mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-11 21:41:18 +00:00
Justin John
225 lines
7.6 KiB
Python
225 lines
7.6 KiB
Python
from cached_path import cached_path
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import torch
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torch.manual_seed(0)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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import random
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random.seed(0)
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import numpy as np
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np.random.seed(0)
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import nltk
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nltk.download('punkt')
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# load packages
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import time
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import random
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import yaml
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from munch import Munch
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import numpy as np
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import torch
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from torch import nn
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import torch.nn.functional as F
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import torchaudio
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import librosa
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from nltk.tokenize import word_tokenize
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from models import *
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from utils import *
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from text_utils import TextCleaner
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textclenaer = TextCleaner()
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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to_mel = torchaudio.transforms.MelSpectrogram(
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n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
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mean, std = -4, 4
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def length_to_mask(lengths):
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mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
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mask = torch.gt(mask+1, lengths.unsqueeze(1))
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return mask
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def preprocess(wave):
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wave_tensor = torch.from_numpy(wave).float()
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mel_tensor = to_mel(wave_tensor)
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mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
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return mel_tensor
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def compute_style(ref_dicts):
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reference_embeddings = {}
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for key, path in ref_dicts.items():
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wave, sr = librosa.load(path, sr=24000)
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audio, index = librosa.effects.trim(wave, top_db=30)
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if sr != 24000:
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audio = librosa.resample(audio, sr, 24000)
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mel_tensor = preprocess(audio).to(device)
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with torch.no_grad():
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ref = model.style_encoder(mel_tensor.unsqueeze(1))
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reference_embeddings[key] = (ref.squeeze(1), audio)
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return reference_embeddings
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# load phonemizer
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import phonemizer
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global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True, words_mismatch='ignore')
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# phonemizer = Phonemizer.from_checkpoint(str(cached_path('https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/en_us_cmudict_ipa_forward.pt')))
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config = yaml.safe_load(open(str(cached_path('hf://yl4579/StyleTTS2-LJSpeech/Models/LJSpeech/config.yml'))))
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# load pretrained ASR model
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ASR_config = config.get('ASR_config', False)
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ASR_path = config.get('ASR_path', False)
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text_aligner = load_ASR_models(ASR_path, ASR_config)
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# load pretrained F0 model
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F0_path = config.get('F0_path', False)
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pitch_extractor = load_F0_models(F0_path)
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# load BERT model
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from Utils.PLBERT.util import load_plbert
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BERT_path = config.get('PLBERT_dir', False)
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plbert = load_plbert(BERT_path)
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model = build_model(recursive_munch(config['model_params']), text_aligner, pitch_extractor, plbert)
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_ = [model[key].eval() for key in model]
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_ = [model[key].to(device) for key in model]
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# params_whole = torch.load("Models/LJSpeech/epoch_2nd_00100.pth", map_location='cpu')
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params_whole = torch.load(str(cached_path('hf://yl4579/StyleTTS2-LJSpeech/Models/LJSpeech/epoch_2nd_00100.pth')), map_location='cpu')
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params = params_whole['net']
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for key in model:
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if key in params:
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print('%s loaded' % key)
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try:
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model[key].load_state_dict(params[key])
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except:
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from collections import OrderedDict
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state_dict = params[key]
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new_state_dict = OrderedDict()
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for k, v in state_dict.items():
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name = k[7:] # remove `module.`
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new_state_dict[name] = v
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# load params
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model[key].load_state_dict(new_state_dict, strict=False)
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# except:
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# _load(params[key], model[key])
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_ = [model[key].eval() for key in model]
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from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
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sampler = DiffusionSampler(
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model.diffusion.diffusion,
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sampler=ADPM2Sampler(),
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sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
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clamp=False
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)
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def inference(text, noise, diffusion_steps=5, embedding_scale=1):
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text = text.strip()
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text = text.replace('"', '')
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ps = global_phonemizer.phonemize([text])
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ps = word_tokenize(ps[0])
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ps = ' '.join(ps)
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tokens = textclenaer(ps)
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tokens.insert(0, 0)
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tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
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with torch.no_grad():
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input_lengths = torch.LongTensor([tokens.shape[-1]]).to(tokens.device)
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text_mask = length_to_mask(input_lengths).to(tokens.device)
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t_en = model.text_encoder(tokens, input_lengths, text_mask)
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bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
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d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
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s_pred = sampler(noise,
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embedding=bert_dur[0].unsqueeze(0), num_steps=diffusion_steps,
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embedding_scale=embedding_scale).squeeze(0)
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s = s_pred[:, 128:]
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ref = s_pred[:, :128]
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d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
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x, _ = model.predictor.lstm(d)
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duration = model.predictor.duration_proj(x)
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duration = torch.sigmoid(duration).sum(axis=-1)
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pred_dur = torch.round(duration.squeeze()).clamp(min=1)
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pred_dur[-1] += 5
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pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
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c_frame = 0
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for i in range(pred_aln_trg.size(0)):
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pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
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c_frame += int(pred_dur[i].data)
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# encode prosody
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en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
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F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
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out = model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(device)),
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F0_pred, N_pred, ref.squeeze().unsqueeze(0))
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return out.squeeze().cpu().numpy()
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def LFinference(text, s_prev, noise, alpha=0.7, diffusion_steps=5, embedding_scale=1):
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text = text.strip()
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text = text.replace('"', '')
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ps = global_phonemizer.phonemize([text])
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ps = word_tokenize(ps[0])
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ps = ' '.join(ps)
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tokens = textclenaer(ps)
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tokens.insert(0, 0)
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tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
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with torch.no_grad():
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input_lengths = torch.LongTensor([tokens.shape[-1]]).to(tokens.device)
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text_mask = length_to_mask(input_lengths).to(tokens.device)
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t_en = model.text_encoder(tokens, input_lengths, text_mask)
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bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
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d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
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s_pred = sampler(noise,
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embedding=bert_dur[0].unsqueeze(0), num_steps=diffusion_steps,
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embedding_scale=embedding_scale).squeeze(0)
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if s_prev is not None:
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# convex combination of previous and current style
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s_pred = alpha * s_prev + (1 - alpha) * s_pred
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s = s_pred[:, 128:]
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ref = s_pred[:, :128]
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d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
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x, _ = model.predictor.lstm(d)
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duration = model.predictor.duration_proj(x)
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duration = torch.sigmoid(duration).sum(axis=-1)
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pred_dur = torch.round(duration.squeeze()).clamp(min=1)
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pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
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c_frame = 0
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for i in range(pred_aln_trg.size(0)):
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pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
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c_frame += int(pred_dur[i].data)
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# encode prosody
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en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
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F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
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out = model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(device)),
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F0_pred, N_pred, ref.squeeze().unsqueeze(0))
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return out.squeeze().cpu().numpy(), s_pred |