max_length: int, features: int):
|
||||
super().__init__()
|
||||
self.max_length = max_length
|
||||
self.embedding = nn.Embedding(max_length, features)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
batch_size, length, device = *x.shape[0:2], x.device
|
||||
assert_message = "Input sequence length must be <= max_length"
|
||||
assert length <= self.max_length, assert_message
|
||||
position = torch.arange(length, device=device)
|
||||
fixed_embedding = self.embedding(position)
|
||||
fixed_embedding = repeat(fixed_embedding, "n d -> b n d", b=batch_size)
|
||||
return fixed_embedding
|
||||
@@ -0,0 +1,685 @@
|
||||
from math import atan, cos, pi, sin, sqrt
|
||||
from typing import Any, Callable, List, Optional, Tuple, Type
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange, reduce
|
||||
from torch import Tensor
|
||||
|
||||
from .utils import *
|
||||
|
||||
"""
|
||||
Diffusion Training
|
||||
"""
|
||||
|
||||
""" Distributions """
|
||||
|
||||
|
||||
class Distribution:
|
||||
def __call__(self, num_samples: int, device: torch.device):
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class LogNormalDistribution(Distribution):
|
||||
def __init__(self, mean: float, std: float):
|
||||
self.mean = mean
|
||||
self.std = std
|
||||
|
||||
def __call__(
|
||||
self, num_samples: int, device: torch.device = torch.device("cpu")
|
||||
) -> Tensor:
|
||||
normal = self.mean + self.std * torch.randn((num_samples,), device=device)
|
||||
return normal.exp()
|
||||
|
||||
|
||||
class UniformDistribution(Distribution):
|
||||
def __call__(self, num_samples: int, device: torch.device = torch.device("cpu")):
|
||||
return torch.rand(num_samples, device=device)
|
||||
|
||||
|
||||
class VKDistribution(Distribution):
|
||||
def __init__(
|
||||
self,
|
||||
min_value: float = 0.0,
|
||||
max_value: float = float("inf"),
|
||||
sigma_data: float = 1.0,
|
||||
):
|
||||
self.min_value = min_value
|
||||
self.max_value = max_value
|
||||
self.sigma_data = sigma_data
|
||||
|
||||
def __call__(
|
||||
self, num_samples: int, device: torch.device = torch.device("cpu")
|
||||
) -> Tensor:
|
||||
sigma_data = self.sigma_data
|
||||
min_cdf = atan(self.min_value / sigma_data) * 2 / pi
|
||||
max_cdf = atan(self.max_value / sigma_data) * 2 / pi
|
||||
u = (max_cdf - min_cdf) * torch.randn((num_samples,), device=device) + min_cdf
|
||||
return torch.tan(u * pi / 2) * sigma_data
|
||||
|
||||
|
||||
""" Diffusion Classes """
|
||||
|
||||
|
||||
def pad_dims(x: Tensor, ndim: int) -> Tensor:
|
||||
# Pads additional ndims to the right of the tensor
|
||||
return x.view(*x.shape, *((1,) * ndim))
|
||||
|
||||
|
||||
def clip(x: Tensor, dynamic_threshold: float = 0.0):
|
||||
if dynamic_threshold == 0.0:
|
||||
return x.clamp(-1.0, 1.0)
|
||||
else:
|
||||
# Dynamic thresholding
|
||||
# Find dynamic threshold quantile for each batch
|
||||
x_flat = rearrange(x, "b ... -> b (...)")
|
||||
scale = torch.quantile(x_flat.abs(), dynamic_threshold, dim=-1)
|
||||
# Clamp to a min of 1.0
|
||||
scale.clamp_(min=1.0)
|
||||
# Clamp all values and scale
|
||||
scale = pad_dims(scale, ndim=x.ndim - scale.ndim)
|
||||
x = x.clamp(-scale, scale) / scale
|
||||
return x
|
||||
|
||||
|
||||
def to_batch(
|
||||
batch_size: int,
|
||||
device: torch.device,
|
||||
x: Optional[float] = None,
|
||||
xs: Optional[Tensor] = None,
|
||||
) -> Tensor:
|
||||
assert exists(x) ^ exists(xs), "Either x or xs must be provided"
|
||||
# If x provided use the same for all batch items
|
||||
if exists(x):
|
||||
xs = torch.full(size=(batch_size,), fill_value=x).to(device)
|
||||
assert exists(xs)
|
||||
return xs
|
||||
|
||||
|
||||
class Diffusion(nn.Module):
|
||||
alias: str = ""
|
||||
|
||||
"""Base diffusion class"""
|
||||
|
||||
def denoise_fn(
|
||||
self,
|
||||
x_noisy: Tensor,
|
||||
sigmas: Optional[Tensor] = None,
|
||||
sigma: Optional[float] = None,
|
||||
**kwargs,
|
||||
) -> Tensor:
|
||||
raise NotImplementedError("Diffusion class missing denoise_fn")
|
||||
|
||||
def forward(self, x: Tensor, noise: Tensor = None, **kwargs) -> Tensor:
|
||||
raise NotImplementedError("Diffusion class missing forward function")
|
||||
|
||||
|
||||
class VDiffusion(Diffusion):
|
||||
alias = "v"
|
||||
|
||||
def __init__(self, net: nn.Module, *, sigma_distribution: Distribution):
|
||||
super().__init__()
|
||||
self.net = net
|
||||
self.sigma_distribution = sigma_distribution
|
||||
|
||||
def get_alpha_beta(self, sigmas: Tensor) -> Tuple[Tensor, Tensor]:
|
||||
angle = sigmas * pi / 2
|
||||
alpha = torch.cos(angle)
|
||||
beta = torch.sin(angle)
|
||||
return alpha, beta
|
||||
|
||||
def denoise_fn(
|
||||
self,
|
||||
x_noisy: Tensor,
|
||||
sigmas: Optional[Tensor] = None,
|
||||
sigma: Optional[float] = None,
|
||||
**kwargs,
|
||||
) -> Tensor:
|
||||
batch_size, device = x_noisy.shape[0], x_noisy.device
|
||||
sigmas = to_batch(x=sigma, xs=sigmas, batch_size=batch_size, device=device)
|
||||
return self.net(x_noisy, sigmas, **kwargs)
|
||||
|
||||
def forward(self, x: Tensor, noise: Tensor = None, **kwargs) -> Tensor:
|
||||
batch_size, device = x.shape[0], x.device
|
||||
|
||||
# Sample amount of noise to add for each batch element
|
||||
sigmas = self.sigma_distribution(num_samples=batch_size, device=device)
|
||||
sigmas_padded = rearrange(sigmas, "b -> b 1 1")
|
||||
|
||||
# Get noise
|
||||
noise = default(noise, lambda: torch.randn_like(x))
|
||||
|
||||
# Combine input and noise weighted by half-circle
|
||||
alpha, beta = self.get_alpha_beta(sigmas_padded)
|
||||
x_noisy = x * alpha + noise * beta
|
||||
x_target = noise * alpha - x * beta
|
||||
|
||||
# Denoise and return loss
|
||||
x_denoised = self.denoise_fn(x_noisy, sigmas, **kwargs)
|
||||
return F.mse_loss(x_denoised, x_target)
|
||||
|
||||
|
||||
class KDiffusion(Diffusion):
|
||||
"""Elucidated Diffusion (Karras et al. 2022): https://arxiv.org/abs/2206.00364"""
|
||||
|
||||
alias = "k"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
net: nn.Module,
|
||||
*,
|
||||
sigma_distribution: Distribution,
|
||||
sigma_data: float, # data distribution standard deviation
|
||||
dynamic_threshold: float = 0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.net = net
|
||||
self.sigma_data = sigma_data
|
||||
self.sigma_distribution = sigma_distribution
|
||||
self.dynamic_threshold = dynamic_threshold
|
||||
|
||||
def get_scale_weights(self, sigmas: Tensor) -> Tuple[Tensor, ...]:
|
||||
sigma_data = self.sigma_data
|
||||
c_noise = torch.log(sigmas) * 0.25
|
||||
sigmas = rearrange(sigmas, "b -> b 1 1")
|
||||
c_skip = (sigma_data**2) / (sigmas**2 + sigma_data**2)
|
||||
c_out = sigmas * sigma_data * (sigma_data**2 + sigmas**2) ** -0.5
|
||||
c_in = (sigmas**2 + sigma_data**2) ** -0.5
|
||||
return c_skip, c_out, c_in, c_noise
|
||||
|
||||
def denoise_fn(
|
||||
self,
|
||||
x_noisy: Tensor,
|
||||
sigmas: Optional[Tensor] = None,
|
||||
sigma: Optional[float] = None,
|
||||
**kwargs,
|
||||
) -> Tensor:
|
||||
batch_size, device = x_noisy.shape[0], x_noisy.device
|
||||
sigmas = to_batch(x=sigma, xs=sigmas, batch_size=batch_size, device=device)
|
||||
|
||||
# Predict network output and add skip connection
|
||||
c_skip, c_out, c_in, c_noise = self.get_scale_weights(sigmas)
|
||||
x_pred = self.net(c_in * x_noisy, c_noise, **kwargs)
|
||||
x_denoised = c_skip * x_noisy + c_out * x_pred
|
||||
|
||||
return x_denoised
|
||||
|
||||
def loss_weight(self, sigmas: Tensor) -> Tensor:
|
||||
# Computes weight depending on data distribution
|
||||
return (sigmas**2 + self.sigma_data**2) * (sigmas * self.sigma_data) ** -2
|
||||
|
||||
def forward(self, x: Tensor, noise: Tensor = None, **kwargs) -> Tensor:
|
||||
batch_size, device = x.shape[0], x.device
|
||||
from einops import rearrange, reduce
|
||||
|
||||
# Sample amount of noise to add for each batch element
|
||||
sigmas = self.sigma_distribution(num_samples=batch_size, device=device)
|
||||
sigmas_padded = rearrange(sigmas, "b -> b 1 1")
|
||||
|
||||
# Add noise to input
|
||||
noise = default(noise, lambda: torch.randn_like(x))
|
||||
x_noisy = x + sigmas_padded * noise
|
||||
|
||||
# Compute denoised values
|
||||
x_denoised = self.denoise_fn(x_noisy, sigmas=sigmas, **kwargs)
|
||||
|
||||
# Compute weighted loss
|
||||
losses = F.mse_loss(x_denoised, x, reduction="none")
|
||||
losses = reduce(losses, "b ... -> b", "mean")
|
||||
losses = losses * self.loss_weight(sigmas)
|
||||
loss = losses.mean()
|
||||
return loss
|
||||
|
||||
|
||||
class VKDiffusion(Diffusion):
|
||||
alias = "vk"
|
||||
|
||||
def __init__(self, net: nn.Module, *, sigma_distribution: Distribution):
|
||||
super().__init__()
|
||||
self.net = net
|
||||
self.sigma_distribution = sigma_distribution
|
||||
|
||||
def get_scale_weights(self, sigmas: Tensor) -> Tuple[Tensor, ...]:
|
||||
sigma_data = 1.0
|
||||
sigmas = rearrange(sigmas, "b -> b 1 1")
|
||||
c_skip = (sigma_data**2) / (sigmas**2 + sigma_data**2)
|
||||
c_out = -sigmas * sigma_data * (sigma_data**2 + sigmas**2) ** -0.5
|
||||
c_in = (sigmas**2 + sigma_data**2) ** -0.5
|
||||
return c_skip, c_out, c_in
|
||||
|
||||
def sigma_to_t(self, sigmas: Tensor) -> Tensor:
|
||||
return sigmas.atan() / pi * 2
|
||||
|
||||
def t_to_sigma(self, t: Tensor) -> Tensor:
|
||||
return (t * pi / 2).tan()
|
||||
|
||||
def denoise_fn(
|
||||
self,
|
||||
x_noisy: Tensor,
|
||||
sigmas: Optional[Tensor] = None,
|
||||
sigma: Optional[float] = None,
|
||||
**kwargs,
|
||||
) -> Tensor:
|
||||
batch_size, device = x_noisy.shape[0], x_noisy.device
|
||||
sigmas = to_batch(x=sigma, xs=sigmas, batch_size=batch_size, device=device)
|
||||
|
||||
# Predict network output and add skip connection
|
||||
c_skip, c_out, c_in = self.get_scale_weights(sigmas)
|
||||
x_pred = self.net(c_in * x_noisy, self.sigma_to_t(sigmas), **kwargs)
|
||||
x_denoised = c_skip * x_noisy + c_out * x_pred
|
||||
return x_denoised
|
||||
|
||||
def forward(self, x: Tensor, noise: Tensor = None, **kwargs) -> Tensor:
|
||||
batch_size, device = x.shape[0], x.device
|
||||
|
||||
# Sample amount of noise to add for each batch element
|
||||
sigmas = self.sigma_distribution(num_samples=batch_size, device=device)
|
||||
sigmas_padded = rearrange(sigmas, "b -> b 1 1")
|
||||
|
||||
# Add noise to input
|
||||
noise = default(noise, lambda: torch.randn_like(x))
|
||||
x_noisy = x + sigmas_padded * noise
|
||||
|
||||
# Compute model output
|
||||
c_skip, c_out, c_in = self.get_scale_weights(sigmas)
|
||||
x_pred = self.net(c_in * x_noisy, self.sigma_to_t(sigmas), **kwargs)
|
||||
|
||||
# Compute v-objective target
|
||||
v_target = (x - c_skip * x_noisy) / (c_out + 1e-7)
|
||||
|
||||
# Compute loss
|
||||
loss = F.mse_loss(x_pred, v_target)
|
||||
return loss
|
||||
|
||||
|
||||
"""
|
||||
Diffusion Sampling
|
||||
"""
|
||||
|
||||
""" Schedules """
|
||||
|
||||
|
||||
class Schedule(nn.Module):
|
||||
"""Interface used by different sampling schedules"""
|
||||
|
||||
def forward(self, num_steps: int, device: torch.device) -> Tensor:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class LinearSchedule(Schedule):
|
||||
def forward(self, num_steps: int, device: Any) -> Tensor:
|
||||
sigmas = torch.linspace(1, 0, num_steps + 1)[:-1]
|
||||
return sigmas
|
||||
|
||||
|
||||
class KarrasSchedule(Schedule):
|
||||
"""https://arxiv.org/abs/2206.00364 equation 5"""
|
||||
|
||||
def __init__(self, sigma_min: float, sigma_max: float, rho: float = 7.0):
|
||||
super().__init__()
|
||||
self.sigma_min = sigma_min
|
||||
self.sigma_max = sigma_max
|
||||
self.rho = rho
|
||||
|
||||
def forward(self, num_steps: int, device: Any) -> Tensor:
|
||||
rho_inv = 1.0 / self.rho
|
||||
steps = torch.arange(num_steps, device=device, dtype=torch.float32)
|
||||
sigmas = (
|
||||
self.sigma_max**rho_inv
|
||||
+ (steps / (num_steps - 1))
|
||||
* (self.sigma_min**rho_inv - self.sigma_max**rho_inv)
|
||||
) ** self.rho
|
||||
sigmas = F.pad(sigmas, pad=(0, 1), value=0.0)
|
||||
return sigmas
|
||||
|
||||
|
||||
""" Samplers """
|
||||
|
||||
|
||||
class Sampler(nn.Module):
|
||||
diffusion_types: List[Type[Diffusion]] = []
|
||||
|
||||
def forward(
|
||||
self, noise: Tensor, fn: Callable, sigmas: Tensor, num_steps: int
|
||||
) -> Tensor:
|
||||
raise NotImplementedError()
|
||||
|
||||
def inpaint(
|
||||
self,
|
||||
source: Tensor,
|
||||
mask: Tensor,
|
||||
fn: Callable,
|
||||
sigmas: Tensor,
|
||||
num_steps: int,
|
||||
num_resamples: int,
|
||||
) -> Tensor:
|
||||
raise NotImplementedError("Inpainting not available with current sampler")
|
||||
|
||||
|
||||
class VSampler(Sampler):
|
||||
diffusion_types = [VDiffusion]
|
||||
|
||||
def get_alpha_beta(self, sigma: float) -> Tuple[float, float]:
|
||||
angle = sigma * pi / 2
|
||||
alpha = cos(angle)
|
||||
beta = sin(angle)
|
||||
return alpha, beta
|
||||
|
||||
def forward(
|
||||
self, noise: Tensor, fn: Callable, sigmas: Tensor, num_steps: int
|
||||
) -> Tensor:
|
||||
x = sigmas[0] * noise
|
||||
alpha, beta = self.get_alpha_beta(sigmas[0].item())
|
||||
|
||||
for i in range(num_steps - 1):
|
||||
is_last = i == num_steps - 1
|
||||
|
||||
x_denoised = fn(x, sigma=sigmas[i])
|
||||
x_pred = x * alpha - x_denoised * beta
|
||||
x_eps = x * beta + x_denoised * alpha
|
||||
|
||||
if not is_last:
|
||||
alpha, beta = self.get_alpha_beta(sigmas[i + 1].item())
|
||||
x = x_pred * alpha + x_eps * beta
|
||||
|
||||
return x_pred
|
||||
|
||||
|
||||
class KarrasSampler(Sampler):
|
||||
"""https://arxiv.org/abs/2206.00364 algorithm 1"""
|
||||
|
||||
diffusion_types = [KDiffusion, VKDiffusion]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
s_tmin: float = 0,
|
||||
s_tmax: float = float("inf"),
|
||||
s_churn: float = 0.0,
|
||||
s_noise: float = 1.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.s_tmin = s_tmin
|
||||
self.s_tmax = s_tmax
|
||||
self.s_noise = s_noise
|
||||
self.s_churn = s_churn
|
||||
|
||||
def step(
|
||||
self, x: Tensor, fn: Callable, sigma: float, sigma_next: float, gamma: float
|
||||
) -> Tensor:
|
||||
"""Algorithm 2 (step)"""
|
||||
# Select temporarily increased noise level
|
||||
sigma_hat = sigma + gamma * sigma
|
||||
# Add noise to move from sigma to sigma_hat
|
||||
epsilon = self.s_noise * torch.randn_like(x)
|
||||
x_hat = x + sqrt(sigma_hat**2 - sigma**2) * epsilon
|
||||
# Evaluate ∂x/∂sigma at sigma_hat
|
||||
d = (x_hat - fn(x_hat, sigma=sigma_hat)) / sigma_hat
|
||||
# Take euler step from sigma_hat to sigma_next
|
||||
x_next = x_hat + (sigma_next - sigma_hat) * d
|
||||
# Second order correction
|
||||
if sigma_next != 0:
|
||||
model_out_next = fn(x_next, sigma=sigma_next)
|
||||
d_prime = (x_next - model_out_next) / sigma_next
|
||||
x_next = x_hat + 0.5 * (sigma - sigma_hat) * (d + d_prime)
|
||||
return x_next
|
||||
|
||||
def forward(
|
||||
self, noise: Tensor, fn: Callable, sigmas: Tensor, num_steps: int
|
||||
) -> Tensor:
|
||||
x = sigmas[0] * noise
|
||||
# Compute gammas
|
||||
gammas = torch.where(
|
||||
(sigmas >= self.s_tmin) & (sigmas <= self.s_tmax),
|
||||
min(self.s_churn / num_steps, sqrt(2) - 1),
|
||||
0.0,
|
||||
)
|
||||
# Denoise to sample
|
||||
for i in range(num_steps - 1):
|
||||
x = self.step(
|
||||
x, fn=fn, sigma=sigmas[i], sigma_next=sigmas[i + 1], gamma=gammas[i] # type: ignore # noqa
|
||||
)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class AEulerSampler(Sampler):
|
||||
diffusion_types = [KDiffusion, VKDiffusion]
|
||||
|
||||
def get_sigmas(self, sigma: float, sigma_next: float) -> Tuple[float, float]:
|
||||
sigma_up = sqrt(sigma_next**2 * (sigma**2 - sigma_next**2) / sigma**2)
|
||||
sigma_down = sqrt(sigma_next**2 - sigma_up**2)
|
||||
return sigma_up, sigma_down
|
||||
|
||||
def step(self, x: Tensor, fn: Callable, sigma: float, sigma_next: float) -> Tensor:
|
||||
# Sigma steps
|
||||
sigma_up, sigma_down = self.get_sigmas(sigma, sigma_next)
|
||||
# Derivative at sigma (∂x/∂sigma)
|
||||
d = (x - fn(x, sigma=sigma)) / sigma
|
||||
# Euler method
|
||||
x_next = x + d * (sigma_down - sigma)
|
||||
# Add randomness
|
||||
x_next = x_next + torch.randn_like(x) * sigma_up
|
||||
return x_next
|
||||
|
||||
def forward(
|
||||
self, noise: Tensor, fn: Callable, sigmas: Tensor, num_steps: int
|
||||
) -> Tensor:
|
||||
x = sigmas[0] * noise
|
||||
# Denoise to sample
|
||||
for i in range(num_steps - 1):
|
||||
x = self.step(x, fn=fn, sigma=sigmas[i], sigma_next=sigmas[i + 1]) # type: ignore # noqa
|
||||
return x
|
||||
|
||||
|
||||
class ADPM2Sampler(Sampler):
|
||||
"""https://www.desmos.com/calculator/jbxjlqd9mb"""
|
||||
|
||||
diffusion_types = [KDiffusion, VKDiffusion]
|
||||
|
||||
def __init__(self, rho: float = 1.0):
|
||||
super().__init__()
|
||||
self.rho = rho
|
||||
|
||||
def get_sigmas(self, sigma: float, sigma_next: float) -> Tuple[float, float, float]:
|
||||
r = self.rho
|
||||
sigma_up = sqrt(sigma_next**2 * (sigma**2 - sigma_next**2) / sigma**2)
|
||||
sigma_down = sqrt(sigma_next**2 - sigma_up**2)
|
||||
sigma_mid = ((sigma ** (1 / r) + sigma_down ** (1 / r)) / 2) ** r
|
||||
return sigma_up, sigma_down, sigma_mid
|
||||
|
||||
def step(self, x: Tensor, fn: Callable, sigma: float, sigma_next: float) -> Tensor:
|
||||
# Sigma steps
|
||||
sigma_up, sigma_down, sigma_mid = self.get_sigmas(sigma, sigma_next)
|
||||
# Derivative at sigma (∂x/∂sigma)
|
||||
d = (x - fn(x, sigma=sigma)) / sigma
|
||||
# Denoise to midpoint
|
||||
x_mid = x + d * (sigma_mid - sigma)
|
||||
# Derivative at sigma_mid (∂x_mid/∂sigma_mid)
|
||||
d_mid = (x_mid - fn(x_mid, sigma=sigma_mid)) / sigma_mid
|
||||
# Denoise to next
|
||||
x = x + d_mid * (sigma_down - sigma)
|
||||
# Add randomness
|
||||
x_next = x + torch.randn_like(x) * sigma_up
|
||||
return x_next
|
||||
|
||||
def forward(
|
||||
self, noise: Tensor, fn: Callable, sigmas: Tensor, num_steps: int
|
||||
) -> Tensor:
|
||||
x = sigmas[0] * noise
|
||||
# Denoise to sample
|
||||
for i in range(num_steps - 1):
|
||||
x = self.step(x, fn=fn, sigma=sigmas[i], sigma_next=sigmas[i + 1]) # type: ignore # noqa
|
||||
return x
|
||||
|
||||
def inpaint(
|
||||
self,
|
||||
source: Tensor,
|
||||
mask: Tensor,
|
||||
fn: Callable,
|
||||
sigmas: Tensor,
|
||||
num_steps: int,
|
||||
num_resamples: int,
|
||||
) -> Tensor:
|
||||
x = sigmas[0] * torch.randn_like(source)
|
||||
|
||||
for i in range(num_steps - 1):
|
||||
# Noise source to current noise level
|
||||
source_noisy = source + sigmas[i] * torch.randn_like(source)
|
||||
for r in range(num_resamples):
|
||||
# Merge noisy source and current then denoise
|
||||
x = source_noisy * mask + x * ~mask
|
||||
x = self.step(x, fn=fn, sigma=sigmas[i], sigma_next=sigmas[i + 1]) # type: ignore # noqa
|
||||
# Renoise if not last resample step
|
||||
if r < num_resamples - 1:
|
||||
sigma = sqrt(sigmas[i] ** 2 - sigmas[i + 1] ** 2)
|
||||
x = x + sigma * torch.randn_like(x)
|
||||
|
||||
return source * mask + x * ~mask
|
||||
|
||||
|
||||
""" Main Classes """
|
||||
|
||||
|
||||
class DiffusionSampler(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
diffusion: Diffusion,
|
||||
*,
|
||||
sampler: Sampler,
|
||||
sigma_schedule: Schedule,
|
||||
num_steps: Optional[int] = None,
|
||||
clamp: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
self.denoise_fn = diffusion.denoise_fn
|
||||
self.sampler = sampler
|
||||
self.sigma_schedule = sigma_schedule
|
||||
self.num_steps = num_steps
|
||||
self.clamp = clamp
|
||||
|
||||
# Check sampler is compatible with diffusion type
|
||||
sampler_class = sampler.__class__.__name__
|
||||
diffusion_class = diffusion.__class__.__name__
|
||||
message = f"{sampler_class} incompatible with {diffusion_class}"
|
||||
assert diffusion.alias in [t.alias for t in sampler.diffusion_types], message
|
||||
|
||||
def forward(
|
||||
self, noise: Tensor, num_steps: Optional[int] = None, **kwargs
|
||||
) -> Tensor:
|
||||
device = noise.device
|
||||
num_steps = default(num_steps, self.num_steps) # type: ignore
|
||||
assert exists(num_steps), "Parameter `num_steps` must be provided"
|
||||
# Compute sigmas using schedule
|
||||
sigmas = self.sigma_schedule(num_steps, device)
|
||||
# Append additional kwargs to denoise function (used e.g. for conditional unet)
|
||||
fn = lambda *a, **ka: self.denoise_fn(*a, **{**ka, **kwargs}) # noqa
|
||||
# Sample using sampler
|
||||
x = self.sampler(noise, fn=fn, sigmas=sigmas, num_steps=num_steps)
|
||||
x = x.clamp(-1.0, 1.0) if self.clamp else x
|
||||
return x
|
||||
|
||||
|
||||
class DiffusionInpainter(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
diffusion: Diffusion,
|
||||
*,
|
||||
num_steps: int,
|
||||
num_resamples: int,
|
||||
sampler: Sampler,
|
||||
sigma_schedule: Schedule,
|
||||
):
|
||||
super().__init__()
|
||||
self.denoise_fn = diffusion.denoise_fn
|
||||
self.num_steps = num_steps
|
||||
self.num_resamples = num_resamples
|
||||
self.inpaint_fn = sampler.inpaint
|
||||
self.sigma_schedule = sigma_schedule
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, inpaint: Tensor, inpaint_mask: Tensor) -> Tensor:
|
||||
x = self.inpaint_fn(
|
||||
source=inpaint,
|
||||
mask=inpaint_mask,
|
||||
fn=self.denoise_fn,
|
||||
sigmas=self.sigma_schedule(self.num_steps, inpaint.device),
|
||||
num_steps=self.num_steps,
|
||||
num_resamples=self.num_resamples,
|
||||
)
|
||||
return x
|
||||
|
||||
|
||||
def sequential_mask(like: Tensor, start: int) -> Tensor:
|
||||
length, device = like.shape[2], like.device
|
||||
mask = torch.ones_like(like, dtype=torch.bool)
|
||||
mask[:, :, start:] = torch.zeros((length - start,), device=device)
|
||||
return mask
|
||||
|
||||
|
||||
class SpanBySpanComposer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
inpainter: DiffusionInpainter,
|
||||
*,
|
||||
num_spans: int,
|
||||
):
|
||||
super().__init__()
|
||||
self.inpainter = inpainter
|
||||
self.num_spans = num_spans
|
||||
|
||||
def forward(self, start: Tensor, keep_start: bool = False) -> Tensor:
|
||||
half_length = start.shape[2] // 2
|
||||
|
||||
spans = list(start.chunk(chunks=2, dim=-1)) if keep_start else []
|
||||
# Inpaint second half from first half
|
||||
inpaint = torch.zeros_like(start)
|
||||
inpaint[:, :, :half_length] = start[:, :, half_length:]
|
||||
inpaint_mask = sequential_mask(like=start, start=half_length)
|
||||
|
||||
for i in range(self.num_spans):
|
||||
# Inpaint second half
|
||||
span = self.inpainter(inpaint=inpaint, inpaint_mask=inpaint_mask)
|
||||
# Replace first half with generated second half
|
||||
second_half = span[:, :, half_length:]
|
||||
inpaint[:, :, :half_length] = second_half
|
||||
# Save generated span
|
||||
spans.append(second_half)
|
||||
|
||||
return torch.cat(spans, dim=2)
|
||||
|
||||
|
||||
class XDiffusion(nn.Module):
|
||||
def __init__(self, type: str, net: nn.Module, **kwargs):
|
||||
super().__init__()
|
||||
|
||||
diffusion_classes = [VDiffusion, KDiffusion, VKDiffusion]
|
||||
aliases = [t.alias for t in diffusion_classes] # type: ignore
|
||||
message = f"type='{type}' must be one of {*aliases,}"
|
||||
assert type in aliases, message
|
||||
self.net = net
|
||||
|
||||
for XDiffusion in diffusion_classes:
|
||||
if XDiffusion.alias == type: # type: ignore
|
||||
self.diffusion = XDiffusion(net=net, **kwargs)
|
||||
|
||||
def forward(self, *args, **kwargs) -> Tensor:
|
||||
return self.diffusion(*args, **kwargs)
|
||||
|
||||
def sample(
|
||||
self,
|
||||
noise: Tensor,
|
||||
num_steps: int,
|
||||
sigma_schedule: Schedule,
|
||||
sampler: Sampler,
|
||||
clamp: bool,
|
||||
**kwargs,
|
||||
) -> Tensor:
|
||||
diffusion_sampler = DiffusionSampler(
|
||||
diffusion=self.diffusion,
|
||||
sampler=sampler,
|
||||
sigma_schedule=sigma_schedule,
|
||||
num_steps=num_steps,
|
||||
clamp=clamp,
|
||||
)
|
||||
return diffusion_sampler(noise, **kwargs)
|
||||
@@ -0,0 +1,83 @@
|
||||
from functools import reduce
|
||||
from inspect import isfunction
|
||||
from math import ceil, floor, log2, pi
|
||||
from typing import Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
from torch import Generator, Tensor
|
||||
from typing_extensions import TypeGuard
|
||||
|
||||
T = TypeVar("T")
|
||||
|
||||
|
||||
def exists(val: Optional[T]) -> TypeGuard[T]:
|
||||
return val is not None
|
||||
|
||||
|
||||
def iff(condition: bool, value: T) -> Optional[T]:
|
||||
return value if condition else None
|
||||
|
||||
|
||||
def is_sequence(obj: T) -> TypeGuard[Union[list, tuple]]:
|
||||
return isinstance(obj, list) or isinstance(obj, tuple)
|
||||
|
||||
|
||||
def default(val: Optional[T], d: Union[Callable[..., T], T]) -> T:
|
||||
if exists(val):
|
||||
return val
|
||||
return d() if isfunction(d) else d
|
||||
|
||||
|
||||
def to_list(val: Union[T, Sequence[T]]) -> List[T]:
|
||||
if isinstance(val, tuple):
|
||||
return list(val)
|
||||
if isinstance(val, list):
|
||||
return val
|
||||
return [val] # type: ignore
|
||||
|
||||
|
||||
def prod(vals: Sequence[int]) -> int:
|
||||
return reduce(lambda x, y: x * y, vals)
|
||||
|
||||
|
||||
def closest_power_2(x: float) -> int:
|
||||
exponent = log2(x)
|
||||
distance_fn = lambda z: abs(x - 2**z) # noqa
|
||||
exponent_closest = min((floor(exponent), ceil(exponent)), key=distance_fn)
|
||||
return 2 ** int(exponent_closest)
|
||||
|
||||
|
||||
def rand_bool(shape, proba, device=None):
|
||||
if proba == 1:
|
||||
return torch.ones(shape, device=device, dtype=torch.bool)
|
||||
elif proba == 0:
|
||||
return torch.zeros(shape, device=device, dtype=torch.bool)
|
||||
else:
|
||||
return torch.bernoulli(torch.full(shape, proba, device=device)).to(torch.bool)
|
||||
|
||||
|
||||
"""
|
||||
Kwargs Utils
|
||||
"""
|
||||
|
||||
|
||||
def group_dict_by_prefix(prefix: str, d: Dict) -> Tuple[Dict, Dict]:
|
||||
return_dicts: Tuple[Dict, Dict] = ({}, {})
|
||||
for key in d.keys():
|
||||
no_prefix = int(not key.startswith(prefix))
|
||||
return_dicts[no_prefix][key] = d[key]
|
||||
return return_dicts
|
||||
|
||||
|
||||
def groupby(prefix: str, d: Dict, keep_prefix: bool = False) -> Tuple[Dict, Dict]:
|
||||
kwargs_with_prefix, kwargs = group_dict_by_prefix(prefix, d)
|
||||
if keep_prefix:
|
||||
return kwargs_with_prefix, kwargs
|
||||
kwargs_no_prefix = {k[len(prefix) :]: v for k, v in kwargs_with_prefix.items()}
|
||||
return kwargs_no_prefix, kwargs
|
||||
|
||||
|
||||
def prefix_dict(prefix: str, d: Dict) -> Dict:
|
||||
return {prefix + str(k): v for k, v in d.items()}
|
||||
@@ -0,0 +1,267 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.nn as nn
|
||||
from torch.nn import Conv1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, spectral_norm
|
||||
|
||||
from .utils import get_padding
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
def stft(x, fft_size, hop_size, win_length, window):
|
||||
"""Perform STFT and convert to magnitude spectrogram.
|
||||
Args:
|
||||
x (Tensor): Input signal tensor (B, T).
|
||||
fft_size (int): FFT size.
|
||||
hop_size (int): Hop size.
|
||||
win_length (int): Window length.
|
||||
window (str): Window function type.
|
||||
Returns:
|
||||
Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
|
||||
"""
|
||||
x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True)
|
||||
real = x_stft[..., 0]
|
||||
imag = x_stft[..., 1]
|
||||
|
||||
return torch.abs(x_stft).transpose(2, 1)
|
||||
|
||||
|
||||
class SpecDiscriminator(nn.Module):
|
||||
"""docstring for Discriminator."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
fft_size=1024,
|
||||
shift_size=120,
|
||||
win_length=600,
|
||||
window="hann_window",
|
||||
use_spectral_norm=False,
|
||||
):
|
||||
super(SpecDiscriminator, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.fft_size = fft_size
|
||||
self.shift_size = shift_size
|
||||
self.win_length = win_length
|
||||
self.window = getattr(torch, window)(win_length)
|
||||
self.discriminators = nn.ModuleList(
|
||||
[
|
||||
norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
|
||||
norm_f(
|
||||
nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))
|
||||
),
|
||||
norm_f(
|
||||
nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))
|
||||
),
|
||||
norm_f(
|
||||
nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))
|
||||
),
|
||||
norm_f(
|
||||
nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))
|
||||
|
||||
def forward(self, y):
|
||||
fmap = []
|
||||
y = y.squeeze(1)
|
||||
y = stft(
|
||||
y,
|
||||
self.fft_size,
|
||||
self.shift_size,
|
||||
self.win_length,
|
||||
self.window.to(y.get_device()),
|
||||
)
|
||||
y = y.unsqueeze(1)
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y = d(y)
|
||||
y = F.leaky_relu(y, LRELU_SLOPE)
|
||||
fmap.append(y)
|
||||
|
||||
y = self.out(y)
|
||||
fmap.append(y)
|
||||
|
||||
return torch.flatten(y, 1, -1), fmap
|
||||
|
||||
|
||||
class MultiResSpecDiscriminator(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
fft_sizes=[1024, 2048, 512],
|
||||
hop_sizes=[120, 240, 50],
|
||||
win_lengths=[600, 1200, 240],
|
||||
window="hann_window",
|
||||
):
|
||||
super(MultiResSpecDiscriminator, self).__init__()
|
||||
self.discriminators = nn.ModuleList(
|
||||
[
|
||||
SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
|
||||
SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
|
||||
SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window),
|
||||
]
|
||||
)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
norm_f(
|
||||
Conv2d(
|
||||
1,
|
||||
32,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(5, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
32,
|
||||
128,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(5, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
128,
|
||||
512,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(5, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
Conv2d(
|
||||
512,
|
||||
1024,
|
||||
(kernel_size, 1),
|
||||
(stride, 1),
|
||||
padding=(get_padding(5, 1), 0),
|
||||
)
|
||||
),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
|
||||
]
|
||||
)
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
self.discriminators = nn.ModuleList(
|
||||
[
|
||||
DiscriminatorP(2),
|
||||
DiscriminatorP(3),
|
||||
DiscriminatorP(5),
|
||||
DiscriminatorP(7),
|
||||
DiscriminatorP(11),
|
||||
]
|
||||
)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class WavLMDiscriminator(nn.Module):
|
||||
"""docstring for Discriminator."""
|
||||
|
||||
def __init__(
|
||||
self, slm_hidden=768, slm_layers=13, initial_channel=64, use_spectral_norm=False
|
||||
):
|
||||
super(WavLMDiscriminator, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.pre = norm_f(
|
||||
Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
|
||||
)
|
||||
|
||||
self.convs = nn.ModuleList(
|
||||
[
|
||||
norm_f(
|
||||
nn.Conv1d(
|
||||
initial_channel, initial_channel * 2, kernel_size=5, padding=2
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
nn.Conv1d(
|
||||
initial_channel * 2,
|
||||
initial_channel * 4,
|
||||
kernel_size=5,
|
||||
padding=2,
|
||||
)
|
||||
),
|
||||
norm_f(
|
||||
nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pre(x)
|
||||
|
||||
fmap = []
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x
|
||||
@@ -0,0 +1,643 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.nn as nn
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from .utils import init_weights, get_padding
|
||||
|
||||
import math
|
||||
import random
|
||||
import numpy as np
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class AdaIN1d(nn.Module):
|
||||
def __init__(self, style_dim, num_features):
|
||||
super().__init__()
|
||||
self.norm = nn.InstanceNorm1d(num_features, affine=False)
|
||||
self.fc = nn.Linear(style_dim, num_features * 2)
|
||||
|
||||
def forward(self, x, s):
|
||||
h = self.fc(s)
|
||||
h = h.view(h.size(0), h.size(1), 1)
|
||||
gamma, beta = torch.chunk(h, chunks=2, dim=1)
|
||||
return (1 + gamma) * self.norm(x) + beta
|
||||
|
||||
|
||||
class AdaINResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64):
|
||||
super(AdaINResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList(
|
||||
[
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2]),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList(
|
||||
[
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
self.adain1 = nn.ModuleList(
|
||||
[
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
]
|
||||
)
|
||||
|
||||
self.adain2 = nn.ModuleList(
|
||||
[
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
]
|
||||
)
|
||||
|
||||
self.alpha1 = nn.ParameterList(
|
||||
[nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))]
|
||||
)
|
||||
self.alpha2 = nn.ParameterList(
|
||||
[nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))]
|
||||
)
|
||||
|
||||
def forward(self, x, s):
|
||||
for c1, c2, n1, n2, a1, a2 in zip(
|
||||
self.convs1, self.convs2, self.adain1, self.adain2, self.alpha1, self.alpha2
|
||||
):
|
||||
xt = n1(x, s)
|
||||
xt = xt + (1 / a1) * (torch.sin(a1 * xt) ** 2) # Snake1D
|
||||
xt = c1(xt)
|
||||
xt = n2(xt, s)
|
||||
xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2) # Snake1D
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class SineGen(torch.nn.Module):
|
||||
"""Definition of sine generator
|
||||
SineGen(samp_rate, harmonic_num = 0,
|
||||
sine_amp = 0.1, noise_std = 0.003,
|
||||
voiced_threshold = 0,
|
||||
flag_for_pulse=False)
|
||||
samp_rate: sampling rate in Hz
|
||||
harmonic_num: number of harmonic overtones (default 0)
|
||||
sine_amp: amplitude of sine-wavefrom (default 0.1)
|
||||
noise_std: std of Gaussian noise (default 0.003)
|
||||
voiced_thoreshold: F0 threshold for U/V classification (default 0)
|
||||
flag_for_pulse: this SinGen is used inside PulseGen (default False)
|
||||
Note: when flag_for_pulse is True, the first time step of a voiced
|
||||
segment is always sin(np.pi) or cos(0)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
samp_rate,
|
||||
upsample_scale,
|
||||
harmonic_num=0,
|
||||
sine_amp=0.1,
|
||||
noise_std=0.003,
|
||||
voiced_threshold=0,
|
||||
flag_for_pulse=False,
|
||||
):
|
||||
super(SineGen, self).__init__()
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = noise_std
|
||||
self.harmonic_num = harmonic_num
|
||||
self.dim = self.harmonic_num + 1
|
||||
self.sampling_rate = samp_rate
|
||||
self.voiced_threshold = voiced_threshold
|
||||
self.flag_for_pulse = flag_for_pulse
|
||||
self.upsample_scale = upsample_scale
|
||||
|
||||
def _f02uv(self, f0):
|
||||
# generate uv signal
|
||||
uv = (f0 > self.voiced_threshold).type(torch.float32)
|
||||
return uv
|
||||
|
||||
def _f02sine(self, f0_values):
|
||||
"""f0_values: (batchsize, length, dim)
|
||||
where dim indicates fundamental tone and overtones
|
||||
"""
|
||||
# convert to F0 in rad. The interger part n can be ignored
|
||||
# because 2 * np.pi * n doesn't affect phase
|
||||
rad_values = (f0_values / self.sampling_rate) % 1
|
||||
|
||||
# initial phase noise (no noise for fundamental component)
|
||||
rand_ini = torch.rand(
|
||||
f0_values.shape[0], f0_values.shape[2], device=f0_values.device
|
||||
)
|
||||
rand_ini[:, 0] = 0
|
||||
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
|
||||
|
||||
# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
|
||||
if not self.flag_for_pulse:
|
||||
# # for normal case
|
||||
|
||||
# # To prevent torch.cumsum numerical overflow,
|
||||
# # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
|
||||
# # Buffer tmp_over_one_idx indicates the time step to add -1.
|
||||
# # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
|
||||
# tmp_over_one = torch.cumsum(rad_values, 1) % 1
|
||||
# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
|
||||
# cumsum_shift = torch.zeros_like(rad_values)
|
||||
# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||||
|
||||
# phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
|
||||
rad_values = torch.nn.functional.interpolate(
|
||||
rad_values.transpose(1, 2),
|
||||
scale_factor=1 / self.upsample_scale,
|
||||
mode="linear",
|
||||
).transpose(1, 2)
|
||||
|
||||
# tmp_over_one = torch.cumsum(rad_values, 1) % 1
|
||||
# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
|
||||
# cumsum_shift = torch.zeros_like(rad_values)
|
||||
# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||||
|
||||
phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
|
||||
phase = torch.nn.functional.interpolate(
|
||||
phase.transpose(1, 2) * self.upsample_scale,
|
||||
scale_factor=self.upsample_scale,
|
||||
mode="linear",
|
||||
).transpose(1, 2)
|
||||
sines = torch.sin(phase)
|
||||
|
||||
else:
|
||||
# If necessary, make sure that the first time step of every
|
||||
# voiced segments is sin(pi) or cos(0)
|
||||
# This is used for pulse-train generation
|
||||
|
||||
# identify the last time step in unvoiced segments
|
||||
uv = self._f02uv(f0_values)
|
||||
uv_1 = torch.roll(uv, shifts=-1, dims=1)
|
||||
uv_1[:, -1, :] = 1
|
||||
u_loc = (uv < 1) * (uv_1 > 0)
|
||||
|
||||
# get the instantanouse phase
|
||||
tmp_cumsum = torch.cumsum(rad_values, dim=1)
|
||||
# different batch needs to be processed differently
|
||||
for idx in range(f0_values.shape[0]):
|
||||
temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
|
||||
temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
|
||||
# stores the accumulation of i.phase within
|
||||
# each voiced segments
|
||||
tmp_cumsum[idx, :, :] = 0
|
||||
tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
|
||||
|
||||
# rad_values - tmp_cumsum: remove the accumulation of i.phase
|
||||
# within the previous voiced segment.
|
||||
i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
|
||||
|
||||
# get the sines
|
||||
sines = torch.cos(i_phase * 2 * np.pi)
|
||||
return sines
|
||||
|
||||
def forward(self, f0):
|
||||
"""sine_tensor, uv = forward(f0)
|
||||
input F0: tensor(batchsize=1, length, dim=1)
|
||||
f0 for unvoiced steps should be 0
|
||||
output sine_tensor: tensor(batchsize=1, length, dim)
|
||||
output uv: tensor(batchsize=1, length, 1)
|
||||
"""
|
||||
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
|
||||
# fundamental component
|
||||
fn = torch.multiply(
|
||||
f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)
|
||||
)
|
||||
|
||||
# generate sine waveforms
|
||||
sine_waves = self._f02sine(fn) * self.sine_amp
|
||||
|
||||
# generate uv signal
|
||||
# uv = torch.ones(f0.shape)
|
||||
# uv = uv * (f0 > self.voiced_threshold)
|
||||
uv = self._f02uv(f0)
|
||||
|
||||
# noise: for unvoiced should be similar to sine_amp
|
||||
# std = self.sine_amp/3 -> max value ~ self.sine_amp
|
||||
# . for voiced regions is self.noise_std
|
||||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||||
noise = noise_amp * torch.randn_like(sine_waves)
|
||||
|
||||
# first: set the unvoiced part to 0 by uv
|
||||
# then: additive noise
|
||||
sine_waves = sine_waves * uv + noise
|
||||
return sine_waves, uv, noise
|
||||
|
||||
|
||||
class SourceModuleHnNSF(torch.nn.Module):
|
||||
"""SourceModule for hn-nsf
|
||||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0)
|
||||
sampling_rate: sampling_rate in Hz
|
||||
harmonic_num: number of harmonic above F0 (default: 0)
|
||||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||||
note that amplitude of noise in unvoiced is decided
|
||||
by sine_amp
|
||||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
uv (batchsize, length, 1)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sampling_rate,
|
||||
upsample_scale,
|
||||
harmonic_num=0,
|
||||
sine_amp=0.1,
|
||||
add_noise_std=0.003,
|
||||
voiced_threshod=0,
|
||||
):
|
||||
super(SourceModuleHnNSF, self).__init__()
|
||||
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = add_noise_std
|
||||
|
||||
# to produce sine waveforms
|
||||
self.l_sin_gen = SineGen(
|
||||
sampling_rate,
|
||||
upsample_scale,
|
||||
harmonic_num,
|
||||
sine_amp,
|
||||
add_noise_std,
|
||||
voiced_threshod,
|
||||
)
|
||||
|
||||
# to merge source harmonics into a single excitation
|
||||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||||
self.l_tanh = torch.nn.Tanh()
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
"""
|
||||
# source for harmonic branch
|
||||
with torch.no_grad():
|
||||
sine_wavs, uv, _ = self.l_sin_gen(x)
|
||||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||||
|
||||
# source for noise branch, in the same shape as uv
|
||||
noise = torch.randn_like(uv) * self.sine_amp / 3
|
||||
return sine_merge, noise, uv
|
||||
|
||||
|
||||
def padDiff(x):
|
||||
return F.pad(
|
||||
F.pad(x, (0, 0, -1, 1), "constant", 0) - x, (0, 0, 0, -1), "constant", 0
|
||||
)
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
style_dim,
|
||||
resblock_kernel_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
resblock_dilation_sizes,
|
||||
upsample_kernel_sizes,
|
||||
):
|
||||
super(Generator, self).__init__()
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
resblock = AdaINResBlock1
|
||||
|
||||
self.m_source = SourceModuleHnNSF(
|
||||
sampling_rate=24000,
|
||||
upsample_scale=np.prod(upsample_rates),
|
||||
harmonic_num=8,
|
||||
voiced_threshod=10,
|
||||
)
|
||||
|
||||
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
|
||||
self.noise_convs = nn.ModuleList()
|
||||
self.ups = nn.ModuleList()
|
||||
self.noise_res = nn.ModuleList()
|
||||
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
c_cur = upsample_initial_channel // (2 ** (i + 1))
|
||||
|
||||
self.ups.append(
|
||||
weight_norm(
|
||||
ConvTranspose1d(
|
||||
upsample_initial_channel // (2**i),
|
||||
upsample_initial_channel // (2 ** (i + 1)),
|
||||
k,
|
||||
u,
|
||||
padding=(u // 2 + u % 2),
|
||||
output_padding=u % 2,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
if i + 1 < len(upsample_rates): #
|
||||
stride_f0 = np.prod(upsample_rates[i + 1 :])
|
||||
self.noise_convs.append(
|
||||
Conv1d(
|
||||
1,
|
||||
c_cur,
|
||||
kernel_size=stride_f0 * 2,
|
||||
stride=stride_f0,
|
||||
padding=(stride_f0 + 1) // 2,
|
||||
)
|
||||
)
|
||||
self.noise_res.append(resblock(c_cur, 7, [1, 3, 5], style_dim))
|
||||
else:
|
||||
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
|
||||
self.noise_res.append(resblock(c_cur, 11, [1, 3, 5], style_dim))
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
|
||||
self.alphas = nn.ParameterList()
|
||||
self.alphas.append(nn.Parameter(torch.ones(1, upsample_initial_channel, 1)))
|
||||
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||||
self.alphas.append(nn.Parameter(torch.ones(1, ch, 1)))
|
||||
|
||||
for j, (k, d) in enumerate(
|
||||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||||
):
|
||||
self.resblocks.append(resblock(ch, k, d, style_dim))
|
||||
|
||||
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
||||
self.ups.apply(init_weights)
|
||||
self.conv_post.apply(init_weights)
|
||||
|
||||
def forward(self, x, s, f0):
|
||||
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
|
||||
|
||||
har_source, noi_source, uv = self.m_source(f0)
|
||||
har_source = har_source.transpose(1, 2)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
x = x + (1 / self.alphas[i]) * (torch.sin(self.alphas[i] * x) ** 2)
|
||||
x_source = self.noise_convs[i](har_source)
|
||||
x_source = self.noise_res[i](x_source, s)
|
||||
|
||||
x = self.ups[i](x)
|
||||
x = x + x_source
|
||||
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x, s)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x, s)
|
||||
x = xs / self.num_kernels
|
||||
x = x + (1 / self.alphas[i + 1]) * (torch.sin(self.alphas[i + 1] * x) ** 2)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print("Removing weight norm...")
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
remove_weight_norm(self.conv_pre)
|
||||
remove_weight_norm(self.conv_post)
|
||||
|
||||
|
||||
class AdainResBlk1d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_in,
|
||||
dim_out,
|
||||
style_dim=64,
|
||||
actv=nn.LeakyReLU(0.2),
|
||||
upsample="none",
|
||||
dropout_p=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.actv = actv
|
||||
self.upsample_type = upsample
|
||||
self.upsample = UpSample1d(upsample)
|
||||
self.learned_sc = dim_in != dim_out
|
||||
self._build_weights(dim_in, dim_out, style_dim)
|
||||
self.dropout = nn.Dropout(dropout_p)
|
||||
|
||||
if upsample == "none":
|
||||
self.pool = nn.Identity()
|
||||
else:
|
||||
self.pool = weight_norm(
|
||||
nn.ConvTranspose1d(
|
||||
dim_in,
|
||||
dim_in,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
groups=dim_in,
|
||||
padding=1,
|
||||
output_padding=1,
|
||||
)
|
||||
)
|
||||
|
||||
def _build_weights(self, dim_in, dim_out, style_dim):
|
||||
self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
|
||||
self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
|
||||
self.norm1 = AdaIN1d(style_dim, dim_in)
|
||||
self.norm2 = AdaIN1d(style_dim, dim_out)
|
||||
if self.learned_sc:
|
||||
self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
|
||||
|
||||
def _shortcut(self, x):
|
||||
x = self.upsample(x)
|
||||
if self.learned_sc:
|
||||
x = self.conv1x1(x)
|
||||
return x
|
||||
|
||||
def _residual(self, x, s):
|
||||
x = self.norm1(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.pool(x)
|
||||
x = self.conv1(self.dropout(x))
|
||||
x = self.norm2(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.conv2(self.dropout(x))
|
||||
return x
|
||||
|
||||
def forward(self, x, s):
|
||||
out = self._residual(x, s)
|
||||
out = (out + self._shortcut(x)) / math.sqrt(2)
|
||||
return out
|
||||
|
||||
|
||||
class UpSample1d(nn.Module):
|
||||
def __init__(self, layer_type):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
def forward(self, x):
|
||||
if self.layer_type == "none":
|
||||
return x
|
||||
else:
|
||||
return F.interpolate(x, scale_factor=2, mode="nearest")
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_in=512,
|
||||
F0_channel=512,
|
||||
style_dim=64,
|
||||
dim_out=80,
|
||||
resblock_kernel_sizes=[3, 7, 11],
|
||||
upsample_rates=[10, 5, 3, 2],
|
||||
upsample_initial_channel=512,
|
||||
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
upsample_kernel_sizes=[20, 10, 6, 4],
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.decode = nn.ModuleList()
|
||||
|
||||
self.encode = AdainResBlk1d(dim_in + 2, 1024, style_dim)
|
||||
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 512, style_dim, upsample=True))
|
||||
|
||||
self.F0_conv = weight_norm(
|
||||
nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)
|
||||
)
|
||||
|
||||
self.N_conv = weight_norm(
|
||||
nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)
|
||||
)
|
||||
|
||||
self.asr_res = nn.Sequential(
|
||||
weight_norm(nn.Conv1d(512, 64, kernel_size=1)),
|
||||
)
|
||||
|
||||
self.generator = Generator(
|
||||
style_dim,
|
||||
resblock_kernel_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
resblock_dilation_sizes,
|
||||
upsample_kernel_sizes,
|
||||
)
|
||||
|
||||
def forward(self, asr, F0_curve, N, s):
|
||||
if self.training:
|
||||
downlist = [0, 3, 7]
|
||||
F0_down = downlist[random.randint(0, 2)]
|
||||
downlist = [0, 3, 7, 15]
|
||||
N_down = downlist[random.randint(0, 3)]
|
||||
if F0_down:
|
||||
F0_curve = (
|
||||
nn.functional.conv1d(
|
||||
F0_curve.unsqueeze(1),
|
||||
torch.ones(1, 1, F0_down).to("cuda"),
|
||||
padding=F0_down // 2,
|
||||
).squeeze(1)
|
||||
/ F0_down
|
||||
)
|
||||
if N_down:
|
||||
N = (
|
||||
nn.functional.conv1d(
|
||||
N.unsqueeze(1),
|
||||
torch.ones(1, 1, N_down).to("cuda"),
|
||||
padding=N_down // 2,
|
||||
).squeeze(1)
|
||||
/ N_down
|
||||
)
|
||||
|
||||
F0 = self.F0_conv(F0_curve.unsqueeze(1))
|
||||
N = self.N_conv(N.unsqueeze(1))
|
||||
|
||||
x = torch.cat([asr, F0, N], axis=1)
|
||||
x = self.encode(x, s)
|
||||
|
||||
asr_res = self.asr_res(asr)
|
||||
|
||||
res = True
|
||||
for block in self.decode:
|
||||
if res:
|
||||
x = torch.cat([x, asr_res, F0, N], axis=1)
|
||||
x = block(x, s)
|
||||
if block.upsample_type != "none":
|
||||
res = False
|
||||
|
||||
x = self.generator(x, s, F0_curve)
|
||||
return x
|
||||
@@ -0,0 +1,720 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.nn as nn
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from .utils import init_weights, get_padding
|
||||
|
||||
import math
|
||||
import random
|
||||
import numpy as np
|
||||
from scipy.signal import get_window
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class AdaIN1d(nn.Module):
|
||||
def __init__(self, style_dim, num_features):
|
||||
super().__init__()
|
||||
self.norm = nn.InstanceNorm1d(num_features, affine=False)
|
||||
self.fc = nn.Linear(style_dim, num_features * 2)
|
||||
|
||||
def forward(self, x, s):
|
||||
h = self.fc(s)
|
||||
h = h.view(h.size(0), h.size(1), 1)
|
||||
gamma, beta = torch.chunk(h, chunks=2, dim=1)
|
||||
return (1 + gamma) * self.norm(x) + beta
|
||||
|
||||
|
||||
class AdaINResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64):
|
||||
super(AdaINResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList(
|
||||
[
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2]),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList(
|
||||
[
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
weight_norm(
|
||||
Conv1d(
|
||||
channels,
|
||||
channels,
|
||||
kernel_size,
|
||||
1,
|
||||
dilation=1,
|
||||
padding=get_padding(kernel_size, 1),
|
||||
)
|
||||
),
|
||||
]
|
||||
)
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
self.adain1 = nn.ModuleList(
|
||||
[
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
]
|
||||
)
|
||||
|
||||
self.adain2 = nn.ModuleList(
|
||||
[
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
AdaIN1d(style_dim, channels),
|
||||
]
|
||||
)
|
||||
|
||||
self.alpha1 = nn.ParameterList(
|
||||
[nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))]
|
||||
)
|
||||
self.alpha2 = nn.ParameterList(
|
||||
[nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))]
|
||||
)
|
||||
|
||||
def forward(self, x, s):
|
||||
for c1, c2, n1, n2, a1, a2 in zip(
|
||||
self.convs1, self.convs2, self.adain1, self.adain2, self.alpha1, self.alpha2
|
||||
):
|
||||
xt = n1(x, s)
|
||||
xt = xt + (1 / a1) * (torch.sin(a1 * xt) ** 2) # Snake1D
|
||||
xt = c1(xt)
|
||||
xt = n2(xt, s)
|
||||
xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2) # Snake1D
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class TorchSTFT(torch.nn.Module):
|
||||
def __init__(
|
||||
self, filter_length=800, hop_length=200, win_length=800, window="hann"
|
||||
):
|
||||
super().__init__()
|
||||
self.filter_length = filter_length
|
||||
self.hop_length = hop_length
|
||||
self.win_length = win_length
|
||||
self.window = torch.from_numpy(
|
||||
get_window(window, win_length, fftbins=True).astype(np.float32)
|
||||
)
|
||||
|
||||
def transform(self, input_data):
|
||||
forward_transform = torch.stft(
|
||||
input_data,
|
||||
self.filter_length,
|
||||
self.hop_length,
|
||||
self.win_length,
|
||||
window=self.window.to(input_data.device),
|
||||
return_complex=True,
|
||||
)
|
||||
|
||||
return torch.abs(forward_transform), torch.angle(forward_transform)
|
||||
|
||||
def inverse(self, magnitude, phase):
|
||||
inverse_transform = torch.istft(
|
||||
magnitude * torch.exp(phase * 1j),
|
||||
self.filter_length,
|
||||
self.hop_length,
|
||||
self.win_length,
|
||||
window=self.window.to(magnitude.device),
|
||||
)
|
||||
|
||||
return inverse_transform.unsqueeze(
|
||||
-2
|
||||
) # unsqueeze to stay consistent with conv_transpose1d implementation
|
||||
|
||||
def forward(self, input_data):
|
||||
self.magnitude, self.phase = self.transform(input_data)
|
||||
reconstruction = self.inverse(self.magnitude, self.phase)
|
||||
return reconstruction
|
||||
|
||||
|
||||
class SineGen(torch.nn.Module):
|
||||
"""Definition of sine generator
|
||||
SineGen(samp_rate, harmonic_num = 0,
|
||||
sine_amp = 0.1, noise_std = 0.003,
|
||||
voiced_threshold = 0,
|
||||
flag_for_pulse=False)
|
||||
samp_rate: sampling rate in Hz
|
||||
harmonic_num: number of harmonic overtones (default 0)
|
||||
sine_amp: amplitude of sine-wavefrom (default 0.1)
|
||||
noise_std: std of Gaussian noise (default 0.003)
|
||||
voiced_thoreshold: F0 threshold for U/V classification (default 0)
|
||||
flag_for_pulse: this SinGen is used inside PulseGen (default False)
|
||||
Note: when flag_for_pulse is True, the first time step of a voiced
|
||||
segment is always sin(np.pi) or cos(0)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
samp_rate,
|
||||
upsample_scale,
|
||||
harmonic_num=0,
|
||||
sine_amp=0.1,
|
||||
noise_std=0.003,
|
||||
voiced_threshold=0,
|
||||
flag_for_pulse=False,
|
||||
):
|
||||
super(SineGen, self).__init__()
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = noise_std
|
||||
self.harmonic_num = harmonic_num
|
||||
self.dim = self.harmonic_num + 1
|
||||
self.sampling_rate = samp_rate
|
||||
self.voiced_threshold = voiced_threshold
|
||||
self.flag_for_pulse = flag_for_pulse
|
||||
self.upsample_scale = upsample_scale
|
||||
|
||||
def _f02uv(self, f0):
|
||||
# generate uv signal
|
||||
uv = (f0 > self.voiced_threshold).type(torch.float32)
|
||||
return uv
|
||||
|
||||
def _f02sine(self, f0_values):
|
||||
"""f0_values: (batchsize, length, dim)
|
||||
where dim indicates fundamental tone and overtones
|
||||
"""
|
||||
# convert to F0 in rad. The interger part n can be ignored
|
||||
# because 2 * np.pi * n doesn't affect phase
|
||||
rad_values = (f0_values / self.sampling_rate) % 1
|
||||
|
||||
# initial phase noise (no noise for fundamental component)
|
||||
rand_ini = torch.rand(
|
||||
f0_values.shape[0], f0_values.shape[2], device=f0_values.device
|
||||
)
|
||||
rand_ini[:, 0] = 0
|
||||
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
|
||||
|
||||
# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
|
||||
if not self.flag_for_pulse:
|
||||
# # for normal case
|
||||
|
||||
# # To prevent torch.cumsum numerical overflow,
|
||||
# # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
|
||||
# # Buffer tmp_over_one_idx indicates the time step to add -1.
|
||||
# # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
|
||||
# tmp_over_one = torch.cumsum(rad_values, 1) % 1
|
||||
# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
|
||||
# cumsum_shift = torch.zeros_like(rad_values)
|
||||
# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||||
|
||||
# phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
|
||||
rad_values = torch.nn.functional.interpolate(
|
||||
rad_values.transpose(1, 2),
|
||||
scale_factor=1 / self.upsample_scale,
|
||||
mode="linear",
|
||||
).transpose(1, 2)
|
||||
|
||||
# tmp_over_one = torch.cumsum(rad_values, 1) % 1
|
||||
# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
|
||||
# cumsum_shift = torch.zeros_like(rad_values)
|
||||
# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||||
|
||||
phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
|
||||
phase = torch.nn.functional.interpolate(
|
||||
phase.transpose(1, 2) * self.upsample_scale,
|
||||
scale_factor=self.upsample_scale,
|
||||
mode="linear",
|
||||
).transpose(1, 2)
|
||||
sines = torch.sin(phase)
|
||||
|
||||
else:
|
||||
# If necessary, make sure that the first time step of every
|
||||
# voiced segments is sin(pi) or cos(0)
|
||||
# This is used for pulse-train generation
|
||||
|
||||
# identify the last time step in unvoiced segments
|
||||
uv = self._f02uv(f0_values)
|
||||
uv_1 = torch.roll(uv, shifts=-1, dims=1)
|
||||
uv_1[:, -1, :] = 1
|
||||
u_loc = (uv < 1) * (uv_1 > 0)
|
||||
|
||||
# get the instantanouse phase
|
||||
tmp_cumsum = torch.cumsum(rad_values, dim=1)
|
||||
# different batch needs to be processed differently
|
||||
for idx in range(f0_values.shape[0]):
|
||||
temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
|
||||
temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
|
||||
# stores the accumulation of i.phase within
|
||||
# each voiced segments
|
||||
tmp_cumsum[idx, :, :] = 0
|
||||
tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
|
||||
|
||||
# rad_values - tmp_cumsum: remove the accumulation of i.phase
|
||||
# within the previous voiced segment.
|
||||
i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
|
||||
|
||||
# get the sines
|
||||
sines = torch.cos(i_phase * 2 * np.pi)
|
||||
return sines
|
||||
|
||||
def forward(self, f0):
|
||||
"""sine_tensor, uv = forward(f0)
|
||||
input F0: tensor(batchsize=1, length, dim=1)
|
||||
f0 for unvoiced steps should be 0
|
||||
output sine_tensor: tensor(batchsize=1, length, dim)
|
||||
output uv: tensor(batchsize=1, length, 1)
|
||||
"""
|
||||
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
|
||||
# fundamental component
|
||||
fn = torch.multiply(
|
||||
f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)
|
||||
)
|
||||
|
||||
# generate sine waveforms
|
||||
sine_waves = self._f02sine(fn) * self.sine_amp
|
||||
|
||||
# generate uv signal
|
||||
# uv = torch.ones(f0.shape)
|
||||
# uv = uv * (f0 > self.voiced_threshold)
|
||||
uv = self._f02uv(f0)
|
||||
|
||||
# noise: for unvoiced should be similar to sine_amp
|
||||
# std = self.sine_amp/3 -> max value ~ self.sine_amp
|
||||
# . for voiced regions is self.noise_std
|
||||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||||
noise = noise_amp * torch.randn_like(sine_waves)
|
||||
|
||||
# first: set the unvoiced part to 0 by uv
|
||||
# then: additive noise
|
||||
sine_waves = sine_waves * uv + noise
|
||||
return sine_waves, uv, noise
|
||||
|
||||
|
||||
class SourceModuleHnNSF(torch.nn.Module):
|
||||
"""SourceModule for hn-nsf
|
||||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0)
|
||||
sampling_rate: sampling_rate in Hz
|
||||
harmonic_num: number of harmonic above F0 (default: 0)
|
||||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||||
note that amplitude of noise in unvoiced is decided
|
||||
by sine_amp
|
||||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
uv (batchsize, length, 1)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sampling_rate,
|
||||
upsample_scale,
|
||||
harmonic_num=0,
|
||||
sine_amp=0.1,
|
||||
add_noise_std=0.003,
|
||||
voiced_threshod=0,
|
||||
):
|
||||
super(SourceModuleHnNSF, self).__init__()
|
||||
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = add_noise_std
|
||||
|
||||
# to produce sine waveforms
|
||||
self.l_sin_gen = SineGen(
|
||||
sampling_rate,
|
||||
upsample_scale,
|
||||
harmonic_num,
|
||||
sine_amp,
|
||||
add_noise_std,
|
||||
voiced_threshod,
|
||||
)
|
||||
|
||||
# to merge source harmonics into a single excitation
|
||||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||||
self.l_tanh = torch.nn.Tanh()
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
"""
|
||||
# source for harmonic branch
|
||||
with torch.no_grad():
|
||||
sine_wavs, uv, _ = self.l_sin_gen(x)
|
||||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||||
|
||||
# source for noise branch, in the same shape as uv
|
||||
noise = torch.randn_like(uv) * self.sine_amp / 3
|
||||
return sine_merge, noise, uv
|
||||
|
||||
|
||||
def padDiff(x):
|
||||
return F.pad(
|
||||
F.pad(x, (0, 0, -1, 1), "constant", 0) - x, (0, 0, 0, -1), "constant", 0
|
||||
)
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
style_dim,
|
||||
resblock_kernel_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
resblock_dilation_sizes,
|
||||
upsample_kernel_sizes,
|
||||
gen_istft_n_fft,
|
||||
gen_istft_hop_size,
|
||||
):
|
||||
super(Generator, self).__init__()
|
||||
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
resblock = AdaINResBlock1
|
||||
|
||||
self.m_source = SourceModuleHnNSF(
|
||||
sampling_rate=24000,
|
||||
upsample_scale=np.prod(upsample_rates) * gen_istft_hop_size,
|
||||
harmonic_num=8,
|
||||
voiced_threshod=10,
|
||||
)
|
||||
self.f0_upsamp = torch.nn.Upsample(
|
||||
scale_factor=np.prod(upsample_rates) * gen_istft_hop_size
|
||||
)
|
||||
self.noise_convs = nn.ModuleList()
|
||||
self.noise_res = nn.ModuleList()
|
||||
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
self.ups.append(
|
||||
weight_norm(
|
||||
ConvTranspose1d(
|
||||
upsample_initial_channel // (2**i),
|
||||
upsample_initial_channel // (2 ** (i + 1)),
|
||||
k,
|
||||
u,
|
||||
padding=(k - u) // 2,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||||
for j, (k, d) in enumerate(
|
||||
zip(resblock_kernel_sizes, resblock_dilation_sizes)
|
||||
):
|
||||
self.resblocks.append(resblock(ch, k, d, style_dim))
|
||||
|
||||
c_cur = upsample_initial_channel // (2 ** (i + 1))
|
||||
|
||||
if i + 1 < len(upsample_rates): #
|
||||
stride_f0 = np.prod(upsample_rates[i + 1 :])
|
||||
self.noise_convs.append(
|
||||
Conv1d(
|
||||
gen_istft_n_fft + 2,
|
||||
c_cur,
|
||||
kernel_size=stride_f0 * 2,
|
||||
stride=stride_f0,
|
||||
padding=(stride_f0 + 1) // 2,
|
||||
)
|
||||
)
|
||||
self.noise_res.append(resblock(c_cur, 7, [1, 3, 5], style_dim))
|
||||
else:
|
||||
self.noise_convs.append(
|
||||
Conv1d(gen_istft_n_fft + 2, c_cur, kernel_size=1)
|
||||
)
|
||||
self.noise_res.append(resblock(c_cur, 11, [1, 3, 5], style_dim))
|
||||
|
||||
self.post_n_fft = gen_istft_n_fft
|
||||
self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
|
||||
self.ups.apply(init_weights)
|
||||
self.conv_post.apply(init_weights)
|
||||
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
|
||||
self.stft = TorchSTFT(
|
||||
filter_length=gen_istft_n_fft,
|
||||
hop_length=gen_istft_hop_size,
|
||||
win_length=gen_istft_n_fft,
|
||||
)
|
||||
|
||||
def forward(self, x, s, f0):
|
||||
with torch.no_grad():
|
||||
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
|
||||
|
||||
har_source, noi_source, uv = self.m_source(f0)
|
||||
har_source = har_source.transpose(1, 2).squeeze(1)
|
||||
har_spec, har_phase = self.stft.transform(har_source)
|
||||
har = torch.cat([har_spec, har_phase], dim=1)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
x_source = self.noise_convs[i](har)
|
||||
x_source = self.noise_res[i](x_source, s)
|
||||
|
||||
x = self.ups[i](x)
|
||||
if i == self.num_upsamples - 1:
|
||||
x = self.reflection_pad(x)
|
||||
|
||||
x = x + x_source
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x, s)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x, s)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
spec = torch.exp(x[:, : self.post_n_fft // 2 + 1, :])
|
||||
phase = torch.sin(x[:, self.post_n_fft // 2 + 1 :, :])
|
||||
return self.stft.inverse(spec, phase)
|
||||
|
||||
def fw_phase(self, x, s):
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
x = self.ups[i](x)
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x, s)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x, s)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.reflection_pad(x)
|
||||
x = self.conv_post(x)
|
||||
spec = torch.exp(x[:, : self.post_n_fft // 2 + 1, :])
|
||||
phase = torch.sin(x[:, self.post_n_fft // 2 + 1 :, :])
|
||||
return spec, phase
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print("Removing weight norm...")
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
remove_weight_norm(self.conv_pre)
|
||||
remove_weight_norm(self.conv_post)
|
||||
|
||||
|
||||
class AdainResBlk1d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_in,
|
||||
dim_out,
|
||||
style_dim=64,
|
||||
actv=nn.LeakyReLU(0.2),
|
||||
upsample="none",
|
||||
dropout_p=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.actv = actv
|
||||
self.upsample_type = upsample
|
||||
self.upsample = UpSample1d(upsample)
|
||||
self.learned_sc = dim_in != dim_out
|
||||
self._build_weights(dim_in, dim_out, style_dim)
|
||||
self.dropout = nn.Dropout(dropout_p)
|
||||
|
||||
if upsample == "none":
|
||||
self.pool = nn.Identity()
|
||||
else:
|
||||
self.pool = weight_norm(
|
||||
nn.ConvTranspose1d(
|
||||
dim_in,
|
||||
dim_in,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
groups=dim_in,
|
||||
padding=1,
|
||||
output_padding=1,
|
||||
)
|
||||
)
|
||||
|
||||
def _build_weights(self, dim_in, dim_out, style_dim):
|
||||
self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
|
||||
self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
|
||||
self.norm1 = AdaIN1d(style_dim, dim_in)
|
||||
self.norm2 = AdaIN1d(style_dim, dim_out)
|
||||
if self.learned_sc:
|
||||
self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
|
||||
|
||||
def _shortcut(self, x):
|
||||
x = self.upsample(x)
|
||||
if self.learned_sc:
|
||||
x = self.conv1x1(x)
|
||||
return x
|
||||
|
||||
def _residual(self, x, s):
|
||||
x = self.norm1(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.pool(x)
|
||||
x = self.conv1(self.dropout(x))
|
||||
x = self.norm2(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.conv2(self.dropout(x))
|
||||
return x
|
||||
|
||||
def forward(self, x, s):
|
||||
out = self._residual(x, s)
|
||||
out = (out + self._shortcut(x)) / math.sqrt(2)
|
||||
return out
|
||||
|
||||
|
||||
class UpSample1d(nn.Module):
|
||||
def __init__(self, layer_type):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
def forward(self, x):
|
||||
if self.layer_type == "none":
|
||||
return x
|
||||
else:
|
||||
return F.interpolate(x, scale_factor=2, mode="nearest")
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_in=512,
|
||||
F0_channel=512,
|
||||
style_dim=64,
|
||||
dim_out=80,
|
||||
resblock_kernel_sizes=[3, 7, 11],
|
||||
upsample_rates=[10, 6],
|
||||
upsample_initial_channel=512,
|
||||
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
upsample_kernel_sizes=[20, 12],
|
||||
gen_istft_n_fft=20,
|
||||
gen_istft_hop_size=5,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.decode = nn.ModuleList()
|
||||
|
||||
self.encode = AdainResBlk1d(dim_in + 2, 1024, style_dim)
|
||||
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 512, style_dim, upsample=True))
|
||||
|
||||
self.F0_conv = weight_norm(
|
||||
nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)
|
||||
)
|
||||
|
||||
self.N_conv = weight_norm(
|
||||
nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)
|
||||
)
|
||||
|
||||
self.asr_res = nn.Sequential(
|
||||
weight_norm(nn.Conv1d(512, 64, kernel_size=1)),
|
||||
)
|
||||
|
||||
self.generator = Generator(
|
||||
style_dim,
|
||||
resblock_kernel_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
resblock_dilation_sizes,
|
||||
upsample_kernel_sizes,
|
||||
gen_istft_n_fft,
|
||||
gen_istft_hop_size,
|
||||
)
|
||||
|
||||
def forward(self, asr, F0_curve, N, s):
|
||||
if self.training:
|
||||
downlist = [0, 3, 7]
|
||||
F0_down = downlist[random.randint(0, 2)]
|
||||
downlist = [0, 3, 7, 15]
|
||||
N_down = downlist[random.randint(0, 3)]
|
||||
if F0_down:
|
||||
F0_curve = (
|
||||
nn.functional.conv1d(
|
||||
F0_curve.unsqueeze(1),
|
||||
torch.ones(1, 1, F0_down).to("cuda"),
|
||||
padding=F0_down // 2,
|
||||
).squeeze(1)
|
||||
/ F0_down
|
||||
)
|
||||
if N_down:
|
||||
N = (
|
||||
nn.functional.conv1d(
|
||||
N.unsqueeze(1),
|
||||
torch.ones(1, 1, N_down).to("cuda"),
|
||||
padding=N_down // 2,
|
||||
).squeeze(1)
|
||||
/ N_down
|
||||
)
|
||||
|
||||
F0 = self.F0_conv(F0_curve.unsqueeze(1))
|
||||
N = self.N_conv(N.unsqueeze(1))
|
||||
|
||||
x = torch.cat([asr, F0, N], axis=1)
|
||||
x = self.encode(x, s)
|
||||
|
||||
asr_res = self.asr_res(asr)
|
||||
|
||||
res = True
|
||||
for block in self.decode:
|
||||
if res:
|
||||
x = torch.cat([x, asr_res, F0, N], axis=1)
|
||||
x = block(x, s)
|
||||
if block.upsample_type != "none":
|
||||
res = False
|
||||
|
||||
x = self.generator(x, s, F0_curve)
|
||||
return x
|
||||
@@ -0,0 +1,256 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class SLMAdversarialLoss(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
model,
|
||||
wl,
|
||||
sampler,
|
||||
min_len,
|
||||
max_len,
|
||||
batch_percentage=0.5,
|
||||
skip_update=10,
|
||||
sig=1.5,
|
||||
):
|
||||
super(SLMAdversarialLoss, self).__init__()
|
||||
self.model = model
|
||||
self.wl = wl
|
||||
self.sampler = sampler
|
||||
|
||||
self.min_len = min_len
|
||||
self.max_len = max_len
|
||||
self.batch_percentage = batch_percentage
|
||||
|
||||
self.sig = sig
|
||||
self.skip_update = skip_update
|
||||
|
||||
def forward(
|
||||
self,
|
||||
iters,
|
||||
y_rec_gt,
|
||||
y_rec_gt_pred,
|
||||
waves,
|
||||
mel_input_length,
|
||||
ref_text,
|
||||
ref_lengths,
|
||||
use_ind,
|
||||
s_trg,
|
||||
ref_s=None,
|
||||
):
|
||||
text_mask = length_to_mask(ref_lengths).to(ref_text.device)
|
||||
bert_dur = self.model.bert(ref_text, attention_mask=(~text_mask).int())
|
||||
d_en = self.model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
if use_ind and np.random.rand() < 0.5:
|
||||
s_preds = s_trg
|
||||
else:
|
||||
num_steps = np.random.randint(3, 5)
|
||||
if ref_s is not None:
|
||||
s_preds = self.sampler(
|
||||
noise=torch.randn_like(s_trg).unsqueeze(1).to(ref_text.device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=1,
|
||||
features=ref_s, # reference from the same speaker as the embedding
|
||||
embedding_mask_proba=0.1,
|
||||
num_steps=num_steps,
|
||||
).squeeze(1)
|
||||
else:
|
||||
s_preds = self.sampler(
|
||||
noise=torch.randn_like(s_trg).unsqueeze(1).to(ref_text.device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=1,
|
||||
embedding_mask_proba=0.1,
|
||||
num_steps=num_steps,
|
||||
).squeeze(1)
|
||||
|
||||
s_dur = s_preds[:, 128:]
|
||||
s = s_preds[:, :128]
|
||||
|
||||
d, _ = self.model.predictor(
|
||||
d_en,
|
||||
s_dur,
|
||||
ref_lengths,
|
||||
torch.randn(ref_lengths.shape[0], ref_lengths.max(), 2).to(ref_text.device),
|
||||
text_mask,
|
||||
)
|
||||
|
||||
bib = 0
|
||||
|
||||
output_lengths = []
|
||||
attn_preds = []
|
||||
|
||||
# differentiable duration modeling
|
||||
for _s2s_pred, _text_length in zip(d, ref_lengths):
|
||||
_s2s_pred_org = _s2s_pred[:_text_length, :]
|
||||
|
||||
_s2s_pred = torch.sigmoid(_s2s_pred_org)
|
||||
_dur_pred = _s2s_pred.sum(axis=-1)
|
||||
|
||||
l = int(torch.round(_s2s_pred.sum()).item())
|
||||
t = torch.arange(0, l).expand(l)
|
||||
|
||||
t = (
|
||||
torch.arange(0, l)
|
||||
.unsqueeze(0)
|
||||
.expand((len(_s2s_pred), l))
|
||||
.to(ref_text.device)
|
||||
)
|
||||
loc = torch.cumsum(_dur_pred, dim=0) - _dur_pred / 2
|
||||
|
||||
h = torch.exp(
|
||||
-0.5 * torch.square(t - (l - loc.unsqueeze(-1))) / (self.sig) ** 2
|
||||
)
|
||||
|
||||
out = torch.nn.functional.conv1d(
|
||||
_s2s_pred_org.unsqueeze(0),
|
||||
h.unsqueeze(1),
|
||||
padding=h.shape[-1] - 1,
|
||||
groups=int(_text_length),
|
||||
)[..., :l]
|
||||
attn_preds.append(F.softmax(out.squeeze(), dim=0))
|
||||
|
||||
output_lengths.append(l)
|
||||
|
||||
max_len = max(output_lengths)
|
||||
|
||||
with torch.no_grad():
|
||||
t_en = self.model.text_encoder(ref_text, ref_lengths, text_mask)
|
||||
|
||||
s2s_attn = torch.zeros(len(ref_lengths), int(ref_lengths.max()), max_len).to(
|
||||
ref_text.device
|
||||
)
|
||||
for bib in range(len(output_lengths)):
|
||||
s2s_attn[bib, : ref_lengths[bib], : output_lengths[bib]] = attn_preds[bib]
|
||||
|
||||
asr_pred = t_en @ s2s_attn
|
||||
|
||||
_, p_pred = self.model.predictor(d_en, s_dur, ref_lengths, s2s_attn, text_mask)
|
||||
|
||||
mel_len = max(int(min(output_lengths) / 2 - 1), self.min_len // 2)
|
||||
mel_len = min(mel_len, self.max_len // 2)
|
||||
|
||||
# get clips
|
||||
|
||||
en = []
|
||||
p_en = []
|
||||
sp = []
|
||||
|
||||
F0_fakes = []
|
||||
N_fakes = []
|
||||
|
||||
wav = []
|
||||
|
||||
for bib in range(len(output_lengths)):
|
||||
mel_length_pred = output_lengths[bib]
|
||||
mel_length_gt = int(mel_input_length[bib].item() / 2)
|
||||
if mel_length_gt <= mel_len or mel_length_pred <= mel_len:
|
||||
continue
|
||||
|
||||
sp.append(s_preds[bib])
|
||||
|
||||
random_start = np.random.randint(0, mel_length_pred - mel_len)
|
||||
en.append(asr_pred[bib, :, random_start : random_start + mel_len])
|
||||
p_en.append(p_pred[bib, :, random_start : random_start + mel_len])
|
||||
|
||||
# get ground truth clips
|
||||
random_start = np.random.randint(0, mel_length_gt - mel_len)
|
||||
y = waves[bib][
|
||||
(random_start * 2) * 300 : ((random_start + mel_len) * 2) * 300
|
||||
]
|
||||
wav.append(torch.from_numpy(y).to(ref_text.device))
|
||||
|
||||
if len(wav) >= self.batch_percentage * len(
|
||||
waves
|
||||
): # prevent OOM due to longer lengths
|
||||
break
|
||||
|
||||
if len(sp) <= 1:
|
||||
return None
|
||||
|
||||
sp = torch.stack(sp)
|
||||
wav = torch.stack(wav).float()
|
||||
en = torch.stack(en)
|
||||
p_en = torch.stack(p_en)
|
||||
|
||||
F0_fake, N_fake = self.model.predictor.F0Ntrain(p_en, sp[:, 128:])
|
||||
y_pred = self.model.decoder(en, F0_fake, N_fake, sp[:, :128])
|
||||
|
||||
# discriminator loss
|
||||
if (iters + 1) % self.skip_update == 0:
|
||||
if np.random.randint(0, 2) == 0:
|
||||
wav = y_rec_gt_pred
|
||||
use_rec = True
|
||||
else:
|
||||
use_rec = False
|
||||
|
||||
crop_size = min(wav.size(-1), y_pred.size(-1))
|
||||
if (
|
||||
use_rec
|
||||
): # use reconstructed (shorter lengths), do length invariant regularization
|
||||
if wav.size(-1) > y_pred.size(-1):
|
||||
real_GP = wav[:, :, :crop_size]
|
||||
out_crop = self.wl.discriminator_forward(real_GP.detach().squeeze())
|
||||
out_org = self.wl.discriminator_forward(wav.detach().squeeze())
|
||||
loss_reg = F.l1_loss(out_crop, out_org[..., : out_crop.size(-1)])
|
||||
|
||||
if np.random.randint(0, 2) == 0:
|
||||
d_loss = self.wl.discriminator(
|
||||
real_GP.detach().squeeze(), y_pred.detach().squeeze()
|
||||
).mean()
|
||||
else:
|
||||
d_loss = self.wl.discriminator(
|
||||
wav.detach().squeeze(), y_pred.detach().squeeze()
|
||||
).mean()
|
||||
else:
|
||||
real_GP = y_pred[:, :, :crop_size]
|
||||
out_crop = self.wl.discriminator_forward(real_GP.detach().squeeze())
|
||||
out_org = self.wl.discriminator_forward(y_pred.detach().squeeze())
|
||||
loss_reg = F.l1_loss(out_crop, out_org[..., : out_crop.size(-1)])
|
||||
|
||||
if np.random.randint(0, 2) == 0:
|
||||
d_loss = self.wl.discriminator(
|
||||
wav.detach().squeeze(), real_GP.detach().squeeze()
|
||||
).mean()
|
||||
else:
|
||||
d_loss = self.wl.discriminator(
|
||||
wav.detach().squeeze(), y_pred.detach().squeeze()
|
||||
).mean()
|
||||
|
||||
# regularization (ignore length variation)
|
||||
d_loss += loss_reg
|
||||
|
||||
out_gt = self.wl.discriminator_forward(y_rec_gt.detach().squeeze())
|
||||
out_rec = self.wl.discriminator_forward(
|
||||
y_rec_gt_pred.detach().squeeze()
|
||||
)
|
||||
|
||||
# regularization (ignore reconstruction artifacts)
|
||||
d_loss += F.l1_loss(out_gt, out_rec)
|
||||
|
||||
else:
|
||||
d_loss = self.wl.discriminator(
|
||||
wav.detach().squeeze(), y_pred.detach().squeeze()
|
||||
).mean()
|
||||
else:
|
||||
d_loss = 0
|
||||
|
||||
# generator loss
|
||||
gen_loss = self.wl.generator(y_pred.squeeze())
|
||||
|
||||
gen_loss = gen_loss.mean()
|
||||
|
||||
return d_loss, gen_loss, y_pred.detach().cpu().numpy()
|
||||
|
||||
|
||||
def length_to_mask(lengths):
|
||||
mask = (
|
||||
torch.arange(lengths.max())
|
||||
.unsqueeze(0)
|
||||
.expand(lengths.shape[0], -1)
|
||||
.type_as(lengths)
|
||||
)
|
||||
mask = torch.gt(mask + 1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
@@ -0,0 +1,14 @@
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def apply_weight_norm(m):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
weight_norm(m)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size * dilation - dilation) / 2)
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1,29 @@
|
||||
log_dir: "logs/20201006"
|
||||
save_freq: 5
|
||||
device: "cuda"
|
||||
epochs: 180
|
||||
batch_size: 64
|
||||
pretrained_model: ""
|
||||
train_data: "ASRDataset/train_list.txt"
|
||||
val_data: "ASRDataset/val_list.txt"
|
||||
|
||||
dataset_params:
|
||||
data_augmentation: false
|
||||
|
||||
preprocess_parasm:
|
||||
sr: 24000
|
||||
spect_params:
|
||||
n_fft: 2048
|
||||
win_length: 1200
|
||||
hop_length: 300
|
||||
mel_params:
|
||||
n_mels: 80
|
||||
|
||||
model_params:
|
||||
input_dim: 80
|
||||
hidden_dim: 256
|
||||
n_token: 178
|
||||
token_embedding_dim: 512
|
||||
|
||||
optimizer_params:
|
||||
lr: 0.0005
|
||||
Binary file not shown.
@@ -0,0 +1,455 @@
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from typing import Optional, Any
|
||||
from torch import Tensor
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import torchaudio.functional as audio_F
|
||||
|
||||
import random
|
||||
|
||||
random.seed(0)
|
||||
|
||||
|
||||
def _get_activation_fn(activ):
|
||||
if activ == "relu":
|
||||
return nn.ReLU()
|
||||
elif activ == "lrelu":
|
||||
return nn.LeakyReLU(0.2)
|
||||
elif activ == "swish":
|
||||
return lambda x: x * torch.sigmoid(x)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Unexpected activ type %s, expected [relu, lrelu, swish]" % activ
|
||||
)
|
||||
|
||||
|
||||
class LinearNorm(torch.nn.Module):
|
||||
def __init__(self, in_dim, out_dim, bias=True, w_init_gain="linear"):
|
||||
super(LinearNorm, self).__init__()
|
||||
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
|
||||
|
||||
torch.nn.init.xavier_uniform_(
|
||||
self.linear_layer.weight, gain=torch.nn.init.calculate_gain(w_init_gain)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.linear_layer(x)
|
||||
|
||||
|
||||
class ConvNorm(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=None,
|
||||
dilation=1,
|
||||
bias=True,
|
||||
w_init_gain="linear",
|
||||
param=None,
|
||||
):
|
||||
super(ConvNorm, self).__init__()
|
||||
if padding is None:
|
||||
assert kernel_size % 2 == 1
|
||||
padding = int(dilation * (kernel_size - 1) / 2)
|
||||
|
||||
self.conv = torch.nn.Conv1d(
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
bias=bias,
|
||||
)
|
||||
|
||||
torch.nn.init.xavier_uniform_(
|
||||
self.conv.weight,
|
||||
gain=torch.nn.init.calculate_gain(w_init_gain, param=param),
|
||||
)
|
||||
|
||||
def forward(self, signal):
|
||||
conv_signal = self.conv(signal)
|
||||
return conv_signal
|
||||
|
||||
|
||||
class CausualConv(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=1,
|
||||
dilation=1,
|
||||
bias=True,
|
||||
w_init_gain="linear",
|
||||
param=None,
|
||||
):
|
||||
super(CausualConv, self).__init__()
|
||||
if padding is None:
|
||||
assert kernel_size % 2 == 1
|
||||
padding = int(dilation * (kernel_size - 1) / 2) * 2
|
||||
else:
|
||||
self.padding = padding * 2
|
||||
self.conv = nn.Conv1d(
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=self.padding,
|
||||
dilation=dilation,
|
||||
bias=bias,
|
||||
)
|
||||
|
||||
torch.nn.init.xavier_uniform_(
|
||||
self.conv.weight,
|
||||
gain=torch.nn.init.calculate_gain(w_init_gain, param=param),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv(x)
|
||||
x = x[:, :, : -self.padding]
|
||||
return x
|
||||
|
||||
|
||||
class CausualBlock(nn.Module):
|
||||
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ="lrelu"):
|
||||
super(CausualBlock, self).__init__()
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
self._get_conv(
|
||||
hidden_dim, dilation=3**i, activ=activ, dropout_p=dropout_p
|
||||
)
|
||||
for i in range(n_conv)
|
||||
]
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
res = x
|
||||
x = block(x)
|
||||
x += res
|
||||
return x
|
||||
|
||||
def _get_conv(self, hidden_dim, dilation, activ="lrelu", dropout_p=0.2):
|
||||
layers = [
|
||||
CausualConv(
|
||||
hidden_dim,
|
||||
hidden_dim,
|
||||
kernel_size=3,
|
||||
padding=dilation,
|
||||
dilation=dilation,
|
||||
),
|
||||
_get_activation_fn(activ),
|
||||
nn.BatchNorm1d(hidden_dim),
|
||||
nn.Dropout(p=dropout_p),
|
||||
CausualConv(hidden_dim, hidden_dim, kernel_size=3, padding=1, dilation=1),
|
||||
_get_activation_fn(activ),
|
||||
nn.Dropout(p=dropout_p),
|
||||
]
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
class ConvBlock(nn.Module):
|
||||
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ="relu"):
|
||||
super().__init__()
|
||||
self._n_groups = 8
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
self._get_conv(
|
||||
hidden_dim, dilation=3**i, activ=activ, dropout_p=dropout_p
|
||||
)
|
||||
for i in range(n_conv)
|
||||
]
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
res = x
|
||||
x = block(x)
|
||||
x += res
|
||||
return x
|
||||
|
||||
def _get_conv(self, hidden_dim, dilation, activ="relu", dropout_p=0.2):
|
||||
layers = [
|
||||
ConvNorm(
|
||||
hidden_dim,
|
||||
hidden_dim,
|
||||
kernel_size=3,
|
||||
padding=dilation,
|
||||
dilation=dilation,
|
||||
),
|
||||
_get_activation_fn(activ),
|
||||
nn.GroupNorm(num_groups=self._n_groups, num_channels=hidden_dim),
|
||||
nn.Dropout(p=dropout_p),
|
||||
ConvNorm(hidden_dim, hidden_dim, kernel_size=3, padding=1, dilation=1),
|
||||
_get_activation_fn(activ),
|
||||
nn.Dropout(p=dropout_p),
|
||||
]
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
class LocationLayer(nn.Module):
|
||||
def __init__(self, attention_n_filters, attention_kernel_size, attention_dim):
|
||||
super(LocationLayer, self).__init__()
|
||||
padding = int((attention_kernel_size - 1) / 2)
|
||||
self.location_conv = ConvNorm(
|
||||
2,
|
||||
attention_n_filters,
|
||||
kernel_size=attention_kernel_size,
|
||||
padding=padding,
|
||||
bias=False,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
)
|
||||
self.location_dense = LinearNorm(
|
||||
attention_n_filters, attention_dim, bias=False, w_init_gain="tanh"
|
||||
)
|
||||
|
||||
def forward(self, attention_weights_cat):
|
||||
processed_attention = self.location_conv(attention_weights_cat)
|
||||
processed_attention = processed_attention.transpose(1, 2)
|
||||
processed_attention = self.location_dense(processed_attention)
|
||||
return processed_attention
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
attention_rnn_dim,
|
||||
embedding_dim,
|
||||
attention_dim,
|
||||
attention_location_n_filters,
|
||||
attention_location_kernel_size,
|
||||
):
|
||||
super(Attention, self).__init__()
|
||||
self.query_layer = LinearNorm(
|
||||
attention_rnn_dim, attention_dim, bias=False, w_init_gain="tanh"
|
||||
)
|
||||
self.memory_layer = LinearNorm(
|
||||
embedding_dim, attention_dim, bias=False, w_init_gain="tanh"
|
||||
)
|
||||
self.v = LinearNorm(attention_dim, 1, bias=False)
|
||||
self.location_layer = LocationLayer(
|
||||
attention_location_n_filters, attention_location_kernel_size, attention_dim
|
||||
)
|
||||
self.score_mask_value = -float("inf")
|
||||
|
||||
def get_alignment_energies(self, query, processed_memory, attention_weights_cat):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
query: decoder output (batch, n_mel_channels * n_frames_per_step)
|
||||
processed_memory: processed encoder outputs (B, T_in, attention_dim)
|
||||
attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
|
||||
RETURNS
|
||||
-------
|
||||
alignment (batch, max_time)
|
||||
"""
|
||||
|
||||
processed_query = self.query_layer(query.unsqueeze(1))
|
||||
processed_attention_weights = self.location_layer(attention_weights_cat)
|
||||
energies = self.v(
|
||||
torch.tanh(processed_query + processed_attention_weights + processed_memory)
|
||||
)
|
||||
|
||||
energies = energies.squeeze(-1)
|
||||
return energies
|
||||
|
||||
def forward(
|
||||
self,
|
||||
attention_hidden_state,
|
||||
memory,
|
||||
processed_memory,
|
||||
attention_weights_cat,
|
||||
mask,
|
||||
):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
attention_hidden_state: attention rnn last output
|
||||
memory: encoder outputs
|
||||
processed_memory: processed encoder outputs
|
||||
attention_weights_cat: previous and cummulative attention weights
|
||||
mask: binary mask for padded data
|
||||
"""
|
||||
alignment = self.get_alignment_energies(
|
||||
attention_hidden_state, processed_memory, attention_weights_cat
|
||||
)
|
||||
|
||||
if mask is not None:
|
||||
alignment.data.masked_fill_(mask, self.score_mask_value)
|
||||
|
||||
attention_weights = F.softmax(alignment, dim=1)
|
||||
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
|
||||
attention_context = attention_context.squeeze(1)
|
||||
|
||||
return attention_context, attention_weights
|
||||
|
||||
|
||||
class ForwardAttentionV2(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
attention_rnn_dim,
|
||||
embedding_dim,
|
||||
attention_dim,
|
||||
attention_location_n_filters,
|
||||
attention_location_kernel_size,
|
||||
):
|
||||
super(ForwardAttentionV2, self).__init__()
|
||||
self.query_layer = LinearNorm(
|
||||
attention_rnn_dim, attention_dim, bias=False, w_init_gain="tanh"
|
||||
)
|
||||
self.memory_layer = LinearNorm(
|
||||
embedding_dim, attention_dim, bias=False, w_init_gain="tanh"
|
||||
)
|
||||
self.v = LinearNorm(attention_dim, 1, bias=False)
|
||||
self.location_layer = LocationLayer(
|
||||
attention_location_n_filters, attention_location_kernel_size, attention_dim
|
||||
)
|
||||
self.score_mask_value = -float(1e20)
|
||||
|
||||
def get_alignment_energies(self, query, processed_memory, attention_weights_cat):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
query: decoder output (batch, n_mel_channels * n_frames_per_step)
|
||||
processed_memory: processed encoder outputs (B, T_in, attention_dim)
|
||||
attention_weights_cat: prev. and cumulative att weights (B, 2, max_time)
|
||||
RETURNS
|
||||
-------
|
||||
alignment (batch, max_time)
|
||||
"""
|
||||
|
||||
processed_query = self.query_layer(query.unsqueeze(1))
|
||||
processed_attention_weights = self.location_layer(attention_weights_cat)
|
||||
energies = self.v(
|
||||
torch.tanh(processed_query + processed_attention_weights + processed_memory)
|
||||
)
|
||||
|
||||
energies = energies.squeeze(-1)
|
||||
return energies
|
||||
|
||||
def forward(
|
||||
self,
|
||||
attention_hidden_state,
|
||||
memory,
|
||||
processed_memory,
|
||||
attention_weights_cat,
|
||||
mask,
|
||||
log_alpha,
|
||||
):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
attention_hidden_state: attention rnn last output
|
||||
memory: encoder outputs
|
||||
processed_memory: processed encoder outputs
|
||||
attention_weights_cat: previous and cummulative attention weights
|
||||
mask: binary mask for padded data
|
||||
"""
|
||||
log_energy = self.get_alignment_energies(
|
||||
attention_hidden_state, processed_memory, attention_weights_cat
|
||||
)
|
||||
|
||||
# log_energy =
|
||||
|
||||
if mask is not None:
|
||||
log_energy.data.masked_fill_(mask, self.score_mask_value)
|
||||
|
||||
# attention_weights = F.softmax(alignment, dim=1)
|
||||
|
||||
# content_score = log_energy.unsqueeze(1) #[B, MAX_TIME] -> [B, 1, MAX_TIME]
|
||||
# log_alpha = log_alpha.unsqueeze(2) #[B, MAX_TIME] -> [B, MAX_TIME, 1]
|
||||
|
||||
# log_total_score = log_alpha + content_score
|
||||
|
||||
# previous_attention_weights = attention_weights_cat[:,0,:]
|
||||
|
||||
log_alpha_shift_padded = []
|
||||
max_time = log_energy.size(1)
|
||||
for sft in range(2):
|
||||
shifted = log_alpha[:, : max_time - sft]
|
||||
shift_padded = F.pad(shifted, (sft, 0), "constant", self.score_mask_value)
|
||||
log_alpha_shift_padded.append(shift_padded.unsqueeze(2))
|
||||
|
||||
biased = torch.logsumexp(torch.cat(log_alpha_shift_padded, 2), 2)
|
||||
|
||||
log_alpha_new = biased + log_energy
|
||||
|
||||
attention_weights = F.softmax(log_alpha_new, dim=1)
|
||||
|
||||
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
|
||||
attention_context = attention_context.squeeze(1)
|
||||
|
||||
return attention_context, attention_weights, log_alpha_new
|
||||
|
||||
|
||||
class PhaseShuffle2d(nn.Module):
|
||||
def __init__(self, n=2):
|
||||
super(PhaseShuffle2d, self).__init__()
|
||||
self.n = n
|
||||
self.random = random.Random(1)
|
||||
|
||||
def forward(self, x, move=None):
|
||||
# x.size = (B, C, M, L)
|
||||
if move is None:
|
||||
move = self.random.randint(-self.n, self.n)
|
||||
|
||||
if move == 0:
|
||||
return x
|
||||
else:
|
||||
left = x[:, :, :, :move]
|
||||
right = x[:, :, :, move:]
|
||||
shuffled = torch.cat([right, left], dim=3)
|
||||
return shuffled
|
||||
|
||||
|
||||
class PhaseShuffle1d(nn.Module):
|
||||
def __init__(self, n=2):
|
||||
super(PhaseShuffle1d, self).__init__()
|
||||
self.n = n
|
||||
self.random = random.Random(1)
|
||||
|
||||
def forward(self, x, move=None):
|
||||
# x.size = (B, C, M, L)
|
||||
if move is None:
|
||||
move = self.random.randint(-self.n, self.n)
|
||||
|
||||
if move == 0:
|
||||
return x
|
||||
else:
|
||||
left = x[:, :, :move]
|
||||
right = x[:, :, move:]
|
||||
shuffled = torch.cat([right, left], dim=2)
|
||||
|
||||
return shuffled
|
||||
|
||||
|
||||
class MFCC(nn.Module):
|
||||
def __init__(self, n_mfcc=40, n_mels=80):
|
||||
super(MFCC, self).__init__()
|
||||
self.n_mfcc = n_mfcc
|
||||
self.n_mels = n_mels
|
||||
self.norm = "ortho"
|
||||
dct_mat = audio_F.create_dct(self.n_mfcc, self.n_mels, self.norm)
|
||||
self.register_buffer("dct_mat", dct_mat)
|
||||
|
||||
def forward(self, mel_specgram):
|
||||
if len(mel_specgram.shape) == 2:
|
||||
mel_specgram = mel_specgram.unsqueeze(0)
|
||||
unsqueezed = True
|
||||
else:
|
||||
unsqueezed = False
|
||||
# (channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc)
|
||||
# -> (channel, time, n_mfcc).tranpose(...)
|
||||
mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2)
|
||||
|
||||
# unpack batch
|
||||
if unsqueezed:
|
||||
mfcc = mfcc.squeeze(0)
|
||||
return mfcc
|
||||
@@ -0,0 +1,217 @@
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import TransformerEncoder
|
||||
import torch.nn.functional as F
|
||||
from .layers import MFCC, Attention, LinearNorm, ConvNorm, ConvBlock
|
||||
|
||||
|
||||
class ASRCNN(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
input_dim=80,
|
||||
hidden_dim=256,
|
||||
n_token=35,
|
||||
n_layers=6,
|
||||
token_embedding_dim=256,
|
||||
):
|
||||
super().__init__()
|
||||
self.n_token = n_token
|
||||
self.n_down = 1
|
||||
self.to_mfcc = MFCC()
|
||||
self.init_cnn = ConvNorm(
|
||||
input_dim // 2, hidden_dim, kernel_size=7, padding=3, stride=2
|
||||
)
|
||||
self.cnns = nn.Sequential(
|
||||
*[
|
||||
nn.Sequential(
|
||||
ConvBlock(hidden_dim),
|
||||
nn.GroupNorm(num_groups=1, num_channels=hidden_dim),
|
||||
)
|
||||
for n in range(n_layers)
|
||||
]
|
||||
)
|
||||
self.projection = ConvNorm(hidden_dim, hidden_dim // 2)
|
||||
self.ctc_linear = nn.Sequential(
|
||||
LinearNorm(hidden_dim // 2, hidden_dim),
|
||||
nn.ReLU(),
|
||||
LinearNorm(hidden_dim, n_token),
|
||||
)
|
||||
self.asr_s2s = ASRS2S(
|
||||
embedding_dim=token_embedding_dim,
|
||||
hidden_dim=hidden_dim // 2,
|
||||
n_token=n_token,
|
||||
)
|
||||
|
||||
def forward(self, x, src_key_padding_mask=None, text_input=None):
|
||||
x = self.to_mfcc(x)
|
||||
x = self.init_cnn(x)
|
||||
x = self.cnns(x)
|
||||
x = self.projection(x)
|
||||
x = x.transpose(1, 2)
|
||||
ctc_logit = self.ctc_linear(x)
|
||||
if text_input is not None:
|
||||
_, s2s_logit, s2s_attn = self.asr_s2s(x, src_key_padding_mask, text_input)
|
||||
return ctc_logit, s2s_logit, s2s_attn
|
||||
else:
|
||||
return ctc_logit
|
||||
|
||||
def get_feature(self, x):
|
||||
x = self.to_mfcc(x.squeeze(1))
|
||||
x = self.init_cnn(x)
|
||||
x = self.cnns(x)
|
||||
x = self.projection(x)
|
||||
return x
|
||||
|
||||
def length_to_mask(self, lengths):
|
||||
mask = (
|
||||
torch.arange(lengths.max())
|
||||
.unsqueeze(0)
|
||||
.expand(lengths.shape[0], -1)
|
||||
.type_as(lengths)
|
||||
)
|
||||
mask = torch.gt(mask + 1, lengths.unsqueeze(1)).to(lengths.device)
|
||||
return mask
|
||||
|
||||
def get_future_mask(self, out_length, unmask_future_steps=0):
|
||||
"""
|
||||
Args:
|
||||
out_length (int): returned mask shape is (out_length, out_length).
|
||||
unmask_futre_steps (int): unmasking future step size.
|
||||
Return:
|
||||
mask (torch.BoolTensor): mask future timesteps mask[i, j] = True if i > j + unmask_future_steps else False
|
||||
"""
|
||||
index_tensor = torch.arange(out_length).unsqueeze(0).expand(out_length, -1)
|
||||
mask = torch.gt(index_tensor, index_tensor.T + unmask_future_steps)
|
||||
return mask
|
||||
|
||||
|
||||
class ASRS2S(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
embedding_dim=256,
|
||||
hidden_dim=512,
|
||||
n_location_filters=32,
|
||||
location_kernel_size=63,
|
||||
n_token=40,
|
||||
):
|
||||
super(ASRS2S, self).__init__()
|
||||
self.embedding = nn.Embedding(n_token, embedding_dim)
|
||||
val_range = math.sqrt(6 / hidden_dim)
|
||||
self.embedding.weight.data.uniform_(-val_range, val_range)
|
||||
|
||||
self.decoder_rnn_dim = hidden_dim
|
||||
self.project_to_n_symbols = nn.Linear(self.decoder_rnn_dim, n_token)
|
||||
self.attention_layer = Attention(
|
||||
self.decoder_rnn_dim,
|
||||
hidden_dim,
|
||||
hidden_dim,
|
||||
n_location_filters,
|
||||
location_kernel_size,
|
||||
)
|
||||
self.decoder_rnn = nn.LSTMCell(
|
||||
self.decoder_rnn_dim + embedding_dim, self.decoder_rnn_dim
|
||||
)
|
||||
self.project_to_hidden = nn.Sequential(
|
||||
LinearNorm(self.decoder_rnn_dim * 2, hidden_dim), nn.Tanh()
|
||||
)
|
||||
self.sos = 1
|
||||
self.eos = 2
|
||||
|
||||
def initialize_decoder_states(self, memory, mask):
|
||||
"""
|
||||
moemory.shape = (B, L, H) = (Batchsize, Maxtimestep, Hiddendim)
|
||||
"""
|
||||
B, L, H = memory.shape
|
||||
self.decoder_hidden = torch.zeros((B, self.decoder_rnn_dim)).type_as(memory)
|
||||
self.decoder_cell = torch.zeros((B, self.decoder_rnn_dim)).type_as(memory)
|
||||
self.attention_weights = torch.zeros((B, L)).type_as(memory)
|
||||
self.attention_weights_cum = torch.zeros((B, L)).type_as(memory)
|
||||
self.attention_context = torch.zeros((B, H)).type_as(memory)
|
||||
self.memory = memory
|
||||
self.processed_memory = self.attention_layer.memory_layer(memory)
|
||||
self.mask = mask
|
||||
self.unk_index = 3
|
||||
self.random_mask = 0.1
|
||||
|
||||
def forward(self, memory, memory_mask, text_input):
|
||||
"""
|
||||
moemory.shape = (B, L, H) = (Batchsize, Maxtimestep, Hiddendim)
|
||||
moemory_mask.shape = (B, L, )
|
||||
texts_input.shape = (B, T)
|
||||
"""
|
||||
self.initialize_decoder_states(memory, memory_mask)
|
||||
# text random mask
|
||||
random_mask = (torch.rand(text_input.shape) < self.random_mask).to(
|
||||
text_input.device
|
||||
)
|
||||
_text_input = text_input.clone()
|
||||
_text_input.masked_fill_(random_mask, self.unk_index)
|
||||
decoder_inputs = self.embedding(_text_input).transpose(
|
||||
0, 1
|
||||
) # -> [T, B, channel]
|
||||
start_embedding = self.embedding(
|
||||
torch.LongTensor([self.sos] * decoder_inputs.size(1)).to(
|
||||
decoder_inputs.device
|
||||
)
|
||||
)
|
||||
decoder_inputs = torch.cat(
|
||||
(start_embedding.unsqueeze(0), decoder_inputs), dim=0
|
||||
)
|
||||
|
||||
hidden_outputs, logit_outputs, alignments = [], [], []
|
||||
while len(hidden_outputs) < decoder_inputs.size(0):
|
||||
decoder_input = decoder_inputs[len(hidden_outputs)]
|
||||
hidden, logit, attention_weights = self.decode(decoder_input)
|
||||
hidden_outputs += [hidden]
|
||||
logit_outputs += [logit]
|
||||
alignments += [attention_weights]
|
||||
|
||||
hidden_outputs, logit_outputs, alignments = self.parse_decoder_outputs(
|
||||
hidden_outputs, logit_outputs, alignments
|
||||
)
|
||||
|
||||
return hidden_outputs, logit_outputs, alignments
|
||||
|
||||
def decode(self, decoder_input):
|
||||
cell_input = torch.cat((decoder_input, self.attention_context), -1)
|
||||
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
|
||||
cell_input, (self.decoder_hidden, self.decoder_cell)
|
||||
)
|
||||
|
||||
attention_weights_cat = torch.cat(
|
||||
(
|
||||
self.attention_weights.unsqueeze(1),
|
||||
self.attention_weights_cum.unsqueeze(1),
|
||||
),
|
||||
dim=1,
|
||||
)
|
||||
|
||||
self.attention_context, self.attention_weights = self.attention_layer(
|
||||
self.decoder_hidden,
|
||||
self.memory,
|
||||
self.processed_memory,
|
||||
attention_weights_cat,
|
||||
self.mask,
|
||||
)
|
||||
|
||||
self.attention_weights_cum += self.attention_weights
|
||||
|
||||
hidden_and_context = torch.cat(
|
||||
(self.decoder_hidden, self.attention_context), -1
|
||||
)
|
||||
hidden = self.project_to_hidden(hidden_and_context)
|
||||
|
||||
# dropout to increasing g
|
||||
logit = self.project_to_n_symbols(F.dropout(hidden, 0.5, self.training))
|
||||
|
||||
return hidden, logit, self.attention_weights
|
||||
|
||||
def parse_decoder_outputs(self, hidden, logit, alignments):
|
||||
# -> [B, T_out + 1, max_time]
|
||||
alignments = torch.stack(alignments).transpose(0, 1)
|
||||
# [T_out + 1, B, n_symbols] -> [B, T_out + 1, n_symbols]
|
||||
logit = torch.stack(logit).transpose(0, 1).contiguous()
|
||||
hidden = torch.stack(hidden).transpose(0, 1).contiguous()
|
||||
|
||||
return hidden, logit, alignments
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
Binary file not shown.
@@ -0,0 +1,212 @@
|
||||
"""
|
||||
Implementation of model from:
|
||||
Kum et al. - "Joint Detection and Classification of Singing Voice Melody Using
|
||||
Convolutional Recurrent Neural Networks" (2019)
|
||||
Link: https://www.semanticscholar.org/paper/Joint-Detection-and-Classification-of-Singing-Voice-Kum-Nam/60a2ad4c7db43bace75805054603747fcd062c0d
|
||||
"""
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
|
||||
class JDCNet(nn.Module):
|
||||
"""
|
||||
Joint Detection and Classification Network model for singing voice melody.
|
||||
"""
|
||||
|
||||
def __init__(self, num_class=722, seq_len=31, leaky_relu_slope=0.01):
|
||||
super().__init__()
|
||||
self.num_class = num_class
|
||||
|
||||
# input = (b, 1, 31, 513), b = batch size
|
||||
self.conv_block = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_channels=1, out_channels=64, kernel_size=3, padding=1, bias=False
|
||||
), # out: (b, 64, 31, 513)
|
||||
nn.BatchNorm2d(num_features=64),
|
||||
nn.LeakyReLU(leaky_relu_slope, inplace=True),
|
||||
nn.Conv2d(64, 64, 3, padding=1, bias=False), # (b, 64, 31, 513)
|
||||
)
|
||||
|
||||
# res blocks
|
||||
self.res_block1 = ResBlock(
|
||||
in_channels=64, out_channels=128
|
||||
) # (b, 128, 31, 128)
|
||||
self.res_block2 = ResBlock(
|
||||
in_channels=128, out_channels=192
|
||||
) # (b, 192, 31, 32)
|
||||
self.res_block3 = ResBlock(in_channels=192, out_channels=256) # (b, 256, 31, 8)
|
||||
|
||||
# pool block
|
||||
self.pool_block = nn.Sequential(
|
||||
nn.BatchNorm2d(num_features=256),
|
||||
nn.LeakyReLU(leaky_relu_slope, inplace=True),
|
||||
nn.MaxPool2d(kernel_size=(1, 4)), # (b, 256, 31, 2)
|
||||
nn.Dropout(p=0.2),
|
||||
)
|
||||
|
||||
# maxpool layers (for auxiliary network inputs)
|
||||
# in = (b, 128, 31, 513) from conv_block, out = (b, 128, 31, 2)
|
||||
self.maxpool1 = nn.MaxPool2d(kernel_size=(1, 40))
|
||||
# in = (b, 128, 31, 128) from res_block1, out = (b, 128, 31, 2)
|
||||
self.maxpool2 = nn.MaxPool2d(kernel_size=(1, 20))
|
||||
# in = (b, 128, 31, 32) from res_block2, out = (b, 128, 31, 2)
|
||||
self.maxpool3 = nn.MaxPool2d(kernel_size=(1, 10))
|
||||
|
||||
# in = (b, 640, 31, 2), out = (b, 256, 31, 2)
|
||||
self.detector_conv = nn.Sequential(
|
||||
nn.Conv2d(640, 256, 1, bias=False),
|
||||
nn.BatchNorm2d(256),
|
||||
nn.LeakyReLU(leaky_relu_slope, inplace=True),
|
||||
nn.Dropout(p=0.2),
|
||||
)
|
||||
|
||||
# input: (b, 31, 512) - resized from (b, 256, 31, 2)
|
||||
self.bilstm_classifier = nn.LSTM(
|
||||
input_size=512, hidden_size=256, batch_first=True, bidirectional=True
|
||||
) # (b, 31, 512)
|
||||
|
||||
# input: (b, 31, 512) - resized from (b, 256, 31, 2)
|
||||
self.bilstm_detector = nn.LSTM(
|
||||
input_size=512, hidden_size=256, batch_first=True, bidirectional=True
|
||||
) # (b, 31, 512)
|
||||
|
||||
# input: (b * 31, 512)
|
||||
self.classifier = nn.Linear(
|
||||
in_features=512, out_features=self.num_class
|
||||
) # (b * 31, num_class)
|
||||
|
||||
# input: (b * 31, 512)
|
||||
self.detector = nn.Linear(
|
||||
in_features=512, out_features=2
|
||||
) # (b * 31, 2) - binary classifier
|
||||
|
||||
# initialize weights
|
||||
self.apply(self.init_weights)
|
||||
|
||||
def get_feature_GAN(self, x):
|
||||
seq_len = x.shape[-2]
|
||||
x = x.float().transpose(-1, -2)
|
||||
|
||||
convblock_out = self.conv_block(x)
|
||||
|
||||
resblock1_out = self.res_block1(convblock_out)
|
||||
resblock2_out = self.res_block2(resblock1_out)
|
||||
resblock3_out = self.res_block3(resblock2_out)
|
||||
poolblock_out = self.pool_block[0](resblock3_out)
|
||||
poolblock_out = self.pool_block[1](poolblock_out)
|
||||
|
||||
return poolblock_out.transpose(-1, -2)
|
||||
|
||||
def get_feature(self, x):
|
||||
seq_len = x.shape[-2]
|
||||
x = x.float().transpose(-1, -2)
|
||||
|
||||
convblock_out = self.conv_block(x)
|
||||
|
||||
resblock1_out = self.res_block1(convblock_out)
|
||||
resblock2_out = self.res_block2(resblock1_out)
|
||||
resblock3_out = self.res_block3(resblock2_out)
|
||||
poolblock_out = self.pool_block[0](resblock3_out)
|
||||
poolblock_out = self.pool_block[1](poolblock_out)
|
||||
|
||||
return self.pool_block[2](poolblock_out)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Returns:
|
||||
classification_prediction, detection_prediction
|
||||
sizes: (b, 31, 722), (b, 31, 2)
|
||||
"""
|
||||
###############################
|
||||
# forward pass for classifier #
|
||||
###############################
|
||||
seq_len = x.shape[-1]
|
||||
x = x.float().transpose(-1, -2)
|
||||
|
||||
convblock_out = self.conv_block(x)
|
||||
|
||||
resblock1_out = self.res_block1(convblock_out)
|
||||
resblock2_out = self.res_block2(resblock1_out)
|
||||
resblock3_out = self.res_block3(resblock2_out)
|
||||
|
||||
poolblock_out = self.pool_block[0](resblock3_out)
|
||||
poolblock_out = self.pool_block[1](poolblock_out)
|
||||
GAN_feature = poolblock_out.transpose(-1, -2)
|
||||
poolblock_out = self.pool_block[2](poolblock_out)
|
||||
|
||||
# (b, 256, 31, 2) => (b, 31, 256, 2) => (b, 31, 512)
|
||||
classifier_out = (
|
||||
poolblock_out.permute(0, 2, 1, 3).contiguous().view((-1, seq_len, 512))
|
||||
)
|
||||
classifier_out, _ = self.bilstm_classifier(
|
||||
classifier_out
|
||||
) # ignore the hidden states
|
||||
|
||||
classifier_out = classifier_out.contiguous().view((-1, 512)) # (b * 31, 512)
|
||||
classifier_out = self.classifier(classifier_out)
|
||||
classifier_out = classifier_out.view(
|
||||
(-1, seq_len, self.num_class)
|
||||
) # (b, 31, num_class)
|
||||
|
||||
# sizes: (b, 31, 722), (b, 31, 2)
|
||||
# classifier output consists of predicted pitch classes per frame
|
||||
# detector output consists of: (isvoice, notvoice) estimates per frame
|
||||
return torch.abs(classifier_out.squeeze()), GAN_feature, poolblock_out
|
||||
|
||||
@staticmethod
|
||||
def init_weights(m):
|
||||
if isinstance(m, nn.Linear):
|
||||
nn.init.kaiming_uniform_(m.weight)
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
nn.init.xavier_normal_(m.weight)
|
||||
elif isinstance(m, nn.LSTM) or isinstance(m, nn.LSTMCell):
|
||||
for p in m.parameters():
|
||||
if p.data is None:
|
||||
continue
|
||||
|
||||
if len(p.shape) >= 2:
|
||||
nn.init.orthogonal_(p.data)
|
||||
else:
|
||||
nn.init.normal_(p.data)
|
||||
|
||||
|
||||
class ResBlock(nn.Module):
|
||||
def __init__(self, in_channels: int, out_channels: int, leaky_relu_slope=0.01):
|
||||
super().__init__()
|
||||
self.downsample = in_channels != out_channels
|
||||
|
||||
# BN / LReLU / MaxPool layer before the conv layer - see Figure 1b in the paper
|
||||
self.pre_conv = nn.Sequential(
|
||||
nn.BatchNorm2d(num_features=in_channels),
|
||||
nn.LeakyReLU(leaky_relu_slope, inplace=True),
|
||||
nn.MaxPool2d(kernel_size=(1, 2)), # apply downsampling on the y axis only
|
||||
)
|
||||
|
||||
# conv layers
|
||||
self.conv = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=3,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
nn.BatchNorm2d(out_channels),
|
||||
nn.LeakyReLU(leaky_relu_slope, inplace=True),
|
||||
nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),
|
||||
)
|
||||
|
||||
# 1 x 1 convolution layer to match the feature dimensions
|
||||
self.conv1by1 = None
|
||||
if self.downsample:
|
||||
self.conv1by1 = nn.Conv2d(in_channels, out_channels, 1, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.pre_conv(x)
|
||||
if self.downsample:
|
||||
x = self.conv(x) + self.conv1by1(x)
|
||||
else:
|
||||
x = self.conv(x) + x
|
||||
return x
|
||||
@@ -0,0 +1,30 @@
|
||||
log_dir: "Checkpoint"
|
||||
mixed_precision: "fp16"
|
||||
data_folder: "wikipedia_20220301.en.processed"
|
||||
batch_size: 192
|
||||
save_interval: 5000
|
||||
log_interval: 10
|
||||
num_process: 1 # number of GPUs
|
||||
num_steps: 1000000
|
||||
|
||||
dataset_params:
|
||||
tokenizer: "transfo-xl-wt103"
|
||||
token_separator: " " # token used for phoneme separator (space)
|
||||
token_mask: "M" # token used for phoneme mask (M)
|
||||
word_separator: 3039 # token used for word separator (<formula>)
|
||||
token_maps: "token_maps.pkl" # token map path
|
||||
|
||||
max_mel_length: 512 # max phoneme length
|
||||
|
||||
word_mask_prob: 0.15 # probability to mask the entire word
|
||||
phoneme_mask_prob: 0.1 # probability to mask each phoneme
|
||||
replace_prob: 0.2 # probablity to replace phonemes
|
||||
|
||||
model_params:
|
||||
vocab_size: 178
|
||||
hidden_size: 768
|
||||
num_attention_heads: 12
|
||||
intermediate_size: 2048
|
||||
max_position_embeddings: 512
|
||||
num_hidden_layers: 12
|
||||
dropout: 0.1
|
||||
Binary file not shown.
@@ -0,0 +1,49 @@
|
||||
import os
|
||||
import yaml
|
||||
import torch
|
||||
from transformers import AlbertConfig, AlbertModel
|
||||
|
||||
|
||||
class CustomAlbert(AlbertModel):
|
||||
def forward(self, *args, **kwargs):
|
||||
# Call the original forward method
|
||||
outputs = super().forward(*args, **kwargs)
|
||||
|
||||
# Only return the last_hidden_state
|
||||
return outputs.last_hidden_state
|
||||
|
||||
|
||||
def load_plbert(log_dir):
|
||||
config_path = os.path.join(log_dir, "config.yml")
|
||||
plbert_config = yaml.safe_load(open(config_path))
|
||||
|
||||
albert_base_configuration = AlbertConfig(**plbert_config["model_params"])
|
||||
bert = CustomAlbert(albert_base_configuration)
|
||||
|
||||
files = os.listdir(log_dir)
|
||||
ckpts = []
|
||||
for f in os.listdir(log_dir):
|
||||
if f.startswith("step_"):
|
||||
ckpts.append(f)
|
||||
|
||||
iters = [
|
||||
int(f.split("_")[-1].split(".")[0])
|
||||
for f in ckpts
|
||||
if os.path.isfile(os.path.join(log_dir, f))
|
||||
]
|
||||
iters = sorted(iters)[-1]
|
||||
|
||||
checkpoint = torch.load(log_dir + "/step_" + str(iters) + ".t7", map_location="cpu")
|
||||
state_dict = checkpoint["net"]
|
||||
from collections import OrderedDict
|
||||
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in state_dict.items():
|
||||
name = k[7:] # remove `module.`
|
||||
if name.startswith("encoder."):
|
||||
name = name[8:] # remove `encoder.`
|
||||
new_state_dict[name] = v
|
||||
del new_state_dict["embeddings.position_ids"]
|
||||
bert.load_state_dict(new_state_dict, strict=False)
|
||||
|
||||
return bert
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1,371 @@
|
||||
from cached_path import cached_path
|
||||
|
||||
from dp.phonemizer import Phonemizer
|
||||
print("NLTK")
|
||||
import nltk
|
||||
nltk.download('punkt')
|
||||
print("SCIPY")
|
||||
from scipy.io.wavfile import write
|
||||
print("TORCH STUFF")
|
||||
import torch
|
||||
print("START")
|
||||
torch.manual_seed(0)
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
import random
|
||||
random.seed(0)
|
||||
|
||||
import numpy as np
|
||||
np.random.seed(0)
|
||||
|
||||
# load packages
|
||||
import time
|
||||
import random
|
||||
import yaml
|
||||
from munch import Munch
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
from nltk.tokenize import word_tokenize
|
||||
|
||||
from models import *
|
||||
from utils import *
|
||||
from text_utils import TextCleaner
|
||||
textclenaer = TextCleaner()
|
||||
|
||||
|
||||
to_mel = torchaudio.transforms.MelSpectrogram(
|
||||
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
|
||||
mean, std = -4, 4
|
||||
|
||||
def length_to_mask(lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
def preprocess(wave):
|
||||
wave_tensor = torch.from_numpy(wave).float()
|
||||
mel_tensor = to_mel(wave_tensor)
|
||||
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
|
||||
return mel_tensor
|
||||
|
||||
def compute_style(path):
|
||||
wave, sr = librosa.load(path, sr=24000)
|
||||
audio, index = librosa.effects.trim(wave, top_db=30)
|
||||
if sr != 24000:
|
||||
audio = librosa.resample(audio, sr, 24000)
|
||||
mel_tensor = preprocess(audio).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
ref_s = model.style_encoder(mel_tensor.unsqueeze(1))
|
||||
ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1))
|
||||
|
||||
return torch.cat([ref_s, ref_p], dim=1)
|
||||
|
||||
device = 'cpu'
|
||||
if torch.cuda.is_available():
|
||||
device = 'cuda'
|
||||
elif torch.backends.mps.is_available():
|
||||
print("MPS would be available but cannot be used rn")
|
||||
# device = 'mps'
|
||||
|
||||
|
||||
# global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True)
|
||||
phonemizer = Phonemizer.from_checkpoint(str(cached_path('https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/en_us_cmudict_ipa_forward.pt')))
|
||||
|
||||
|
||||
config = yaml.safe_load(open("Models/LibriTTS/config.yml"))
|
||||
|
||||
# load pretrained ASR model
|
||||
ASR_config = config.get('ASR_config', False)
|
||||
ASR_path = config.get('ASR_path', False)
|
||||
text_aligner = load_ASR_models(ASR_path, ASR_config)
|
||||
|
||||
# load pretrained F0 model
|
||||
F0_path = config.get('F0_path', False)
|
||||
pitch_extractor = load_F0_models(F0_path)
|
||||
|
||||
# load BERT model
|
||||
from Utils.PLBERT.util import load_plbert
|
||||
BERT_path = config.get('PLBERT_dir', False)
|
||||
plbert = load_plbert(BERT_path)
|
||||
|
||||
model_params = recursive_munch(config['model_params'])
|
||||
model = build_model(model_params, text_aligner, pitch_extractor, plbert)
|
||||
_ = [model[key].eval() for key in model]
|
||||
_ = [model[key].to(device) for key in model]
|
||||
|
||||
params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu')
|
||||
params = params_whole['net']
|
||||
|
||||
for key in model:
|
||||
if key in params:
|
||||
print('%s loaded' % key)
|
||||
try:
|
||||
model[key].load_state_dict(params[key])
|
||||
except:
|
||||
from collections import OrderedDict
|
||||
state_dict = params[key]
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in state_dict.items():
|
||||
name = k[7:] # remove `module.`
|
||||
new_state_dict[name] = v
|
||||
# load params
|
||||
model[key].load_state_dict(new_state_dict, strict=False)
|
||||
# except:
|
||||
# _load(params[key], model[key])
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
|
||||
|
||||
sampler = DiffusionSampler(
|
||||
model.diffusion.diffusion,
|
||||
sampler=ADPM2Sampler(),
|
||||
sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
|
||||
clamp=False
|
||||
)
|
||||
|
||||
def inference(text, ref_s, alpha = 0.3, beta = 0.7, diffusion_steps=5, embedding_scale=1):
|
||||
text = text.strip()
|
||||
ps = phonemizer([text], lang='en_us')
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=embedding_scale,
|
||||
features=ref_s, # reference from the same speaker as the embedding
|
||||
num_steps=diffusion_steps).squeeze(1)
|
||||
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
|
||||
s = beta * s + (1 - beta) * ref_s[:, 128:]
|
||||
|
||||
d = model.predictor.text_encoder(d_en,
|
||||
s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(en)
|
||||
asr_new[:, :, 0] = en[:, :, 0]
|
||||
asr_new[:, :, 1:] = en[:, :, 0:-1]
|
||||
en = asr_new
|
||||
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
|
||||
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(asr)
|
||||
asr_new[:, :, 0] = asr[:, :, 0]
|
||||
asr_new[:, :, 1:] = asr[:, :, 0:-1]
|
||||
asr = asr_new
|
||||
|
||||
out = model.decoder(asr,
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
|
||||
return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later
|
||||
|
||||
def LFinference(text, s_prev, ref_s, alpha = 0.3, beta = 0.7, t = 0.7, diffusion_steps=5, embedding_scale=1):
|
||||
text = text.strip()
|
||||
ps = phonemizer([text], lang='en_us')
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
ps = ps.replace('``', '"')
|
||||
ps = ps.replace("''", '"')
|
||||
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=embedding_scale,
|
||||
features=ref_s, # reference from the same speaker as the embedding
|
||||
num_steps=diffusion_steps).squeeze(1)
|
||||
|
||||
if s_prev is not None:
|
||||
# convex combination of previous and current style
|
||||
s_pred = t * s_prev + (1 - t) * s_pred
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
|
||||
s = beta * s + (1 - beta) * ref_s[:, 128:]
|
||||
|
||||
s_pred = torch.cat([ref, s], dim=-1)
|
||||
|
||||
d = model.predictor.text_encoder(d_en,
|
||||
s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(en)
|
||||
asr_new[:, :, 0] = en[:, :, 0]
|
||||
asr_new[:, :, 1:] = en[:, :, 0:-1]
|
||||
en = asr_new
|
||||
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
|
||||
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(asr)
|
||||
asr_new[:, :, 0] = asr[:, :, 0]
|
||||
asr_new[:, :, 1:] = asr[:, :, 0:-1]
|
||||
asr = asr_new
|
||||
|
||||
out = model.decoder(asr,
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
|
||||
return out.squeeze().cpu().numpy()[..., :-100], s_pred # weird pulse at the end of the model, need to be fixed later
|
||||
|
||||
def STinference(text, ref_s, ref_text, alpha = 0.3, beta = 0.7, diffusion_steps=5, embedding_scale=1):
|
||||
text = text.strip()
|
||||
ps = phonemizer([text], lang='en_us')
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
ref_text = ref_text.strip()
|
||||
ps = phonemizer([ref_text], lang='en_us')
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
|
||||
ref_tokens = textclenaer(ps)
|
||||
ref_tokens.insert(0, 0)
|
||||
ref_tokens = torch.LongTensor(ref_tokens).to(device).unsqueeze(0)
|
||||
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
ref_input_lengths = torch.LongTensor([ref_tokens.shape[-1]]).to(device)
|
||||
ref_text_mask = length_to_mask(ref_input_lengths).to(device)
|
||||
ref_bert_dur = model.bert(ref_tokens, attention_mask=(~ref_text_mask).int())
|
||||
s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=embedding_scale,
|
||||
features=ref_s, # reference from the same speaker as the embedding
|
||||
num_steps=diffusion_steps).squeeze(1)
|
||||
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
|
||||
s = beta * s + (1 - beta) * ref_s[:, 128:]
|
||||
|
||||
d = model.predictor.text_encoder(d_en,
|
||||
s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(en)
|
||||
asr_new[:, :, 0] = en[:, :, 0]
|
||||
asr_new[:, :, 1:] = en[:, :, 0:-1]
|
||||
en = asr_new
|
||||
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
|
||||
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(asr)
|
||||
asr_new[:, :, 0] = asr[:, :, 0]
|
||||
asr_new[:, :, 1:] = asr[:, :, 0:-1]
|
||||
asr = asr_new
|
||||
|
||||
out = model.decoder(asr,
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
|
||||
return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later
|
||||
print("Time to synthesize!")
|
||||
ref_s = compute_style('./voice/voice.wav')
|
||||
while True:
|
||||
text = input("What to say? > ")
|
||||
start = time.time()
|
||||
wav = inference(text, ref_s, alpha=0.3, beta=0.7, diffusion_steps=15, embedding_scale=1)
|
||||
rtf = (time.time() - start) / (len(wav) / 24000)
|
||||
print(f"RTF = {rtf:5f}")
|
||||
print(k + ' Synthesized:')
|
||||
# display(ipd.Audio(wav, rate=24000, normalize=False))
|
||||
write('result.wav', 24000, wav)
|
||||
print("Saved to result.wav")
|
||||
@@ -0,0 +1,138 @@
|
||||
from cached_path import cached_path
|
||||
|
||||
# from dp.phonemizer import Phonemizer
|
||||
print("NLTK")
|
||||
import nltk
|
||||
nltk.download('punkt')
|
||||
print("SCIPY")
|
||||
from scipy.io.wavfile import write
|
||||
print("TORCH STUFF")
|
||||
import torch
|
||||
print("START")
|
||||
torch.manual_seed(0)
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
import random
|
||||
random.seed(0)
|
||||
|
||||
import numpy as np
|
||||
np.random.seed(0)
|
||||
|
||||
# load packages
|
||||
import time
|
||||
import random
|
||||
import yaml
|
||||
from munch import Munch
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
from nltk.tokenize import word_tokenize
|
||||
|
||||
from models import *
|
||||
from utils import *
|
||||
from text_utils import TextCleaner
|
||||
textclenaer = TextCleaner()
|
||||
|
||||
|
||||
to_mel = torchaudio.transforms.MelSpectrogram(
|
||||
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
|
||||
mean, std = -4, 4
|
||||
|
||||
def length_to_mask(lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
def preprocess(wave):
|
||||
wave_tensor = torch.from_numpy(wave).float()
|
||||
mel_tensor = to_mel(wave_tensor)
|
||||
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
|
||||
return mel_tensor
|
||||
|
||||
def compute_style(path):
|
||||
wave, sr = librosa.load(path, sr=24000)
|
||||
audio, index = librosa.effects.trim(wave, top_db=30)
|
||||
if sr != 24000:
|
||||
audio = librosa.resample(audio, sr, 24000)
|
||||
mel_tensor = preprocess(audio).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
ref_s = model.style_encoder(mel_tensor.unsqueeze(1))
|
||||
ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1))
|
||||
|
||||
return torch.cat([ref_s, ref_p], dim=1)
|
||||
|
||||
device = 'cpu'
|
||||
if torch.cuda.is_available():
|
||||
device = 'cuda'
|
||||
elif torch.backends.mps.is_available():
|
||||
print("MPS would be available but cannot be used rn")
|
||||
# device = 'mps'
|
||||
|
||||
|
||||
|
||||
# config = yaml.safe_load(open("Models/LibriTTS/config.yml"))
|
||||
config = yaml.safe_load(open(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/config.yml"))))
|
||||
|
||||
# load pretrained ASR model
|
||||
ASR_config = config.get('ASR_config', False)
|
||||
ASR_path = config.get('ASR_path', False)
|
||||
text_aligner = load_ASR_models(ASR_path, ASR_config)
|
||||
|
||||
# load pretrained F0 model
|
||||
F0_path = config.get('F0_path', False)
|
||||
pitch_extractor = load_F0_models(F0_path)
|
||||
|
||||
# load BERT model
|
||||
from Utils.PLBERT.util import load_plbert
|
||||
BERT_path = config.get('PLBERT_dir', False)
|
||||
plbert = load_plbert(BERT_path)
|
||||
|
||||
model_params = recursive_munch(config['model_params'])
|
||||
model = build_model(model_params, text_aligner, pitch_extractor, plbert)
|
||||
_ = [model[key].eval() for key in model]
|
||||
_ = [model[key].to(device) for key in model]
|
||||
|
||||
# params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu')
|
||||
params_whole = torch.load(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/epochs_2nd_00020.pth")), map_location='cpu')
|
||||
params = params_whole['net']
|
||||
|
||||
for key in model:
|
||||
if key in params:
|
||||
print('%s loaded' % key)
|
||||
try:
|
||||
model[key].load_state_dict(params[key])
|
||||
except:
|
||||
from collections import OrderedDict
|
||||
state_dict = params[key]
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in state_dict.items():
|
||||
name = k[7:] # remove `module.`
|
||||
new_state_dict[name] = v
|
||||
# load params
|
||||
model[key].load_state_dict(new_state_dict, strict=False)
|
||||
# except:
|
||||
# _load(params[key], model[key])
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
|
||||
|
||||
sampler = DiffusionSampler(
|
||||
model.diffusion.diffusion,
|
||||
sampler=ADPM2Sampler(),
|
||||
sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
|
||||
clamp=False
|
||||
)
|
||||
voicelist = ['f-us-1', 'f-us-2', 'f-us-3', 'f-us-4', 'm-us-1', 'm-us-2', 'm-us-3', 'm-us-4']
|
||||
voices = {}
|
||||
# todo: cache computed style, load using pickle
|
||||
for v in voicelist:
|
||||
print(f"Loading voice {v}")
|
||||
voices[v] = compute_style(f'voices/{v}.wav')
|
||||
import pickle
|
||||
with open('voices.pkl', 'wb') as f:
|
||||
pickle.dump(voices, f)
|
||||
@@ -0,0 +1,37 @@
|
||||
# FakeYou StyleTTS 2 Inference
|
||||
|
||||
import argparse
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
from styletts2importable import compute_style, inference
|
||||
from tortoise.utils.text import split_and_recombine_text
|
||||
|
||||
def clsynthesize(text, voice_path, vcsteps):
|
||||
# Load the custom voice
|
||||
custom_voice = compute_style(voice_path)
|
||||
|
||||
# Split and recombine long text into manageable chunks
|
||||
texts = split_and_recombine_text(text)
|
||||
|
||||
audios = []
|
||||
for t in texts:
|
||||
# Generate audio for each text chunk
|
||||
audios.append(inference(t, custom_voice, alpha=0.3, beta=0.7, diffusion_steps=vcsteps, embedding_scale=1))
|
||||
|
||||
return 24000, np.concatenate(audios)
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description='CLI for StyleTTS Voice Synthesis')
|
||||
parser.add_argument('--text', type=str, required=True, help='Text to synthesize')
|
||||
parser.add_argument('--voice', type=str, required=True, help='Path to the custom voice audio file')
|
||||
parser.add_argument('--vcsteps', type=int, required=True, help='Number of diffusion steps')
|
||||
parser.add_argument('--output', type=str, required=True, help='Output path for the synthesized audio file')
|
||||
args = parser.parse_args()
|
||||
|
||||
sample_rate, audio = clsynthesize(args.text, args.voice, args.vcsteps)
|
||||
|
||||
# Save the synthesized audio to the specified output path
|
||||
sf.write(args.output, audio, sample_rate)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,10 @@
|
||||
from gruut import sentences
|
||||
|
||||
|
||||
def gphonemize(text):
|
||||
phonemes = ''
|
||||
for sent in sentences(text, lang="en-us"):
|
||||
for word in sent:
|
||||
if word.phonemes:
|
||||
phonemes += ''.join(word.phonemes)
|
||||
return phonemes
|
||||
@@ -0,0 +1,225 @@
|
||||
from cached_path import cached_path
|
||||
|
||||
|
||||
import torch
|
||||
torch.manual_seed(0)
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
import random
|
||||
random.seed(0)
|
||||
|
||||
import numpy as np
|
||||
np.random.seed(0)
|
||||
|
||||
import nltk
|
||||
nltk.download('punkt')
|
||||
|
||||
# load packages
|
||||
import time
|
||||
import random
|
||||
import yaml
|
||||
from munch import Munch
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
from nltk.tokenize import word_tokenize
|
||||
|
||||
from models import *
|
||||
from utils import *
|
||||
from text_utils import TextCleaner
|
||||
textclenaer = TextCleaner()
|
||||
|
||||
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
|
||||
to_mel = torchaudio.transforms.MelSpectrogram(
|
||||
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
|
||||
mean, std = -4, 4
|
||||
|
||||
def length_to_mask(lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
def preprocess(wave):
|
||||
wave_tensor = torch.from_numpy(wave).float()
|
||||
mel_tensor = to_mel(wave_tensor)
|
||||
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
|
||||
return mel_tensor
|
||||
|
||||
def compute_style(ref_dicts):
|
||||
reference_embeddings = {}
|
||||
for key, path in ref_dicts.items():
|
||||
wave, sr = librosa.load(path, sr=24000)
|
||||
audio, index = librosa.effects.trim(wave, top_db=30)
|
||||
if sr != 24000:
|
||||
audio = librosa.resample(audio, sr, 24000)
|
||||
mel_tensor = preprocess(audio).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
ref = model.style_encoder(mel_tensor.unsqueeze(1))
|
||||
reference_embeddings[key] = (ref.squeeze(1), audio)
|
||||
|
||||
return reference_embeddings
|
||||
|
||||
# load phonemizer
|
||||
import phonemizer
|
||||
global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True, words_mismatch='ignore')
|
||||
|
||||
# phonemizer = Phonemizer.from_checkpoint(str(cached_path('https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/en_us_cmudict_ipa_forward.pt')))
|
||||
|
||||
|
||||
config = yaml.safe_load(open(str(cached_path('hf://yl4579/StyleTTS2-LJSpeech/Models/LJSpeech/config.yml'))))
|
||||
|
||||
# load pretrained ASR model
|
||||
ASR_config = config.get('ASR_config', False)
|
||||
ASR_path = config.get('ASR_path', False)
|
||||
text_aligner = load_ASR_models(ASR_path, ASR_config)
|
||||
|
||||
# load pretrained F0 model
|
||||
F0_path = config.get('F0_path', False)
|
||||
pitch_extractor = load_F0_models(F0_path)
|
||||
|
||||
# load BERT model
|
||||
from Utils.PLBERT.util import load_plbert
|
||||
BERT_path = config.get('PLBERT_dir', False)
|
||||
plbert = load_plbert(BERT_path)
|
||||
|
||||
model = build_model(recursive_munch(config['model_params']), text_aligner, pitch_extractor, plbert)
|
||||
_ = [model[key].eval() for key in model]
|
||||
_ = [model[key].to(device) for key in model]
|
||||
|
||||
# params_whole = torch.load("Models/LJSpeech/epoch_2nd_00100.pth", map_location='cpu')
|
||||
params_whole = torch.load(str(cached_path('hf://yl4579/StyleTTS2-LJSpeech/Models/LJSpeech/epoch_2nd_00100.pth')), map_location='cpu')
|
||||
params = params_whole['net']
|
||||
|
||||
for key in model:
|
||||
if key in params:
|
||||
print('%s loaded' % key)
|
||||
try:
|
||||
model[key].load_state_dict(params[key])
|
||||
except:
|
||||
from collections import OrderedDict
|
||||
state_dict = params[key]
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in state_dict.items():
|
||||
name = k[7:] # remove `module.`
|
||||
new_state_dict[name] = v
|
||||
# load params
|
||||
model[key].load_state_dict(new_state_dict, strict=False)
|
||||
# except:
|
||||
# _load(params[key], model[key])
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
|
||||
|
||||
sampler = DiffusionSampler(
|
||||
model.diffusion.diffusion,
|
||||
sampler=ADPM2Sampler(),
|
||||
sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
|
||||
clamp=False
|
||||
)
|
||||
|
||||
def inference(text, noise, diffusion_steps=5, embedding_scale=1):
|
||||
text = text.strip()
|
||||
text = text.replace('"', '')
|
||||
ps = global_phonemizer.phonemize([text])
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(tokens.device)
|
||||
text_mask = length_to_mask(input_lengths).to(tokens.device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
s_pred = sampler(noise,
|
||||
embedding=bert_dur[0].unsqueeze(0), num_steps=diffusion_steps,
|
||||
embedding_scale=embedding_scale).squeeze(0)
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
pred_dur[-1] += 5
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
out = model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(device)),
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
return out.squeeze().cpu().numpy()
|
||||
|
||||
def LFinference(text, s_prev, noise, alpha=0.7, diffusion_steps=5, embedding_scale=1):
|
||||
text = text.strip()
|
||||
text = text.replace('"', '')
|
||||
ps = global_phonemizer.phonemize([text])
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(tokens.device)
|
||||
text_mask = length_to_mask(input_lengths).to(tokens.device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
s_pred = sampler(noise,
|
||||
embedding=bert_dur[0].unsqueeze(0), num_steps=diffusion_steps,
|
||||
embedding_scale=embedding_scale).squeeze(0)
|
||||
|
||||
if s_prev is not None:
|
||||
# convex combination of previous and current style
|
||||
s_pred = alpha * s_prev + (1 - alpha) * s_pred
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
out = model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(device)),
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
return out.squeeze().cpu().numpy(), s_pred
|
||||
@@ -0,0 +1,303 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
from transformers import AutoModel
|
||||
|
||||
|
||||
class SpectralConvergengeLoss(torch.nn.Module):
|
||||
"""Spectral convergence loss module."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initilize spectral convergence loss module."""
|
||||
super(SpectralConvergengeLoss, self).__init__()
|
||||
|
||||
def forward(self, x_mag, y_mag):
|
||||
"""Calculate forward propagation.
|
||||
Args:
|
||||
x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
|
||||
y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
|
||||
Returns:
|
||||
Tensor: Spectral convergence loss value.
|
||||
"""
|
||||
return torch.norm(y_mag - x_mag, p=1) / torch.norm(y_mag, p=1)
|
||||
|
||||
|
||||
class STFTLoss(torch.nn.Module):
|
||||
"""STFT loss module."""
|
||||
|
||||
def __init__(
|
||||
self, fft_size=1024, shift_size=120, win_length=600, window=torch.hann_window
|
||||
):
|
||||
"""Initialize STFT loss module."""
|
||||
super(STFTLoss, self).__init__()
|
||||
self.fft_size = fft_size
|
||||
self.shift_size = shift_size
|
||||
self.win_length = win_length
|
||||
self.to_mel = torchaudio.transforms.MelSpectrogram(
|
||||
sample_rate=24000,
|
||||
n_fft=fft_size,
|
||||
win_length=win_length,
|
||||
hop_length=shift_size,
|
||||
window_fn=window,
|
||||
)
|
||||
|
||||
self.spectral_convergenge_loss = SpectralConvergengeLoss()
|
||||
|
||||
def forward(self, x, y):
|
||||
"""Calculate forward propagation.
|
||||
Args:
|
||||
x (Tensor): Predicted signal (B, T).
|
||||
y (Tensor): Groundtruth signal (B, T).
|
||||
Returns:
|
||||
Tensor: Spectral convergence loss value.
|
||||
Tensor: Log STFT magnitude loss value.
|
||||
"""
|
||||
x_mag = self.to_mel(x)
|
||||
mean, std = -4, 4
|
||||
x_mag = (torch.log(1e-5 + x_mag) - mean) / std
|
||||
|
||||
y_mag = self.to_mel(y)
|
||||
mean, std = -4, 4
|
||||
y_mag = (torch.log(1e-5 + y_mag) - mean) / std
|
||||
|
||||
sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
|
||||
return sc_loss
|
||||
|
||||
|
||||
class MultiResolutionSTFTLoss(torch.nn.Module):
|
||||
"""Multi resolution STFT loss module."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
fft_sizes=[1024, 2048, 512],
|
||||
hop_sizes=[120, 240, 50],
|
||||
win_lengths=[600, 1200, 240],
|
||||
window=torch.hann_window,
|
||||
):
|
||||
"""Initialize Multi resolution STFT loss module.
|
||||
Args:
|
||||
fft_sizes (list): List of FFT sizes.
|
||||
hop_sizes (list): List of hop sizes.
|
||||
win_lengths (list): List of window lengths.
|
||||
window (str): Window function type.
|
||||
"""
|
||||
super(MultiResolutionSTFTLoss, self).__init__()
|
||||
assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
|
||||
self.stft_losses = torch.nn.ModuleList()
|
||||
for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
|
||||
self.stft_losses += [STFTLoss(fs, ss, wl, window)]
|
||||
|
||||
def forward(self, x, y):
|
||||
"""Calculate forward propagation.
|
||||
Args:
|
||||
x (Tensor): Predicted signal (B, T).
|
||||
y (Tensor): Groundtruth signal (B, T).
|
||||
Returns:
|
||||
Tensor: Multi resolution spectral convergence loss value.
|
||||
Tensor: Multi resolution log STFT magnitude loss value.
|
||||
"""
|
||||
sc_loss = 0.0
|
||||
for f in self.stft_losses:
|
||||
sc_l = f(x, y)
|
||||
sc_loss += sc_l
|
||||
sc_loss /= len(self.stft_losses)
|
||||
|
||||
return sc_loss
|
||||
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
r_loss = torch.mean((1 - dr) ** 2)
|
||||
g_loss = torch.mean(dg**2)
|
||||
loss += r_loss + g_loss
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_loss(disc_outputs):
|
||||
loss = 0
|
||||
gen_losses = []
|
||||
for dg in disc_outputs:
|
||||
l = torch.mean((1 - dg) ** 2)
|
||||
gen_losses.append(l)
|
||||
loss += l
|
||||
|
||||
return loss, gen_losses
|
||||
|
||||
|
||||
""" https://dl.acm.org/doi/abs/10.1145/3573834.3574506 """
|
||||
|
||||
|
||||
def discriminator_TPRLS_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
tau = 0.04
|
||||
m_DG = torch.median((dr - dg))
|
||||
L_rel = torch.mean((((dr - dg) - m_DG) ** 2)[dr < dg + m_DG])
|
||||
loss += tau - F.relu(tau - L_rel)
|
||||
return loss
|
||||
|
||||
|
||||
def generator_TPRLS_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
for dg, dr in zip(disc_real_outputs, disc_generated_outputs):
|
||||
tau = 0.04
|
||||
m_DG = torch.median((dr - dg))
|
||||
L_rel = torch.mean((((dr - dg) - m_DG) ** 2)[dr < dg + m_DG])
|
||||
loss += tau - F.relu(tau - L_rel)
|
||||
return loss
|
||||
|
||||
|
||||
class GeneratorLoss(torch.nn.Module):
|
||||
def __init__(self, mpd, msd):
|
||||
super(GeneratorLoss, self).__init__()
|
||||
self.mpd = mpd
|
||||
self.msd = msd
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = self.mpd(y, y_hat)
|
||||
y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = self.msd(y, y_hat)
|
||||
loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
|
||||
loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
|
||||
loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
|
||||
loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
|
||||
|
||||
loss_rel = generator_TPRLS_loss(y_df_hat_r, y_df_hat_g) + generator_TPRLS_loss(
|
||||
y_ds_hat_r, y_ds_hat_g
|
||||
)
|
||||
|
||||
loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_rel
|
||||
|
||||
return loss_gen_all.mean()
|
||||
|
||||
|
||||
class DiscriminatorLoss(torch.nn.Module):
|
||||
def __init__(self, mpd, msd):
|
||||
super(DiscriminatorLoss, self).__init__()
|
||||
self.mpd = mpd
|
||||
self.msd = msd
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
# MPD
|
||||
y_df_hat_r, y_df_hat_g, _, _ = self.mpd(y, y_hat)
|
||||
loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(
|
||||
y_df_hat_r, y_df_hat_g
|
||||
)
|
||||
# MSD
|
||||
y_ds_hat_r, y_ds_hat_g, _, _ = self.msd(y, y_hat)
|
||||
loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(
|
||||
y_ds_hat_r, y_ds_hat_g
|
||||
)
|
||||
|
||||
loss_rel = discriminator_TPRLS_loss(
|
||||
y_df_hat_r, y_df_hat_g
|
||||
) + discriminator_TPRLS_loss(y_ds_hat_r, y_ds_hat_g)
|
||||
|
||||
d_loss = loss_disc_s + loss_disc_f + loss_rel
|
||||
|
||||
return d_loss.mean()
|
||||
|
||||
|
||||
class WavLMLoss(torch.nn.Module):
|
||||
def __init__(self, model, wd, model_sr, slm_sr=16000):
|
||||
super(WavLMLoss, self).__init__()
|
||||
self.wavlm = AutoModel.from_pretrained(model)
|
||||
self.wd = wd
|
||||
self.resample = torchaudio.transforms.Resample(model_sr, slm_sr)
|
||||
|
||||
def forward(self, wav, y_rec):
|
||||
with torch.no_grad():
|
||||
wav_16 = self.resample(wav)
|
||||
wav_embeddings = self.wavlm(
|
||||
input_values=wav_16, output_hidden_states=True
|
||||
).hidden_states
|
||||
y_rec_16 = self.resample(y_rec)
|
||||
y_rec_embeddings = self.wavlm(
|
||||
input_values=y_rec_16.squeeze(), output_hidden_states=True
|
||||
).hidden_states
|
||||
|
||||
floss = 0
|
||||
for er, eg in zip(wav_embeddings, y_rec_embeddings):
|
||||
floss += torch.mean(torch.abs(er - eg))
|
||||
|
||||
return floss.mean()
|
||||
|
||||
def generator(self, y_rec):
|
||||
y_rec_16 = self.resample(y_rec)
|
||||
y_rec_embeddings = self.wavlm(
|
||||
input_values=y_rec_16, output_hidden_states=True
|
||||
).hidden_states
|
||||
y_rec_embeddings = (
|
||||
torch.stack(y_rec_embeddings, dim=1)
|
||||
.transpose(-1, -2)
|
||||
.flatten(start_dim=1, end_dim=2)
|
||||
)
|
||||
y_df_hat_g = self.wd(y_rec_embeddings)
|
||||
loss_gen = torch.mean((1 - y_df_hat_g) ** 2)
|
||||
|
||||
return loss_gen
|
||||
|
||||
def discriminator(self, wav, y_rec):
|
||||
with torch.no_grad():
|
||||
wav_16 = self.resample(wav)
|
||||
wav_embeddings = self.wavlm(
|
||||
input_values=wav_16, output_hidden_states=True
|
||||
).hidden_states
|
||||
y_rec_16 = self.resample(y_rec)
|
||||
y_rec_embeddings = self.wavlm(
|
||||
input_values=y_rec_16, output_hidden_states=True
|
||||
).hidden_states
|
||||
|
||||
y_embeddings = (
|
||||
torch.stack(wav_embeddings, dim=1)
|
||||
.transpose(-1, -2)
|
||||
.flatten(start_dim=1, end_dim=2)
|
||||
)
|
||||
y_rec_embeddings = (
|
||||
torch.stack(y_rec_embeddings, dim=1)
|
||||
.transpose(-1, -2)
|
||||
.flatten(start_dim=1, end_dim=2)
|
||||
)
|
||||
|
||||
y_d_rs = self.wd(y_embeddings)
|
||||
y_d_gs = self.wd(y_rec_embeddings)
|
||||
|
||||
y_df_hat_r, y_df_hat_g = y_d_rs, y_d_gs
|
||||
|
||||
r_loss = torch.mean((1 - y_df_hat_r) ** 2)
|
||||
g_loss = torch.mean((y_df_hat_g) ** 2)
|
||||
|
||||
loss_disc_f = r_loss + g_loss
|
||||
|
||||
return loss_disc_f.mean()
|
||||
|
||||
def discriminator_forward(self, wav):
|
||||
with torch.no_grad():
|
||||
wav_16 = self.resample(wav)
|
||||
wav_embeddings = self.wavlm(
|
||||
input_values=wav_16, output_hidden_states=True
|
||||
).hidden_states
|
||||
y_embeddings = (
|
||||
torch.stack(wav_embeddings, dim=1)
|
||||
.transpose(-1, -2)
|
||||
.flatten(start_dim=1, end_dim=2)
|
||||
)
|
||||
|
||||
y_d_rs = self.wd(y_embeddings)
|
||||
|
||||
return y_d_rs
|
||||
@@ -0,0 +1,294 @@
|
||||
# coding: utf-8
|
||||
import os
|
||||
import os.path as osp
|
||||
import time
|
||||
import random
|
||||
import numpy as np
|
||||
import random
|
||||
import soundfile as sf
|
||||
import librosa
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
import pandas as pd
|
||||
|
||||
_pad = "$"
|
||||
_punctuation = ';:,.!?¡¿—…"«»“” '
|
||||
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
|
||||
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
|
||||
|
||||
dicts = {}
|
||||
for i in range(len((symbols))):
|
||||
dicts[symbols[i]] = i
|
||||
|
||||
|
||||
class TextCleaner:
|
||||
def __init__(self, dummy=None):
|
||||
self.word_index_dictionary = dicts
|
||||
|
||||
def __call__(self, text):
|
||||
indexes = []
|
||||
for char in text:
|
||||
try:
|
||||
indexes.append(self.word_index_dictionary[char])
|
||||
except KeyError:
|
||||
print(text)
|
||||
return indexes
|
||||
|
||||
|
||||
np.random.seed(1)
|
||||
random.seed(1)
|
||||
SPECT_PARAMS = {"n_fft": 2048, "win_length": 1200, "hop_length": 300}
|
||||
MEL_PARAMS = {
|
||||
"n_mels": 80,
|
||||
}
|
||||
|
||||
to_mel = torchaudio.transforms.MelSpectrogram(
|
||||
n_mels=80, n_fft=2048, win_length=1200, hop_length=300
|
||||
)
|
||||
mean, std = -4, 4
|
||||
|
||||
|
||||
def preprocess(wave):
|
||||
wave_tensor = torch.from_numpy(wave).float()
|
||||
mel_tensor = to_mel(wave_tensor)
|
||||
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
|
||||
return mel_tensor
|
||||
|
||||
|
||||
class FilePathDataset(torch.utils.data.Dataset):
|
||||
def __init__(
|
||||
self,
|
||||
data_list,
|
||||
root_path,
|
||||
sr=24000,
|
||||
data_augmentation=False,
|
||||
validation=False,
|
||||
OOD_data="Data/OOD_texts.txt",
|
||||
min_length=50,
|
||||
):
|
||||
spect_params = SPECT_PARAMS
|
||||
mel_params = MEL_PARAMS
|
||||
|
||||
_data_list = [l[:-1].split("|") for l in data_list]
|
||||
self.data_list = [data if len(data) == 3 else (*data, 0) for data in _data_list]
|
||||
self.text_cleaner = TextCleaner()
|
||||
self.sr = sr
|
||||
|
||||
self.df = pd.DataFrame(self.data_list)
|
||||
|
||||
self.to_melspec = torchaudio.transforms.MelSpectrogram(**MEL_PARAMS)
|
||||
|
||||
self.mean, self.std = -4, 4
|
||||
self.data_augmentation = data_augmentation and (not validation)
|
||||
self.max_mel_length = 192
|
||||
|
||||
self.min_length = min_length
|
||||
with open(OOD_data, "r") as f:
|
||||
tl = f.readlines()
|
||||
idx = 1 if ".wav" in tl[0].split("|")[0] else 0
|
||||
self.ptexts = [t.split("|")[idx] for t in tl]
|
||||
|
||||
self.root_path = root_path
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data_list)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
data = self.data_list[idx]
|
||||
path = data[0]
|
||||
|
||||
wave, text_tensor, speaker_id = self._load_tensor(data)
|
||||
|
||||
mel_tensor = preprocess(wave).squeeze()
|
||||
|
||||
acoustic_feature = mel_tensor.squeeze()
|
||||
length_feature = acoustic_feature.size(1)
|
||||
acoustic_feature = acoustic_feature[:, : (length_feature - length_feature % 2)]
|
||||
|
||||
# get reference sample
|
||||
ref_data = (self.df[self.df[2] == str(speaker_id)]).sample(n=1).iloc[0].tolist()
|
||||
ref_mel_tensor, ref_label = self._load_data(ref_data[:3])
|
||||
|
||||
# get OOD text
|
||||
|
||||
ps = ""
|
||||
|
||||
while len(ps) < self.min_length:
|
||||
rand_idx = np.random.randint(0, len(self.ptexts) - 1)
|
||||
ps = self.ptexts[rand_idx]
|
||||
|
||||
text = self.text_cleaner(ps)
|
||||
text.insert(0, 0)
|
||||
text.append(0)
|
||||
|
||||
ref_text = torch.LongTensor(text)
|
||||
|
||||
return (
|
||||
speaker_id,
|
||||
acoustic_feature,
|
||||
text_tensor,
|
||||
ref_text,
|
||||
ref_mel_tensor,
|
||||
ref_label,
|
||||
path,
|
||||
wave,
|
||||
)
|
||||
|
||||
def _load_tensor(self, data):
|
||||
wave_path, text, speaker_id = data
|
||||
speaker_id = int(speaker_id)
|
||||
wave, sr = sf.read(osp.join(self.root_path, wave_path))
|
||||
if wave.shape[-1] == 2:
|
||||
wave = wave[:, 0].squeeze()
|
||||
if sr != 24000:
|
||||
wave = librosa.resample(wave, orig_sr=sr, target_sr=24000)
|
||||
print(wave_path, sr)
|
||||
|
||||
wave = np.concatenate([np.zeros([5000]), wave, np.zeros([5000])], axis=0)
|
||||
|
||||
text = self.text_cleaner(text)
|
||||
|
||||
text.insert(0, 0)
|
||||
text.append(0)
|
||||
|
||||
text = torch.LongTensor(text)
|
||||
|
||||
return wave, text, speaker_id
|
||||
|
||||
def _load_data(self, data):
|
||||
wave, text_tensor, speaker_id = self._load_tensor(data)
|
||||
mel_tensor = preprocess(wave).squeeze()
|
||||
|
||||
mel_length = mel_tensor.size(1)
|
||||
if mel_length > self.max_mel_length:
|
||||
random_start = np.random.randint(0, mel_length - self.max_mel_length)
|
||||
mel_tensor = mel_tensor[
|
||||
:, random_start : random_start + self.max_mel_length
|
||||
]
|
||||
|
||||
return mel_tensor, speaker_id
|
||||
|
||||
|
||||
class Collater(object):
|
||||
"""
|
||||
Args:
|
||||
adaptive_batch_size (bool): if true, decrease batch size when long data comes.
|
||||
"""
|
||||
|
||||
def __init__(self, return_wave=False):
|
||||
self.text_pad_index = 0
|
||||
self.min_mel_length = 192
|
||||
self.max_mel_length = 192
|
||||
self.return_wave = return_wave
|
||||
|
||||
def __call__(self, batch):
|
||||
# batch[0] = wave, mel, text, f0, speakerid
|
||||
batch_size = len(batch)
|
||||
|
||||
# sort by mel length
|
||||
lengths = [b[1].shape[1] for b in batch]
|
||||
batch_indexes = np.argsort(lengths)[::-1]
|
||||
batch = [batch[bid] for bid in batch_indexes]
|
||||
|
||||
nmels = batch[0][1].size(0)
|
||||
max_mel_length = max([b[1].shape[1] for b in batch])
|
||||
max_text_length = max([b[2].shape[0] for b in batch])
|
||||
max_rtext_length = max([b[3].shape[0] for b in batch])
|
||||
|
||||
labels = torch.zeros((batch_size)).long()
|
||||
mels = torch.zeros((batch_size, nmels, max_mel_length)).float()
|
||||
texts = torch.zeros((batch_size, max_text_length)).long()
|
||||
ref_texts = torch.zeros((batch_size, max_rtext_length)).long()
|
||||
|
||||
input_lengths = torch.zeros(batch_size).long()
|
||||
ref_lengths = torch.zeros(batch_size).long()
|
||||
output_lengths = torch.zeros(batch_size).long()
|
||||
ref_mels = torch.zeros((batch_size, nmels, self.max_mel_length)).float()
|
||||
ref_labels = torch.zeros((batch_size)).long()
|
||||
paths = ["" for _ in range(batch_size)]
|
||||
waves = [None for _ in range(batch_size)]
|
||||
|
||||
for bid, (
|
||||
label,
|
||||
mel,
|
||||
text,
|
||||
ref_text,
|
||||
ref_mel,
|
||||
ref_label,
|
||||
path,
|
||||
wave,
|
||||
) in enumerate(batch):
|
||||
mel_size = mel.size(1)
|
||||
text_size = text.size(0)
|
||||
rtext_size = ref_text.size(0)
|
||||
labels[bid] = label
|
||||
mels[bid, :, :mel_size] = mel
|
||||
texts[bid, :text_size] = text
|
||||
ref_texts[bid, :rtext_size] = ref_text
|
||||
input_lengths[bid] = text_size
|
||||
ref_lengths[bid] = rtext_size
|
||||
output_lengths[bid] = mel_size
|
||||
paths[bid] = path
|
||||
ref_mel_size = ref_mel.size(1)
|
||||
ref_mels[bid, :, :ref_mel_size] = ref_mel
|
||||
|
||||
ref_labels[bid] = ref_label
|
||||
waves[bid] = wave
|
||||
|
||||
return (
|
||||
waves,
|
||||
texts,
|
||||
input_lengths,
|
||||
ref_texts,
|
||||
ref_lengths,
|
||||
mels,
|
||||
output_lengths,
|
||||
ref_mels,
|
||||
)
|
||||
|
||||
|
||||
def build_dataloader(
|
||||
path_list,
|
||||
root_path,
|
||||
validation=False,
|
||||
OOD_data="Data/OOD_texts.txt",
|
||||
min_length=50,
|
||||
batch_size=4,
|
||||
num_workers=1,
|
||||
device="cpu",
|
||||
collate_config={},
|
||||
dataset_config={},
|
||||
):
|
||||
dataset = FilePathDataset(
|
||||
path_list,
|
||||
root_path,
|
||||
OOD_data=OOD_data,
|
||||
min_length=min_length,
|
||||
validation=validation,
|
||||
**dataset_config
|
||||
)
|
||||
collate_fn = Collater(**collate_config)
|
||||
data_loader = DataLoader(
|
||||
dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=(not validation),
|
||||
num_workers=num_workers,
|
||||
drop_last=(not validation),
|
||||
collate_fn=collate_fn,
|
||||
pin_memory=(device != "cpu"),
|
||||
)
|
||||
|
||||
return data_loader
|
||||
@@ -0,0 +1,881 @@
|
||||
# coding:utf-8
|
||||
|
||||
import os
|
||||
import os.path as osp
|
||||
|
||||
import copy
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
|
||||
from Utils.ASR.models import ASRCNN
|
||||
from Utils.JDC.model import JDCNet
|
||||
|
||||
from Modules.diffusion.sampler import KDiffusion, LogNormalDistribution
|
||||
from Modules.diffusion.modules import Transformer1d, StyleTransformer1d
|
||||
from Modules.diffusion.diffusion import AudioDiffusionConditional
|
||||
|
||||
from Modules.discriminators import (
|
||||
MultiPeriodDiscriminator,
|
||||
MultiResSpecDiscriminator,
|
||||
WavLMDiscriminator,
|
||||
)
|
||||
|
||||
from munch import Munch
|
||||
import yaml
|
||||
|
||||
|
||||
class LearnedDownSample(nn.Module):
|
||||
def __init__(self, layer_type, dim_in):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
if self.layer_type == "none":
|
||||
self.conv = nn.Identity()
|
||||
elif self.layer_type == "timepreserve":
|
||||
self.conv = spectral_norm(
|
||||
nn.Conv2d(
|
||||
dim_in,
|
||||
dim_in,
|
||||
kernel_size=(3, 1),
|
||||
stride=(2, 1),
|
||||
groups=dim_in,
|
||||
padding=(1, 0),
|
||||
)
|
||||
)
|
||||
elif self.layer_type == "half":
|
||||
self.conv = spectral_norm(
|
||||
nn.Conv2d(
|
||||
dim_in,
|
||||
dim_in,
|
||||
kernel_size=(3, 3),
|
||||
stride=(2, 2),
|
||||
groups=dim_in,
|
||||
padding=1,
|
||||
)
|
||||
)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Got unexpected donwsampletype %s, expected is [none, timepreserve, half]"
|
||||
% self.layer_type
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv(x)
|
||||
|
||||
|
||||
class LearnedUpSample(nn.Module):
|
||||
def __init__(self, layer_type, dim_in):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
if self.layer_type == "none":
|
||||
self.conv = nn.Identity()
|
||||
elif self.layer_type == "timepreserve":
|
||||
self.conv = nn.ConvTranspose2d(
|
||||
dim_in,
|
||||
dim_in,
|
||||
kernel_size=(3, 1),
|
||||
stride=(2, 1),
|
||||
groups=dim_in,
|
||||
output_padding=(1, 0),
|
||||
padding=(1, 0),
|
||||
)
|
||||
elif self.layer_type == "half":
|
||||
self.conv = nn.ConvTranspose2d(
|
||||
dim_in,
|
||||
dim_in,
|
||||
kernel_size=(3, 3),
|
||||
stride=(2, 2),
|
||||
groups=dim_in,
|
||||
output_padding=1,
|
||||
padding=1,
|
||||
)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Got unexpected upsampletype %s, expected is [none, timepreserve, half]"
|
||||
% self.layer_type
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv(x)
|
||||
|
||||
|
||||
class DownSample(nn.Module):
|
||||
def __init__(self, layer_type):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
def forward(self, x):
|
||||
if self.layer_type == "none":
|
||||
return x
|
||||
elif self.layer_type == "timepreserve":
|
||||
return F.avg_pool2d(x, (2, 1))
|
||||
elif self.layer_type == "half":
|
||||
if x.shape[-1] % 2 != 0:
|
||||
x = torch.cat([x, x[..., -1].unsqueeze(-1)], dim=-1)
|
||||
return F.avg_pool2d(x, 2)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Got unexpected donwsampletype %s, expected is [none, timepreserve, half]"
|
||||
% self.layer_type
|
||||
)
|
||||
|
||||
|
||||
class UpSample(nn.Module):
|
||||
def __init__(self, layer_type):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
def forward(self, x):
|
||||
if self.layer_type == "none":
|
||||
return x
|
||||
elif self.layer_type == "timepreserve":
|
||||
return F.interpolate(x, scale_factor=(2, 1), mode="nearest")
|
||||
elif self.layer_type == "half":
|
||||
return F.interpolate(x, scale_factor=2, mode="nearest")
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Got unexpected upsampletype %s, expected is [none, timepreserve, half]"
|
||||
% self.layer_type
|
||||
)
|
||||
|
||||
|
||||
class ResBlk(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_in,
|
||||
dim_out,
|
||||
actv=nn.LeakyReLU(0.2),
|
||||
normalize=False,
|
||||
downsample="none",
|
||||
):
|
||||
super().__init__()
|
||||
self.actv = actv
|
||||
self.normalize = normalize
|
||||
self.downsample = DownSample(downsample)
|
||||
self.downsample_res = LearnedDownSample(downsample, dim_in)
|
||||
self.learned_sc = dim_in != dim_out
|
||||
self._build_weights(dim_in, dim_out)
|
||||
|
||||
def _build_weights(self, dim_in, dim_out):
|
||||
self.conv1 = spectral_norm(nn.Conv2d(dim_in, dim_in, 3, 1, 1))
|
||||
self.conv2 = spectral_norm(nn.Conv2d(dim_in, dim_out, 3, 1, 1))
|
||||
if self.normalize:
|
||||
self.norm1 = nn.InstanceNorm2d(dim_in, affine=True)
|
||||
self.norm2 = nn.InstanceNorm2d(dim_in, affine=True)
|
||||
if self.learned_sc:
|
||||
self.conv1x1 = spectral_norm(
|
||||
nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False)
|
||||
)
|
||||
|
||||
def _shortcut(self, x):
|
||||
if self.learned_sc:
|
||||
x = self.conv1x1(x)
|
||||
if self.downsample:
|
||||
x = self.downsample(x)
|
||||
return x
|
||||
|
||||
def _residual(self, x):
|
||||
if self.normalize:
|
||||
x = self.norm1(x)
|
||||
x = self.actv(x)
|
||||
x = self.conv1(x)
|
||||
x = self.downsample_res(x)
|
||||
if self.normalize:
|
||||
x = self.norm2(x)
|
||||
x = self.actv(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self._shortcut(x) + self._residual(x)
|
||||
return x / math.sqrt(2) # unit variance
|
||||
|
||||
|
||||
class StyleEncoder(nn.Module):
|
||||
def __init__(self, dim_in=48, style_dim=48, max_conv_dim=384):
|
||||
super().__init__()
|
||||
blocks = []
|
||||
blocks += [spectral_norm(nn.Conv2d(1, dim_in, 3, 1, 1))]
|
||||
|
||||
repeat_num = 4
|
||||
for _ in range(repeat_num):
|
||||
dim_out = min(dim_in * 2, max_conv_dim)
|
||||
blocks += [ResBlk(dim_in, dim_out, downsample="half")]
|
||||
dim_in = dim_out
|
||||
|
||||
blocks += [nn.LeakyReLU(0.2)]
|
||||
blocks += [spectral_norm(nn.Conv2d(dim_out, dim_out, 5, 1, 0))]
|
||||
blocks += [nn.AdaptiveAvgPool2d(1)]
|
||||
blocks += [nn.LeakyReLU(0.2)]
|
||||
self.shared = nn.Sequential(*blocks)
|
||||
|
||||
self.unshared = nn.Linear(dim_out, style_dim)
|
||||
|
||||
def forward(self, x):
|
||||
h = self.shared(x)
|
||||
h = h.view(h.size(0), -1)
|
||||
s = self.unshared(h)
|
||||
|
||||
return s
|
||||
|
||||
|
||||
class LinearNorm(torch.nn.Module):
|
||||
def __init__(self, in_dim, out_dim, bias=True, w_init_gain="linear"):
|
||||
super(LinearNorm, self).__init__()
|
||||
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
|
||||
|
||||
torch.nn.init.xavier_uniform_(
|
||||
self.linear_layer.weight, gain=torch.nn.init.calculate_gain(w_init_gain)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.linear_layer(x)
|
||||
|
||||
|
||||
class Discriminator2d(nn.Module):
|
||||
def __init__(self, dim_in=48, num_domains=1, max_conv_dim=384, repeat_num=4):
|
||||
super().__init__()
|
||||
blocks = []
|
||||
blocks += [spectral_norm(nn.Conv2d(1, dim_in, 3, 1, 1))]
|
||||
|
||||
for lid in range(repeat_num):
|
||||
dim_out = min(dim_in * 2, max_conv_dim)
|
||||
blocks += [ResBlk(dim_in, dim_out, downsample="half")]
|
||||
dim_in = dim_out
|
||||
|
||||
blocks += [nn.LeakyReLU(0.2)]
|
||||
blocks += [spectral_norm(nn.Conv2d(dim_out, dim_out, 5, 1, 0))]
|
||||
blocks += [nn.LeakyReLU(0.2)]
|
||||
blocks += [nn.AdaptiveAvgPool2d(1)]
|
||||
blocks += [spectral_norm(nn.Conv2d(dim_out, num_domains, 1, 1, 0))]
|
||||
self.main = nn.Sequential(*blocks)
|
||||
|
||||
def get_feature(self, x):
|
||||
features = []
|
||||
for l in self.main:
|
||||
x = l(x)
|
||||
features.append(x)
|
||||
out = features[-1]
|
||||
out = out.view(out.size(0), -1) # (batch, num_domains)
|
||||
return out, features
|
||||
|
||||
def forward(self, x):
|
||||
out, features = self.get_feature(x)
|
||||
out = out.squeeze() # (batch)
|
||||
return out, features
|
||||
|
||||
|
||||
class ResBlk1d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_in,
|
||||
dim_out,
|
||||
actv=nn.LeakyReLU(0.2),
|
||||
normalize=False,
|
||||
downsample="none",
|
||||
dropout_p=0.2,
|
||||
):
|
||||
super().__init__()
|
||||
self.actv = actv
|
||||
self.normalize = normalize
|
||||
self.downsample_type = downsample
|
||||
self.learned_sc = dim_in != dim_out
|
||||
self._build_weights(dim_in, dim_out)
|
||||
self.dropout_p = dropout_p
|
||||
|
||||
if self.downsample_type == "none":
|
||||
self.pool = nn.Identity()
|
||||
else:
|
||||
self.pool = weight_norm(
|
||||
nn.Conv1d(
|
||||
dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1
|
||||
)
|
||||
)
|
||||
|
||||
def _build_weights(self, dim_in, dim_out):
|
||||
self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_in, 3, 1, 1))
|
||||
self.conv2 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
|
||||
if self.normalize:
|
||||
self.norm1 = nn.InstanceNorm1d(dim_in, affine=True)
|
||||
self.norm2 = nn.InstanceNorm1d(dim_in, affine=True)
|
||||
if self.learned_sc:
|
||||
self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
|
||||
|
||||
def downsample(self, x):
|
||||
if self.downsample_type == "none":
|
||||
return x
|
||||
else:
|
||||
if x.shape[-1] % 2 != 0:
|
||||
x = torch.cat([x, x[..., -1].unsqueeze(-1)], dim=-1)
|
||||
return F.avg_pool1d(x, 2)
|
||||
|
||||
def _shortcut(self, x):
|
||||
if self.learned_sc:
|
||||
x = self.conv1x1(x)
|
||||
x = self.downsample(x)
|
||||
return x
|
||||
|
||||
def _residual(self, x):
|
||||
if self.normalize:
|
||||
x = self.norm1(x)
|
||||
x = self.actv(x)
|
||||
x = F.dropout(x, p=self.dropout_p, training=self.training)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.pool(x)
|
||||
if self.normalize:
|
||||
x = self.norm2(x)
|
||||
|
||||
x = self.actv(x)
|
||||
x = F.dropout(x, p=self.dropout_p, training=self.training)
|
||||
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self._shortcut(x) + self._residual(x)
|
||||
return x / math.sqrt(2) # unit variance
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = nn.Parameter(torch.ones(channels))
|
||||
self.beta = nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(1, -1)
|
||||
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
||||
return x.transpose(1, -1)
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)):
|
||||
super().__init__()
|
||||
self.embedding = nn.Embedding(n_symbols, channels)
|
||||
|
||||
padding = (kernel_size - 1) // 2
|
||||
self.cnn = nn.ModuleList()
|
||||
for _ in range(depth):
|
||||
self.cnn.append(
|
||||
nn.Sequential(
|
||||
weight_norm(
|
||||
nn.Conv1d(
|
||||
channels, channels, kernel_size=kernel_size, padding=padding
|
||||
)
|
||||
),
|
||||
LayerNorm(channels),
|
||||
actv,
|
||||
nn.Dropout(0.2),
|
||||
)
|
||||
)
|
||||
# self.cnn = nn.Sequential(*self.cnn)
|
||||
|
||||
self.lstm = nn.LSTM(
|
||||
channels, channels // 2, 1, batch_first=True, bidirectional=True
|
||||
)
|
||||
|
||||
def forward(self, x, input_lengths, m):
|
||||
x = self.embedding(x) # [B, T, emb]
|
||||
x = x.transpose(1, 2) # [B, emb, T]
|
||||
m = m.to(input_lengths.device).unsqueeze(1)
|
||||
x.masked_fill_(m, 0.0)
|
||||
|
||||
for c in self.cnn:
|
||||
x = c(x)
|
||||
x.masked_fill_(m, 0.0)
|
||||
|
||||
x = x.transpose(1, 2) # [B, T, chn]
|
||||
|
||||
input_lengths = input_lengths.cpu().numpy()
|
||||
x = nn.utils.rnn.pack_padded_sequence(
|
||||
x, input_lengths, batch_first=True, enforce_sorted=False
|
||||
)
|
||||
|
||||
self.lstm.flatten_parameters()
|
||||
x, _ = self.lstm(x)
|
||||
x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
|
||||
|
||||
x = x.transpose(-1, -2)
|
||||
x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
|
||||
|
||||
x_pad[:, :, : x.shape[-1]] = x
|
||||
x = x_pad.to(x.device)
|
||||
|
||||
x.masked_fill_(m, 0.0)
|
||||
|
||||
return x
|
||||
|
||||
def inference(self, x):
|
||||
x = self.embedding(x)
|
||||
x = x.transpose(1, 2)
|
||||
x = self.cnn(x)
|
||||
x = x.transpose(1, 2)
|
||||
self.lstm.flatten_parameters()
|
||||
x, _ = self.lstm(x)
|
||||
return x
|
||||
|
||||
def length_to_mask(self, lengths):
|
||||
mask = (
|
||||
torch.arange(lengths.max())
|
||||
.unsqueeze(0)
|
||||
.expand(lengths.shape[0], -1)
|
||||
.type_as(lengths)
|
||||
)
|
||||
mask = torch.gt(mask + 1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
|
||||
class AdaIN1d(nn.Module):
|
||||
def __init__(self, style_dim, num_features):
|
||||
super().__init__()
|
||||
self.norm = nn.InstanceNorm1d(num_features, affine=False)
|
||||
self.fc = nn.Linear(style_dim, num_features * 2)
|
||||
|
||||
def forward(self, x, s):
|
||||
h = self.fc(s)
|
||||
h = h.view(h.size(0), h.size(1), 1)
|
||||
gamma, beta = torch.chunk(h, chunks=2, dim=1)
|
||||
return (1 + gamma) * self.norm(x) + beta
|
||||
|
||||
|
||||
class UpSample1d(nn.Module):
|
||||
def __init__(self, layer_type):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
def forward(self, x):
|
||||
if self.layer_type == "none":
|
||||
return x
|
||||
else:
|
||||
return F.interpolate(x, scale_factor=2, mode="nearest")
|
||||
|
||||
|
||||
class AdainResBlk1d(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_in,
|
||||
dim_out,
|
||||
style_dim=64,
|
||||
actv=nn.LeakyReLU(0.2),
|
||||
upsample="none",
|
||||
dropout_p=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.actv = actv
|
||||
self.upsample_type = upsample
|
||||
self.upsample = UpSample1d(upsample)
|
||||
self.learned_sc = dim_in != dim_out
|
||||
self._build_weights(dim_in, dim_out, style_dim)
|
||||
self.dropout = nn.Dropout(dropout_p)
|
||||
|
||||
if upsample == "none":
|
||||
self.pool = nn.Identity()
|
||||
else:
|
||||
self.pool = weight_norm(
|
||||
nn.ConvTranspose1d(
|
||||
dim_in,
|
||||
dim_in,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
groups=dim_in,
|
||||
padding=1,
|
||||
output_padding=1,
|
||||
)
|
||||
)
|
||||
|
||||
def _build_weights(self, dim_in, dim_out, style_dim):
|
||||
self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
|
||||
self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
|
||||
self.norm1 = AdaIN1d(style_dim, dim_in)
|
||||
self.norm2 = AdaIN1d(style_dim, dim_out)
|
||||
if self.learned_sc:
|
||||
self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
|
||||
|
||||
def _shortcut(self, x):
|
||||
x = self.upsample(x)
|
||||
if self.learned_sc:
|
||||
x = self.conv1x1(x)
|
||||
return x
|
||||
|
||||
def _residual(self, x, s):
|
||||
x = self.norm1(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.pool(x)
|
||||
x = self.conv1(self.dropout(x))
|
||||
x = self.norm2(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.conv2(self.dropout(x))
|
||||
return x
|
||||
|
||||
def forward(self, x, s):
|
||||
out = self._residual(x, s)
|
||||
out = (out + self._shortcut(x)) / math.sqrt(2)
|
||||
return out
|
||||
|
||||
|
||||
class AdaLayerNorm(nn.Module):
|
||||
def __init__(self, style_dim, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.fc = nn.Linear(style_dim, channels * 2)
|
||||
|
||||
def forward(self, x, s):
|
||||
x = x.transpose(-1, -2)
|
||||
x = x.transpose(1, -1)
|
||||
|
||||
h = self.fc(s)
|
||||
h = h.view(h.size(0), h.size(1), 1)
|
||||
gamma, beta = torch.chunk(h, chunks=2, dim=1)
|
||||
gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
|
||||
|
||||
x = F.layer_norm(x, (self.channels,), eps=self.eps)
|
||||
x = (1 + gamma) * x + beta
|
||||
return x.transpose(1, -1).transpose(-1, -2)
|
||||
|
||||
|
||||
class ProsodyPredictor(nn.Module):
|
||||
def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1):
|
||||
super().__init__()
|
||||
|
||||
self.text_encoder = DurationEncoder(
|
||||
sty_dim=style_dim, d_model=d_hid, nlayers=nlayers, dropout=dropout
|
||||
)
|
||||
|
||||
self.lstm = nn.LSTM(
|
||||
d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True
|
||||
)
|
||||
self.duration_proj = LinearNorm(d_hid, max_dur)
|
||||
|
||||
self.shared = nn.LSTM(
|
||||
d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True
|
||||
)
|
||||
self.F0 = nn.ModuleList()
|
||||
self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
|
||||
self.F0.append(
|
||||
AdainResBlk1d(
|
||||
d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout
|
||||
)
|
||||
)
|
||||
self.F0.append(
|
||||
AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)
|
||||
)
|
||||
|
||||
self.N = nn.ModuleList()
|
||||
self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
|
||||
self.N.append(
|
||||
AdainResBlk1d(
|
||||
d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout
|
||||
)
|
||||
)
|
||||
self.N.append(
|
||||
AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)
|
||||
)
|
||||
|
||||
self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
|
||||
self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
|
||||
|
||||
def forward(self, texts, style, text_lengths, alignment, m):
|
||||
d = self.text_encoder(texts, style, text_lengths, m)
|
||||
|
||||
batch_size = d.shape[0]
|
||||
text_size = d.shape[1]
|
||||
|
||||
# predict duration
|
||||
input_lengths = text_lengths.cpu().numpy()
|
||||
x = nn.utils.rnn.pack_padded_sequence(
|
||||
d, input_lengths, batch_first=True, enforce_sorted=False
|
||||
)
|
||||
|
||||
m = m.to(text_lengths.device).unsqueeze(1)
|
||||
|
||||
self.lstm.flatten_parameters()
|
||||
x, _ = self.lstm(x)
|
||||
x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
|
||||
|
||||
x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]])
|
||||
|
||||
x_pad[:, : x.shape[1], :] = x
|
||||
x = x_pad.to(x.device)
|
||||
|
||||
duration = self.duration_proj(
|
||||
nn.functional.dropout(x, 0.5, training=self.training)
|
||||
)
|
||||
|
||||
en = d.transpose(-1, -2) @ alignment
|
||||
|
||||
return duration.squeeze(-1), en
|
||||
|
||||
def F0Ntrain(self, x, s):
|
||||
x, _ = self.shared(x.transpose(-1, -2))
|
||||
|
||||
F0 = x.transpose(-1, -2)
|
||||
for block in self.F0:
|
||||
F0 = block(F0, s)
|
||||
F0 = self.F0_proj(F0)
|
||||
|
||||
N = x.transpose(-1, -2)
|
||||
for block in self.N:
|
||||
N = block(N, s)
|
||||
N = self.N_proj(N)
|
||||
|
||||
return F0.squeeze(1), N.squeeze(1)
|
||||
|
||||
def length_to_mask(self, lengths):
|
||||
mask = (
|
||||
torch.arange(lengths.max())
|
||||
.unsqueeze(0)
|
||||
.expand(lengths.shape[0], -1)
|
||||
.type_as(lengths)
|
||||
)
|
||||
mask = torch.gt(mask + 1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
|
||||
class DurationEncoder(nn.Module):
|
||||
def __init__(self, sty_dim, d_model, nlayers, dropout=0.1):
|
||||
super().__init__()
|
||||
self.lstms = nn.ModuleList()
|
||||
for _ in range(nlayers):
|
||||
self.lstms.append(
|
||||
nn.LSTM(
|
||||
d_model + sty_dim,
|
||||
d_model // 2,
|
||||
num_layers=1,
|
||||
batch_first=True,
|
||||
bidirectional=True,
|
||||
dropout=dropout,
|
||||
)
|
||||
)
|
||||
self.lstms.append(AdaLayerNorm(sty_dim, d_model))
|
||||
|
||||
self.dropout = dropout
|
||||
self.d_model = d_model
|
||||
self.sty_dim = sty_dim
|
||||
|
||||
def forward(self, x, style, text_lengths, m):
|
||||
masks = m.to(text_lengths.device)
|
||||
|
||||
x = x.permute(2, 0, 1)
|
||||
s = style.expand(x.shape[0], x.shape[1], -1)
|
||||
x = torch.cat([x, s], axis=-1)
|
||||
x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0)
|
||||
|
||||
x = x.transpose(0, 1)
|
||||
input_lengths = text_lengths.cpu().numpy()
|
||||
x = x.transpose(-1, -2)
|
||||
|
||||
for block in self.lstms:
|
||||
if isinstance(block, AdaLayerNorm):
|
||||
x = block(x.transpose(-1, -2), style).transpose(-1, -2)
|
||||
x = torch.cat([x, s.permute(1, -1, 0)], axis=1)
|
||||
x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0)
|
||||
else:
|
||||
x = x.transpose(-1, -2)
|
||||
x = nn.utils.rnn.pack_padded_sequence(
|
||||
x, input_lengths, batch_first=True, enforce_sorted=False
|
||||
)
|
||||
block.flatten_parameters()
|
||||
x, _ = block(x)
|
||||
x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
|
||||
x = F.dropout(x, p=self.dropout, training=self.training)
|
||||
x = x.transpose(-1, -2)
|
||||
|
||||
x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
|
||||
|
||||
x_pad[:, :, : x.shape[-1]] = x
|
||||
x = x_pad.to(x.device)
|
||||
|
||||
return x.transpose(-1, -2)
|
||||
|
||||
def inference(self, x, style):
|
||||
x = self.embedding(x.transpose(-1, -2)) * math.sqrt(self.d_model)
|
||||
style = style.expand(x.shape[0], x.shape[1], -1)
|
||||
x = torch.cat([x, style], axis=-1)
|
||||
src = self.pos_encoder(x)
|
||||
output = self.transformer_encoder(src).transpose(0, 1)
|
||||
return output
|
||||
|
||||
def length_to_mask(self, lengths):
|
||||
mask = (
|
||||
torch.arange(lengths.max())
|
||||
.unsqueeze(0)
|
||||
.expand(lengths.shape[0], -1)
|
||||
.type_as(lengths)
|
||||
)
|
||||
mask = torch.gt(mask + 1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
|
||||
def load_F0_models(path):
|
||||
# load F0 model
|
||||
|
||||
F0_model = JDCNet(num_class=1, seq_len=192)
|
||||
params = torch.load(path, map_location="cpu")["net"]
|
||||
F0_model.load_state_dict(params)
|
||||
_ = F0_model.train()
|
||||
|
||||
return F0_model
|
||||
|
||||
|
||||
def load_ASR_models(ASR_MODEL_PATH, ASR_MODEL_CONFIG):
|
||||
# load ASR model
|
||||
def _load_config(path):
|
||||
with open(path) as f:
|
||||
config = yaml.safe_load(f)
|
||||
model_config = config["model_params"]
|
||||
return model_config
|
||||
|
||||
def _load_model(model_config, model_path):
|
||||
model = ASRCNN(**model_config)
|
||||
params = torch.load(model_path, map_location="cpu")["model"]
|
||||
model.load_state_dict(params)
|
||||
return model
|
||||
|
||||
asr_model_config = _load_config(ASR_MODEL_CONFIG)
|
||||
asr_model = _load_model(asr_model_config, ASR_MODEL_PATH)
|
||||
_ = asr_model.train()
|
||||
|
||||
return asr_model
|
||||
|
||||
|
||||
def build_model(args, text_aligner, pitch_extractor, bert):
|
||||
assert args.decoder.type in ["istftnet", "hifigan"], "Decoder type unknown"
|
||||
|
||||
if args.decoder.type == "istftnet":
|
||||
from Modules.istftnet import Decoder
|
||||
|
||||
decoder = Decoder(
|
||||
dim_in=args.hidden_dim,
|
||||
style_dim=args.style_dim,
|
||||
dim_out=args.n_mels,
|
||||
resblock_kernel_sizes=args.decoder.resblock_kernel_sizes,
|
||||
upsample_rates=args.decoder.upsample_rates,
|
||||
upsample_initial_channel=args.decoder.upsample_initial_channel,
|
||||
resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
|
||||
upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
|
||||
gen_istft_n_fft=args.decoder.gen_istft_n_fft,
|
||||
gen_istft_hop_size=args.decoder.gen_istft_hop_size,
|
||||
)
|
||||
else:
|
||||
from Modules.hifigan import Decoder
|
||||
|
||||
decoder = Decoder(
|
||||
dim_in=args.hidden_dim,
|
||||
style_dim=args.style_dim,
|
||||
dim_out=args.n_mels,
|
||||
resblock_kernel_sizes=args.decoder.resblock_kernel_sizes,
|
||||
upsample_rates=args.decoder.upsample_rates,
|
||||
upsample_initial_channel=args.decoder.upsample_initial_channel,
|
||||
resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
|
||||
upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
|
||||
)
|
||||
|
||||
text_encoder = TextEncoder(
|
||||
channels=args.hidden_dim,
|
||||
kernel_size=5,
|
||||
depth=args.n_layer,
|
||||
n_symbols=args.n_token,
|
||||
)
|
||||
|
||||
predictor = ProsodyPredictor(
|
||||
style_dim=args.style_dim,
|
||||
d_hid=args.hidden_dim,
|
||||
nlayers=args.n_layer,
|
||||
max_dur=args.max_dur,
|
||||
dropout=args.dropout,
|
||||
)
|
||||
|
||||
style_encoder = StyleEncoder(
|
||||
dim_in=args.dim_in, style_dim=args.style_dim, max_conv_dim=args.hidden_dim
|
||||
) # acoustic style encoder
|
||||
predictor_encoder = StyleEncoder(
|
||||
dim_in=args.dim_in, style_dim=args.style_dim, max_conv_dim=args.hidden_dim
|
||||
) # prosodic style encoder
|
||||
|
||||
# define diffusion model
|
||||
if args.multispeaker:
|
||||
transformer = StyleTransformer1d(
|
||||
channels=args.style_dim * 2,
|
||||
context_embedding_features=bert.config.hidden_size,
|
||||
context_features=args.style_dim * 2,
|
||||
**args.diffusion.transformer
|
||||
)
|
||||
else:
|
||||
transformer = Transformer1d(
|
||||
channels=args.style_dim * 2,
|
||||
context_embedding_features=bert.config.hidden_size,
|
||||
**args.diffusion.transformer
|
||||
)
|
||||
|
||||
diffusion = AudioDiffusionConditional(
|
||||
in_channels=1,
|
||||
embedding_max_length=bert.config.max_position_embeddings,
|
||||
embedding_features=bert.config.hidden_size,
|
||||
embedding_mask_proba=args.diffusion.embedding_mask_proba, # Conditional dropout of batch elements,
|
||||
channels=args.style_dim * 2,
|
||||
context_features=args.style_dim * 2,
|
||||
)
|
||||
|
||||
diffusion.diffusion = KDiffusion(
|
||||
net=diffusion.unet,
|
||||
sigma_distribution=LogNormalDistribution(
|
||||
mean=args.diffusion.dist.mean, std=args.diffusion.dist.std
|
||||
),
|
||||
sigma_data=args.diffusion.dist.sigma_data, # a placeholder, will be changed dynamically when start training diffusion model
|
||||
dynamic_threshold=0.0,
|
||||
)
|
||||
diffusion.diffusion.net = transformer
|
||||
diffusion.unet = transformer
|
||||
|
||||
nets = Munch(
|
||||
bert=bert,
|
||||
bert_encoder=nn.Linear(bert.config.hidden_size, args.hidden_dim),
|
||||
predictor=predictor,
|
||||
decoder=decoder,
|
||||
text_encoder=text_encoder,
|
||||
predictor_encoder=predictor_encoder,
|
||||
style_encoder=style_encoder,
|
||||
diffusion=diffusion,
|
||||
text_aligner=text_aligner,
|
||||
pitch_extractor=pitch_extractor,
|
||||
mpd=MultiPeriodDiscriminator(),
|
||||
msd=MultiResSpecDiscriminator(),
|
||||
# slm discriminator head
|
||||
wd=WavLMDiscriminator(
|
||||
args.slm.hidden, args.slm.nlayers, args.slm.initial_channel
|
||||
),
|
||||
)
|
||||
|
||||
return nets
|
||||
|
||||
|
||||
def load_checkpoint(model, optimizer, path, load_only_params=True, ignore_modules=[]):
|
||||
state = torch.load(path, map_location="cpu")
|
||||
params = state["net"]
|
||||
for key in model:
|
||||
if key in params and key not in ignore_modules:
|
||||
print("%s loaded" % key)
|
||||
model[key].load_state_dict(params[key], strict=False)
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
if not load_only_params:
|
||||
epoch = state["epoch"]
|
||||
iters = state["iters"]
|
||||
optimizer.load_state_dict(state["optimizer"])
|
||||
else:
|
||||
epoch = 0
|
||||
iters = 0
|
||||
|
||||
return model, optimizer, epoch, iters
|
||||
@@ -0,0 +1,86 @@
|
||||
# coding:utf-8
|
||||
import os, sys
|
||||
import os.path as osp
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.optim import Optimizer
|
||||
from functools import reduce
|
||||
from torch.optim import AdamW
|
||||
|
||||
|
||||
class MultiOptimizer:
|
||||
def __init__(self, optimizers={}, schedulers={}):
|
||||
self.optimizers = optimizers
|
||||
self.schedulers = schedulers
|
||||
self.keys = list(optimizers.keys())
|
||||
self.param_groups = reduce(
|
||||
lambda x, y: x + y, [v.param_groups for v in self.optimizers.values()]
|
||||
)
|
||||
|
||||
def state_dict(self):
|
||||
state_dicts = [(key, self.optimizers[key].state_dict()) for key in self.keys]
|
||||
return state_dicts
|
||||
|
||||
def load_state_dict(self, state_dict):
|
||||
for key, val in state_dict:
|
||||
try:
|
||||
self.optimizers[key].load_state_dict(val)
|
||||
except:
|
||||
print("Unloaded %s" % key)
|
||||
|
||||
def step(self, key=None, scaler=None):
|
||||
keys = [key] if key is not None else self.keys
|
||||
_ = [self._step(key, scaler) for key in keys]
|
||||
|
||||
def _step(self, key, scaler=None):
|
||||
if scaler is not None:
|
||||
scaler.step(self.optimizers[key])
|
||||
scaler.update()
|
||||
else:
|
||||
self.optimizers[key].step()
|
||||
|
||||
def zero_grad(self, key=None):
|
||||
if key is not None:
|
||||
self.optimizers[key].zero_grad()
|
||||
else:
|
||||
_ = [self.optimizers[key].zero_grad() for key in self.keys]
|
||||
|
||||
def scheduler(self, *args, key=None):
|
||||
if key is not None:
|
||||
self.schedulers[key].step(*args)
|
||||
else:
|
||||
_ = [self.schedulers[key].step(*args) for key in self.keys]
|
||||
|
||||
|
||||
def define_scheduler(optimizer, params):
|
||||
scheduler = torch.optim.lr_scheduler.OneCycleLR(
|
||||
optimizer,
|
||||
max_lr=params.get("max_lr", 2e-4),
|
||||
epochs=params.get("epochs", 200),
|
||||
steps_per_epoch=params.get("steps_per_epoch", 1000),
|
||||
pct_start=params.get("pct_start", 0.0),
|
||||
div_factor=1,
|
||||
final_div_factor=1,
|
||||
)
|
||||
|
||||
return scheduler
|
||||
|
||||
|
||||
def build_optimizer(parameters_dict, scheduler_params_dict, lr):
|
||||
optim = dict(
|
||||
[
|
||||
(key, AdamW(params, lr=lr, weight_decay=1e-4, betas=(0.0, 0.99), eps=1e-9))
|
||||
for key, params in parameters_dict.items()
|
||||
]
|
||||
)
|
||||
|
||||
schedulers = dict(
|
||||
[
|
||||
(key, define_scheduler(opt, scheduler_params_dict[key]))
|
||||
for key, opt in optim.items()
|
||||
]
|
||||
)
|
||||
|
||||
multi_optim = MultiOptimizer(optim, schedulers)
|
||||
return multi_optim
|
||||
@@ -0,0 +1 @@
|
||||
espeak-ng
|
||||
@@ -0,0 +1,20 @@
|
||||
SoundFile
|
||||
torchaudio
|
||||
munch
|
||||
torch
|
||||
pydub
|
||||
pyyaml
|
||||
librosa
|
||||
nltk
|
||||
matplotlib
|
||||
accelerate
|
||||
transformers
|
||||
einops
|
||||
einops-exts
|
||||
tqdm
|
||||
typing
|
||||
typing-extensions
|
||||
git+https://github.com/resemble-ai/monotonic_align.git
|
||||
scipy
|
||||
phonemizer
|
||||
tortoise-tts
|
||||
@@ -0,0 +1,354 @@
|
||||
print("NLTK")
|
||||
import nltk
|
||||
nltk.download('punkt')
|
||||
print("SCIPY")
|
||||
from scipy.io.wavfile import write
|
||||
print("TORCH STUFF")
|
||||
import torch
|
||||
print("START")
|
||||
torch.manual_seed(0)
|
||||
torch.backends.cudnn.benchmark = False
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
import random
|
||||
random.seed(0)
|
||||
|
||||
import numpy as np
|
||||
np.random.seed(0)
|
||||
|
||||
# load packages
|
||||
import time
|
||||
import random
|
||||
import yaml
|
||||
from munch import Munch
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
from nltk.tokenize import word_tokenize
|
||||
|
||||
from models import *
|
||||
from utils import *
|
||||
from text_utils import TextCleaner
|
||||
textclenaer = TextCleaner()
|
||||
|
||||
|
||||
to_mel = torchaudio.transforms.MelSpectrogram(
|
||||
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
|
||||
mean, std = -4, 4
|
||||
|
||||
def length_to_mask(lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
def preprocess(wave):
|
||||
wave_tensor = torch.from_numpy(wave).float()
|
||||
mel_tensor = to_mel(wave_tensor)
|
||||
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
|
||||
return mel_tensor
|
||||
|
||||
def compute_style(path):
|
||||
wave, sr = librosa.load(path, sr=24000)
|
||||
audio, index = librosa.effects.trim(wave, top_db=30)
|
||||
if sr != 24000:
|
||||
audio = librosa.resample(audio, sr, 24000)
|
||||
mel_tensor = preprocess(audio).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
ref_s = model.style_encoder(mel_tensor.unsqueeze(1))
|
||||
ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1))
|
||||
|
||||
return torch.cat([ref_s, ref_p], dim=1)
|
||||
|
||||
device = 'cpu'
|
||||
if torch.cuda.is_available():
|
||||
device = 'cuda'
|
||||
elif torch.backends.mps.is_available():
|
||||
print("MPS would be available but cannot be used rn")
|
||||
# device = 'mps'
|
||||
|
||||
import phonemizer
|
||||
global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True)
|
||||
|
||||
config = yaml.safe_load(open("Models/LibriTTS/config.yml"))
|
||||
|
||||
# load pretrained ASR model
|
||||
ASR_config = config.get('ASR_config', False)
|
||||
ASR_path = config.get('ASR_path', False)
|
||||
text_aligner = load_ASR_models(ASR_path, ASR_config)
|
||||
|
||||
# load pretrained F0 model
|
||||
F0_path = config.get('F0_path', False)
|
||||
pitch_extractor = load_F0_models(F0_path)
|
||||
|
||||
# load BERT model
|
||||
from Utils.PLBERT.util import load_plbert
|
||||
BERT_path = config.get('PLBERT_dir', False)
|
||||
plbert = load_plbert(BERT_path)
|
||||
|
||||
model_params = recursive_munch(config['model_params'])
|
||||
model = build_model(model_params, text_aligner, pitch_extractor, plbert)
|
||||
_ = [model[key].eval() for key in model]
|
||||
_ = [model[key].to(device) for key in model]
|
||||
|
||||
params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu')
|
||||
params = params_whole['net']
|
||||
|
||||
for key in model:
|
||||
if key in params:
|
||||
print('%s loaded' % key)
|
||||
try:
|
||||
model[key].load_state_dict(params[key])
|
||||
except:
|
||||
from collections import OrderedDict
|
||||
state_dict = params[key]
|
||||
new_state_dict = OrderedDict()
|
||||
for k, v in state_dict.items():
|
||||
name = k[7:] # remove `module.`
|
||||
new_state_dict[name] = v
|
||||
# load params
|
||||
model[key].load_state_dict(new_state_dict, strict=False)
|
||||
# except:
|
||||
# _load(params[key], model[key])
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
|
||||
|
||||
sampler = DiffusionSampler(
|
||||
model.diffusion.diffusion,
|
||||
sampler=ADPM2Sampler(),
|
||||
sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
|
||||
clamp=False
|
||||
)
|
||||
|
||||
def inference(text, ref_s, alpha = 0.3, beta = 0.7, diffusion_steps=5, embedding_scale=1, use_gruut=False):
|
||||
text = text.strip()
|
||||
ps = global_phonemizer.phonemize([text])
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=embedding_scale,
|
||||
features=ref_s, # reference from the same speaker as the embedding
|
||||
num_steps=diffusion_steps).squeeze(1)
|
||||
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
|
||||
s = beta * s + (1 - beta) * ref_s[:, 128:]
|
||||
|
||||
d = model.predictor.text_encoder(d_en,
|
||||
s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(en)
|
||||
asr_new[:, :, 0] = en[:, :, 0]
|
||||
asr_new[:, :, 1:] = en[:, :, 0:-1]
|
||||
en = asr_new
|
||||
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
|
||||
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(asr)
|
||||
asr_new[:, :, 0] = asr[:, :, 0]
|
||||
asr_new[:, :, 1:] = asr[:, :, 0:-1]
|
||||
asr = asr_new
|
||||
|
||||
out = model.decoder(asr,
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
|
||||
return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later
|
||||
|
||||
def LFinference(text, s_prev, ref_s, alpha = 0.3, beta = 0.7, t = 0.7, diffusion_steps=5, embedding_scale=1, use_gruut=False):
|
||||
text = text.strip()
|
||||
ps = global_phonemizer.phonemize([text])
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
ps = ps.replace('``', '"')
|
||||
ps = ps.replace("''", '"')
|
||||
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=embedding_scale,
|
||||
features=ref_s, # reference from the same speaker as the embedding
|
||||
num_steps=diffusion_steps).squeeze(1)
|
||||
|
||||
if s_prev is not None:
|
||||
# convex combination of previous and current style
|
||||
s_pred = t * s_prev + (1 - t) * s_pred
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
|
||||
s = beta * s + (1 - beta) * ref_s[:, 128:]
|
||||
|
||||
s_pred = torch.cat([ref, s], dim=-1)
|
||||
|
||||
d = model.predictor.text_encoder(d_en,
|
||||
s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(en)
|
||||
asr_new[:, :, 0] = en[:, :, 0]
|
||||
asr_new[:, :, 1:] = en[:, :, 0:-1]
|
||||
en = asr_new
|
||||
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
|
||||
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(asr)
|
||||
asr_new[:, :, 0] = asr[:, :, 0]
|
||||
asr_new[:, :, 1:] = asr[:, :, 0:-1]
|
||||
asr = asr_new
|
||||
|
||||
out = model.decoder(asr,
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
|
||||
return out.squeeze().cpu().numpy()[..., :-100], s_pred # weird pulse at the end of the model, need to be fixed later
|
||||
|
||||
def STinference(text, ref_s, ref_text, alpha = 0.3, beta = 0.7, diffusion_steps=5, embedding_scale=1, use_gruut=False):
|
||||
text = text.strip()
|
||||
ps = global_phonemizer.phonemize([text])
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
|
||||
tokens = textclenaer(ps)
|
||||
tokens.insert(0, 0)
|
||||
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
|
||||
|
||||
ref_text = ref_text.strip()
|
||||
ps = global_phonemizer.phonemize([ref_text])
|
||||
ps = word_tokenize(ps[0])
|
||||
ps = ' '.join(ps)
|
||||
|
||||
ref_tokens = textclenaer(ps)
|
||||
ref_tokens.insert(0, 0)
|
||||
ref_tokens = torch.LongTensor(ref_tokens).to(device).unsqueeze(0)
|
||||
|
||||
|
||||
with torch.no_grad():
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
ref_input_lengths = torch.LongTensor([ref_tokens.shape[-1]]).to(device)
|
||||
ref_text_mask = length_to_mask(ref_input_lengths).to(device)
|
||||
ref_bert_dur = model.bert(ref_tokens, attention_mask=(~ref_text_mask).int())
|
||||
s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=embedding_scale,
|
||||
features=ref_s, # reference from the same speaker as the embedding
|
||||
num_steps=diffusion_steps).squeeze(1)
|
||||
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
|
||||
s = beta * s + (1 - beta) * ref_s[:, 128:]
|
||||
|
||||
d = model.predictor.text_encoder(d_en,
|
||||
s, input_lengths, text_mask)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
|
||||
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(en)
|
||||
asr_new[:, :, 0] = en[:, :, 0]
|
||||
asr_new[:, :, 1:] = en[:, :, 0:-1]
|
||||
en = asr_new
|
||||
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
|
||||
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
|
||||
if model_params.decoder.type == "hifigan":
|
||||
asr_new = torch.zeros_like(asr)
|
||||
asr_new[:, :, 0] = asr[:, :, 0]
|
||||
asr_new[:, :, 1:] = asr[:, :, 0:-1]
|
||||
asr = asr_new
|
||||
|
||||
out = model.decoder(asr,
|
||||
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
|
||||
|
||||
|
||||
return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later
|
||||
@@ -0,0 +1,28 @@
|
||||
# IPA Phonemizer: https://github.com/bootphon/phonemizer
|
||||
|
||||
_pad = "$"
|
||||
_punctuation = ';:,.!?¡¿—…"«»“” '
|
||||
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
|
||||
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
|
||||
|
||||
dicts = {}
|
||||
for i in range(len((symbols))):
|
||||
dicts[symbols[i]] = i
|
||||
|
||||
|
||||
class TextCleaner:
|
||||
def __init__(self, dummy=None):
|
||||
self.word_index_dictionary = dicts
|
||||
print(len(dicts))
|
||||
|
||||
def __call__(self, text):
|
||||
indexes = []
|
||||
for char in text:
|
||||
try:
|
||||
indexes.append(self.word_index_dictionary[char])
|
||||
except KeyError:
|
||||
print(text)
|
||||
return indexes
|
||||
@@ -0,0 +1,839 @@
|
||||
# load packages
|
||||
import random
|
||||
import yaml
|
||||
import time
|
||||
from munch import Munch
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
import click
|
||||
import shutil
|
||||
import warnings
|
||||
|
||||
warnings.simplefilter("ignore")
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
from meldataset import build_dataloader
|
||||
|
||||
from Utils.ASR.models import ASRCNN
|
||||
from Utils.JDC.model import JDCNet
|
||||
from Utils.PLBERT.util import load_plbert
|
||||
|
||||
from models import *
|
||||
from losses import *
|
||||
from utils import *
|
||||
|
||||
from Modules.slmadv import SLMAdversarialLoss
|
||||
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
|
||||
|
||||
from optimizers import build_optimizer
|
||||
|
||||
|
||||
# simple fix for dataparallel that allows access to class attributes
|
||||
class MyDataParallel(torch.nn.DataParallel):
|
||||
def __getattr__(self, name):
|
||||
try:
|
||||
return super().__getattr__(name)
|
||||
except AttributeError:
|
||||
return getattr(self.module, name)
|
||||
|
||||
|
||||
import logging
|
||||
from logging import StreamHandler
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
handler = StreamHandler()
|
||||
handler.setLevel(logging.DEBUG)
|
||||
logger.addHandler(handler)
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option("-p", "--config_path", default="Configs/config_ft.yml", type=str)
|
||||
def main(config_path):
|
||||
config = yaml.safe_load(open(config_path))
|
||||
|
||||
log_dir = config["log_dir"]
|
||||
if not osp.exists(log_dir):
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
|
||||
writer = SummaryWriter(log_dir + "/tensorboard")
|
||||
|
||||
# write logs
|
||||
file_handler = logging.FileHandler(osp.join(log_dir, "train.log"))
|
||||
file_handler.setLevel(logging.DEBUG)
|
||||
file_handler.setFormatter(
|
||||
logging.Formatter("%(levelname)s:%(asctime)s: %(message)s")
|
||||
)
|
||||
logger.addHandler(file_handler)
|
||||
|
||||
batch_size = config.get("batch_size", 10)
|
||||
|
||||
epochs = config.get("epochs", 200)
|
||||
save_freq = config.get("save_freq", 2)
|
||||
log_interval = config.get("log_interval", 10)
|
||||
saving_epoch = config.get("save_freq", 2)
|
||||
|
||||
data_params = config.get("data_params", None)
|
||||
sr = config["preprocess_params"].get("sr", 24000)
|
||||
train_path = data_params["train_data"]
|
||||
val_path = data_params["val_data"]
|
||||
root_path = data_params["root_path"]
|
||||
min_length = data_params["min_length"]
|
||||
OOD_data = data_params["OOD_data"]
|
||||
|
||||
max_len = config.get("max_len", 200)
|
||||
|
||||
loss_params = Munch(config["loss_params"])
|
||||
diff_epoch = loss_params.diff_epoch
|
||||
joint_epoch = loss_params.joint_epoch
|
||||
|
||||
optimizer_params = Munch(config["optimizer_params"])
|
||||
|
||||
train_list, val_list = get_data_path_list(train_path, val_path)
|
||||
device = "cuda"
|
||||
|
||||
train_dataloader = build_dataloader(
|
||||
train_list,
|
||||
root_path,
|
||||
OOD_data=OOD_data,
|
||||
min_length=min_length,
|
||||
batch_size=batch_size,
|
||||
num_workers=2,
|
||||
dataset_config={},
|
||||
device=device,
|
||||
)
|
||||
|
||||
val_dataloader = build_dataloader(
|
||||
val_list,
|
||||
root_path,
|
||||
OOD_data=OOD_data,
|
||||
min_length=min_length,
|
||||
batch_size=batch_size,
|
||||
validation=True,
|
||||
num_workers=0,
|
||||
device=device,
|
||||
dataset_config={},
|
||||
)
|
||||
|
||||
# load pretrained ASR model
|
||||
ASR_config = config.get("ASR_config", False)
|
||||
ASR_path = config.get("ASR_path", False)
|
||||
text_aligner = load_ASR_models(ASR_path, ASR_config)
|
||||
|
||||
# load pretrained F0 model
|
||||
F0_path = config.get("F0_path", False)
|
||||
pitch_extractor = load_F0_models(F0_path)
|
||||
|
||||
# load PL-BERT model
|
||||
BERT_path = config.get("PLBERT_dir", False)
|
||||
plbert = load_plbert(BERT_path)
|
||||
|
||||
# build model
|
||||
model_params = recursive_munch(config["model_params"])
|
||||
multispeaker = model_params.multispeaker
|
||||
model = build_model(model_params, text_aligner, pitch_extractor, plbert)
|
||||
_ = [model[key].to(device) for key in model]
|
||||
|
||||
# DP
|
||||
for key in model:
|
||||
if key != "mpd" and key != "msd" and key != "wd":
|
||||
model[key] = MyDataParallel(model[key])
|
||||
|
||||
start_epoch = 0
|
||||
iters = 0
|
||||
|
||||
load_pretrained = config.get("pretrained_model", "") != "" and config.get(
|
||||
"second_stage_load_pretrained", False
|
||||
)
|
||||
|
||||
if not load_pretrained:
|
||||
if config.get("first_stage_path", "") != "":
|
||||
first_stage_path = osp.join(
|
||||
log_dir, config.get("first_stage_path", "first_stage.pth")
|
||||
)
|
||||
print("Loading the first stage model at %s ..." % first_stage_path)
|
||||
model, _, start_epoch, iters = load_checkpoint(
|
||||
model,
|
||||
None,
|
||||
first_stage_path,
|
||||
load_only_params=True,
|
||||
ignore_modules=[
|
||||
"bert",
|
||||
"bert_encoder",
|
||||
"predictor",
|
||||
"predictor_encoder",
|
||||
"msd",
|
||||
"mpd",
|
||||
"wd",
|
||||
"diffusion",
|
||||
],
|
||||
) # keep starting epoch for tensorboard log
|
||||
|
||||
# these epochs should be counted from the start epoch
|
||||
diff_epoch += start_epoch
|
||||
joint_epoch += start_epoch
|
||||
epochs += start_epoch
|
||||
|
||||
model.predictor_encoder = copy.deepcopy(model.style_encoder)
|
||||
else:
|
||||
raise ValueError("You need to specify the path to the first stage model.")
|
||||
|
||||
gl = GeneratorLoss(model.mpd, model.msd).to(device)
|
||||
dl = DiscriminatorLoss(model.mpd, model.msd).to(device)
|
||||
wl = WavLMLoss(model_params.slm.model, model.wd, sr, model_params.slm.sr).to(device)
|
||||
|
||||
gl = MyDataParallel(gl)
|
||||
dl = MyDataParallel(dl)
|
||||
wl = MyDataParallel(wl)
|
||||
|
||||
sampler = DiffusionSampler(
|
||||
model.diffusion.diffusion,
|
||||
sampler=ADPM2Sampler(),
|
||||
sigma_schedule=KarrasSchedule(
|
||||
sigma_min=0.0001, sigma_max=3.0, rho=9.0
|
||||
), # empirical parameters
|
||||
clamp=False,
|
||||
)
|
||||
|
||||
scheduler_params = {
|
||||
"max_lr": optimizer_params.lr,
|
||||
"pct_start": float(0),
|
||||
"epochs": epochs,
|
||||
"steps_per_epoch": len(train_dataloader),
|
||||
}
|
||||
scheduler_params_dict = {key: scheduler_params.copy() for key in model}
|
||||
scheduler_params_dict["bert"]["max_lr"] = optimizer_params.bert_lr * 2
|
||||
scheduler_params_dict["decoder"]["max_lr"] = optimizer_params.ft_lr * 2
|
||||
scheduler_params_dict["style_encoder"]["max_lr"] = optimizer_params.ft_lr * 2
|
||||
|
||||
optimizer = build_optimizer(
|
||||
{key: model[key].parameters() for key in model},
|
||||
scheduler_params_dict=scheduler_params_dict,
|
||||
lr=optimizer_params.lr,
|
||||
)
|
||||
|
||||
# adjust BERT learning rate
|
||||
for g in optimizer.optimizers["bert"].param_groups:
|
||||
g["betas"] = (0.9, 0.99)
|
||||
g["lr"] = optimizer_params.bert_lr
|
||||
g["initial_lr"] = optimizer_params.bert_lr
|
||||
g["min_lr"] = 0
|
||||
g["weight_decay"] = 0.01
|
||||
|
||||
# adjust acoustic module learning rate
|
||||
for module in ["decoder", "style_encoder"]:
|
||||
for g in optimizer.optimizers[module].param_groups:
|
||||
g["betas"] = (0.0, 0.99)
|
||||
g["lr"] = optimizer_params.ft_lr
|
||||
g["initial_lr"] = optimizer_params.ft_lr
|
||||
g["min_lr"] = 0
|
||||
g["weight_decay"] = 1e-4
|
||||
|
||||
# load models if there is a model
|
||||
if load_pretrained:
|
||||
model, optimizer, start_epoch, iters = load_checkpoint(
|
||||
model,
|
||||
optimizer,
|
||||
config["pretrained_model"],
|
||||
load_only_params=config.get("load_only_params", True),
|
||||
)
|
||||
|
||||
n_down = model.text_aligner.n_down
|
||||
|
||||
best_loss = float("inf") # best test loss
|
||||
loss_train_record = list([])
|
||||
loss_test_record = list([])
|
||||
iters = 0
|
||||
|
||||
criterion = nn.L1Loss() # F0 loss (regression)
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
stft_loss = MultiResolutionSTFTLoss().to(device)
|
||||
|
||||
print("BERT", optimizer.optimizers["bert"])
|
||||
print("decoder", optimizer.optimizers["decoder"])
|
||||
|
||||
start_ds = False
|
||||
|
||||
running_std = []
|
||||
|
||||
slmadv_params = Munch(config["slmadv_params"])
|
||||
slmadv = SLMAdversarialLoss(
|
||||
model,
|
||||
wl,
|
||||
sampler,
|
||||
slmadv_params.min_len,
|
||||
slmadv_params.max_len,
|
||||
batch_percentage=slmadv_params.batch_percentage,
|
||||
skip_update=slmadv_params.iter,
|
||||
sig=slmadv_params.sig,
|
||||
)
|
||||
|
||||
for epoch in range(start_epoch, epochs):
|
||||
running_loss = 0
|
||||
start_time = time.time()
|
||||
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
model.text_aligner.train()
|
||||
model.text_encoder.train()
|
||||
|
||||
model.predictor.train()
|
||||
model.bert_encoder.train()
|
||||
model.bert.train()
|
||||
model.msd.train()
|
||||
model.mpd.train()
|
||||
|
||||
for i, batch in enumerate(train_dataloader):
|
||||
waves = batch[0]
|
||||
batch = [b.to(device) for b in batch[1:]]
|
||||
(
|
||||
texts,
|
||||
input_lengths,
|
||||
ref_texts,
|
||||
ref_lengths,
|
||||
mels,
|
||||
mel_input_length,
|
||||
ref_mels,
|
||||
) = batch
|
||||
with torch.no_grad():
|
||||
mask = length_to_mask(mel_input_length // (2**n_down)).to(device)
|
||||
mel_mask = length_to_mask(mel_input_length).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(texts.device)
|
||||
|
||||
# compute reference styles
|
||||
if multispeaker and epoch >= diff_epoch:
|
||||
ref_ss = model.style_encoder(ref_mels.unsqueeze(1))
|
||||
ref_sp = model.predictor_encoder(ref_mels.unsqueeze(1))
|
||||
ref = torch.cat([ref_ss, ref_sp], dim=1)
|
||||
|
||||
try:
|
||||
ppgs, s2s_pred, s2s_attn = model.text_aligner(mels, mask, texts)
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
s2s_attn = s2s_attn[..., 1:]
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
except:
|
||||
continue
|
||||
|
||||
mask_ST = mask_from_lens(
|
||||
s2s_attn, input_lengths, mel_input_length // (2**n_down)
|
||||
)
|
||||
s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
|
||||
|
||||
# encode
|
||||
t_en = model.text_encoder(texts, input_lengths, text_mask)
|
||||
|
||||
# 50% of chance of using monotonic version
|
||||
if bool(random.getrandbits(1)):
|
||||
asr = t_en @ s2s_attn
|
||||
else:
|
||||
asr = t_en @ s2s_attn_mono
|
||||
|
||||
d_gt = s2s_attn_mono.sum(axis=-1).detach()
|
||||
|
||||
# compute the style of the entire utterance
|
||||
# this operation cannot be done in batch because of the avgpool layer (may need to work on masked avgpool)
|
||||
ss = []
|
||||
gs = []
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item())
|
||||
mel = mels[bib, :, : mel_input_length[bib]]
|
||||
s = model.predictor_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
ss.append(s)
|
||||
s = model.style_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
gs.append(s)
|
||||
|
||||
s_dur = torch.stack(ss).squeeze() # global prosodic styles
|
||||
gs = torch.stack(gs).squeeze() # global acoustic styles
|
||||
s_trg = torch.cat([gs, s_dur], dim=-1).detach() # ground truth for denoiser
|
||||
|
||||
bert_dur = model.bert(texts, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
# denoiser training
|
||||
if epoch >= diff_epoch:
|
||||
num_steps = np.random.randint(3, 5)
|
||||
|
||||
if model_params.diffusion.dist.estimate_sigma_data:
|
||||
model.diffusion.module.diffusion.sigma_data = (
|
||||
s_trg.std(axis=-1).mean().item()
|
||||
) # batch-wise std estimation
|
||||
running_std.append(model.diffusion.module.diffusion.sigma_data)
|
||||
|
||||
if multispeaker:
|
||||
s_preds = sampler(
|
||||
noise=torch.randn_like(s_trg).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=1,
|
||||
features=ref, # reference from the same speaker as the embedding
|
||||
embedding_mask_proba=0.1,
|
||||
num_steps=num_steps,
|
||||
).squeeze(1)
|
||||
loss_diff = model.diffusion(
|
||||
s_trg.unsqueeze(1), embedding=bert_dur, features=ref
|
||||
).mean() # EDM loss
|
||||
loss_sty = F.l1_loss(
|
||||
s_preds, s_trg.detach()
|
||||
) # style reconstruction loss
|
||||
else:
|
||||
s_preds = sampler(
|
||||
noise=torch.randn_like(s_trg).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=1,
|
||||
embedding_mask_proba=0.1,
|
||||
num_steps=num_steps,
|
||||
).squeeze(1)
|
||||
loss_diff = model.diffusion.module.diffusion(
|
||||
s_trg.unsqueeze(1), embedding=bert_dur
|
||||
).mean() # EDM loss
|
||||
loss_sty = F.l1_loss(
|
||||
s_preds, s_trg.detach()
|
||||
) # style reconstruction loss
|
||||
else:
|
||||
loss_sty = 0
|
||||
loss_diff = 0
|
||||
|
||||
s_loss = 0
|
||||
|
||||
d, p = model.predictor(d_en, s_dur, input_lengths, s2s_attn_mono, text_mask)
|
||||
|
||||
mel_len_st = int(mel_input_length.min().item() / 2 - 1)
|
||||
mel_len = min(int(mel_input_length.min().item() / 2 - 1), max_len // 2)
|
||||
en = []
|
||||
gt = []
|
||||
p_en = []
|
||||
wav = []
|
||||
st = []
|
||||
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item() / 2)
|
||||
|
||||
random_start = np.random.randint(0, mel_length - mel_len)
|
||||
en.append(asr[bib, :, random_start : random_start + mel_len])
|
||||
p_en.append(p[bib, :, random_start : random_start + mel_len])
|
||||
gt.append(
|
||||
mels[bib, :, (random_start * 2) : ((random_start + mel_len) * 2)]
|
||||
)
|
||||
|
||||
y = waves[bib][
|
||||
(random_start * 2) * 300 : ((random_start + mel_len) * 2) * 300
|
||||
]
|
||||
wav.append(torch.from_numpy(y).to(device))
|
||||
|
||||
# style reference (better to be different from the GT)
|
||||
random_start = np.random.randint(0, mel_length - mel_len_st)
|
||||
st.append(
|
||||
mels[bib, :, (random_start * 2) : ((random_start + mel_len_st) * 2)]
|
||||
)
|
||||
|
||||
wav = torch.stack(wav).float().detach()
|
||||
|
||||
en = torch.stack(en)
|
||||
p_en = torch.stack(p_en)
|
||||
gt = torch.stack(gt).detach()
|
||||
st = torch.stack(st).detach()
|
||||
|
||||
if gt.size(-1) < 80:
|
||||
continue
|
||||
|
||||
s = model.style_encoder(gt.unsqueeze(1))
|
||||
s_dur = model.predictor_encoder(gt.unsqueeze(1))
|
||||
|
||||
with torch.no_grad():
|
||||
F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
|
||||
F0 = F0.reshape(F0.shape[0], F0.shape[1] * 2, F0.shape[2], 1).squeeze()
|
||||
|
||||
N_real = log_norm(gt.unsqueeze(1)).squeeze(1)
|
||||
|
||||
y_rec_gt = wav.unsqueeze(1)
|
||||
y_rec_gt_pred = model.decoder(en, F0_real, N_real, s)
|
||||
|
||||
wav = y_rec_gt
|
||||
|
||||
F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s_dur)
|
||||
|
||||
y_rec = model.decoder(en, F0_fake, N_fake, s)
|
||||
|
||||
loss_F0_rec = (F.smooth_l1_loss(F0_real, F0_fake)) / 10
|
||||
loss_norm_rec = F.smooth_l1_loss(N_real, N_fake)
|
||||
|
||||
optimizer.zero_grad()
|
||||
d_loss = dl(wav.detach(), y_rec.detach()).mean()
|
||||
d_loss.backward()
|
||||
optimizer.step("msd")
|
||||
optimizer.step("mpd")
|
||||
|
||||
# generator loss
|
||||
optimizer.zero_grad()
|
||||
|
||||
loss_mel = stft_loss(y_rec, wav)
|
||||
loss_gen_all = gl(wav, y_rec).mean()
|
||||
loss_lm = wl(wav.detach().squeeze(), y_rec.squeeze()).mean()
|
||||
|
||||
loss_ce = 0
|
||||
loss_dur = 0
|
||||
for _s2s_pred, _text_input, _text_length in zip(d, (d_gt), input_lengths):
|
||||
_s2s_pred = _s2s_pred[:_text_length, :]
|
||||
_text_input = _text_input[:_text_length].long()
|
||||
_s2s_trg = torch.zeros_like(_s2s_pred)
|
||||
for p in range(_s2s_trg.shape[0]):
|
||||
_s2s_trg[p, : _text_input[p]] = 1
|
||||
_dur_pred = torch.sigmoid(_s2s_pred).sum(axis=1)
|
||||
|
||||
loss_dur += F.l1_loss(
|
||||
_dur_pred[1 : _text_length - 1], _text_input[1 : _text_length - 1]
|
||||
)
|
||||
loss_ce += F.binary_cross_entropy_with_logits(
|
||||
_s2s_pred.flatten(), _s2s_trg.flatten()
|
||||
)
|
||||
|
||||
loss_ce /= texts.size(0)
|
||||
loss_dur /= texts.size(0)
|
||||
|
||||
loss_s2s = 0
|
||||
for _s2s_pred, _text_input, _text_length in zip(
|
||||
s2s_pred, texts, input_lengths
|
||||
):
|
||||
loss_s2s += F.cross_entropy(
|
||||
_s2s_pred[:_text_length], _text_input[:_text_length]
|
||||
)
|
||||
loss_s2s /= texts.size(0)
|
||||
|
||||
loss_mono = F.l1_loss(s2s_attn, s2s_attn_mono) * 10
|
||||
|
||||
g_loss = (
|
||||
loss_params.lambda_mel * loss_mel
|
||||
+ loss_params.lambda_F0 * loss_F0_rec
|
||||
+ loss_params.lambda_ce * loss_ce
|
||||
+ loss_params.lambda_norm * loss_norm_rec
|
||||
+ loss_params.lambda_dur * loss_dur
|
||||
+ loss_params.lambda_gen * loss_gen_all
|
||||
+ loss_params.lambda_slm * loss_lm
|
||||
+ loss_params.lambda_sty * loss_sty
|
||||
+ loss_params.lambda_diff * loss_diff
|
||||
+ loss_params.lambda_mono * loss_mono
|
||||
+ loss_params.lambda_s2s * loss_s2s
|
||||
)
|
||||
|
||||
running_loss += loss_mel.item()
|
||||
g_loss.backward()
|
||||
if torch.isnan(g_loss):
|
||||
from IPython.core.debugger import set_trace
|
||||
|
||||
set_trace()
|
||||
|
||||
optimizer.step("bert_encoder")
|
||||
optimizer.step("bert")
|
||||
optimizer.step("predictor")
|
||||
optimizer.step("predictor_encoder")
|
||||
optimizer.step("style_encoder")
|
||||
optimizer.step("decoder")
|
||||
|
||||
optimizer.step("text_encoder")
|
||||
optimizer.step("text_aligner")
|
||||
|
||||
if epoch >= diff_epoch:
|
||||
optimizer.step("diffusion")
|
||||
|
||||
if epoch >= joint_epoch:
|
||||
# randomly pick whether to use in-distribution text
|
||||
if np.random.rand() < 0.5:
|
||||
use_ind = True
|
||||
else:
|
||||
use_ind = False
|
||||
|
||||
if use_ind:
|
||||
ref_lengths = input_lengths
|
||||
ref_texts = texts
|
||||
|
||||
slm_out = slmadv(
|
||||
i,
|
||||
y_rec_gt,
|
||||
y_rec_gt_pred,
|
||||
waves,
|
||||
mel_input_length,
|
||||
ref_texts,
|
||||
ref_lengths,
|
||||
use_ind,
|
||||
s_trg.detach(),
|
||||
ref if multispeaker else None,
|
||||
)
|
||||
|
||||
if slm_out is None:
|
||||
continue
|
||||
|
||||
d_loss_slm, loss_gen_lm, y_pred = slm_out
|
||||
|
||||
# SLM discriminator loss
|
||||
if d_loss_slm != 0:
|
||||
optimizer.zero_grad()
|
||||
d_loss_slm.backward()
|
||||
optimizer.step("wd")
|
||||
|
||||
# SLM generator loss
|
||||
optimizer.zero_grad()
|
||||
loss_gen_lm.backward()
|
||||
|
||||
# compute the gradient norm
|
||||
total_norm = {}
|
||||
for key in model.keys():
|
||||
total_norm[key] = 0
|
||||
parameters = [
|
||||
p
|
||||
for p in model[key].parameters()
|
||||
if p.grad is not None and p.requires_grad
|
||||
]
|
||||
for p in parameters:
|
||||
param_norm = p.grad.detach().data.norm(2)
|
||||
total_norm[key] += param_norm.item() ** 2
|
||||
total_norm[key] = total_norm[key] ** 0.5
|
||||
|
||||
# gradient scaling
|
||||
if total_norm["predictor"] > slmadv_params.thresh:
|
||||
for key in model.keys():
|
||||
for p in model[key].parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= 1 / total_norm["predictor"]
|
||||
|
||||
for p in model.predictor.duration_proj.parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= slmadv_params.scale
|
||||
|
||||
for p in model.predictor.lstm.parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= slmadv_params.scale
|
||||
|
||||
for p in model.diffusion.parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= slmadv_params.scale
|
||||
|
||||
optimizer.step("bert_encoder")
|
||||
optimizer.step("bert")
|
||||
optimizer.step("predictor")
|
||||
optimizer.step("diffusion")
|
||||
|
||||
else:
|
||||
d_loss_slm, loss_gen_lm = 0, 0
|
||||
|
||||
iters = iters + 1
|
||||
|
||||
if (i + 1) % log_interval == 0:
|
||||
logger.info(
|
||||
"Epoch [%d/%d], Step [%d/%d], Loss: %.5f, Disc Loss: %.5f, Dur Loss: %.5f, CE Loss: %.5f, Norm Loss: %.5f, F0 Loss: %.5f, LM Loss: %.5f, Gen Loss: %.5f, Sty Loss: %.5f, Diff Loss: %.5f, DiscLM Loss: %.5f, GenLM Loss: %.5f, SLoss: %.5f, S2S Loss: %.5f, Mono Loss: %.5f"
|
||||
% (
|
||||
epoch + 1,
|
||||
epochs,
|
||||
i + 1,
|
||||
len(train_list) // batch_size,
|
||||
running_loss / log_interval,
|
||||
d_loss,
|
||||
loss_dur,
|
||||
loss_ce,
|
||||
loss_norm_rec,
|
||||
loss_F0_rec,
|
||||
loss_lm,
|
||||
loss_gen_all,
|
||||
loss_sty,
|
||||
loss_diff,
|
||||
d_loss_slm,
|
||||
loss_gen_lm,
|
||||
s_loss,
|
||||
loss_s2s,
|
||||
loss_mono,
|
||||
)
|
||||
)
|
||||
|
||||
writer.add_scalar("train/mel_loss", running_loss / log_interval, iters)
|
||||
writer.add_scalar("train/gen_loss", loss_gen_all, iters)
|
||||
writer.add_scalar("train/d_loss", d_loss, iters)
|
||||
writer.add_scalar("train/ce_loss", loss_ce, iters)
|
||||
writer.add_scalar("train/dur_loss", loss_dur, iters)
|
||||
writer.add_scalar("train/slm_loss", loss_lm, iters)
|
||||
writer.add_scalar("train/norm_loss", loss_norm_rec, iters)
|
||||
writer.add_scalar("train/F0_loss", loss_F0_rec, iters)
|
||||
writer.add_scalar("train/sty_loss", loss_sty, iters)
|
||||
writer.add_scalar("train/diff_loss", loss_diff, iters)
|
||||
writer.add_scalar("train/d_loss_slm", d_loss_slm, iters)
|
||||
writer.add_scalar("train/gen_loss_slm", loss_gen_lm, iters)
|
||||
|
||||
running_loss = 0
|
||||
|
||||
print("Time elasped:", time.time() - start_time)
|
||||
|
||||
loss_test = 0
|
||||
loss_align = 0
|
||||
loss_f = 0
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
with torch.no_grad():
|
||||
iters_test = 0
|
||||
for batch_idx, batch in enumerate(val_dataloader):
|
||||
optimizer.zero_grad()
|
||||
|
||||
try:
|
||||
waves = batch[0]
|
||||
batch = [b.to(device) for b in batch[1:]]
|
||||
(
|
||||
texts,
|
||||
input_lengths,
|
||||
ref_texts,
|
||||
ref_lengths,
|
||||
mels,
|
||||
mel_input_length,
|
||||
ref_mels,
|
||||
) = batch
|
||||
with torch.no_grad():
|
||||
mask = length_to_mask(mel_input_length // (2**n_down)).to(
|
||||
"cuda"
|
||||
)
|
||||
text_mask = length_to_mask(input_lengths).to(texts.device)
|
||||
|
||||
_, _, s2s_attn = model.text_aligner(mels, mask, texts)
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
s2s_attn = s2s_attn[..., 1:]
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
|
||||
mask_ST = mask_from_lens(
|
||||
s2s_attn, input_lengths, mel_input_length // (2**n_down)
|
||||
)
|
||||
s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
|
||||
|
||||
# encode
|
||||
t_en = model.text_encoder(texts, input_lengths, text_mask)
|
||||
asr = t_en @ s2s_attn_mono
|
||||
|
||||
d_gt = s2s_attn_mono.sum(axis=-1).detach()
|
||||
|
||||
ss = []
|
||||
gs = []
|
||||
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item())
|
||||
mel = mels[bib, :, : mel_input_length[bib]]
|
||||
s = model.predictor_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
ss.append(s)
|
||||
s = model.style_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
gs.append(s)
|
||||
|
||||
s = torch.stack(ss).squeeze()
|
||||
gs = torch.stack(gs).squeeze()
|
||||
s_trg = torch.cat([s, gs], dim=-1).detach()
|
||||
|
||||
bert_dur = model.bert(texts, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
d, p = model.predictor(
|
||||
d_en, s, input_lengths, s2s_attn_mono, text_mask
|
||||
)
|
||||
# get clips
|
||||
mel_len = int(mel_input_length.min().item() / 2 - 1)
|
||||
en = []
|
||||
gt = []
|
||||
|
||||
p_en = []
|
||||
wav = []
|
||||
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item() / 2)
|
||||
|
||||
random_start = np.random.randint(0, mel_length - mel_len)
|
||||
en.append(asr[bib, :, random_start : random_start + mel_len])
|
||||
p_en.append(p[bib, :, random_start : random_start + mel_len])
|
||||
|
||||
gt.append(
|
||||
mels[
|
||||
bib,
|
||||
:,
|
||||
(random_start * 2) : ((random_start + mel_len) * 2),
|
||||
]
|
||||
)
|
||||
y = waves[bib][
|
||||
(random_start * 2)
|
||||
* 300 : ((random_start + mel_len) * 2)
|
||||
* 300
|
||||
]
|
||||
wav.append(torch.from_numpy(y).to(device))
|
||||
|
||||
wav = torch.stack(wav).float().detach()
|
||||
|
||||
en = torch.stack(en)
|
||||
p_en = torch.stack(p_en)
|
||||
gt = torch.stack(gt).detach()
|
||||
s = model.predictor_encoder(gt.unsqueeze(1))
|
||||
|
||||
F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s)
|
||||
|
||||
loss_dur = 0
|
||||
for _s2s_pred, _text_input, _text_length in zip(
|
||||
d, (d_gt), input_lengths
|
||||
):
|
||||
_s2s_pred = _s2s_pred[:_text_length, :]
|
||||
_text_input = _text_input[:_text_length].long()
|
||||
_s2s_trg = torch.zeros_like(_s2s_pred)
|
||||
for bib in range(_s2s_trg.shape[0]):
|
||||
_s2s_trg[bib, : _text_input[bib]] = 1
|
||||
_dur_pred = torch.sigmoid(_s2s_pred).sum(axis=1)
|
||||
loss_dur += F.l1_loss(
|
||||
_dur_pred[1 : _text_length - 1],
|
||||
_text_input[1 : _text_length - 1],
|
||||
)
|
||||
|
||||
loss_dur /= texts.size(0)
|
||||
|
||||
s = model.style_encoder(gt.unsqueeze(1))
|
||||
|
||||
y_rec = model.decoder(en, F0_fake, N_fake, s)
|
||||
loss_mel = stft_loss(y_rec.squeeze(), wav.detach())
|
||||
|
||||
F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
|
||||
|
||||
loss_F0 = F.l1_loss(F0_real, F0_fake) / 10
|
||||
|
||||
loss_test += (loss_mel).mean()
|
||||
loss_align += (loss_dur).mean()
|
||||
loss_f += (loss_F0).mean()
|
||||
|
||||
iters_test += 1
|
||||
except:
|
||||
continue
|
||||
|
||||
print("Epochs:", epoch + 1)
|
||||
logger.info(
|
||||
"Validation loss: %.3f, Dur loss: %.3f, F0 loss: %.3f"
|
||||
% (loss_test / iters_test, loss_align / iters_test, loss_f / iters_test)
|
||||
+ "\n\n\n"
|
||||
)
|
||||
print("\n\n\n")
|
||||
writer.add_scalar("eval/mel_loss", loss_test / iters_test, epoch + 1)
|
||||
writer.add_scalar("eval/dur_loss", loss_test / iters_test, epoch + 1)
|
||||
writer.add_scalar("eval/F0_loss", loss_f / iters_test, epoch + 1)
|
||||
|
||||
if (epoch + 1) % save_freq == 0:
|
||||
if (loss_test / iters_test) < best_loss:
|
||||
best_loss = loss_test / iters_test
|
||||
print("Saving..")
|
||||
state = {
|
||||
"net": {key: model[key].state_dict() for key in model},
|
||||
"optimizer": optimizer.state_dict(),
|
||||
"iters": iters,
|
||||
"val_loss": loss_test / iters_test,
|
||||
"epoch": epoch,
|
||||
}
|
||||
save_path = osp.join(log_dir, "epoch_2nd_%05d.pth" % epoch)
|
||||
torch.save(state, save_path)
|
||||
|
||||
# if estimate sigma, save the estimated simga
|
||||
if model_params.diffusion.dist.estimate_sigma_data:
|
||||
config["model_params"]["diffusion"]["dist"]["sigma_data"] = float(
|
||||
np.mean(running_std)
|
||||
)
|
||||
|
||||
with open(osp.join(log_dir, osp.basename(config_path)), "w") as outfile:
|
||||
yaml.dump(config, outfile, default_flow_style=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,540 @@
|
||||
import os
|
||||
import os.path as osp
|
||||
import re
|
||||
import sys
|
||||
import yaml
|
||||
import shutil
|
||||
import numpy as np
|
||||
import torch
|
||||
import click
|
||||
import warnings
|
||||
|
||||
warnings.simplefilter("ignore")
|
||||
|
||||
# load packages
|
||||
import random
|
||||
import yaml
|
||||
from munch import Munch
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
|
||||
from models import *
|
||||
from meldataset import build_dataloader
|
||||
from utils import *
|
||||
from losses import *
|
||||
from optimizers import build_optimizer
|
||||
import time
|
||||
|
||||
from accelerate import Accelerator
|
||||
from accelerate.utils import LoggerType
|
||||
from accelerate import DistributedDataParallelKwargs
|
||||
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
import logging
|
||||
from accelerate.logging import get_logger
|
||||
|
||||
logger = get_logger(__name__, log_level="DEBUG")
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option("-p", "--config_path", default="Configs/config.yml", type=str)
|
||||
def main(config_path):
|
||||
config = yaml.safe_load(open(config_path))
|
||||
|
||||
log_dir = config["log_dir"]
|
||||
if not osp.exists(log_dir):
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
|
||||
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
|
||||
accelerator = Accelerator(
|
||||
project_dir=log_dir, split_batches=True, kwargs_handlers=[ddp_kwargs]
|
||||
)
|
||||
if accelerator.is_main_process:
|
||||
writer = SummaryWriter(log_dir + "/tensorboard")
|
||||
|
||||
# write logs
|
||||
file_handler = logging.FileHandler(osp.join(log_dir, "train.log"))
|
||||
file_handler.setLevel(logging.DEBUG)
|
||||
file_handler.setFormatter(
|
||||
logging.Formatter("%(levelname)s:%(asctime)s: %(message)s")
|
||||
)
|
||||
logger.logger.addHandler(file_handler)
|
||||
|
||||
batch_size = config.get("batch_size", 10)
|
||||
device = accelerator.device
|
||||
|
||||
epochs = config.get("epochs_1st", 200)
|
||||
save_freq = config.get("save_freq", 2)
|
||||
log_interval = config.get("log_interval", 10)
|
||||
saving_epoch = config.get("save_freq", 2)
|
||||
|
||||
data_params = config.get("data_params", None)
|
||||
sr = config["preprocess_params"].get("sr", 24000)
|
||||
train_path = data_params["train_data"]
|
||||
val_path = data_params["val_data"]
|
||||
root_path = data_params["root_path"]
|
||||
min_length = data_params["min_length"]
|
||||
OOD_data = data_params["OOD_data"]
|
||||
|
||||
max_len = config.get("max_len", 200)
|
||||
|
||||
# load data
|
||||
train_list, val_list = get_data_path_list(train_path, val_path)
|
||||
|
||||
train_dataloader = build_dataloader(
|
||||
train_list,
|
||||
root_path,
|
||||
OOD_data=OOD_data,
|
||||
min_length=min_length,
|
||||
batch_size=batch_size,
|
||||
num_workers=2,
|
||||
dataset_config={},
|
||||
device=device,
|
||||
)
|
||||
|
||||
val_dataloader = build_dataloader(
|
||||
val_list,
|
||||
root_path,
|
||||
OOD_data=OOD_data,
|
||||
min_length=min_length,
|
||||
batch_size=batch_size,
|
||||
validation=True,
|
||||
num_workers=0,
|
||||
device=device,
|
||||
dataset_config={},
|
||||
)
|
||||
|
||||
with accelerator.main_process_first():
|
||||
# load pretrained ASR model
|
||||
ASR_config = config.get("ASR_config", False)
|
||||
ASR_path = config.get("ASR_path", False)
|
||||
text_aligner = load_ASR_models(ASR_path, ASR_config)
|
||||
|
||||
# load pretrained F0 model
|
||||
F0_path = config.get("F0_path", False)
|
||||
pitch_extractor = load_F0_models(F0_path)
|
||||
|
||||
# load BERT model
|
||||
from Utils.PLBERT.util import load_plbert
|
||||
|
||||
BERT_path = config.get("PLBERT_dir", False)
|
||||
plbert = load_plbert(BERT_path)
|
||||
|
||||
scheduler_params = {
|
||||
"max_lr": float(config["optimizer_params"].get("lr", 1e-4)),
|
||||
"pct_start": float(config["optimizer_params"].get("pct_start", 0.0)),
|
||||
"epochs": epochs,
|
||||
"steps_per_epoch": len(train_dataloader),
|
||||
}
|
||||
|
||||
model_params = recursive_munch(config["model_params"])
|
||||
multispeaker = model_params.multispeaker
|
||||
model = build_model(model_params, text_aligner, pitch_extractor, plbert)
|
||||
|
||||
best_loss = float("inf") # best test loss
|
||||
loss_train_record = list([])
|
||||
loss_test_record = list([])
|
||||
|
||||
loss_params = Munch(config["loss_params"])
|
||||
TMA_epoch = loss_params.TMA_epoch
|
||||
|
||||
for k in model:
|
||||
model[k] = accelerator.prepare(model[k])
|
||||
|
||||
train_dataloader, val_dataloader = accelerator.prepare(
|
||||
train_dataloader, val_dataloader
|
||||
)
|
||||
|
||||
_ = [model[key].to(device) for key in model]
|
||||
|
||||
# initialize optimizers after preparing models for compatibility with FSDP
|
||||
optimizer = build_optimizer(
|
||||
{key: model[key].parameters() for key in model},
|
||||
scheduler_params_dict={key: scheduler_params.copy() for key in model},
|
||||
lr=float(config["optimizer_params"].get("lr", 1e-4)),
|
||||
)
|
||||
|
||||
for k, v in optimizer.optimizers.items():
|
||||
optimizer.optimizers[k] = accelerator.prepare(optimizer.optimizers[k])
|
||||
optimizer.schedulers[k] = accelerator.prepare(optimizer.schedulers[k])
|
||||
|
||||
with accelerator.main_process_first():
|
||||
if config.get("pretrained_model", "") != "":
|
||||
model, optimizer, start_epoch, iters = load_checkpoint(
|
||||
model,
|
||||
optimizer,
|
||||
config["pretrained_model"],
|
||||
load_only_params=config.get("load_only_params", True),
|
||||
)
|
||||
else:
|
||||
start_epoch = 0
|
||||
iters = 0
|
||||
|
||||
# in case not distributed
|
||||
try:
|
||||
n_down = model.text_aligner.module.n_down
|
||||
except:
|
||||
n_down = model.text_aligner.n_down
|
||||
|
||||
# wrapped losses for compatibility with mixed precision
|
||||
stft_loss = MultiResolutionSTFTLoss().to(device)
|
||||
gl = GeneratorLoss(model.mpd, model.msd).to(device)
|
||||
dl = DiscriminatorLoss(model.mpd, model.msd).to(device)
|
||||
wl = WavLMLoss(model_params.slm.model, model.wd, sr, model_params.slm.sr).to(device)
|
||||
|
||||
for epoch in range(start_epoch, epochs):
|
||||
running_loss = 0
|
||||
start_time = time.time()
|
||||
|
||||
_ = [model[key].train() for key in model]
|
||||
|
||||
for i, batch in enumerate(train_dataloader):
|
||||
waves = batch[0]
|
||||
batch = [b.to(device) for b in batch[1:]]
|
||||
texts, input_lengths, _, _, mels, mel_input_length, _ = batch
|
||||
|
||||
with torch.no_grad():
|
||||
mask = length_to_mask(mel_input_length // (2**n_down)).to("cuda")
|
||||
text_mask = length_to_mask(input_lengths).to(texts.device)
|
||||
|
||||
ppgs, s2s_pred, s2s_attn = model.text_aligner(mels, mask, texts)
|
||||
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
s2s_attn = s2s_attn[..., 1:]
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
|
||||
with torch.no_grad():
|
||||
attn_mask = (
|
||||
(~mask)
|
||||
.unsqueeze(-1)
|
||||
.expand(mask.shape[0], mask.shape[1], text_mask.shape[-1])
|
||||
.float()
|
||||
.transpose(-1, -2)
|
||||
)
|
||||
attn_mask = (
|
||||
attn_mask.float()
|
||||
* (~text_mask)
|
||||
.unsqueeze(-1)
|
||||
.expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1])
|
||||
.float()
|
||||
)
|
||||
attn_mask = attn_mask < 1
|
||||
|
||||
s2s_attn.masked_fill_(attn_mask, 0.0)
|
||||
|
||||
with torch.no_grad():
|
||||
mask_ST = mask_from_lens(
|
||||
s2s_attn, input_lengths, mel_input_length // (2**n_down)
|
||||
)
|
||||
s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
|
||||
|
||||
# encode
|
||||
t_en = model.text_encoder(texts, input_lengths, text_mask)
|
||||
|
||||
# 50% of chance of using monotonic version
|
||||
if bool(random.getrandbits(1)):
|
||||
asr = t_en @ s2s_attn
|
||||
else:
|
||||
asr = t_en @ s2s_attn_mono
|
||||
|
||||
# get clips
|
||||
mel_input_length_all = accelerator.gather(
|
||||
mel_input_length
|
||||
) # for balanced load
|
||||
mel_len = min(
|
||||
[int(mel_input_length_all.min().item() / 2 - 1), max_len // 2]
|
||||
)
|
||||
mel_len_st = int(mel_input_length.min().item() / 2 - 1)
|
||||
|
||||
en = []
|
||||
gt = []
|
||||
wav = []
|
||||
st = []
|
||||
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item() / 2)
|
||||
|
||||
random_start = np.random.randint(0, mel_length - mel_len)
|
||||
en.append(asr[bib, :, random_start : random_start + mel_len])
|
||||
gt.append(
|
||||
mels[bib, :, (random_start * 2) : ((random_start + mel_len) * 2)]
|
||||
)
|
||||
|
||||
y = waves[bib][
|
||||
(random_start * 2) * 300 : ((random_start + mel_len) * 2) * 300
|
||||
]
|
||||
wav.append(torch.from_numpy(y).to(device))
|
||||
|
||||
# style reference (better to be different from the GT)
|
||||
random_start = np.random.randint(0, mel_length - mel_len_st)
|
||||
st.append(
|
||||
mels[bib, :, (random_start * 2) : ((random_start + mel_len_st) * 2)]
|
||||
)
|
||||
|
||||
en = torch.stack(en)
|
||||
gt = torch.stack(gt).detach()
|
||||
st = torch.stack(st).detach()
|
||||
|
||||
wav = torch.stack(wav).float().detach()
|
||||
|
||||
# clip too short to be used by the style encoder
|
||||
if gt.shape[-1] < 80:
|
||||
continue
|
||||
|
||||
with torch.no_grad():
|
||||
real_norm = log_norm(gt.unsqueeze(1)).squeeze(1).detach()
|
||||
F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))
|
||||
|
||||
s = model.style_encoder(
|
||||
st.unsqueeze(1) if multispeaker else gt.unsqueeze(1)
|
||||
)
|
||||
|
||||
y_rec = model.decoder(en, F0_real, real_norm, s)
|
||||
|
||||
# discriminator loss
|
||||
|
||||
if epoch >= TMA_epoch:
|
||||
optimizer.zero_grad()
|
||||
d_loss = dl(wav.detach().unsqueeze(1).float(), y_rec.detach()).mean()
|
||||
accelerator.backward(d_loss)
|
||||
optimizer.step("msd")
|
||||
optimizer.step("mpd")
|
||||
else:
|
||||
d_loss = 0
|
||||
|
||||
# generator loss
|
||||
optimizer.zero_grad()
|
||||
loss_mel = stft_loss(y_rec.squeeze(), wav.detach())
|
||||
|
||||
if epoch >= TMA_epoch: # start TMA training
|
||||
loss_s2s = 0
|
||||
for _s2s_pred, _text_input, _text_length in zip(
|
||||
s2s_pred, texts, input_lengths
|
||||
):
|
||||
loss_s2s += F.cross_entropy(
|
||||
_s2s_pred[:_text_length], _text_input[:_text_length]
|
||||
)
|
||||
loss_s2s /= texts.size(0)
|
||||
|
||||
loss_mono = F.l1_loss(s2s_attn, s2s_attn_mono) * 10
|
||||
|
||||
loss_gen_all = gl(wav.detach().unsqueeze(1).float(), y_rec).mean()
|
||||
loss_slm = wl(wav.detach(), y_rec).mean()
|
||||
|
||||
g_loss = (
|
||||
loss_params.lambda_mel * loss_mel
|
||||
+ loss_params.lambda_mono * loss_mono
|
||||
+ loss_params.lambda_s2s * loss_s2s
|
||||
+ loss_params.lambda_gen * loss_gen_all
|
||||
+ loss_params.lambda_slm * loss_slm
|
||||
)
|
||||
|
||||
else:
|
||||
loss_s2s = 0
|
||||
loss_mono = 0
|
||||
loss_gen_all = 0
|
||||
loss_slm = 0
|
||||
g_loss = loss_mel
|
||||
|
||||
running_loss += accelerator.gather(loss_mel).mean().item()
|
||||
|
||||
accelerator.backward(g_loss)
|
||||
|
||||
optimizer.step("text_encoder")
|
||||
optimizer.step("style_encoder")
|
||||
optimizer.step("decoder")
|
||||
|
||||
if epoch >= TMA_epoch:
|
||||
optimizer.step("text_aligner")
|
||||
optimizer.step("pitch_extractor")
|
||||
|
||||
iters = iters + 1
|
||||
|
||||
if (i + 1) % log_interval == 0 and accelerator.is_main_process:
|
||||
log_print(
|
||||
"Epoch [%d/%d], Step [%d/%d], Mel Loss: %.5f, Gen Loss: %.5f, Disc Loss: %.5f, Mono Loss: %.5f, S2S Loss: %.5f, SLM Loss: %.5f"
|
||||
% (
|
||||
epoch + 1,
|
||||
epochs,
|
||||
i + 1,
|
||||
len(train_list) // batch_size,
|
||||
running_loss / log_interval,
|
||||
loss_gen_all,
|
||||
d_loss,
|
||||
loss_mono,
|
||||
loss_s2s,
|
||||
loss_slm,
|
||||
),
|
||||
logger,
|
||||
)
|
||||
|
||||
writer.add_scalar("train/mel_loss", running_loss / log_interval, iters)
|
||||
writer.add_scalar("train/gen_loss", loss_gen_all, iters)
|
||||
writer.add_scalar("train/d_loss", d_loss, iters)
|
||||
writer.add_scalar("train/mono_loss", loss_mono, iters)
|
||||
writer.add_scalar("train/s2s_loss", loss_s2s, iters)
|
||||
writer.add_scalar("train/slm_loss", loss_slm, iters)
|
||||
|
||||
running_loss = 0
|
||||
|
||||
print("Time elasped:", time.time() - start_time)
|
||||
|
||||
loss_test = 0
|
||||
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
with torch.no_grad():
|
||||
iters_test = 0
|
||||
for batch_idx, batch in enumerate(val_dataloader):
|
||||
optimizer.zero_grad()
|
||||
|
||||
waves = batch[0]
|
||||
batch = [b.to(device) for b in batch[1:]]
|
||||
texts, input_lengths, _, _, mels, mel_input_length, _ = batch
|
||||
|
||||
with torch.no_grad():
|
||||
mask = length_to_mask(mel_input_length // (2**n_down)).to("cuda")
|
||||
ppgs, s2s_pred, s2s_attn = model.text_aligner(mels, mask, texts)
|
||||
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
s2s_attn = s2s_attn[..., 1:]
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
|
||||
text_mask = length_to_mask(input_lengths).to(texts.device)
|
||||
attn_mask = (
|
||||
(~mask)
|
||||
.unsqueeze(-1)
|
||||
.expand(mask.shape[0], mask.shape[1], text_mask.shape[-1])
|
||||
.float()
|
||||
.transpose(-1, -2)
|
||||
)
|
||||
attn_mask = (
|
||||
attn_mask.float()
|
||||
* (~text_mask)
|
||||
.unsqueeze(-1)
|
||||
.expand(text_mask.shape[0], text_mask.shape[1], mask.shape[-1])
|
||||
.float()
|
||||
)
|
||||
attn_mask = attn_mask < 1
|
||||
s2s_attn.masked_fill_(attn_mask, 0.0)
|
||||
|
||||
# encode
|
||||
t_en = model.text_encoder(texts, input_lengths, text_mask)
|
||||
|
||||
asr = t_en @ s2s_attn
|
||||
|
||||
# get clips
|
||||
mel_input_length_all = accelerator.gather(
|
||||
mel_input_length
|
||||
) # for balanced load
|
||||
mel_len = min(
|
||||
[int(mel_input_length.min().item() / 2 - 1), max_len // 2]
|
||||
)
|
||||
|
||||
en = []
|
||||
gt = []
|
||||
wav = []
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item() / 2)
|
||||
|
||||
random_start = np.random.randint(0, mel_length - mel_len)
|
||||
en.append(asr[bib, :, random_start : random_start + mel_len])
|
||||
gt.append(
|
||||
mels[
|
||||
bib, :, (random_start * 2) : ((random_start + mel_len) * 2)
|
||||
]
|
||||
)
|
||||
y = waves[bib][
|
||||
(random_start * 2) * 300 : ((random_start + mel_len) * 2) * 300
|
||||
]
|
||||
wav.append(torch.from_numpy(y).to("cuda"))
|
||||
|
||||
wav = torch.stack(wav).float().detach()
|
||||
|
||||
en = torch.stack(en)
|
||||
gt = torch.stack(gt).detach()
|
||||
|
||||
F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
|
||||
s = model.style_encoder(gt.unsqueeze(1))
|
||||
real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
|
||||
y_rec = model.decoder(en, F0_real, real_norm, s)
|
||||
|
||||
loss_mel = stft_loss(y_rec.squeeze(), wav.detach())
|
||||
|
||||
loss_test += accelerator.gather(loss_mel).mean().item()
|
||||
iters_test += 1
|
||||
|
||||
if accelerator.is_main_process:
|
||||
print("Epochs:", epoch + 1)
|
||||
log_print(
|
||||
"Validation loss: %.3f" % (loss_test / iters_test) + "\n\n\n\n", logger
|
||||
)
|
||||
print("\n\n\n")
|
||||
writer.add_scalar("eval/mel_loss", loss_test / iters_test, epoch + 1)
|
||||
attn_image = get_image(s2s_attn[0].cpu().numpy().squeeze())
|
||||
writer.add_figure("eval/attn", attn_image, epoch)
|
||||
|
||||
with torch.no_grad():
|
||||
for bib in range(len(asr)):
|
||||
mel_length = int(mel_input_length[bib].item())
|
||||
gt = mels[bib, :, :mel_length].unsqueeze(0)
|
||||
en = asr[bib, :, : mel_length // 2].unsqueeze(0)
|
||||
|
||||
F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))
|
||||
F0_real = F0_real.unsqueeze(0)
|
||||
s = model.style_encoder(gt.unsqueeze(1))
|
||||
real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
|
||||
|
||||
y_rec = model.decoder(en, F0_real, real_norm, s)
|
||||
|
||||
writer.add_audio(
|
||||
"eval/y" + str(bib),
|
||||
y_rec.cpu().numpy().squeeze(),
|
||||
epoch,
|
||||
sample_rate=sr,
|
||||
)
|
||||
if epoch == 0:
|
||||
writer.add_audio(
|
||||
"gt/y" + str(bib),
|
||||
waves[bib].squeeze(),
|
||||
epoch,
|
||||
sample_rate=sr,
|
||||
)
|
||||
|
||||
if bib >= 6:
|
||||
break
|
||||
|
||||
if epoch % saving_epoch == 0:
|
||||
if (loss_test / iters_test) < best_loss:
|
||||
best_loss = loss_test / iters_test
|
||||
print("Saving..")
|
||||
state = {
|
||||
"net": {key: model[key].state_dict() for key in model},
|
||||
"optimizer": optimizer.state_dict(),
|
||||
"iters": iters,
|
||||
"val_loss": loss_test / iters_test,
|
||||
"epoch": epoch,
|
||||
}
|
||||
save_path = osp.join(log_dir, "epoch_1st_%05d.pth" % epoch)
|
||||
torch.save(state, save_path)
|
||||
|
||||
if accelerator.is_main_process:
|
||||
print("Saving..")
|
||||
state = {
|
||||
"net": {key: model[key].state_dict() for key in model},
|
||||
"optimizer": optimizer.state_dict(),
|
||||
"iters": iters,
|
||||
"val_loss": loss_test / iters_test,
|
||||
"epoch": epoch,
|
||||
}
|
||||
save_path = osp.join(log_dir, config.get("first_stage_path", "first_stage.pth"))
|
||||
torch.save(state, save_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,958 @@
|
||||
# load packages
|
||||
import random
|
||||
import yaml
|
||||
import time
|
||||
from munch import Munch
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
import click
|
||||
import shutil
|
||||
import warnings
|
||||
|
||||
warnings.simplefilter("ignore")
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
from meldataset import build_dataloader
|
||||
|
||||
from Utils.ASR.models import ASRCNN
|
||||
from Utils.JDC.model import JDCNet
|
||||
from Utils.PLBERT.util import load_plbert
|
||||
|
||||
from models import *
|
||||
from losses import *
|
||||
from utils import *
|
||||
|
||||
from Modules.slmadv import SLMAdversarialLoss
|
||||
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
|
||||
|
||||
from optimizers import build_optimizer
|
||||
|
||||
|
||||
# simple fix for dataparallel that allows access to class attributes
|
||||
class MyDataParallel(torch.nn.DataParallel):
|
||||
def __getattr__(self, name):
|
||||
try:
|
||||
return super().__getattr__(name)
|
||||
except AttributeError:
|
||||
return getattr(self.module, name)
|
||||
|
||||
|
||||
import logging
|
||||
from logging import StreamHandler
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
handler = StreamHandler()
|
||||
handler.setLevel(logging.DEBUG)
|
||||
logger.addHandler(handler)
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option("-p", "--config_path", default="Configs/config.yml", type=str)
|
||||
def main(config_path):
|
||||
config = yaml.safe_load(open(config_path))
|
||||
|
||||
log_dir = config["log_dir"]
|
||||
if not osp.exists(log_dir):
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
|
||||
writer = SummaryWriter(log_dir + "/tensorboard")
|
||||
|
||||
# write logs
|
||||
file_handler = logging.FileHandler(osp.join(log_dir, "train.log"))
|
||||
file_handler.setLevel(logging.DEBUG)
|
||||
file_handler.setFormatter(
|
||||
logging.Formatter("%(levelname)s:%(asctime)s: %(message)s")
|
||||
)
|
||||
logger.addHandler(file_handler)
|
||||
|
||||
batch_size = config.get("batch_size", 10)
|
||||
|
||||
epochs = config.get("epochs_2nd", 200)
|
||||
save_freq = config.get("save_freq", 2)
|
||||
log_interval = config.get("log_interval", 10)
|
||||
saving_epoch = config.get("save_freq", 2)
|
||||
|
||||
data_params = config.get("data_params", None)
|
||||
sr = config["preprocess_params"].get("sr", 24000)
|
||||
train_path = data_params["train_data"]
|
||||
val_path = data_params["val_data"]
|
||||
root_path = data_params["root_path"]
|
||||
min_length = data_params["min_length"]
|
||||
OOD_data = data_params["OOD_data"]
|
||||
|
||||
max_len = config.get("max_len", 200)
|
||||
|
||||
loss_params = Munch(config["loss_params"])
|
||||
diff_epoch = loss_params.diff_epoch
|
||||
joint_epoch = loss_params.joint_epoch
|
||||
|
||||
optimizer_params = Munch(config["optimizer_params"])
|
||||
|
||||
train_list, val_list = get_data_path_list(train_path, val_path)
|
||||
device = "cuda"
|
||||
|
||||
train_dataloader = build_dataloader(
|
||||
train_list,
|
||||
root_path,
|
||||
OOD_data=OOD_data,
|
||||
min_length=min_length,
|
||||
batch_size=batch_size,
|
||||
num_workers=2,
|
||||
dataset_config={},
|
||||
device=device,
|
||||
)
|
||||
|
||||
val_dataloader = build_dataloader(
|
||||
val_list,
|
||||
root_path,
|
||||
OOD_data=OOD_data,
|
||||
min_length=min_length,
|
||||
batch_size=batch_size,
|
||||
validation=True,
|
||||
num_workers=0,
|
||||
device=device,
|
||||
dataset_config={},
|
||||
)
|
||||
|
||||
# load pretrained ASR model
|
||||
ASR_config = config.get("ASR_config", False)
|
||||
ASR_path = config.get("ASR_path", False)
|
||||
text_aligner = load_ASR_models(ASR_path, ASR_config)
|
||||
|
||||
# load pretrained F0 model
|
||||
F0_path = config.get("F0_path", False)
|
||||
pitch_extractor = load_F0_models(F0_path)
|
||||
|
||||
# load PL-BERT model
|
||||
BERT_path = config.get("PLBERT_dir", False)
|
||||
plbert = load_plbert(BERT_path)
|
||||
|
||||
# build model
|
||||
model_params = recursive_munch(config["model_params"])
|
||||
multispeaker = model_params.multispeaker
|
||||
model = build_model(model_params, text_aligner, pitch_extractor, plbert)
|
||||
_ = [model[key].to(device) for key in model]
|
||||
|
||||
# DP
|
||||
for key in model:
|
||||
if key != "mpd" and key != "msd" and key != "wd":
|
||||
model[key] = MyDataParallel(model[key])
|
||||
|
||||
start_epoch = 0
|
||||
iters = 0
|
||||
|
||||
load_pretrained = config.get("pretrained_model", "") != "" and config.get(
|
||||
"second_stage_load_pretrained", False
|
||||
)
|
||||
|
||||
if not load_pretrained:
|
||||
if config.get("first_stage_path", "") != "":
|
||||
first_stage_path = osp.join(
|
||||
log_dir, config.get("first_stage_path", "first_stage.pth")
|
||||
)
|
||||
print("Loading the first stage model at %s ..." % first_stage_path)
|
||||
model, _, start_epoch, iters = load_checkpoint(
|
||||
model,
|
||||
None,
|
||||
first_stage_path,
|
||||
load_only_params=True,
|
||||
ignore_modules=[
|
||||
"bert",
|
||||
"bert_encoder",
|
||||
"predictor",
|
||||
"predictor_encoder",
|
||||
"msd",
|
||||
"mpd",
|
||||
"wd",
|
||||
"diffusion",
|
||||
],
|
||||
) # keep starting epoch for tensorboard log
|
||||
|
||||
# these epochs should be counted from the start epoch
|
||||
diff_epoch += start_epoch
|
||||
joint_epoch += start_epoch
|
||||
epochs += start_epoch
|
||||
|
||||
model.predictor_encoder = copy.deepcopy(model.style_encoder)
|
||||
else:
|
||||
raise ValueError("You need to specify the path to the first stage model.")
|
||||
|
||||
gl = GeneratorLoss(model.mpd, model.msd).to(device)
|
||||
dl = DiscriminatorLoss(model.mpd, model.msd).to(device)
|
||||
wl = WavLMLoss(model_params.slm.model, model.wd, sr, model_params.slm.sr).to(device)
|
||||
|
||||
gl = MyDataParallel(gl)
|
||||
dl = MyDataParallel(dl)
|
||||
wl = MyDataParallel(wl)
|
||||
|
||||
sampler = DiffusionSampler(
|
||||
model.diffusion.diffusion,
|
||||
sampler=ADPM2Sampler(),
|
||||
sigma_schedule=KarrasSchedule(
|
||||
sigma_min=0.0001, sigma_max=3.0, rho=9.0
|
||||
), # empirical parameters
|
||||
clamp=False,
|
||||
)
|
||||
|
||||
scheduler_params = {
|
||||
"max_lr": optimizer_params.lr,
|
||||
"pct_start": float(0),
|
||||
"epochs": epochs,
|
||||
"steps_per_epoch": len(train_dataloader),
|
||||
}
|
||||
scheduler_params_dict = {key: scheduler_params.copy() for key in model}
|
||||
scheduler_params_dict["bert"]["max_lr"] = optimizer_params.bert_lr * 2
|
||||
scheduler_params_dict["decoder"]["max_lr"] = optimizer_params.ft_lr * 2
|
||||
scheduler_params_dict["style_encoder"]["max_lr"] = optimizer_params.ft_lr * 2
|
||||
|
||||
optimizer = build_optimizer(
|
||||
{key: model[key].parameters() for key in model},
|
||||
scheduler_params_dict=scheduler_params_dict,
|
||||
lr=optimizer_params.lr,
|
||||
)
|
||||
|
||||
# adjust BERT learning rate
|
||||
for g in optimizer.optimizers["bert"].param_groups:
|
||||
g["betas"] = (0.9, 0.99)
|
||||
g["lr"] = optimizer_params.bert_lr
|
||||
g["initial_lr"] = optimizer_params.bert_lr
|
||||
g["min_lr"] = 0
|
||||
g["weight_decay"] = 0.01
|
||||
|
||||
# adjust acoustic module learning rate
|
||||
for module in ["decoder", "style_encoder"]:
|
||||
for g in optimizer.optimizers[module].param_groups:
|
||||
g["betas"] = (0.0, 0.99)
|
||||
g["lr"] = optimizer_params.ft_lr
|
||||
g["initial_lr"] = optimizer_params.ft_lr
|
||||
g["min_lr"] = 0
|
||||
g["weight_decay"] = 1e-4
|
||||
|
||||
# load models if there is a model
|
||||
if load_pretrained:
|
||||
model, optimizer, start_epoch, iters = load_checkpoint(
|
||||
model,
|
||||
optimizer,
|
||||
config["pretrained_model"],
|
||||
load_only_params=config.get("load_only_params", True),
|
||||
)
|
||||
|
||||
n_down = model.text_aligner.n_down
|
||||
|
||||
best_loss = float("inf") # best test loss
|
||||
loss_train_record = list([])
|
||||
loss_test_record = list([])
|
||||
iters = 0
|
||||
|
||||
criterion = nn.L1Loss() # F0 loss (regression)
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
stft_loss = MultiResolutionSTFTLoss().to(device)
|
||||
|
||||
print("BERT", optimizer.optimizers["bert"])
|
||||
print("decoder", optimizer.optimizers["decoder"])
|
||||
|
||||
start_ds = False
|
||||
|
||||
running_std = []
|
||||
|
||||
slmadv_params = Munch(config["slmadv_params"])
|
||||
slmadv = SLMAdversarialLoss(
|
||||
model,
|
||||
wl,
|
||||
sampler,
|
||||
slmadv_params.min_len,
|
||||
slmadv_params.max_len,
|
||||
batch_percentage=slmadv_params.batch_percentage,
|
||||
skip_update=slmadv_params.iter,
|
||||
sig=slmadv_params.sig,
|
||||
)
|
||||
|
||||
for epoch in range(start_epoch, epochs):
|
||||
running_loss = 0
|
||||
start_time = time.time()
|
||||
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
model.predictor.train()
|
||||
model.bert_encoder.train()
|
||||
model.bert.train()
|
||||
model.msd.train()
|
||||
model.mpd.train()
|
||||
|
||||
if epoch >= diff_epoch:
|
||||
start_ds = True
|
||||
|
||||
for i, batch in enumerate(train_dataloader):
|
||||
waves = batch[0]
|
||||
batch = [b.to(device) for b in batch[1:]]
|
||||
(
|
||||
texts,
|
||||
input_lengths,
|
||||
ref_texts,
|
||||
ref_lengths,
|
||||
mels,
|
||||
mel_input_length,
|
||||
ref_mels,
|
||||
) = batch
|
||||
|
||||
with torch.no_grad():
|
||||
mask = length_to_mask(mel_input_length // (2**n_down)).to(device)
|
||||
mel_mask = length_to_mask(mel_input_length).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(texts.device)
|
||||
|
||||
try:
|
||||
_, _, s2s_attn = model.text_aligner(mels, mask, texts)
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
s2s_attn = s2s_attn[..., 1:]
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
except:
|
||||
continue
|
||||
|
||||
mask_ST = mask_from_lens(
|
||||
s2s_attn, input_lengths, mel_input_length // (2**n_down)
|
||||
)
|
||||
s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
|
||||
|
||||
# encode
|
||||
t_en = model.text_encoder(texts, input_lengths, text_mask)
|
||||
asr = t_en @ s2s_attn_mono
|
||||
|
||||
d_gt = s2s_attn_mono.sum(axis=-1).detach()
|
||||
|
||||
# compute reference styles
|
||||
if multispeaker and epoch >= diff_epoch:
|
||||
ref_ss = model.style_encoder(ref_mels.unsqueeze(1))
|
||||
ref_sp = model.predictor_encoder(ref_mels.unsqueeze(1))
|
||||
ref = torch.cat([ref_ss, ref_sp], dim=1)
|
||||
|
||||
# compute the style of the entire utterance
|
||||
# this operation cannot be done in batch because of the avgpool layer (may need to work on masked avgpool)
|
||||
ss = []
|
||||
gs = []
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item())
|
||||
mel = mels[bib, :, : mel_input_length[bib]]
|
||||
s = model.predictor_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
ss.append(s)
|
||||
s = model.style_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
gs.append(s)
|
||||
|
||||
s_dur = torch.stack(ss).squeeze() # global prosodic styles
|
||||
gs = torch.stack(gs).squeeze() # global acoustic styles
|
||||
s_trg = torch.cat([gs, s_dur], dim=-1).detach() # ground truth for denoiser
|
||||
|
||||
bert_dur = model.bert(texts, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
|
||||
# denoiser training
|
||||
if epoch >= diff_epoch:
|
||||
num_steps = np.random.randint(3, 5)
|
||||
|
||||
if model_params.diffusion.dist.estimate_sigma_data:
|
||||
model.diffusion.module.diffusion.sigma_data = (
|
||||
s_trg.std(axis=-1).mean().item()
|
||||
) # batch-wise std estimation
|
||||
running_std.append(model.diffusion.module.diffusion.sigma_data)
|
||||
|
||||
if multispeaker:
|
||||
s_preds = sampler(
|
||||
noise=torch.randn_like(s_trg).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=1,
|
||||
features=ref, # reference from the same speaker as the embedding
|
||||
embedding_mask_proba=0.1,
|
||||
num_steps=num_steps,
|
||||
).squeeze(1)
|
||||
loss_diff = model.diffusion(
|
||||
s_trg.unsqueeze(1), embedding=bert_dur, features=ref
|
||||
).mean() # EDM loss
|
||||
loss_sty = F.l1_loss(
|
||||
s_preds, s_trg.detach()
|
||||
) # style reconstruction loss
|
||||
else:
|
||||
s_preds = sampler(
|
||||
noise=torch.randn_like(s_trg).unsqueeze(1).to(device),
|
||||
embedding=bert_dur,
|
||||
embedding_scale=1,
|
||||
embedding_mask_proba=0.1,
|
||||
num_steps=num_steps,
|
||||
).squeeze(1)
|
||||
loss_diff = model.diffusion.module.diffusion(
|
||||
s_trg.unsqueeze(1), embedding=bert_dur
|
||||
).mean() # EDM loss
|
||||
loss_sty = F.l1_loss(
|
||||
s_preds, s_trg.detach()
|
||||
) # style reconstruction loss
|
||||
else:
|
||||
loss_sty = 0
|
||||
loss_diff = 0
|
||||
|
||||
d, p = model.predictor(d_en, s_dur, input_lengths, s2s_attn_mono, text_mask)
|
||||
|
||||
mel_len = min(int(mel_input_length.min().item() / 2 - 1), max_len // 2)
|
||||
mel_len_st = int(mel_input_length.min().item() / 2 - 1)
|
||||
en = []
|
||||
gt = []
|
||||
st = []
|
||||
p_en = []
|
||||
wav = []
|
||||
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item() / 2)
|
||||
|
||||
random_start = np.random.randint(0, mel_length - mel_len)
|
||||
en.append(asr[bib, :, random_start : random_start + mel_len])
|
||||
p_en.append(p[bib, :, random_start : random_start + mel_len])
|
||||
gt.append(
|
||||
mels[bib, :, (random_start * 2) : ((random_start + mel_len) * 2)]
|
||||
)
|
||||
|
||||
y = waves[bib][
|
||||
(random_start * 2) * 300 : ((random_start + mel_len) * 2) * 300
|
||||
]
|
||||
wav.append(torch.from_numpy(y).to(device))
|
||||
|
||||
# style reference (better to be different from the GT)
|
||||
random_start = np.random.randint(0, mel_length - mel_len_st)
|
||||
st.append(
|
||||
mels[bib, :, (random_start * 2) : ((random_start + mel_len_st) * 2)]
|
||||
)
|
||||
|
||||
wav = torch.stack(wav).float().detach()
|
||||
|
||||
en = torch.stack(en)
|
||||
p_en = torch.stack(p_en)
|
||||
gt = torch.stack(gt).detach()
|
||||
st = torch.stack(st).detach()
|
||||
|
||||
if gt.size(-1) < 80:
|
||||
continue
|
||||
|
||||
s_dur = model.predictor_encoder(
|
||||
st.unsqueeze(1) if multispeaker else gt.unsqueeze(1)
|
||||
)
|
||||
s = model.style_encoder(
|
||||
st.unsqueeze(1) if multispeaker else gt.unsqueeze(1)
|
||||
)
|
||||
|
||||
with torch.no_grad():
|
||||
F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
|
||||
F0 = F0.reshape(F0.shape[0], F0.shape[1] * 2, F0.shape[2], 1).squeeze()
|
||||
|
||||
asr_real = model.text_aligner.get_feature(gt)
|
||||
|
||||
N_real = log_norm(gt.unsqueeze(1)).squeeze(1)
|
||||
|
||||
y_rec_gt = wav.unsqueeze(1)
|
||||
y_rec_gt_pred = model.decoder(en, F0_real, N_real, s)
|
||||
|
||||
if epoch >= joint_epoch:
|
||||
# ground truth from recording
|
||||
wav = y_rec_gt # use recording since decoder is tuned
|
||||
else:
|
||||
# ground truth from reconstruction
|
||||
wav = y_rec_gt_pred # use reconstruction since decoder is fixed
|
||||
|
||||
F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s_dur)
|
||||
|
||||
y_rec = model.decoder(en, F0_fake, N_fake, s)
|
||||
|
||||
loss_F0_rec = (F.smooth_l1_loss(F0_real, F0_fake)) / 10
|
||||
loss_norm_rec = F.smooth_l1_loss(N_real, N_fake)
|
||||
|
||||
if start_ds:
|
||||
optimizer.zero_grad()
|
||||
d_loss = dl(wav.detach(), y_rec.detach()).mean()
|
||||
d_loss.backward()
|
||||
optimizer.step("msd")
|
||||
optimizer.step("mpd")
|
||||
else:
|
||||
d_loss = 0
|
||||
|
||||
# generator loss
|
||||
optimizer.zero_grad()
|
||||
|
||||
loss_mel = stft_loss(y_rec, wav)
|
||||
if start_ds:
|
||||
loss_gen_all = gl(wav, y_rec).mean()
|
||||
else:
|
||||
loss_gen_all = 0
|
||||
loss_lm = wl(wav.detach().squeeze(), y_rec.squeeze()).mean()
|
||||
|
||||
loss_ce = 0
|
||||
loss_dur = 0
|
||||
for _s2s_pred, _text_input, _text_length in zip(d, (d_gt), input_lengths):
|
||||
_s2s_pred = _s2s_pred[:_text_length, :]
|
||||
_text_input = _text_input[:_text_length].long()
|
||||
_s2s_trg = torch.zeros_like(_s2s_pred)
|
||||
for p in range(_s2s_trg.shape[0]):
|
||||
_s2s_trg[p, : _text_input[p]] = 1
|
||||
_dur_pred = torch.sigmoid(_s2s_pred).sum(axis=1)
|
||||
|
||||
loss_dur += F.l1_loss(
|
||||
_dur_pred[1 : _text_length - 1], _text_input[1 : _text_length - 1]
|
||||
)
|
||||
loss_ce += F.binary_cross_entropy_with_logits(
|
||||
_s2s_pred.flatten(), _s2s_trg.flatten()
|
||||
)
|
||||
|
||||
loss_ce /= texts.size(0)
|
||||
loss_dur /= texts.size(0)
|
||||
|
||||
g_loss = (
|
||||
loss_params.lambda_mel * loss_mel
|
||||
+ loss_params.lambda_F0 * loss_F0_rec
|
||||
+ loss_params.lambda_ce * loss_ce
|
||||
+ loss_params.lambda_norm * loss_norm_rec
|
||||
+ loss_params.lambda_dur * loss_dur
|
||||
+ loss_params.lambda_gen * loss_gen_all
|
||||
+ loss_params.lambda_slm * loss_lm
|
||||
+ loss_params.lambda_sty * loss_sty
|
||||
+ loss_params.lambda_diff * loss_diff
|
||||
)
|
||||
|
||||
running_loss += loss_mel.item()
|
||||
g_loss.backward()
|
||||
if torch.isnan(g_loss):
|
||||
from IPython.core.debugger import set_trace
|
||||
|
||||
set_trace()
|
||||
|
||||
optimizer.step("bert_encoder")
|
||||
optimizer.step("bert")
|
||||
optimizer.step("predictor")
|
||||
optimizer.step("predictor_encoder")
|
||||
|
||||
if epoch >= diff_epoch:
|
||||
optimizer.step("diffusion")
|
||||
|
||||
if epoch >= joint_epoch:
|
||||
optimizer.step("style_encoder")
|
||||
optimizer.step("decoder")
|
||||
|
||||
# randomly pick whether to use in-distribution text
|
||||
if np.random.rand() < 0.5:
|
||||
use_ind = True
|
||||
else:
|
||||
use_ind = False
|
||||
|
||||
if use_ind:
|
||||
ref_lengths = input_lengths
|
||||
ref_texts = texts
|
||||
|
||||
slm_out = slmadv(
|
||||
i,
|
||||
y_rec_gt,
|
||||
y_rec_gt_pred,
|
||||
waves,
|
||||
mel_input_length,
|
||||
ref_texts,
|
||||
ref_lengths,
|
||||
use_ind,
|
||||
s_trg.detach(),
|
||||
ref if multispeaker else None,
|
||||
)
|
||||
|
||||
if slm_out is None:
|
||||
continue
|
||||
|
||||
d_loss_slm, loss_gen_lm, y_pred = slm_out
|
||||
|
||||
# SLM generator loss
|
||||
optimizer.zero_grad()
|
||||
loss_gen_lm.backward()
|
||||
|
||||
# SLM discriminator loss
|
||||
if d_loss_slm != 0:
|
||||
optimizer.zero_grad()
|
||||
d_loss_slm.backward(retain_graph=True)
|
||||
optimizer.step("wd")
|
||||
|
||||
# compute the gradient norm
|
||||
total_norm = {}
|
||||
for key in model.keys():
|
||||
total_norm[key] = 0
|
||||
parameters = [
|
||||
p
|
||||
for p in model[key].parameters()
|
||||
if p.grad is not None and p.requires_grad
|
||||
]
|
||||
for p in parameters:
|
||||
param_norm = p.grad.detach().data.norm(2)
|
||||
total_norm[key] += param_norm.item() ** 2
|
||||
total_norm[key] = total_norm[key] ** 0.5
|
||||
|
||||
# gradient scaling
|
||||
if total_norm["predictor"] > slmadv_params.thresh:
|
||||
for key in model.keys():
|
||||
for p in model[key].parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= 1 / total_norm["predictor"]
|
||||
|
||||
for p in model.predictor.duration_proj.parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= slmadv_params.scale
|
||||
|
||||
for p in model.predictor.lstm.parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= slmadv_params.scale
|
||||
|
||||
for p in model.diffusion.parameters():
|
||||
if p.grad is not None:
|
||||
p.grad *= slmadv_params.scale
|
||||
|
||||
optimizer.step("bert_encoder")
|
||||
optimizer.step("bert")
|
||||
optimizer.step("predictor")
|
||||
optimizer.step("diffusion")
|
||||
else:
|
||||
d_loss_slm, loss_gen_lm = 0, 0
|
||||
|
||||
iters = iters + 1
|
||||
|
||||
if (i + 1) % log_interval == 0:
|
||||
logger.info(
|
||||
"Epoch [%d/%d], Step [%d/%d], Loss: %.5f, Disc Loss: %.5f, Dur Loss: %.5f, CE Loss: %.5f, Norm Loss: %.5f, F0 Loss: %.5f, LM Loss: %.5f, Gen Loss: %.5f, Sty Loss: %.5f, Diff Loss: %.5f, DiscLM Loss: %.5f, GenLM Loss: %.5f"
|
||||
% (
|
||||
epoch + 1,
|
||||
epochs,
|
||||
i + 1,
|
||||
len(train_list) // batch_size,
|
||||
running_loss / log_interval,
|
||||
d_loss,
|
||||
loss_dur,
|
||||
loss_ce,
|
||||
loss_norm_rec,
|
||||
loss_F0_rec,
|
||||
loss_lm,
|
||||
loss_gen_all,
|
||||
loss_sty,
|
||||
loss_diff,
|
||||
d_loss_slm,
|
||||
loss_gen_lm,
|
||||
)
|
||||
)
|
||||
|
||||
writer.add_scalar("train/mel_loss", running_loss / log_interval, iters)
|
||||
writer.add_scalar("train/gen_loss", loss_gen_all, iters)
|
||||
writer.add_scalar("train/d_loss", d_loss, iters)
|
||||
writer.add_scalar("train/ce_loss", loss_ce, iters)
|
||||
writer.add_scalar("train/dur_loss", loss_dur, iters)
|
||||
writer.add_scalar("train/slm_loss", loss_lm, iters)
|
||||
writer.add_scalar("train/norm_loss", loss_norm_rec, iters)
|
||||
writer.add_scalar("train/F0_loss", loss_F0_rec, iters)
|
||||
writer.add_scalar("train/sty_loss", loss_sty, iters)
|
||||
writer.add_scalar("train/diff_loss", loss_diff, iters)
|
||||
writer.add_scalar("train/d_loss_slm", d_loss_slm, iters)
|
||||
writer.add_scalar("train/gen_loss_slm", loss_gen_lm, iters)
|
||||
|
||||
running_loss = 0
|
||||
|
||||
print("Time elasped:", time.time() - start_time)
|
||||
|
||||
loss_test = 0
|
||||
loss_align = 0
|
||||
loss_f = 0
|
||||
_ = [model[key].eval() for key in model]
|
||||
|
||||
with torch.no_grad():
|
||||
iters_test = 0
|
||||
for batch_idx, batch in enumerate(val_dataloader):
|
||||
optimizer.zero_grad()
|
||||
|
||||
try:
|
||||
waves = batch[0]
|
||||
batch = [b.to(device) for b in batch[1:]]
|
||||
(
|
||||
texts,
|
||||
input_lengths,
|
||||
ref_texts,
|
||||
ref_lengths,
|
||||
mels,
|
||||
mel_input_length,
|
||||
ref_mels,
|
||||
) = batch
|
||||
with torch.no_grad():
|
||||
mask = length_to_mask(mel_input_length // (2**n_down)).to(
|
||||
"cuda"
|
||||
)
|
||||
text_mask = length_to_mask(input_lengths).to(texts.device)
|
||||
|
||||
_, _, s2s_attn = model.text_aligner(mels, mask, texts)
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
s2s_attn = s2s_attn[..., 1:]
|
||||
s2s_attn = s2s_attn.transpose(-1, -2)
|
||||
|
||||
mask_ST = mask_from_lens(
|
||||
s2s_attn, input_lengths, mel_input_length // (2**n_down)
|
||||
)
|
||||
s2s_attn_mono = maximum_path(s2s_attn, mask_ST)
|
||||
|
||||
# encode
|
||||
t_en = model.text_encoder(texts, input_lengths, text_mask)
|
||||
asr = t_en @ s2s_attn_mono
|
||||
|
||||
d_gt = s2s_attn_mono.sum(axis=-1).detach()
|
||||
|
||||
ss = []
|
||||
gs = []
|
||||
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item())
|
||||
mel = mels[bib, :, : mel_input_length[bib]]
|
||||
s = model.predictor_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
ss.append(s)
|
||||
s = model.style_encoder(mel.unsqueeze(0).unsqueeze(1))
|
||||
gs.append(s)
|
||||
|
||||
s = torch.stack(ss).squeeze()
|
||||
gs = torch.stack(gs).squeeze()
|
||||
s_trg = torch.cat([s, gs], dim=-1).detach()
|
||||
|
||||
bert_dur = model.bert(texts, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
d, p = model.predictor(
|
||||
d_en, s, input_lengths, s2s_attn_mono, text_mask
|
||||
)
|
||||
# get clips
|
||||
mel_len = int(mel_input_length.min().item() / 2 - 1)
|
||||
en = []
|
||||
gt = []
|
||||
p_en = []
|
||||
wav = []
|
||||
|
||||
for bib in range(len(mel_input_length)):
|
||||
mel_length = int(mel_input_length[bib].item() / 2)
|
||||
|
||||
random_start = np.random.randint(0, mel_length - mel_len)
|
||||
en.append(asr[bib, :, random_start : random_start + mel_len])
|
||||
p_en.append(p[bib, :, random_start : random_start + mel_len])
|
||||
|
||||
gt.append(
|
||||
mels[
|
||||
bib,
|
||||
:,
|
||||
(random_start * 2) : ((random_start + mel_len) * 2),
|
||||
]
|
||||
)
|
||||
|
||||
y = waves[bib][
|
||||
(random_start * 2)
|
||||
* 300 : ((random_start + mel_len) * 2)
|
||||
* 300
|
||||
]
|
||||
wav.append(torch.from_numpy(y).to(device))
|
||||
|
||||
wav = torch.stack(wav).float().detach()
|
||||
|
||||
en = torch.stack(en)
|
||||
p_en = torch.stack(p_en)
|
||||
gt = torch.stack(gt).detach()
|
||||
|
||||
s = model.predictor_encoder(gt.unsqueeze(1))
|
||||
|
||||
F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s)
|
||||
|
||||
loss_dur = 0
|
||||
for _s2s_pred, _text_input, _text_length in zip(
|
||||
d, (d_gt), input_lengths
|
||||
):
|
||||
_s2s_pred = _s2s_pred[:_text_length, :]
|
||||
_text_input = _text_input[:_text_length].long()
|
||||
_s2s_trg = torch.zeros_like(_s2s_pred)
|
||||
for bib in range(_s2s_trg.shape[0]):
|
||||
_s2s_trg[bib, : _text_input[bib]] = 1
|
||||
_dur_pred = torch.sigmoid(_s2s_pred).sum(axis=1)
|
||||
loss_dur += F.l1_loss(
|
||||
_dur_pred[1 : _text_length - 1],
|
||||
_text_input[1 : _text_length - 1],
|
||||
)
|
||||
|
||||
loss_dur /= texts.size(0)
|
||||
|
||||
s = model.style_encoder(gt.unsqueeze(1))
|
||||
|
||||
y_rec = model.decoder(en, F0_fake, N_fake, s)
|
||||
loss_mel = stft_loss(y_rec.squeeze(), wav.detach())
|
||||
|
||||
F0_real, _, F0 = model.pitch_extractor(gt.unsqueeze(1))
|
||||
|
||||
loss_F0 = F.l1_loss(F0_real, F0_fake) / 10
|
||||
|
||||
loss_test += (loss_mel).mean()
|
||||
loss_align += (loss_dur).mean()
|
||||
loss_f += (loss_F0).mean()
|
||||
|
||||
iters_test += 1
|
||||
except:
|
||||
continue
|
||||
|
||||
print("Epochs:", epoch + 1)
|
||||
logger.info(
|
||||
"Validation loss: %.3f, Dur loss: %.3f, F0 loss: %.3f"
|
||||
% (loss_test / iters_test, loss_align / iters_test, loss_f / iters_test)
|
||||
+ "\n\n\n"
|
||||
)
|
||||
print("\n\n\n")
|
||||
writer.add_scalar("eval/mel_loss", loss_test / iters_test, epoch + 1)
|
||||
writer.add_scalar("eval/dur_loss", loss_test / iters_test, epoch + 1)
|
||||
writer.add_scalar("eval/F0_loss", loss_f / iters_test, epoch + 1)
|
||||
|
||||
if epoch < joint_epoch:
|
||||
# generating reconstruction examples with GT duration
|
||||
|
||||
with torch.no_grad():
|
||||
for bib in range(len(asr)):
|
||||
mel_length = int(mel_input_length[bib].item())
|
||||
gt = mels[bib, :, :mel_length].unsqueeze(0)
|
||||
en = asr[bib, :, : mel_length // 2].unsqueeze(0)
|
||||
|
||||
F0_real, _, _ = model.pitch_extractor(gt.unsqueeze(1))
|
||||
F0_real = F0_real.unsqueeze(0)
|
||||
s = model.style_encoder(gt.unsqueeze(1))
|
||||
real_norm = log_norm(gt.unsqueeze(1)).squeeze(1)
|
||||
|
||||
y_rec = model.decoder(en, F0_real, real_norm, s)
|
||||
|
||||
writer.add_audio(
|
||||
"eval/y" + str(bib),
|
||||
y_rec.cpu().numpy().squeeze(),
|
||||
epoch,
|
||||
sample_rate=sr,
|
||||
)
|
||||
|
||||
s_dur = model.predictor_encoder(gt.unsqueeze(1))
|
||||
p_en = p[bib, :, : mel_length // 2].unsqueeze(0)
|
||||
|
||||
F0_fake, N_fake = model.predictor.F0Ntrain(p_en, s_dur)
|
||||
|
||||
y_pred = model.decoder(en, F0_fake, N_fake, s)
|
||||
|
||||
writer.add_audio(
|
||||
"pred/y" + str(bib),
|
||||
y_pred.cpu().numpy().squeeze(),
|
||||
epoch,
|
||||
sample_rate=sr,
|
||||
)
|
||||
|
||||
if epoch == 0:
|
||||
writer.add_audio(
|
||||
"gt/y" + str(bib),
|
||||
waves[bib].squeeze(),
|
||||
epoch,
|
||||
sample_rate=sr,
|
||||
)
|
||||
|
||||
if bib >= 5:
|
||||
break
|
||||
else:
|
||||
# generating sampled speech from text directly
|
||||
with torch.no_grad():
|
||||
# compute reference styles
|
||||
if multispeaker and epoch >= diff_epoch:
|
||||
ref_ss = model.style_encoder(ref_mels.unsqueeze(1))
|
||||
ref_sp = model.predictor_encoder(ref_mels.unsqueeze(1))
|
||||
ref_s = torch.cat([ref_ss, ref_sp], dim=1)
|
||||
|
||||
for bib in range(len(d_en)):
|
||||
if multispeaker:
|
||||
s_pred = sampler(
|
||||
noise=torch.randn((1, 256)).unsqueeze(1).to(texts.device),
|
||||
embedding=bert_dur[bib].unsqueeze(0),
|
||||
embedding_scale=1,
|
||||
features=ref_s[bib].unsqueeze(
|
||||
0
|
||||
), # reference from the same speaker as the embedding
|
||||
num_steps=5,
|
||||
).squeeze(1)
|
||||
else:
|
||||
s_pred = sampler(
|
||||
noise=torch.randn((1, 256)).unsqueeze(1).to(texts.device),
|
||||
embedding=bert_dur[bib].unsqueeze(0),
|
||||
embedding_scale=1,
|
||||
num_steps=5,
|
||||
).squeeze(1)
|
||||
|
||||
s = s_pred[:, 128:]
|
||||
ref = s_pred[:, :128]
|
||||
|
||||
d = model.predictor.text_encoder(
|
||||
d_en[bib, :, : input_lengths[bib]].unsqueeze(0),
|
||||
s,
|
||||
input_lengths[bib, ...].unsqueeze(0),
|
||||
text_mask[bib, : input_lengths[bib]].unsqueeze(0),
|
||||
)
|
||||
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
|
||||
duration = torch.sigmoid(duration).sum(axis=-1)
|
||||
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
|
||||
|
||||
pred_dur[-1] += 5
|
||||
|
||||
pred_aln_trg = torch.zeros(
|
||||
input_lengths[bib], int(pred_dur.sum().data)
|
||||
)
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame : c_frame + int(pred_dur[i].data)] = 1
|
||||
c_frame += int(pred_dur[i].data)
|
||||
|
||||
# encode prosody
|
||||
en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(
|
||||
texts.device
|
||||
)
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
out = model.decoder(
|
||||
(
|
||||
t_en[bib, :, : input_lengths[bib]].unsqueeze(0)
|
||||
@ pred_aln_trg.unsqueeze(0).to(texts.device)
|
||||
),
|
||||
F0_pred,
|
||||
N_pred,
|
||||
ref.squeeze().unsqueeze(0),
|
||||
)
|
||||
|
||||
writer.add_audio(
|
||||
"pred/y" + str(bib),
|
||||
out.cpu().numpy().squeeze(),
|
||||
epoch,
|
||||
sample_rate=sr,
|
||||
)
|
||||
|
||||
if bib >= 5:
|
||||
break
|
||||
|
||||
if epoch % saving_epoch == 0:
|
||||
if (loss_test / iters_test) < best_loss:
|
||||
best_loss = loss_test / iters_test
|
||||
print("Saving..")
|
||||
state = {
|
||||
"net": {key: model[key].state_dict() for key in model},
|
||||
"optimizer": optimizer.state_dict(),
|
||||
"iters": iters,
|
||||
"val_loss": loss_test / iters_test,
|
||||
"epoch": epoch,
|
||||
}
|
||||
save_path = osp.join(log_dir, "epoch_2nd_%05d.pth" % epoch)
|
||||
torch.save(state, save_path)
|
||||
|
||||
# if estimate sigma, save the estimated simga
|
||||
if model_params.diffusion.dist.estimate_sigma_data:
|
||||
config["model_params"]["diffusion"]["dist"]["sigma_data"] = float(
|
||||
np.mean(running_std)
|
||||
)
|
||||
|
||||
with open(osp.join(log_dir, osp.basename(config_path)), "w") as outfile:
|
||||
yaml.dump(config, outfile, default_flow_style=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,89 @@
|
||||
from monotonic_align import maximum_path
|
||||
from monotonic_align import mask_from_lens
|
||||
from monotonic_align.core import maximum_path_c
|
||||
import numpy as np
|
||||
import torch
|
||||
import copy
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio
|
||||
import librosa
|
||||
import matplotlib.pyplot as plt
|
||||
from munch import Munch
|
||||
|
||||
|
||||
def maximum_path(neg_cent, mask):
|
||||
"""Cython optimized version.
|
||||
neg_cent: [b, t_t, t_s]
|
||||
mask: [b, t_t, t_s]
|
||||
"""
|
||||
device = neg_cent.device
|
||||
dtype = neg_cent.dtype
|
||||
neg_cent = np.ascontiguousarray(neg_cent.data.cpu().numpy().astype(np.float32))
|
||||
path = np.ascontiguousarray(np.zeros(neg_cent.shape, dtype=np.int32))
|
||||
|
||||
t_t_max = np.ascontiguousarray(
|
||||
mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
|
||||
)
|
||||
t_s_max = np.ascontiguousarray(
|
||||
mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
|
||||
)
|
||||
maximum_path_c(path, neg_cent, t_t_max, t_s_max)
|
||||
return torch.from_numpy(path).to(device=device, dtype=dtype)
|
||||
|
||||
|
||||
def get_data_path_list(train_path=None, val_path=None):
|
||||
if train_path is None:
|
||||
train_path = "Data/train_list.txt"
|
||||
if val_path is None:
|
||||
val_path = "Data/val_list.txt"
|
||||
|
||||
with open(train_path, "r", encoding="utf-8", errors="ignore") as f:
|
||||
train_list = f.readlines()
|
||||
with open(val_path, "r", encoding="utf-8", errors="ignore") as f:
|
||||
val_list = f.readlines()
|
||||
|
||||
return train_list, val_list
|
||||
|
||||
|
||||
def length_to_mask(lengths):
|
||||
mask = (
|
||||
torch.arange(lengths.max())
|
||||
.unsqueeze(0)
|
||||
.expand(lengths.shape[0], -1)
|
||||
.type_as(lengths)
|
||||
)
|
||||
mask = torch.gt(mask + 1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
|
||||
# for norm consistency loss
|
||||
def log_norm(x, mean=-4, std=4, dim=2):
|
||||
"""
|
||||
normalized log mel -> mel -> norm -> log(norm)
|
||||
"""
|
||||
x = torch.log(torch.exp(x * std + mean).norm(dim=dim))
|
||||
return x
|
||||
|
||||
|
||||
def get_image(arrs):
|
||||
plt.switch_backend("agg")
|
||||
fig = plt.figure()
|
||||
ax = plt.gca()
|
||||
ax.imshow(arrs)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def recursive_munch(d):
|
||||
if isinstance(d, dict):
|
||||
return Munch((k, recursive_munch(v)) for k, v in d.items())
|
||||
elif isinstance(d, list):
|
||||
return [recursive_munch(v) for v in d]
|
||||
else:
|
||||
return d
|
||||
|
||||
|
||||
def log_print(message, logger):
|
||||
logger.info(message)
|
||||
print(message)
|
||||
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Reference in New Issue
Block a user