mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-22 08:16:08 +00:00
Introduce code from Rust monorepo. This is a mess
This commit is contained in:
Brandon Thomas
@@ -0,0 +1,428 @@
|
||||
# This config contains the default values for training a Citrinet model with CTC loss and BPE-based vocabulary.
|
||||
# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs.
|
||||
# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
|
||||
# If training for a short time, you can also reduce weight decay to 0.
|
||||
|
||||
# Training Recipe
|
||||
# This model can be trained using the default settings in this config with FP32 precision.
|
||||
# When training under AMP, increase `warmup_steps` to 5000 for stable training.
|
||||
# In order to create Citrinet-C, find-replace `filters: 384` with `filters: C`.
|
||||
# When reducing the receptive field of these models, it is advised to reduce the amount of augmentation
|
||||
# for larger models from 10x time masking to 5x or 2x time masking.
|
||||
# For further details regarding Citrinet, visit - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#citrinet
|
||||
|
||||
name: &name "Citrinet-384-8x-Stride"
|
||||
|
||||
model:
|
||||
sample_rate: &sample_rate 16000
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
trim_silence: false
|
||||
max_duration: 16.7
|
||||
shuffle: true
|
||||
is_tarred: false
|
||||
tarred_audio_filepaths: null
|
||||
use_start_end_token: false
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
shuffle: false
|
||||
use_start_end_token: false
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
shuffle: false
|
||||
use_start_end_token: false
|
||||
|
||||
model_defaults:
|
||||
repeat: 5
|
||||
dropout: 0.0
|
||||
separable: true
|
||||
se: true
|
||||
se_context_size: -1
|
||||
kernel_size_factor: 1.0
|
||||
|
||||
tokenizer:
|
||||
dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
|
||||
type: ??? # Can be either bpe or wpe
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
sample_rate: *sample_rate
|
||||
normalize: "per_feature"
|
||||
window_size: 0.025
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: &n_mels 80
|
||||
n_fft: 512
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
pad_to: 16
|
||||
stft_conv: false
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
freq_masks: 2
|
||||
time_masks: 2
|
||||
freq_width: 27
|
||||
time_width: 0.05
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- filters: 384
|
||||
repeat: 1
|
||||
kernel: [5]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [11]
|
||||
stride: [2]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
stride_last: true
|
||||
residual_mode: "stride_add"
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [13]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [19]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [21]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [13]
|
||||
stride: [2] # *stride
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
stride_last: true
|
||||
residual_mode: "stride_add"
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [19]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [21]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [23]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [25]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [25]
|
||||
stride: [2] # stride
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
stride_last: true
|
||||
residual_mode: "stride_add"
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [27]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [29]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [31]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [33]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [35]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [37]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 384
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [39]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: &enc_final 640
|
||||
repeat: 1
|
||||
kernel: [41]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: *enc_final
|
||||
num_classes: -1 # filled with vocabulary size from tokenizer at runtime
|
||||
vocabulary: [] # filled with vocabulary from tokenizer at runtime
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
lr: 0.05
|
||||
|
||||
# optimizer arguments
|
||||
betas: [0.8, 0.25]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: CosineAnnealing
|
||||
|
||||
# scheduler config override
|
||||
warmup_steps: 1000
|
||||
warmup_ratio: null
|
||||
min_lr: 1e-5
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 100
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: false # Provided by exp_manager
|
||||
logger: false # Provided by exp_manager
|
||||
log_every_n_steps: 100 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
check_val_every_n_epoch: 1
|
||||
precision: 32
|
||||
sync_batchnorm: false
|
||||
benchmark: false
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: true
|
||||
create_checkpoint_callback: true
|
||||
checkpoint_callback_params:
|
||||
monitor: "val_wer"
|
||||
mode: "min"
|
||||
save_top_k: 3
|
||||
create_wandb_logger: false
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
entity: null
|
||||
resume_if_exists: false
|
||||
resume_ignore_no_checkpoint: false
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,428 @@
|
||||
# This config contains the default values for training a Citrinet model with CTC loss and BPE-based vocabulary.
|
||||
# Default learning parameters in this config are set for effective batch size of 1k on 32 GPUs.
|
||||
# To train it with smaller batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
|
||||
# If training for a short time, you can also reduce weight decay to 0.
|
||||
|
||||
# Training Recipe
|
||||
# This model can be trained using the default settings in this config with FP32 precision.
|
||||
# When training under AMP, increase `warmup_steps` to 5000 for stable training.
|
||||
# In order to create Citrinet-C, find-replace `filters: 384` with `filters: C`.
|
||||
# When reducing the receptive field of these models, it is advised to reduce the amount of augmentation
|
||||
# for larger models from 10x time masking to 5x or 2x time masking.
|
||||
# For further details regarding Citrinet, visit - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#citrinet
|
||||
|
||||
name: &name "Citrinet-512-8x-Stride"
|
||||
|
||||
model:
|
||||
sample_rate: &sample_rate 16000
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
trim_silence: false
|
||||
max_duration: 16.7
|
||||
shuffle: true
|
||||
is_tarred: false
|
||||
tarred_audio_filepaths: null
|
||||
use_start_end_token: false
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
shuffle: false
|
||||
use_start_end_token: false
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
shuffle: false
|
||||
use_start_end_token: false
|
||||
|
||||
model_defaults:
|
||||
repeat: 5
|
||||
dropout: 0.1
|
||||
separable: true
|
||||
se: true
|
||||
se_context_size: -1
|
||||
kernel_size_factor: 1.0
|
||||
|
||||
tokenizer:
|
||||
dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
|
||||
type: ??? # Can be either bpe or wpe
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
sample_rate: *sample_rate
|
||||
normalize: "per_feature"
|
||||
window_size: 0.025
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: &n_mels 80
|
||||
n_fft: 512
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
pad_to: 16
|
||||
stft_conv: false
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
freq_masks: 2
|
||||
time_masks: 10
|
||||
freq_width: 27
|
||||
time_width: 0.05
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- filters: 512
|
||||
repeat: 1
|
||||
kernel: [5]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [11]
|
||||
stride: [2]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
stride_last: true
|
||||
residual_mode: "stride_add"
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [13]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [19]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [21]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [13]
|
||||
stride: [2] # *stride
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
stride_last: true
|
||||
residual_mode: "stride_add"
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [19]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [21]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [23]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [25]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [25]
|
||||
stride: [2] # stride
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
stride_last: true
|
||||
residual_mode: "stride_add"
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [27]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [29]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [31]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [33]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [35]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [37]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: 512
|
||||
repeat: ${model.model_defaults.repeat}
|
||||
kernel: [39]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.model_defaults.dropout}
|
||||
residual: true
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
- filters: &enc_final 640
|
||||
repeat: 1
|
||||
kernel: [41]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: ${model.model_defaults.separable}
|
||||
se: ${model.model_defaults.se}
|
||||
se_context_size: ${model.model_defaults.se_context_size}
|
||||
kernel_size_factor: ${model.model_defaults.kernel_size_factor}
|
||||
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: *enc_final
|
||||
num_classes: -1 # filled with vocabulary size from tokenizer at runtime
|
||||
vocabulary: [] # filled with vocabulary from tokenizer at runtime
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
lr: 0.05
|
||||
|
||||
# optimizer arguments
|
||||
betas: [0.8, 0.25]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: CosineAnnealing
|
||||
|
||||
# scheduler config override
|
||||
warmup_steps: 1000
|
||||
warmup_ratio: null
|
||||
min_lr: 1e-5
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 100
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: false # Provided by exp_manager
|
||||
logger: false # Provided by exp_manager
|
||||
log_every_n_steps: 100 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
check_val_every_n_epoch: 1
|
||||
precision: 32
|
||||
sync_batchnorm: false
|
||||
benchmark: false
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: true
|
||||
create_checkpoint_callback: true
|
||||
checkpoint_callback_params:
|
||||
monitor: "val_wer"
|
||||
mode: "min"
|
||||
save_top_k: 3
|
||||
create_wandb_logger: false
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
entity: null
|
||||
resume_if_exists: false
|
||||
resume_ignore_no_checkpoint: false
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,183 @@
|
||||
name: &name "ContextNet5x1"
|
||||
sample_rate: &sample_rate 16000
|
||||
repeat: &repeat 1
|
||||
dropout: &dropout 0.0
|
||||
separable: &separable true
|
||||
|
||||
model:
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
trim_silence: True
|
||||
max_duration: 16.7
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
shard_strategy: "scatter"
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
batch_size: 32
|
||||
shuffle: False
|
||||
|
||||
tokenizer:
|
||||
dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
|
||||
type: ??? # Can be either bpe or wpe
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
normalize: "per_feature"
|
||||
window_size: 0.02
|
||||
sample_rate: *sample_rate
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: &n_mels 64
|
||||
n_fft: 512
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
rect_freq: 50
|
||||
rect_masks: 5
|
||||
rect_time: 120
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [11]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [13]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [19]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: 1
|
||||
kernel: [21]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: &enc_feat_out 1024
|
||||
repeat: 1
|
||||
kernel: [1]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: 1024
|
||||
num_classes: -1 # filled with vocabulary size from tokenizer at runtime
|
||||
vocabulary: [] # filled with vocabulary from tokenizer at runtime
|
||||
|
||||
optim:
|
||||
name: adam
|
||||
# _target_: nemo.core.optim.optimizers.Adam
|
||||
lr: .1
|
||||
|
||||
# optimizer arguments
|
||||
betas: [0.9, 0.999]
|
||||
weight_decay: 0.0001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: CosineAnnealing
|
||||
|
||||
# scheduler config override
|
||||
warmup_steps: null
|
||||
warmup_ratio: 0.05
|
||||
min_lr: 1e-6
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 5
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
create_wandb_logger: False
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,183 @@
|
||||
name: &name "QuartzNet15x5"
|
||||
sample_rate: &sample_rate 16000
|
||||
repeat: &repeat 1
|
||||
dropout: &dropout 0.0
|
||||
separable: &separable true
|
||||
labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
|
||||
"n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
|
||||
|
||||
model:
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
trim_silence: True
|
||||
max_duration: 16.7
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
tarred_shard_strategy: "scatter"
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
shuffle: False
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
normalize: "per_feature"
|
||||
window_size: 0.02
|
||||
sample_rate: *sample_rate
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: &n_mels 64
|
||||
n_fft: 512
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
stft_conv: false
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
rect_freq: 50
|
||||
rect_masks: 5
|
||||
rect_time: 120
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [11]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [13]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: *repeat
|
||||
kernel: [19]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: *dropout
|
||||
residual: true
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: 256
|
||||
repeat: 1
|
||||
kernel: [21]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
- filters: &enc_feat_out 1024
|
||||
repeat: 1
|
||||
kernel: [1]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: 0.0
|
||||
residual: false
|
||||
separable: *separable
|
||||
se: true
|
||||
se_context_size: -1
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: 1024
|
||||
num_classes: 28
|
||||
vocabulary: *labels
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
# _target_: nemo.core.optim.optimizers.Novograd
|
||||
lr: .01
|
||||
# optimizer arguments
|
||||
betas: [0.8, 0.5]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: CosineAnnealing
|
||||
|
||||
# pytorch lightning args
|
||||
monitor: val_loss
|
||||
reduce_on_plateau: false
|
||||
|
||||
# Scheduler params
|
||||
warmup_steps: null
|
||||
warmup_ratio: null
|
||||
min_lr: 0.0
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 5
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # check once per epoch .25 for 4 times per epoch
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,189 @@
|
||||
# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and sub-word encoding.
|
||||
|
||||
# Architecture and training config:
|
||||
# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective
|
||||
# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
|
||||
# Here are the recommended configs for different variants of Conformer-CTC, other parameters are the same as in this config file.
|
||||
# One extra layer (compared to original paper) is added to the medium and large variants to compensate for replacing the LSTM decoder with a linear one.
|
||||
#
|
||||
# +-------------+---------+---------+----------+------------+-----+
|
||||
# | Model | d_model | n_heads | n_layers | time_masks | lr |
|
||||
# +=============+=========+========+===========+============+=====+
|
||||
# | Small (13M)| 176 | 4 | 16 | 5 | 5.0 |
|
||||
# +-------------+---------+--------+-----------+------------+-----+
|
||||
# | Medium (30M)| 256 | 4 | 18 | 5 | 5.0 |
|
||||
# +-------------+---------+--------+-----------+------------+-----+
|
||||
# | Large (121M)| 512 | 8 | 18 | 10 | 2.0 |
|
||||
# +---------------------------------------------------------------+
|
||||
#
|
||||
# If you do not want to train with AMP, you may use weight decay of 0.0 or reduce the number of time maskings to 2
|
||||
# with time_width=100. It may help when you want to train for fewer epochs and need faster convergence.
|
||||
# With weight_decay=0.0, learning rate may need to get reduced to 2.0.
|
||||
|
||||
# You may find more info about Conformer-CTC here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-ctc
|
||||
# Pre-trained models of Conformer-CTC can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html
|
||||
# The checkpoint of the large model trained on LibriSpeech with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large_ls
|
||||
|
||||
name: "Conformer-CTC-BPE"
|
||||
|
||||
model:
|
||||
sample_rate: 16000
|
||||
log_prediction: true # enables logging sample predictions in the output during training
|
||||
ctc_reduction: 'mean_batch'
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
batch_size: 16 # you may increase batch_size if your memory allows
|
||||
shuffle: true
|
||||
num_workers: 8
|
||||
pin_memory: true
|
||||
use_start_end_token: false
|
||||
trim_silence: false
|
||||
max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset
|
||||
min_duration: 0.1
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
batch_size: 16 # you may increase batch_size if your memory allows
|
||||
shuffle: false
|
||||
num_workers: 8
|
||||
pin_memory: true
|
||||
use_start_end_token: false
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
sample_rate: ${model.sample_rate}
|
||||
batch_size: 16 # you may increase batch_size if your memory allows
|
||||
shuffle: false
|
||||
num_workers: 8
|
||||
pin_memory: true
|
||||
use_start_end_token: false
|
||||
|
||||
# recommend small vocab size of 128 or 256 when using 4x sub-sampling
|
||||
# you may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
|
||||
tokenizer:
|
||||
dir: ??? # path to directory which contains either tokenizer.model (bpe) or vocab.txt (wpe)
|
||||
type: wpe # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
sample_rate: ${model.sample_rate}
|
||||
normalize: "per_feature"
|
||||
window_size: 0.025
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: 80
|
||||
n_fft: 512
|
||||
log: true
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
pad_to: 0
|
||||
pad_value: 0.0
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
freq_masks: 2 # set to zero to disable it
|
||||
# you may use lower time_masks for smaller models to have a faster convergence
|
||||
time_masks: 10 # set to zero to disable it
|
||||
freq_width: 27
|
||||
time_width: 0.05
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConformerEncoder
|
||||
feat_in: ${model.preprocessor.features}
|
||||
feat_out: -1 # you may set it if you need different output size other than the default d_model
|
||||
n_layers: 18
|
||||
d_model: 512
|
||||
|
||||
# Sub-sampling params
|
||||
subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory
|
||||
subsampling_factor: 4 # must be power of 2
|
||||
subsampling_conv_channels: -1 # -1 sets it to d_model
|
||||
|
||||
# Feed forward module's params
|
||||
ff_expansion_factor: 4
|
||||
|
||||
# Multi-headed Attention Module's params
|
||||
self_attention_model: rel_pos # rel_pos or abs_pos
|
||||
n_heads: 8 # may need to be lower for smaller d_models
|
||||
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
|
||||
att_context_size: [-1, -1] # -1 means unlimited context
|
||||
xscaling: true # scales up the input embeddings by sqrt(d_model)
|
||||
untie_biases: true # unties the biases of the TransformerXL layers
|
||||
pos_emb_max_len: 5000
|
||||
|
||||
# Convolution module's params
|
||||
conv_kernel_size: 31
|
||||
|
||||
### regularization
|
||||
dropout: 0.1 # The dropout used in most of the Conformer Modules
|
||||
dropout_emb: 0.0 # The dropout used for embeddings
|
||||
dropout_att: 0.1 # The dropout for multi-headed attention modules
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: null
|
||||
num_classes: -1
|
||||
vocabulary: []
|
||||
|
||||
optim:
|
||||
name: adamw
|
||||
lr: 2.0
|
||||
# optimizer arguments
|
||||
betas: [0.9, 0.98]
|
||||
# less necessity for weight_decay as we already have large augmentations with SpecAug
|
||||
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
|
||||
# weight decay of 0.0 with lr of 2.0 also works fine
|
||||
weight_decay: 1e-3
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: NoamAnnealing
|
||||
d_model: ${model.encoder.d_model}
|
||||
# scheduler config override
|
||||
warmup_steps: 10000
|
||||
warmup_ratio: null
|
||||
min_lr: 1e-6
|
||||
|
||||
trainer:
|
||||
gpus: -1 # number of GPUs, -1 would use all available GPUs
|
||||
num_nodes: 1
|
||||
max_epochs: 1000
|
||||
max_steps: null # computed at runtime if not set
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
gradient_clip_val: 0.0
|
||||
amp_level: O0 # O1/O2 for mixed precision
|
||||
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
|
||||
log_every_n_steps: 10 # Interval of logging.
|
||||
progress_bar_refresh_rate: 10
|
||||
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
|
||||
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
|
||||
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
|
||||
sync_batchnorm: true
|
||||
checkpoint_callback: false # Provided by exp_manager
|
||||
logger: false # Provided by exp_manager
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: ${name}
|
||||
create_tensorboard_logger: true
|
||||
create_checkpoint_callback: true
|
||||
checkpoint_callback_params:
|
||||
# in case of multiple validation sets, first one is used
|
||||
monitor: "val_wer"
|
||||
mode: "min"
|
||||
save_top_k: 3
|
||||
|
||||
# you need to set these two to True to continue the training
|
||||
resume_if_exists: false
|
||||
resume_ignore_no_checkpoint: false
|
||||
|
||||
# You may use this section to create a W&B logger
|
||||
create_wandb_logger: false
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
@@ -0,0 +1,165 @@
|
||||
# It contains the default values for training a Conformer-CTC ASR model, large size (~120M) with CTC loss and char-based vocabulary.
|
||||
# Char-based encoding may give lower accuracy than sub-word encoding (conformer_ctc_bpe.yaml).
|
||||
|
||||
# You may find more detail on Conformer-CTC at `examples/asr/conf/conformer/conformer_ctc_bpe.yaml`
|
||||
|
||||
name: "Conformer-CTC-Char"
|
||||
|
||||
model:
|
||||
sample_rate: 16000
|
||||
labels: [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
|
||||
"n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
|
||||
log_prediction: true # enables logging sample predictions in the output during training
|
||||
ctc_reduction: 'mean_batch'
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
labels: ${model.labels}
|
||||
sample_rate: ${model.sample_rate}
|
||||
batch_size: 16 # you may increase batch_size if your memory allows
|
||||
shuffle: true
|
||||
num_workers: 8
|
||||
pin_memory: true
|
||||
trim_silence: false
|
||||
max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset
|
||||
min_duration: 0.1
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
labels: ${model.labels}
|
||||
sample_rate: ${model.sample_rate}
|
||||
batch_size: 16 # you may increase batch_size if your memory allows
|
||||
shuffle: false
|
||||
num_workers: 8
|
||||
pin_memory: true
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
labels: ${model.labels}
|
||||
sample_rate: ${model.sample_rate}
|
||||
batch_size: 16 # you may increase batch_size if your memory allows
|
||||
shuffle: false
|
||||
num_workers: 8
|
||||
pin_memory: true
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
sample_rate: ${model.sample_rate}
|
||||
normalize: "per_feature"
|
||||
window_size: 0.025
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: 80
|
||||
n_fft: 512
|
||||
log: true
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
pad_to: 0
|
||||
pad_value: 0.0
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
freq_masks: 2 # set to zero to disable it
|
||||
# you may use lower time_masks for smaller models to have a faster convergence
|
||||
time_masks: 10 # set to zero to disable it
|
||||
freq_width: 27
|
||||
time_width: 0.05
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConformerEncoder
|
||||
feat_in: ${model.preprocessor.features}
|
||||
feat_out: -1 # you may set it if you need different output size other than the default d_model
|
||||
n_layers: 16
|
||||
d_model: 256
|
||||
|
||||
# Sub-sampling params
|
||||
subsampling: striding # vggnet or striding, vggnet may give better results but needs more memory
|
||||
subsampling_factor: 4 # must be power of 2
|
||||
subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model
|
||||
|
||||
# Feed forward module's params
|
||||
ff_expansion_factor: 4
|
||||
|
||||
# Multi-headed Attention Module's params
|
||||
self_attention_model: rel_pos # rel_pos or abs_pos
|
||||
n_heads: 8 # may need to be lower for smaller d_models
|
||||
# [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
|
||||
att_context_size: [-1, -1] # -1 means unlimited context
|
||||
xscaling: true # scales up the input embeddings by sqrt(d_model)
|
||||
untie_biases: true # unties the biases of the TransformerXL layers
|
||||
pos_emb_max_len: 5000
|
||||
|
||||
# Convolution module's params
|
||||
conv_kernel_size: 31
|
||||
|
||||
### regularization
|
||||
dropout: 0.1 # The dropout used in most of the Conformer Modules
|
||||
dropout_emb: 0.0 # The dropout used for embeddings
|
||||
dropout_att: 0.1 # The dropout for multi-headed attention modules
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: null
|
||||
num_classes: -1
|
||||
vocabulary: ${model.labels}
|
||||
|
||||
optim:
|
||||
name: adamw
|
||||
lr: 2.0
|
||||
# optimizer arguments
|
||||
betas: [0.9, 0.98]
|
||||
# less necessity for weight_decay as we already have large augmentations with SpecAug
|
||||
# you may need weight_decay for large models, stable AMP training, small datasets, or when lower augmentations are used
|
||||
# weight decay of 0.0 with lr of 2.0 also works fine
|
||||
weight_decay: 1e-3
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: NoamAnnealing
|
||||
d_model: ${model.encoder.d_model}
|
||||
# scheduler config override
|
||||
warmup_steps: 10000
|
||||
warmup_ratio: null
|
||||
min_lr: 1e-6
|
||||
|
||||
trainer:
|
||||
gpus: -1 # number of GPUs, -1 would use all available GPUs
|
||||
num_nodes: 1
|
||||
max_epochs: 1000
|
||||
max_steps: null # computed at runtime if not set
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
gradient_clip_val: 0.0
|
||||
amp_level: O0 # O1/O2 for mixed precision
|
||||
precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
|
||||
log_every_n_steps: 10 # Interval of logging.
|
||||
progress_bar_refresh_rate: 10
|
||||
resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
|
||||
num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
|
||||
check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
|
||||
sync_batchnorm: true
|
||||
checkpoint_callback: false # Provided by exp_manager
|
||||
logger: false # Provided by exp_manager
|
||||
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: ${name}
|
||||
create_tensorboard_logger: true
|
||||
create_checkpoint_callback: true
|
||||
checkpoint_callback_params:
|
||||
# in case of multiple validation sets, first one is used
|
||||
monitor: "val_wer"
|
||||
mode: "min"
|
||||
save_top_k: 3
|
||||
|
||||
# you need to set these two to True to continue the training
|
||||
resume_if_exists: false
|
||||
resume_ignore_no_checkpoint: false
|
||||
|
||||
# You may use this section to create a W&B logger
|
||||
create_wandb_logger: false
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
@@ -0,0 +1,208 @@
|
||||
name: &name "Jasper10x5"
|
||||
|
||||
model:
|
||||
sample_rate: &sample_rate 16000
|
||||
labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
|
||||
"n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
trim_silence: True
|
||||
max_duration: 16.7
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
tarred_shard_strategy: "scatter"
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
shuffle: False
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
normalize: "per_feature"
|
||||
window_size: 0.02
|
||||
sample_rate: *sample_rate
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: &n_mels 64
|
||||
n_fft: 512
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
rect_freq: 50
|
||||
rect_masks: 5
|
||||
rect_time: 120
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
jasper:
|
||||
- dilation: [1]
|
||||
dropout: 0.2
|
||||
filters: 256
|
||||
kernel: [11]
|
||||
repeat: 1
|
||||
residual: false
|
||||
stride: [2]
|
||||
- dilation: [1]
|
||||
dropout: 0.2
|
||||
filters: 256
|
||||
kernel: [11]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.2
|
||||
filters: 256
|
||||
kernel: [11]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.2
|
||||
filters: 384
|
||||
kernel: [13]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.2
|
||||
filters: 384
|
||||
kernel: [13]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.2
|
||||
filters: 512
|
||||
kernel: [17]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.2
|
||||
filters: 512
|
||||
kernel: [17]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.3
|
||||
filters: 640
|
||||
kernel: [21]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.3
|
||||
filters: 640
|
||||
kernel: [21]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.3
|
||||
filters: 768
|
||||
kernel: [25]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.3
|
||||
filters: 768
|
||||
kernel: [25]
|
||||
repeat: 5
|
||||
residual: true
|
||||
residual_dense: true
|
||||
stride: [1]
|
||||
- dilation: [2]
|
||||
dropout: 0.4
|
||||
filters: 896
|
||||
kernel: [29]
|
||||
repeat: 1
|
||||
residual: false
|
||||
stride: [1]
|
||||
- dilation: [1]
|
||||
dropout: 0.4
|
||||
filters: 1024
|
||||
kernel: [1]
|
||||
repeat: 1
|
||||
residual: false
|
||||
stride: [1]
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: 1024
|
||||
num_classes: 28
|
||||
vocabulary: *labels
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
# _target_: nemo.core.optim.optimizers.Novograd
|
||||
lr: .01
|
||||
# optimizer arguments
|
||||
betas: [0.8, 0.5]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: CosineAnnealing
|
||||
|
||||
# pytorch lightning args
|
||||
# monitor: val_loss
|
||||
# reduce_on_plateau: false
|
||||
|
||||
# Scheduler params
|
||||
warmup_steps: null
|
||||
warmup_ratio: null
|
||||
min_lr: 0.0
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 5
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
create_wandb_logger: False
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,180 @@
|
||||
name: &name "MarbleNet-3x2x64"
|
||||
|
||||
model:
|
||||
sample_rate: 16000
|
||||
repeat: 2
|
||||
dropout: 0.0
|
||||
kernel_size_factor: 1.0
|
||||
|
||||
labels: ['background', 'speech']
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
tarred_shard_strategy: "scatter"
|
||||
augmentor:
|
||||
shift:
|
||||
prob: 1.0
|
||||
min_shift_ms: -5.0
|
||||
max_shift_ms: 5.0
|
||||
white_noise:
|
||||
prob: 1.0
|
||||
min_level: -90
|
||||
max_level: -46
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: False
|
||||
val_loss_idx: 0
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: False
|
||||
test_loss_idx: 0
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMFCCPreprocessor
|
||||
window_size: 0.025
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
n_mels: &n_mels 64
|
||||
n_mfcc: *n_mels
|
||||
n_fft: 512
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
freq_masks: 2
|
||||
time_masks: 2
|
||||
freq_width: 15
|
||||
time_width: 25
|
||||
rect_masks: 5
|
||||
rect_time: 25
|
||||
rect_freq: 15
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [11]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [13]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [29]
|
||||
stride: [1]
|
||||
dilation: [2]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: &enc_final_filters 128
|
||||
repeat: 1
|
||||
kernel: [1]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoderClassification
|
||||
feat_in: *enc_final_filters
|
||||
return_logits: true
|
||||
pooling_type: 'avg'
|
||||
|
||||
optim:
|
||||
name: sgd
|
||||
lr: 0.01
|
||||
# optimizer arguments
|
||||
weight_decay: 0.001
|
||||
momentum: 0.9
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: PolynomialHoldDecayAnnealing
|
||||
# Scheduler params
|
||||
power: 2.0
|
||||
warmup_ratio: 0.05
|
||||
hold_ratio: 0.45
|
||||
min_lr: 0.001
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 150
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
create_wandb_logger: False
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,193 @@
|
||||
name: &name "MatchboxNet-3x1x64-v1"
|
||||
|
||||
model:
|
||||
sample_rate: 16000
|
||||
timesteps: 128
|
||||
repeat: 1
|
||||
dropout: 0.0
|
||||
kernel_size_factor: 1.0
|
||||
|
||||
labels_full: ['bed', 'bird', 'cat', 'dog', 'down', 'eight', 'five', 'four', 'go', 'happy', 'house', 'left', 'marvin',
|
||||
'nine', 'no', 'off', 'on', 'one', 'right', 'seven', 'sheila', 'six', 'stop', 'three', 'tree', 'two', 'up',
|
||||
'wow', 'yes', 'zero']
|
||||
|
||||
labels_subset: ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go", "unknown", "silence"]
|
||||
|
||||
labels: ${model.labels_full}
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
tarred_shard_strategy: "scatter"
|
||||
augmentor:
|
||||
shift:
|
||||
prob: 1.0
|
||||
min_shift_ms: -5.0
|
||||
max_shift_ms: 5.0
|
||||
white_noise:
|
||||
prob: 1.0
|
||||
min_level: -90
|
||||
max_level: -46
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: False
|
||||
val_loss_idx: 0
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: False
|
||||
test_loss_idx: 0
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMFCCPreprocessor
|
||||
window_size: 0.025
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
n_mels: &n_mels 64
|
||||
n_mfcc: *n_mels
|
||||
n_fft: 512
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
freq_masks: 2
|
||||
time_masks: 2
|
||||
freq_width: 15
|
||||
time_width: 25
|
||||
rect_masks: 5
|
||||
rect_time: 25
|
||||
rect_freq: 15
|
||||
|
||||
crop_or_pad_augment:
|
||||
_target_: nemo.collections.asr.modules.CropOrPadSpectrogramAugmentation
|
||||
audio_length: ${model.timesteps}
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [11]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [13]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [29]
|
||||
stride: [1]
|
||||
dilation: [2]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: &enc_final_filters 128
|
||||
repeat: 1
|
||||
kernel: [1]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoderClassification
|
||||
feat_in: *enc_final_filters
|
||||
return_logits: true
|
||||
pooling_type: 'avg'
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
# _target_: nemo.core.optim.optimizers.Novograd
|
||||
lr: 0.05
|
||||
# optimizer arguments
|
||||
betas: [0.95, 0.5]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: PolynomialHoldDecayAnnealing
|
||||
|
||||
# Scheduler params
|
||||
power: 2.0
|
||||
warmup_ratio: 0.05
|
||||
hold_ratio: 0.45
|
||||
min_lr: 0.001
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 200
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
create_wandb_logger: False
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,193 @@
|
||||
name: &name "MatchboxNet-3x1x64-v2"
|
||||
|
||||
model:
|
||||
sample_rate: 16000
|
||||
timesteps: 128
|
||||
repeat: 1
|
||||
dropout: 0.0
|
||||
kernel_size_factor: 1.0
|
||||
|
||||
labels_full: ['visual', 'wow', 'learn', 'backward', 'dog', 'two', 'left', 'happy', 'nine', 'go', 'up', 'bed', 'stop',
|
||||
'one', 'zero', 'tree', 'seven', 'on', 'four', 'bird', 'right', 'eight', 'no', 'six', 'forward', 'house',
|
||||
'marvin', 'sheila', 'five', 'off', 'three', 'down', 'cat', 'follow', 'yes']
|
||||
|
||||
labels_subset: ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go", "unknown", "silence"]
|
||||
|
||||
labels: ${model.labels_full}
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
tarred_shard_strategy: "scatter"
|
||||
augmentor:
|
||||
shift:
|
||||
prob: 1.0
|
||||
min_shift_ms: -5.0
|
||||
max_shift_ms: 5.0
|
||||
white_noise:
|
||||
prob: 1.0
|
||||
min_level: -90
|
||||
max_level: -46
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: False
|
||||
val_loss_idx: 0
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
sample_rate: ${model.sample_rate}
|
||||
labels: ${model.labels}
|
||||
batch_size: 128
|
||||
shuffle: False
|
||||
test_loss_idx: 0
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMFCCPreprocessor
|
||||
window_size: 0.025
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
n_mels: &n_mels 64
|
||||
n_mfcc: *n_mels
|
||||
n_fft: 512
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
freq_masks: 2
|
||||
time_masks: 2
|
||||
freq_width: 15
|
||||
time_width: 25
|
||||
rect_masks: 5
|
||||
rect_time: 25
|
||||
rect_freq: 15
|
||||
|
||||
crop_or_pad_augment:
|
||||
_target_: nemo.collections.asr.modules.CropOrPadSpectrogramAugmentation
|
||||
audio_length: ${model.timesteps}
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [11]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [13]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [15]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 64
|
||||
repeat: ${model.repeat}
|
||||
kernel: [17]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: true
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: 128
|
||||
repeat: 1
|
||||
kernel: [29]
|
||||
stride: [1]
|
||||
dilation: [2]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
separable: true
|
||||
kernel_size_factor: ${model.kernel_size_factor}
|
||||
|
||||
- filters: &enc_final_filters 128
|
||||
repeat: 1
|
||||
kernel: [1]
|
||||
stride: [1]
|
||||
dilation: [1]
|
||||
dropout: ${model.dropout}
|
||||
residual: false
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoderClassification
|
||||
feat_in: *enc_final_filters
|
||||
return_logits: true
|
||||
pooling_type: 'avg'
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
# _target_: nemo.core.optim.optimizers.Novograd
|
||||
lr: 0.05
|
||||
# optimizer arguments
|
||||
betas: [0.95, 0.5]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: PolynomialHoldDecayAnnealing
|
||||
|
||||
# Scheduler params
|
||||
power: 2.0
|
||||
warmup_ratio: 0.05
|
||||
hold_ratio: 0.45
|
||||
min_lr: 0.001
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 200
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
create_wandb_logger: False
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,281 @@
|
||||
name: &name "QuartzNet15x5"
|
||||
|
||||
model:
|
||||
sample_rate: &sample_rate 16000
|
||||
repeat: &repeat 5
|
||||
dropout: &dropout 0.0
|
||||
separable: &separable true
|
||||
labels: &labels [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
|
||||
"n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
trim_silence: True
|
||||
max_duration: 16.7
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
tarred_shard_strategy: "scatter"
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
shuffle: False
|
||||
|
||||
test_ds:
|
||||
manifest_filepath: null
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
shuffle: False
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
normalize: "per_feature"
|
||||
window_size: 0.02
|
||||
sample_rate: *sample_rate
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: &n_mels 64
|
||||
n_fft: 512
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
rect_freq: 50
|
||||
rect_masks: 5
|
||||
rect_time: 120
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: 1
|
||||
residual: false
|
||||
separable: *separable
|
||||
stride: [2]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [39]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [39]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [39]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [51]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [51]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [51]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [63]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [63]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [63]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [75]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [75]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [75]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [2]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [87]
|
||||
repeat: 1
|
||||
residual: false
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: &enc_filters 1024
|
||||
kernel: [1]
|
||||
repeat: 1
|
||||
residual: false
|
||||
stride: [1]
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: *enc_filters
|
||||
num_classes: 28
|
||||
vocabulary: *labels
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
# _target_: nemo.core.optim.optimizers.Novograd
|
||||
lr: .01
|
||||
# optimizer arguments
|
||||
betas: [0.8, 0.5]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: CosineAnnealing
|
||||
|
||||
# pytorch lightning args
|
||||
# monitor: val_loss
|
||||
# reduce_on_plateau: false
|
||||
|
||||
# Scheduler params
|
||||
warmup_steps: null
|
||||
warmup_ratio: null
|
||||
min_lr: 0.0
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 5
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
checkpoint_callback_params:
|
||||
monitor: "val_wer"
|
||||
mode: "min"
|
||||
create_wandb_logger: False
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
@@ -0,0 +1,483 @@
|
||||
name: &name "QuartzNet15x5"
|
||||
|
||||
model:
|
||||
sample_rate: &sample_rate 16000
|
||||
repeat: &repeat 5
|
||||
dropout: &dropout 0.0
|
||||
separable: &separable true
|
||||
labels: &labels [' ', '''', A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R,
|
||||
S, T, U, V, W, X, Y, Z, 㶧, 䶮, 一, 丁, 七, 万, 丈, 三, 上, 下, 不, 与, 丐, 丑, 专, 且, 丕,
|
||||
世, 丘, 丙, 业, 丛, 东, 丝, 丞, 丢, 两, 严, 丧, 个, 丫, 中, 丰, 串, 临, 丸, 丹, 为, 主, 丽, 举, 乃,
|
||||
久, 么, 义, 之, 乌, 乍, 乎, 乏, 乐, 乒, 乓, 乔, 乖, 乘, 乙, 九, 乞, 也, 习, 乡, 书, 买, 乱, 乳, 乾,
|
||||
了, 予, 争, 事, 二, 于, 亏, 云, 互, 五, 井, 亘, 亚, 些, 亟, 亡, 亢, 交, 亥, 亦, 产, 亨, 亩, 享, 京,
|
||||
亭, 亮, 亲, 亳, 亵, 人, 亿, 什, 仁, 仄, 仅, 仆, 仇, 今, 介, 仍, 从, 仑, 仓, 仔, 仕, 他, 仗, 付, 仙,
|
||||
仞, 仟, 仡, 代, 令, 以, 仨, 仪, 们, 仰, 仲, 件, 价, 任, 份, 仿, 企, 伉, 伊, 伍, 伎, 伏, 伐, 休, 众,
|
||||
优, 伙, 会, 伞, 伟, 传, 伢, 伤, 伦, 伪, 伫, 伯, 估, 伴, 伶, 伸, 伺, 似, 伽, 佃, 但, 位, 低, 住, 佐,
|
||||
佑, 体, 何, 佗, 佘, 余, 佚, 佛, 作, 佝, 佟, 你, 佣, 佩, 佬, 佯, 佰, 佳, 佶, 佻, 佼, 使, 侃, 侄, 侈,
|
||||
例, 侍, 侏, 侑, 侗, 供, 依, 侠, 侣, 侥, 侦, 侧, 侨, 侬, 侮, 侯, 侵, 便, 促, 俄, 俊, 俎, 俏, 俐, 俑,
|
||||
俗, 俘, 俚, 保, 俞, 俟, 信, 俨, 俩, 俪, 俭, 修, 俯, 俱, 俸, 俺, 俾, 倌, 倍, 倒, 倔, 倘, 候, 倚, 倜,
|
||||
借, 倡, 倦, 倩, 倪, 倭, 债, 值, 倾, 偃, 假, 偈, 偌, 偎, 偏, 偓, 偕, 做, 停, 健, 偶, 偷, 偻, 偿, 傀,
|
||||
傅, 傍, 傣, 傥, 储, 催, 傲, 傻, 像, 僚, 僧, 僮, 僵, 僻, 儋, 儒, 儡, 儿, 兀, 允, 元, 兄, 充, 兆, 先,
|
||||
光, 克, 免, 兑, 兔, 兖, 党, 兜, 兢, 入, 全, 八, 公, 六, 兮, 兰, 共, 关, 兴, 兵, 其, 具, 典, 兹, 养,
|
||||
兼, 兽, 冀, 内, 冈, 冉, 册, 再, 冒, 冕, 冗, 写, 军, 农, 冠, 冢, 冤, 冥, 冬, 冯, 冰, 冲, 决, 况, 冶,
|
||||
冷, 冻, 冼, 冽, 净, 凄, 准, 凇, 凉, 凋, 凌, 减, 凑, 凛, 凝, 几, 凡, 凤, 凭, 凯, 凰, 凳, 凶, 凸, 凹,
|
||||
出, 击, 函, 凿, 刀, 刁, 刃, 分, 切, 刊, 刍, 刎, 刑, 划, 列, 刘, 则, 刚, 创, 初, 删, 判, 刨, 利, 别,
|
||||
刮, 到, 制, 刷, 券, 刹, 刺, 刻, 刽, 剁, 剂, 剃, 削, 剌, 前, 剐, 剑, 剔, 剖, 剜, 剥, 剧, 剩, 剪, 副,
|
||||
割, 剽, 剿, 劈, 力, 劝, 办, 功, 加, 务, 劣, 动, 助, 努, 劫, 劭, 励, 劲, 劳, 劵, 劾, 势, 勃, 勇, 勉,
|
||||
勋, 勐, 勒, 勘, 募, 勤, 勺, 勾, 勿, 匀, 包, 匆, 匈, 匏, 匕, 化, 北, 匙, 匝, 匠, 匡, 匣, 匪, 匮, 匹,
|
||||
区, 医, 匾, 匿, 十, 千, 升, 午, 卉, 半, 华, 协, 卑, 卒, 卓, 单, 卖, 南, 博, 卜, 卞, 占, 卡, 卢, 卤,
|
||||
卦, 卧, 卫, 卯, 印, 危, 卲, 即, 却, 卵, 卷, 卸, 卿, 厂, 厄, 厅, 历, 厉, 压, 厌, 厕, 厘, 厚, 厝, 原,
|
||||
厢, 厥, 厦, 厨, 厩, 厮, 去, 县, 叁, 参, 又, 叉, 及, 友, 双, 反, 发, 叔, 取, 受, 变, 叙, 叛, 叠, 口,
|
||||
古, 句, 另, 叨, 叩, 只, 叫, 召, 叭, 叮, 可, 台, 叱, 史, 右, 叵, 叶, 号, 司, 叹, 叼, 叽, 吁, 吃, 各,
|
||||
吆, 合, 吉, 吊, 吋, 同, 名, 后, 吏, 吐, 向, 吒, 吓, 吕, 吖, 吗, 君, 吝, 吞, 吟, 吠, 否, 吧, 吨, 吩,
|
||||
含, 听, 吭, 吮, 启, 吱, 吴, 吵, 吸, 吹, 吻, 吼, 吾, 呀, 呃, 呆, 呈, 告, 呐, 呕, 呗, 员, 呛, 呜, 呢,
|
||||
呦, 周, 呱, 呲, 味, 呵, 呷, 呸, 呻, 呼, 命, 咀, 咂, 咄, 咆, 咋, 和, 咎, 咏, 咐, 咒, 咔, 咕, 咖, 咘,
|
||||
咙, 咚, 咝, 咣, 咤, 咦, 咧, 咨, 咩, 咪, 咫, 咬, 咭, 咯, 咱, 咳, 咸, 咻, 咽, 哀, 品, 哂, 哄, 哆, 哇,
|
||||
哈, 哉, 响, 哎, 哐, 哑, 哒, 哔, 哕, 哗, 哟, 哥, 哦, 哨, 哩, 哪, 哭, 哮, 哲, 哺, 哼, 哽, 唁, 唆, 唇,
|
||||
唉, 唏, 唐, 唑, 唛, 唠, 唢, 唤, 唧, 唬, 售, 唯, 唰, 唱, 唳, 唷, 唾, 啃, 啄, 商, 啊, 啕, 啖, 啜, 啡,
|
||||
啤, 啥, 啦, 啧, 啪, 啬, 啰, 啲, 啵, 啶, 啸, 啼, 啾, 喀, 喁, 喂, 喃, 善, 喆, 喇, 喉, 喊, 喋, 喔, 喘,
|
||||
喜, 喝, 喟, 喧, 喱, 喳, 喵, 喷, 喻, 喽, 嗄, 嗅, 嗑, 嗒, 嗓, 嗔, 嗖, 嗜, 嗝, 嗡, 嗣, 嗤, 嗦, 嗨, 嗪,
|
||||
嗫, 嗬, 嗯, 嗲, 嗷, 嗽, 嘀, 嘈, 嘉, 嘎, 嘏, 嘘, 嘛, 嘞, 嘟, 嘣, 嘭, 嘱, 嘲, 嘴, 嘶, 嘹, 嘻, 嘿, 噌,
|
||||
噎, 噗, 噘, 噙, 噜, 噢, 噤, 器, 噩, 噪, 噬, 噱, 噶, 噻, 噼, 嚎, 嚏, 嚓, 嚣, 嚷, 嚼, 囊, 囍, 囔, 囗,
|
||||
囚, 四, 回, 因, 团, 囤, 囧, 囫, 园, 囯, 困, 囱, 围, 囵, 囹, 固, 国, 图, 圃, 圄, 圆, 圈, 土, 圣, 在,
|
||||
圩, 圪, 圭, 地, 圳, 圹, 场, 圻, 圾, 址, 坂, 均, 坊, 坍, 坎, 坏, 坐, 坑, 块, 坚, 坛, 坝, 坞, 坟, 坠,
|
||||
坡, 坤, 坦, 坨, 坩, 坪, 坭, 坯, 坳, 坷, 坻, 垂, 垃, 垄, 垅, 型, 垌, 垒, 垚, 垛, 垡, 垢, 垣, 垤, 垦,
|
||||
垩, 垫, 垭, 垮, 埂, 埃, 埇, 埋, 城, 埔, 埕, 埚, 埝, 域, 埠, 埭, 埸, 培, 基, 堀, 堂, 堃, 堆, 堇, 堕,
|
||||
堡, 堤, 堪, 堰, 堵, 堺, 塌, 塍, 塑, 塔, 塘, 塞, 填, 塬, 塾, 境, 墅, 墉, 墓, 増, 墙, 增, 墟, 墨, 墩,
|
||||
壁, 壑, 壕, 壤, 士, 壬, 壮, 声, 壳, 壶, 壹, 处, 备, 复, 夏, 夔, 夕, 外, 夙, 多, 夜, 够, 大, 天, 太,
|
||||
夫, 夭, 央, 夯, 失, 头, 夷, 夸, 夹, 夺, 奁, 奂, 奄, 奇, 奈, 奉, 奋, 奎, 奏, 契, 奔, 奕, 奖, 套, 奘,
|
||||
奚, 奠, 奢, 奥, 女, 奴, 奶, 奸, 她, 好, 如, 妃, 妄, 妆, 妇, 妈, 妊, 妍, 妒, 妓, 妖, 妙, 妞, 妤, 妥,
|
||||
妨, 妩, 妪, 妫, 妮, 妯, 妲, 妹, 妻, 妾, 姆, 姊, 始, 姐, 姑, 姓, 委, 姗, 姚, 姜, 姝, 姣, 姥, 姨, 姬,
|
||||
姻, 姿, 威, 娃, 娄, 娅, 娆, 娇, 娈, 娉, 娌, 娓, 娘, 娜, 娟, 娠, 娣, 娥, 娩, 娱, 娲, 娴, 娶, 娼, 婀,
|
||||
婆, 婉, 婊, 婕, 婚, 婢, 婧, 婪, 婴, 婵, 婶, 婷, 婺, 婿, 媒, 媚, 媛, 媞, 媲, 媳, 嫁, 嫂, 嫉, 嫌, 嫒,
|
||||
嫔, 嫖, 嫚, 嫡, 嫣, 嫦, 嫩, 嫫, 嬅, 嬉, 嬗, 嬛, 嬴, 嬷, 孀, 子, 孑, 孔, 孕, 字, 存, 孙, 孚, 孛, 孜,
|
||||
孝, 孟, 孢, 季, 孤, 学, 孩, 孪, 孬, 孰, 孱, 孳, 孵, 孺, 孽, 宁, 它, 宅, 宇, 守, 安, 宋, 完, 宏, 宓,
|
||||
宕, 宗, 官, 宙, 定, 宛, 宜, 宝, 实, 宠, 审, 客, 宣, 室, 宥, 宦, 宪, 宫, 宰, 害, 宴, 宵, 家, 宸, 容,
|
||||
宽, 宾, 宿, 寂, 寄, 寅, 密, 寇, 富, 寐, 寒, 寓, 寝, 寞, 察, 寡, 寥, 寨, 寮, 寰, 寸, 对, 寺, 寻, 导,
|
||||
寿, 封, 射, 尅, 将, 尉, 尊, 小, 少, 尔, 尕, 尖, 尘, 尚, 尝, 尤, 尧, 尬, 就, 尴, 尸, 尹, 尺, 尼, 尽,
|
||||
尾, 尿, 局, 屁, 层, 居, 屈, 屉, 届, 屋, 屌, 屎, 屏, 屐, 屑, 展, 属, 屠, 屡, 履, 屯, 山, 屹, 屿, 岁,
|
||||
岂, 岌, 岐, 岑, 岔, 岖, 岗, 岚, 岛, 岩, 岬, 岭, 岱, 岳, 岷, 岸, 峁, 峋, 峒, 峙, 峡, 峥, 峦, 峨, 峪,
|
||||
峭, 峰, 峻, 崂, 崃, 崆, 崇, 崎, 崔, 崖, 崛, 崧, 崩, 崭, 崮, 崴, 崽, 嵇, 嵊, 嵋, 嵌, 嵘, 嵛, 嵩, 嵬,
|
||||
嶂, 嶙, 嶝, 巅, 巍, 川, 州, 巡, 巢, 工, 左, 巧, 巨, 巩, 巫, 差, 己, 已, 巳, 巴, 巷, 巾, 币, 市, 布,
|
||||
帅, 帆, 师, 希, 帐, 帕, 帖, 帘, 帚, 帛, 帜, 帝, 带, 帧, 席, 帮, 帷, 常, 帼, 帽, 幂, 幄, 幅, 幌, 幔,
|
||||
幕, 幡, 幢, 干, 平, 年, 并, 幸, 幺, 幻, 幼, 幽, 广, 庄, 庆, 庇, 床, 序, 庐, 库, 应, 底, 庖, 店, 庙,
|
||||
庚, 府, 庞, 废, 度, 座, 庭, 庵, 庶, 康, 庸, 庹, 庾, 廉, 廊, 廓, 廖, 延, 廷, 建, 开, 异, 弃, 弄, 弈,
|
||||
弊, 弋, 式, 弑, 弓, 引, 弗, 弘, 弛, 弟, 张, 弥, 弦, 弧, 弩, 弭, 弯, 弱, 弹, 强, 弼, 归, 当, 录, 彗,
|
||||
彝, 形, 彤, 彦, 彩, 彪, 彬, 彭, 彰, 影, 彷, 役, 彻, 彼, 往, 征, 径, 待, 徇, 很, 徉, 徊, 律, 徐, 徒,
|
||||
得, 徘, 徙, 徜, 御, 徨, 循, 微, 德, 徽, 心, 必, 忆, 忌, 忍, 忏, 忐, 忑, 忒, 忖, 志, 忘, 忙, 忠, 忡,
|
||||
忤, 忧, 忪, 快, 忱, 念, 忻, 忽, 忿, 怀, 态, 怂, 怄, 怅, 怆, 怎, 怒, 怕, 怖, 怜, 思, 怠, 怡, 急, 怦,
|
||||
性, 怨, 怪, 怫, 怯, 怵, 总, 怼, 怿, 恁, 恃, 恋, 恍, 恐, 恒, 恕, 恙, 恢, 恣, 恤, 恨, 恩, 恪, 恬, 恭,
|
||||
息, 恰, 恳, 恶, 恸, 恺, 恻, 恼, 恿, 悄, 悉, 悌, 悍, 悔, 悖, 悚, 悟, 悠, 患, 悦, 您, 悬, 悭, 悯, 悱,
|
||||
悲, 悴, 悸, 悻, 悼, 情, 惆, 惊, 惋, 惑, 惕, 惚, 惜, 惟, 惠, 惦, 惧, 惨, 惩, 惫, 惬, 惭, 惮, 惯, 惰,
|
||||
想, 惶, 惹, 惺, 愁, 愈, 愉, 意, 愕, 愚, 感, 愣, 愤, 愧, 愫, 愿, 慈, 慌, 慎, 慑, 慕, 慢, 慧, 慨, 慰,
|
||||
慵, 慷, 憋, 憎, 憔, 憧, 憨, 憩, 憬, 憷, 憾, 懂, 懈, 懊, 懋, 懑, 懒, 懦, 懵, 懿, 戈, 戊, 戌, 戍, 戎,
|
||||
戏, 成, 我, 戒, 或, 戗, 战, 戚, 戛, 戟, 截, 戬, 戮, 戳, 戴, 户, 戾, 房, 所, 扁, 扇, 扈, 扉, 手, 才,
|
||||
扎, 扑, 扒, 打, 扔, 托, 扛, 扞, 扣, 扦, 执, 扩, 扪, 扫, 扬, 扭, 扮, 扯, 扰, 扳, 扶, 批, 扼, 找, 承,
|
||||
技, 抄, 抉, 把, 抑, 抒, 抓, 投, 抖, 抗, 折, 抚, 抛, 抠, 抡, 抢, 护, 报, 抨, 披, 抬, 抱, 抵, 抹, 押,
|
||||
抽, 抿, 拂, 拄, 担, 拆, 拇, 拈, 拉, 拌, 拍, 拎, 拐, 拒, 拓, 拔, 拖, 拗, 拘, 拙, 拚, 招, 拜, 拟, 拢,
|
||||
拣, 拥, 拦, 拧, 拨, 择, 括, 拭, 拮, 拯, 拱, 拳, 拴, 拷, 拼, 拽, 拾, 拿, 持, 挂, 指, 按, 挎, 挑, 挖,
|
||||
挚, 挛, 挝, 挞, 挟, 挠, 挡, 挣, 挤, 挥, 挨, 挪, 挫, 振, 挺, 挽, 捂, 捅, 捆, 捉, 捋, 捌, 捍, 捎, 捏,
|
||||
捐, 捕, 捞, 损, 捡, 换, 捣, 捧, 据, 捶, 捷, 捺, 捻, 掀, 掂, 掇, 授, 掉, 掌, 掏, 掐, 排, 掖, 掘, 掠,
|
||||
探, 掣, 接, 控, 推, 掩, 措, 掬, 掮, 掰, 掳, 掴, 掷, 掸, 掺, 揄, 揉, 揍, 描, 提, 插, 握, 揣, 揩, 揪,
|
||||
揭, 援, 揶, 揽, 搀, 搁, 搂, 搅, 搏, 搐, 搓, 搔, 搜, 搞, 搡, 搧, 搪, 搬, 搭, 携, 搽, 摁, 摄, 摆, 摇,
|
||||
摈, 摊, 摒, 摔, 摘, 摞, 摧, 摩, 摸, 摹, 撂, 撅, 撇, 撑, 撒, 撕, 撞, 撤, 撩, 撬, 播, 撮, 撰, 撵, 撸,
|
||||
撺, 撼, 擀, 擂, 擅, 操, 擎, 擒, 擘, 擞, 擢, 擦, 攀, 攒, 攘, 攥, 攫, 支, 收, 攸, 改, 攻, 放, 政, 故,
|
||||
效, 敌, 敏, 救, 敕, 敖, 教, 敛, 敝, 敞, 敢, 散, 敦, 敬, 数, 敲, 整, 敷, 文, 斋, 斌, 斐, 斑, 斓, 斗,
|
||||
料, 斛, 斜, 斟, 斡, 斤, 斥, 斧, 斩, 断, 斯, 新, 方, 施, 旁, 旅, 旋, 旌, 族, 旖, 旗, 无, 既, 日, 旦,
|
||||
旧, 旨, 早, 旬, 旭, 旮, 旯, 旱, 时, 旷, 旺, 旻, 昀, 昂, 昆, 昊, 昌, 明, 昏, 易, 昔, 昕, 昙, 昝, 星,
|
||||
映, 春, 昧, 昨, 昭, 是, 昱, 昴, 昵, 昶, 昼, 显, 晃, 晋, 晌, 晏, 晒, 晓, 晔, 晕, 晖, 晗, 晚, 晞, 晟,
|
||||
晤, 晦, 晨, 普, 景, 晰, 晴, 晶, 晷, 智, 晾, 暂, 暄, 暇, 暌, 暑, 暖, 暗, 暧, 暨, 暮, 暴, 暹, 暾, 曈,
|
||||
曙, 曜, 曝, 曦, 曰, 曲, 曳, 更, 曹, 曼, 曾, 替, 最, 月, 有, 朋, 服, 朐, 朔, 朕, 朗, 望, 朝, 期, 朦,
|
||||
木, 未, 末, 本, 札, 术, 朱, 朴, 朵, 机, 朽, 杀, 杂, 权, 杆, 杈, 杉, 李, 杏, 材, 村, 杓, 杖, 杜, 杞,
|
||||
束, 杠, 条, 来, 杨, 杭, 杯, 杰, 杳, 杵, 杷, 松, 板, 极, 构, 枇, 枉, 枋, 析, 枕, 林, 枚, 果, 枝, 枞,
|
||||
枢, 枣, 枥, 枪, 枫, 枭, 枯, 枰, 枳, 架, 枷, 枸, 柃, 柄, 柏, 某, 柑, 柒, 染, 柔, 柘, 柚, 柜, 柞, 柠,
|
||||
查, 柩, 柬, 柯, 柱, 柳, 柴, 柿, 栀, 栅, 标, 栈, 栋, 栌, 栎, 栏, 树, 栓, 栖, 栗, 校, 栩, 株, 样, 核,
|
||||
根, 格, 栽, 栾, 桁, 桂, 桃, 框, 案, 桉, 桌, 桎, 桐, 桑, 桓, 桔, 桠, 桢, 档, 桥, 桦, 桨, 桩, 桴, 桶,
|
||||
桷, 梁, 梅, 梆, 梏, 梓, 梗, 梢, 梦, 梧, 梨, 梭, 梯, 械, 梳, 梵, 检, 棂, 棉, 棋, 棍, 棒, 棕, 棘, 棚,
|
||||
棠, 棣, 森, 棱, 棵, 棺, 椁, 椅, 椋, 植, 椎, 椒, 椟, 椤, 椭, 椰, 椴, 椹, 椿, 楂, 楔, 楚, 楞, 楠, 楣,
|
||||
楷, 楸, 楼, 概, 榄, 榆, 榈, 榉, 榔, 榕, 榛, 榜, 榨, 榫, 榭, 榴, 榷, 榻, 槃, 槌, 槎, 槐, 槛, 槟, 槭,
|
||||
槽, 槿, 樊, 樟, 模, 樨, 横, 樯, 樱, 樵, 樽, 樾, 橄, 橇, 橐, 橘, 橙, 橡, 橱, 檀, 檐, 檗, 檬, 欠, 次,
|
||||
欢, 欣, 欧, 欲, 欸, 欺, 款, 歆, 歇, 歉, 歌, 歙, 止, 正, 此, 步, 武, 歧, 歩, 歪, 歹, 死, 歼, 殁, 殃,
|
||||
殆, 殇, 殉, 殊, 残, 殒, 殓, 殖, 殚, 殡, 殴, 段, 殷, 殿, 毁, 毂, 毅, 毋, 母, 每, 毒, 毓, 比, 毕, 毗,
|
||||
毙, 毛, 毡, 毫, 毯, 毽, 氏, 民, 氓, 气, 氚, 氛, 氟, 氢, 氤, 氦, 氧, 氨, 氪, 氮, 氯, 氰, 氲, 水, 永,
|
||||
汀, 汁, 求, 汇, 汉, 汊, 汐, 汕, 汗, 汛, 汝, 汞, 江, 池, 污, 汤, 汨, 汩, 汪, 汰, 汲, 汴, 汶, 汹, 汽,
|
||||
汾, 沁, 沂, 沃, 沅, 沈, 沉, 沌, 沏, 沐, 沓, 沙, 沛, 沟, 没, 沢, 沣, 沥, 沦, 沧, 沪, 沫, 沭, 沮, 沱,
|
||||
河, 沸, 油, 治, 沼, 沽, 沾, 沿, 泄, 泉, 泊, 泌, 泓, 泔, 法, 泖, 泗, 泛, 泞, 泠, 泡, 波, 泣, 泥, 注,
|
||||
泪, 泫, 泮, 泯, 泰, 泱, 泳, 泵, 泷, 泸, 泺, 泻, 泼, 泽, 泾, 洁, 洋, 洒, 洗, 洙, 洛, 洞, 津, 洪, 洮,
|
||||
洱, 洲, 洵, 洹, 洺, 活, 洼, 洽, 派, 流, 浃, 浅, 浆, 浇, 浈, 浊, 测, 济, 浏, 浐, 浑, 浒, 浓, 浔, 浙,
|
||||
浚, 浜, 浠, 浣, 浦, 浩, 浪, 浮, 浴, 海, 浸, 涂, 涅, 消, 涉, 涌, 涎, 涑, 涓, 涕, 涛, 涝, 涞, 涟, 涠,
|
||||
涡, 涣, 涤, 润, 涧, 涨, 涩, 涪, 涮, 涯, 液, 涵, 涸, 涿, 淀, 淄, 淅, 淆, 淇, 淋, 淌, 淑, 淖, 淘, 淝,
|
||||
淞, 淡, 淤, 淦, 淫, 淬, 淮, 深, 淳, 混, 淹, 添, 淼, 清, 渊, 渌, 渍, 渎, 渐, 渑, 渔, 渗, 渚, 渝, 渠,
|
||||
渡, 渣, 渤, 渥, 温, 渭, 港, 渲, 渴, 游, 渺, 湃, 湄, 湉, 湍, 湎, 湖, 湘, 湛, 湫, 湾, 湿, 溃, 溅, 溆,
|
||||
溉, 溏, 源, 溜, 溟, 溢, 溥, 溧, 溪, 溯, 溶, 溺, 滁, 滇, 滋, 滑, 滔, 滕, 滘, 滚, 滞, 满, 滢, 滤, 滥,
|
||||
滦, 滨, 滩, 滴, 滹, 漂, 漆, 漉, 漏, 漓, 演, 漕, 漠, 漩, 漪, 漫, 漭, 漯, 漱, 漳, 漾, 潆, 潇, 潋, 潍,
|
||||
潘, 潜, 潞, 潢, 潦, 潭, 潮, 潸, 潺, 潼, 澄, 澈, 澍, 澎, 澜, 澡, 澧, 澳, 澶, 激, 濂, 濑, 濒, 濠, 濡,
|
||||
濮, 濯, 瀑, 瀚, 瀛, 灌, 灏, 灞, 火, 灭, 灯, 灰, 灵, 灶, 灸, 灼, 灾, 灿, 炀, 炅, 炉, 炊, 炎, 炒, 炔,
|
||||
炕, 炖, 炙, 炜, 炫, 炬, 炭, 炮, 炯, 炳, 炷, 炸, 点, 炼, 炽, 烀, 烁, 烂, 烃, 烈, 烊, 烘, 烙, 烛, 烟,
|
||||
烤, 烦, 烧, 烨, 烩, 烫, 烬, 热, 烯, 烷, 烹, 烽, 焉, 焊, 焓, 焕, 焖, 焗, 焘, 焙, 焚, 焦, 焯, 焰, 焱,
|
||||
然, 煊, 煌, 煎, 煜, 煞, 煤, 煦, 照, 煨, 煮, 煲, 煳, 煽, 熄, 熊, 熏, 熔, 熙, 熟, 熠, 熨, 熬, 熵, 熹,
|
||||
燃, 燊, 燎, 燕, 燥, 燮, 爆, 爪, 爬, 爱, 爵, 父, 爷, 爸, 爹, 爽, 片, 版, 牌, 牍, 牒, 牙, 牛, 牟, 牠,
|
||||
牡, 牢, 牧, 物, 牲, 牵, 特, 牺, 牾, 犀, 犁, 犄, 犇, 犊, 犒, 犟, 犬, 犯, 状, 犷, 犸, 犹, 狂, 狄, 狈,
|
||||
狐, 狒, 狗, 狙, 狞, 狠, 狡, 狩, 独, 狭, 狮, 狰, 狱, 狸, 狼, 猁, 猎, 猖, 猛, 猜, 猝, 猥, 猩, 猪, 猫,
|
||||
猬, 献, 猴, 猷, 猹, 猾, 猿, 獒, 獗, 獭, 獾, 玄, 率, 玉, 王, 玑, 玖, 玛, 玟, 玥, 玩, 玫, 玮, 环, 现,
|
||||
玲, 玳, 玷, 玹, 玺, 玻, 珀, 珂, 珈, 珉, 珊, 珍, 珏, 珑, 珙, 珞, 珠, 珥, 班, 珮, 珲, 珺, 球, 琅, 理,
|
||||
琉, 琊, 琏, 琐, 琛, 琢, 琤, 琥, 琦, 琨, 琪, 琬, 琮, 琰, 琳, 琴, 琵, 琶, 琼, 瑁, 瑄, 瑕, 瑙, 瑚, 瑛,
|
||||
瑜, 瑞, 瑟, 瑠, 瑭, 瑰, 瑶, 瑷, 瑾, 璀, 璃, 璇, 璋, 璐, 璞, 璟, 璧, 璨, 瓜, 瓢, 瓣, 瓦, 瓮, 瓯, 瓶,
|
||||
瓷, 甄, 甘, 甚, 甜, 生, 甥, 用, 甩, 甫, 甬, 甭, 田, 由, 甲, 申, 电, 男, 甸, 町, 画, 畅, 畈, 畊, 界,
|
||||
畏, 畔, 留, 畜, 略, 番, 畴, 畸, 畿, 疃, 疆, 疏, 疑, 疖, 疗, 疙, 疚, 疝, 疟, 疡, 疣, 疤, 疫, 疮, 疯,
|
||||
疱, 疲, 疴, 疵, 疸, 疹, 疼, 疽, 疾, 病, 症, 痉, 痊, 痍, 痒, 痔, 痕, 痘, 痛, 痞, 痢, 痣, 痧, 痨, 痪,
|
||||
痫, 痰, 痱, 痴, 痹, 痼, 瘀, 瘁, 瘙, 瘟, 瘠, 瘢, 瘤, 瘦, 瘩, 瘪, 瘫, 瘳, 瘴, 瘸, 瘾, 癌, 癖, 癜, 癞,
|
||||
癣, 癫, 登, 白, 百, 皂, 的, 皆, 皇, 皋, 皎, 皑, 皓, 皖, 皙, 皮, 皱, 皿, 盂, 盅, 盆, 盈, 益, 盎, 盏,
|
||||
盐, 监, 盒, 盔, 盖, 盗, 盘, 盛, 盟, 目, 盯, 盱, 盲, 直, 相, 盹, 盼, 盾, 省, 眈, 眉, 看, 眙, 真, 眠,
|
||||
眨, 眩, 眬, 眯, 眶, 眷, 眸, 眺, 眼, 着, 睁, 睇, 睐, 睑, 睛, 睡, 睢, 督, 睦, 睫, 睬, 睹, 睽, 睾, 睿,
|
||||
瞄, 瞅, 瞌, 瞎, 瞑, 瞒, 瞟, 瞠, 瞥, 瞧, 瞩, 瞪, 瞬, 瞭, 瞰, 瞳, 瞻, 瞿, 矍, 矗, 矛, 矜, 矢, 矣, 知,
|
||||
矩, 矫, 矬, 短, 矮, 石, 矶, 矸, 矽, 矾, 矿, 砀, 码, 砂, 砌, 砍, 砒, 研, 砖, 砚, 砝, 砣, 砥, 砭, 砰,
|
||||
破, 砷, 砸, 砺, 砼, 砾, 础, 硅, 硌, 硒, 硕, 硖, 硚, 硝, 硫, 硬, 确, 硼, 碉, 碌, 碍, 碎, 碑, 碓, 碗,
|
||||
碘, 碚, 碜, 碟, 碣, 碧, 碰, 碱, 碳, 碴, 碾, 磁, 磅, 磊, 磋, 磐, 磕, 磨, 磴, 磷, 磺, 礁, 示, 礼, 社,
|
||||
祀, 祁, 祈, 祉, 祎, 祐, 祖, 祚, 祛, 祝, 神, 祟, 祠, 祢, 祥, 票, 祭, 祯, 祷, 祸, 祺, 禀, 禁, 禄, 禅,
|
||||
福, 禧, 禹, 禺, 离, 禽, 禾, 秀, 私, 秃, 秆, 秉, 秋, 种, 科, 秒, 秘, 租, 秣, 秤, 秦, 秧, 秩, 积, 称,
|
||||
秸, 移, 秽, 稀, 程, 稍, 税, 稔, 稚, 稞, 稠, 稣, 稳, 稷, 稹, 稻, 稼, 稽, 稿, 穆, 穗, 穴, 究, 穷, 穹,
|
||||
空, 穿, 突, 窃, 窄, 窈, 窍, 窑, 窒, 窕, 窖, 窗, 窘, 窜, 窝, 窟, 窠, 窥, 窦, 窨, 窿, 立, 竖, 站, 竞,
|
||||
竟, 章, 竣, 童, 竭, 端, 竹, 竺, 竽, 竿, 笃, 笆, 笈, 笋, 笑, 笔, 笙, 笛, 笠, 符, 笨, 第, 笳, 笸, 笼,
|
||||
等, 筋, 筏, 筐, 筑, 筒, 答, 策, 筛, 筝, 筠, 筱, 筵, 筷, 筹, 签, 简, 箍, 箔, 箕, 算, 管, 箩, 箫, 箭,
|
||||
箱, 箴, 篁, 篆, 篇, 篑, 篓, 篝, 篡, 篦, 篪, 篮, 篱, 篷, 篼, 簇, 簋, 簧, 簪, 簸, 簿, 籁, 籍, 米, 类,
|
||||
籼, 籽, 粉, 粑, 粒, 粕, 粗, 粘, 粟, 粤, 粥, 粪, 粮, 粱, 粲, 粳, 粹, 粼, 粽, 精, 糊, 糕, 糖, 糗, 糙,
|
||||
糟, 糠, 糯, 系, 紊, 素, 索, 紧, 紫, 累, 絮, 綦, 繁, 纂, 纠, 纡, 红, 纣, 纤, 约, 级, 纨, 纪, 纫, 纬,
|
||||
纭, 纯, 纰, 纱, 纲, 纳, 纵, 纶, 纷, 纸, 纹, 纺, 纽, 纾, 线, 绀, 练, 组, 绅, 细, 织, 终, 绉, 绊, 绋,
|
||||
绌, 绍, 绎, 经, 绑, 绒, 结, 绔, 绕, 绘, 给, 绚, 绛, 络, 绝, 绞, 统, 绢, 绣, 绥, 继, 绩, 绪, 绫, 续,
|
||||
绮, 绯, 绰, 绳, 维, 绵, 绷, 绸, 绻, 综, 绽, 绿, 缀, 缄, 缅, 缆, 缇, 缉, 缎, 缓, 缔, 缕, 编, 缘, 缙,
|
||||
缚, 缛, 缜, 缝, 缠, 缢, 缤, 缨, 缩, 缪, 缬, 缭, 缮, 缰, 缱, 缴, 缸, 缺, 罂, 罄, 罐, 网, 罔, 罕, 罗,
|
||||
罚, 罡, 罢, 罩, 罪, 置, 署, 罹, 羁, 羊, 羌, 美, 羔, 羚, 羞, 羡, 群, 羧, 羯, 羲, 羸, 羹, 羽, 羿, 翁,
|
||||
翅, 翊, 翌, 翎, 翔, 翘, 翟, 翠, 翡, 翩, 翰, 翱, 翻, 翼, 耀, 老, 考, 耄, 者, 耆, 耋, 而, 耍, 耐, 耒,
|
||||
耕, 耗, 耘, 耙, 耜, 耪, 耳, 耶, 耷, 耸, 耻, 耽, 耿, 聂, 聆, 聊, 聋, 职, 联, 聘, 聚, 聪, 肃, 肆, 肇,
|
||||
肉, 肋, 肌, 肖, 肘, 肚, 肛, 肝, 肠, 股, 肢, 肤, 肥, 肩, 肪, 肮, 肯, 肱, 育, 肴, 肺, 肾, 肿, 胀, 胁,
|
||||
胃, 胆, 背, 胎, 胖, 胗, 胚, 胛, 胜, 胞, 胡, 胤, 胥, 胧, 胫, 胭, 胯, 胰, 胱, 胳, 胶, 胸, 胺, 能, 脂,
|
||||
脆, 脉, 脊, 脍, 脏, 脐, 脑, 脓, 脖, 脚, 脯, 脱, 脸, 脾, 腆, 腈, 腊, 腋, 腌, 腐, 腑, 腓, 腔, 腕, 腥,
|
||||
腩, 腭, 腮, 腰, 腱, 腴, 腹, 腺, 腻, 腼, 腾, 腿, 膀, 膈, 膊, 膏, 膑, 膛, 膜, 膝, 膨, 膳, 膺, 臀, 臂,
|
||||
臃, 臆, 臊, 臣, 臧, 自, 臬, 臭, 至, 致, 臻, 臼, 舀, 舅, 舆, 舌, 舍, 舐, 舒, 舔, 舛, 舜, 舞, 舟, 航,
|
||||
舫, 般, 舰, 舱, 舵, 舶, 舷, 舸, 船, 艇, 艋, 艘, 艮, 良, 艰, 色, 艳, 艺, 艾, 艿, 节, 芊, 芋, 芍, 芒,
|
||||
芗, 芙, 芜, 芝, 芥, 芦, 芩, 芪, 芬, 芭, 芮, 芯, 花, 芳, 芷, 芸, 芹, 芽, 芾, 苇, 苋, 苍, 苏, 苑, 苓,
|
||||
苔, 苗, 苛, 苞, 苟, 苡, 苣, 若, 苦, 苫, 苯, 英, 苷, 苹, 茁, 茂, 范, 茄, 茅, 茆, 茉, 茌, 茎, 茗, 茛,
|
||||
茜, 茧, 茨, 茫, 茬, 茯, 茱, 茳, 茴, 茵, 茶, 茸, 茹, 茼, 荀, 荃, 荆, 荇, 草, 荏, 荐, 荒, 荔, 荚, 荛,
|
||||
荞, 荟, 荠, 荡, 荣, 荤, 荧, 荨, 荫, 药, 荷, 荸, 荻, 荼, 莅, 莆, 莉, 莎, 莒, 莓, 莘, 莜, 莞, 莠, 莪,
|
||||
莫, 莱, 莲, 莴, 获, 莹, 莺, 莽, 菀, 菁, 菅, 菇, 菊, 菌, 菏, 菖, 菘, 菜, 菠, 菡, 菩, 菱, 菲, 萃, 萄,
|
||||
萋, 萌, 萍, 萎, 萝, 萤, 营, 萦, 萧, 萨, 萱, 萸, 落, 葆, 著, 葚, 葛, 葡, 董, 葩, 葫, 葬, 葱, 葳, 葵,
|
||||
葺, 蒂, 蒋, 蒙, 蒜, 蒯, 蒲, 蒸, 蒿, 蓁, 蓄, 蓉, 蓓, 蓝, 蓟, 蓥, 蓦, 蓬, 蓼, 蔑, 蔓, 蔗, 蔚, 蔡, 蔫,
|
||||
蔬, 蔷, 蔺, 蔻, 蔼, 蔽, 蕃, 蕉, 蕊, 蕙, 蕨, 蕲, 蕴, 蕾, 薄, 薇, 薏, 薛, 薪, 薯, 薰, 薷, 藁, 藉, 藏,
|
||||
藐, 藓, 藕, 藜, 藠, 藤, 藩, 藻, 藿, 蘑, 蘸, 虎, 虏, 虐, 虑, 虔, 虚, 虞, 虫, 虱, 虹, 虻, 虽, 虾, 蚀,
|
||||
蚁, 蚂, 蚊, 蚌, 蚓, 蚕, 蚝, 蚣, 蚤, 蚪, 蚬, 蚯, 蚱, 蚴, 蛀, 蛆, 蛇, 蛉, 蛊, 蛋, 蛎, 蛐, 蛔, 蛙, 蛛,
|
||||
蛟, 蛤, 蛮, 蛰, 蛳, 蛹, 蛾, 蜀, 蜂, 蜃, 蜇, 蜈, 蜊, 蜍, 蜒, 蜓, 蜕, 蜗, 蜘, 蜚, 蜜, 蜡, 蜢, 蜥, 蜱,
|
||||
蜴, 蜷, 蜻, 蜿, 蝇, 蝈, 蝉, 蝌, 蝎, 蝗, 蝙, 蝠, 蝮, 蝴, 蝶, 蝽, 螂, 螃, 螈, 融, 螨, 螳, 螺, 蟀, 蟆,
|
||||
蟊, 蟋, 蟑, 蟒, 蟠, 蟹, 蟾, 蠊, 蠕, 蠡, 蠢, 血, 衅, 行, 衍, 衔, 街, 衙, 衡, 衢, 衣, 补, 表, 衩, 衫,
|
||||
衬, 衮, 衰, 衲, 衷, 袁, 袂, 袄, 袅, 袈, 袋, 袍, 袒, 袖, 袜, 被, 袭, 袱, 裁, 裂, 装, 裆, 裔, 裕, 裘,
|
||||
裙, 裟, 裤, 裨, 裱, 裳, 裴, 裸, 裹, 褂, 褐, 褒, 褓, 褔, 褚, 褛, 褥, 褪, 褴, 褶, 襁, 襄, 襟, 西, 要,
|
||||
覃, 覆, 见, 观, 规, 觅, 视, 览, 觉, 觊, 觎, 觐, 觑, 角, 觞, 解, 觥, 触, 言, 訾, 詹, 誉, 誓, 警, 譬,
|
||||
计, 订, 讣, 认, 讥, 讧, 讨, 让, 讪, 训, 议, 讯, 记, 讲, 讳, 讴, 讶, 讷, 许, 讹, 论, 讼, 讽, 设, 访,
|
||||
诀, 证, 诃, 评, 诅, 识, 诈, 诉, 诊, 诋, 词, 诏, 译, 诓, 试, 诗, 诘, 诙, 诚, 诛, 话, 诞, 诟, 诠, 诡,
|
||||
询, 诣, 诤, 该, 详, 诧, 诩, 诫, 诬, 语, 误, 诱, 诲, 说, 诵, 诶, 请, 诸, 诹, 诺, 读, 诽, 课, 诿, 谀,
|
||||
谁, 调, 谅, 谆, 谈, 谊, 谋, 谌, 谍, 谎, 谏, 谐, 谑, 谓, 谕, 谖, 谘, 谙, 谚, 谛, 谜, 谟, 谢, 谣, 谤,
|
||||
谦, 谧, 谨, 谩, 谬, 谭, 谮, 谯, 谱, 谴, 谶, 谷, 豁, 豆, 豇, 豉, 豌, 豚, 象, 豢, 豪, 豫, 豹, 豺, 貂,
|
||||
貅, 貉, 貌, 貔, 贝, 贞, 负, 贡, 财, 责, 贤, 败, 账, 货, 质, 贩, 贪, 贫, 贬, 购, 贮, 贯, 贰, 贱, 贲,
|
||||
贴, 贵, 贷, 贸, 费, 贺, 贻, 贼, 贾, 贿, 赁, 赂, 赃, 资, 赅, 赈, 赉, 赊, 赋, 赌, 赎, 赏, 赐, 赓, 赔,
|
||||
赖, 赘, 赚, 赛, 赝, 赞, 赠, 赡, 赢, 赣, 赤, 赦, 赫, 走, 赳, 赴, 赵, 赶, 起, 趁, 超, 越, 趋, 趟, 趣,
|
||||
足, 趴, 趵, 趸, 趺, 趾, 跃, 跄, 跆, 跋, 跌, 跎, 跑, 跚, 跛, 距, 跟, 跤, 跨, 跪, 跬, 路, 跳, 践, 跶,
|
||||
跷, 跹, 跺, 跻, 踉, 踊, 踌, 踏, 踝, 踞, 踢, 踩, 踪, 踮, 踯, 踱, 踵, 踹, 踺, 蹁, 蹂, 蹄, 蹈, 蹉, 蹊,
|
||||
蹋, 蹒, 蹚, 蹦, 蹩, 蹬, 蹭, 蹲, 蹴, 蹶, 蹼, 蹿, 躁, 躅, 躇, 躏, 身, 躬, 躯, 躲, 躺, 车, 轧, 轨, 轩,
|
||||
轫, 转, 轮, 软, 轰, 轱, 轲, 轳, 轴, 轶, 轸, 轻, 轼, 载, 轿, 较, 辄, 辅, 辆, 辈, 辉, 辊, 辍, 辐, 辑,
|
||||
输, 辕, 辖, 辗, 辘, 辙, 辛, 辜, 辞, 辟, 辣, 辨, 辩, 辫, 辰, 辱, 边, 辽, 达, 迁, 迂, 迄, 迅, 过, 迈,
|
||||
迎, 运, 近, 返, 还, 这, 进, 远, 违, 连, 迟, 迢, 迥, 迦, 迩, 迪, 迫, 迭, 述, 迷, 迸, 迹, 追, 退, 送,
|
||||
适, 逃, 逅, 逆, 选, 逊, 逋, 逍, 透, 逐, 逑, 递, 途, 逗, 通, 逛, 逝, 逞, 速, 造, 逡, 逢, 逮, 逯, 逵,
|
||||
逸, 逻, 逼, 逾, 遁, 遂, 遇, 遍, 遏, 遐, 遑, 道, 遗, 遛, 遢, 遣, 遥, 遨, 遭, 遮, 遴, 遵, 避, 邀, 邂,
|
||||
邃, 邋, 邑, 邓, 邕, 邙, 邛, 邝, 邡, 邢, 那, 邦, 邪, 邬, 邮, 邯, 邰, 邱, 邳, 邵, 邸, 邹, 邺, 邻, 郁,
|
||||
郅, 郇, 郊, 郎, 郑, 郓, 郜, 郝, 郡, 郧, 部, 郫, 郭, 郯, 郴, 郸, 都, 鄂, 鄙, 鄞, 鄢, 鄱, 酉, 酊, 酋,
|
||||
酌, 配, 酐, 酒, 酗, 酚, 酝, 酞, 酣, 酥, 酩, 酪, 酬, 酮, 酯, 酰, 酱, 酵, 酶, 酷, 酸, 酿, 醇, 醉, 醋,
|
||||
醍, 醐, 醒, 醛, 醺, 采, 釉, 释, 里, 重, 野, 量, 金, 釜, 鉴, 銮, 鏖, 鑫, 钇, 针, 钉, 钊, 钎, 钏, 钐,
|
||||
钒, 钓, 钗, 钙, 钛, 钜, 钝, 钞, 钟, 钠, 钢, 钣, 钥, 钦, 钧, 钨, 钩, 钮, 钯, 钰, 钱, 钲, 钳, 钴, 钵,
|
||||
钻, 钼, 钾, 钿, 铀, 铁, 铂, 铃, 铄, 铅, 铆, 铉, 铋, 铍, 铎, 铐, 铑, 铖, 铛, 铜, 铝, 铟, 铠, 铡, 铣,
|
||||
铤, 铧, 铨, 铩, 铬, 铭, 铮, 铰, 铲, 银, 铷, 铸, 铺, 链, 铿, 销, 锁, 锂, 锄, 锅, 锆, 锈, 锉, 锋, 锌,
|
||||
锏, 锐, 锑, 锒, 错, 锚, 锟, 锡, 锢, 锣, 锤, 锥, 锦, 锨, 锭, 键, 锯, 锰, 锲, 锴, 锵, 锷, 锹, 锻, 镀,
|
||||
镁, 镂, 镇, 镉, 镊, 镌, 镍, 镏, 镐, 镑, 镔, 镕, 镖, 镜, 镣, 镭, 镯, 镰, 镳, 镶, 长, 门, 闩, 闪, 闫,
|
||||
闭, 问, 闯, 闰, 闲, 闳, 间, 闵, 闷, 闸, 闹, 闺, 闻, 闽, 闾, 阀, 阁, 阂, 阄, 阅, 阆, 阉, 阎, 阐, 阑,
|
||||
阔, 阕, 阖, 阙, 阚, 阜, 队, 阡, 阪, 阮, 阱, 防, 阳, 阴, 阵, 阶, 阻, 阿, 陀, 陂, 附, 际, 陆, 陇, 陈,
|
||||
陉, 陋, 陌, 降, 限, 陕, 陛, 陡, 院, 除, 陨, 险, 陪, 陬, 陵, 陶, 陷, 隅, 隆, 隋, 隍, 随, 隐, 隔, 隗,
|
||||
隘, 隙, 障, 隧, 隶, 隼, 隽, 难, 雀, 雁, 雄, 雅, 集, 雇, 雉, 雌, 雍, 雏, 雒, 雕, 雨, 雪, 雯, 雳, 零,
|
||||
雷, 雹, 雾, 需, 霁, 霄, 霆, 震, 霈, 霉, 霍, 霎, 霏, 霓, 霖, 霜, 霞, 霪, 露, 霸, 霹, 霾, 靑, 青, 靓,
|
||||
靖, 静, 靛, 非, 靠, 靡, 面, 革, 靳, 靴, 靶, 鞅, 鞋, 鞍, 鞑, 鞘, 鞠, 鞭, 韦, 韧, 韩, 韫, 韬, 韭, 音,
|
||||
韵, 韶, 页, 顶, 顷, 项, 顺, 须, 顽, 顾, 顿, 颀, 颁, 颂, 预, 颅, 领, 颇, 颈, 颊, 颌, 颍, 颐, 频, 颓,
|
||||
颖, 颗, 题, 颚, 颜, 额, 颠, 颢, 颤, 颦, 颧, 风, 飒, 飓, 飘, 飙, 飚, 飞, 食, 飧, 餍, 餐, 餮, 饕, 饥,
|
||||
饨, 饪, 饭, 饮, 饯, 饰, 饱, 饲, 饴, 饵, 饶, 饷, 饺, 饼, 饽, 饿, 馀, 馁, 馄, 馅, 馆, 馈, 馊, 馋, 馍,
|
||||
馏, 馑, 馒, 馕, 首, 馗, 香, 馥, 馨, 马, 驭, 驮, 驯, 驰, 驱, 驳, 驴, 驶, 驷, 驸, 驹, 驻, 驼, 驾, 驿,
|
||||
骁, 骂, 骄, 骅, 骆, 骇, 骈, 骊, 骋, 验, 骏, 骐, 骑, 骓, 骗, 骚, 骛, 骜, 骝, 骞, 骠, 骡, 骤, 骥, 骨,
|
||||
骰, 骷, 骸, 骺, 骼, 髂, 髅, 髋, 髌, 髓, 高, 髦, 髯, 鬃, 鬓, 鬟, 鬼, 魁, 魂, 魄, 魅, 魇, 魉, 魍, 魏,
|
||||
魔, 魟, 鱼, 鱿, 鲁, 鲅, 鲈, 鲍, 鲑, 鲜, 鲟, 鲠, 鲢, 鲤, 鲨, 鲫, 鲭, 鲳, 鲶, 鲷, 鲸, 鲼, 鳃, 鳄, 鳅,
|
||||
鳌, 鳍, 鳕, 鳖, 鳗, 鳝, 鳞, 鳟, 鸟, 鸠, 鸡, 鸢, 鸣, 鸥, 鸦, 鸩, 鸪, 鸫, 鸭, 鸯, 鸳, 鸵, 鸽, 鸾, 鸿,
|
||||
鹁, 鹂, 鹃, 鹅, 鹉, 鹊, 鹌, 鹏, 鹑, 鹜, 鹞, 鹤, 鹦, 鹧, 鹫, 鹭, 鹰, 鹳, 鹿, 麂, 麋, 麒, 麓, 麝, 麟,
|
||||
麦, 麸, 麻, 麾, 黄, 黍, 黎, 黏, 黑, 黔, 默, 黛, 黝, 黟, 黯, 鼎, 鼓, 鼠, 鼬, 鼹, 鼻, 鼾, 齐, 齿, 龃,
|
||||
龄, 龅, 龈, 龉, 龊, 龌, 龙, 龚, 龟, "\U0002B5AF", "\U0002B689"]
|
||||
|
||||
train_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
trim_silence: True
|
||||
normalize: False
|
||||
max_duration: 16.7
|
||||
shuffle: True
|
||||
is_tarred: False
|
||||
tarred_audio_filepaths: null
|
||||
tarred_shard_strategy: "scatter"
|
||||
|
||||
validation_ds:
|
||||
manifest_filepath: ???
|
||||
sample_rate: 16000
|
||||
normalize: False
|
||||
labels: *labels
|
||||
batch_size: 32
|
||||
shuffle: False
|
||||
|
||||
preprocessor:
|
||||
_target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
|
||||
normalize: "per_feature"
|
||||
window_size: 0.02
|
||||
sample_rate: *sample_rate
|
||||
window_stride: 0.01
|
||||
window: "hann"
|
||||
features: &n_mels 64
|
||||
n_fft: 512
|
||||
frame_splicing: 1
|
||||
dither: 0.00001
|
||||
|
||||
spec_augment:
|
||||
_target_: nemo.collections.asr.modules.SpectrogramAugmentation
|
||||
rect_freq: 50
|
||||
rect_masks: 5
|
||||
rect_time: 120
|
||||
|
||||
encoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASREncoder
|
||||
feat_in: *n_mels
|
||||
activation: relu
|
||||
conv_mask: true
|
||||
|
||||
jasper:
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: 1
|
||||
residual: false
|
||||
separable: *separable
|
||||
stride: [2]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [33]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [39]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [39]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 256
|
||||
kernel: [39]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [51]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [51]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [51]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [63]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [63]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [63]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [75]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [75]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [75]
|
||||
repeat: *repeat
|
||||
residual: true
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [2]
|
||||
dropout: *dropout
|
||||
filters: 512
|
||||
kernel: [87]
|
||||
repeat: 1
|
||||
residual: false
|
||||
separable: *separable
|
||||
stride: [1]
|
||||
|
||||
- dilation: [1]
|
||||
dropout: *dropout
|
||||
filters: &enc_filters 1024
|
||||
kernel: [1]
|
||||
repeat: 1
|
||||
residual: false
|
||||
stride: [1]
|
||||
|
||||
decoder:
|
||||
_target_: nemo.collections.asr.modules.ConvASRDecoder
|
||||
feat_in: *enc_filters
|
||||
num_classes: 5206
|
||||
vocabulary: *labels
|
||||
|
||||
optim:
|
||||
name: novograd
|
||||
# _target_: nemo.core.optim.optimizers.Novograd
|
||||
lr: .01
|
||||
# optimizer arguments
|
||||
betas: [0.8, 0.5]
|
||||
weight_decay: 0.001
|
||||
|
||||
# scheduler setup
|
||||
sched:
|
||||
name: CosineAnnealing
|
||||
|
||||
# pytorch lightning args
|
||||
# monitor: val_loss
|
||||
# reduce_on_plateau: false
|
||||
|
||||
# Scheduler params
|
||||
warmup_steps: null
|
||||
warmup_ratio: null
|
||||
min_lr: 0.0
|
||||
last_epoch: -1
|
||||
|
||||
trainer:
|
||||
gpus: 0 # number of gpus
|
||||
max_epochs: 5
|
||||
max_steps: null # computed at runtime if not set
|
||||
num_nodes: 1
|
||||
accelerator: ddp
|
||||
accumulate_grad_batches: 1
|
||||
checkpoint_callback: False # Provided by exp_manager
|
||||
logger: False # Provided by exp_manager
|
||||
log_every_n_steps: 1 # Interval of logging.
|
||||
val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
|
||||
|
||||
exp_manager:
|
||||
exp_dir: null
|
||||
name: *name
|
||||
create_tensorboard_logger: True
|
||||
create_checkpoint_callback: True
|
||||
checkpoint_callback_params:
|
||||
monitor: "val_wer"
|
||||
mode: "min"
|
||||
create_wandb_logger: False
|
||||
wandb_logger_kwargs:
|
||||
name: null
|
||||
project: null
|
||||
|
||||
hydra:
|
||||
run:
|
||||
dir: .
|
||||
job_logging:
|
||||
root:
|
||||
handlers: null
|
||||
Reference in New Issue
Block a user