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| import nltk | |
| nltk.download('punkt') | |
| nltk.download('punkt_tab') | |
| import torch | |
| torch.manual_seed(0) | |
| torch.backends.cudnn.benchmark = False | |
| torch.backends.cudnn.deterministic = True | |
| import os | |
| import random | |
| random.seed(0) | |
| import numpy as np | |
| np.random.seed(0) | |
| # load packages | |
| import time | |
| import random | |
| import yaml | |
| from munch import Munch | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| import torchaudio | |
| import librosa | |
| from nltk.tokenize import word_tokenize | |
| from models import * | |
| from utils import * | |
| from text_utils import TextCleaner | |
| textclenaer = TextCleaner() | |
| import phonemizer | |
| from Utils.PLBERT.util import load_plbert | |
| from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule | |
| class STTS2: | |
| def __init__(self, config_path, model_folder): | |
| torch.manual_seed(0) | |
| torch.backends.cudnn.benchmark = False | |
| torch.backends.cudnn.deterministic = True | |
| random.seed(0) | |
| np.random.seed(0) | |
| self.device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| self.textcleaner = TextCleaner() | |
| self.to_mel = torchaudio.transforms.MelSpectrogram( | |
| n_mels=80, n_fft=2048, win_length=1200, hop_length=300) | |
| self.mean, self.std = -4, 4 | |
| self.global_phonemizer = phonemizer.backend.EspeakBackend(language='fr-fr', preserve_punctuation=True, with_stress=True) | |
| config = yaml.safe_load(open(config_path)) | |
| self.text_aligner = load_ASR_models(config.get('ASR_path', False), config.get('ASR_config', False)) | |
| self.pitch_extractor = load_F0_models(config.get('F0_path', False)) | |
| self.plbert = load_plbert(config.get('PLBERT_dir', False)) | |
| self.model_params = recursive_munch(config['model_params']) | |
| self.model = build_model(self.model_params, self.text_aligner, self.pitch_extractor, self.plbert) | |
| _ = [self.model[key].eval() for key in self.model] | |
| _ = [self.model[key].to(self.device) for key in self.model] | |
| files = [f for f in os.listdir(model_folder+"/") if f.endswith('.pth')] | |
| sorted_files = sorted(files, key=lambda x: int(x.split('_')[-1].split('.')[0])) | |
| print("sorted_files:", sorted_files) | |
| params_whole = torch.load(model_folder+"/" + sorted_files[-1], map_location='cpu') | |
| params = params_whole['net'] | |
| for key in self.model: | |
| if key in params: | |
| print('%s loaded' % key) | |
| try: | |
| self.model[key].load_state_dict(params[key]) | |
| except: | |
| from collections import OrderedDict | |
| state_dict = params[key] | |
| new_state_dict = OrderedDict() | |
| for k, v in state_dict.items(): | |
| name = k[7:] # remove `module.` | |
| new_state_dict[name] = v | |
| self.model[key].load_state_dict(new_state_dict, strict=False) | |
| _ = [self.model[key].eval() for key in self.model] | |
| self.sampler = DiffusionSampler( | |
| self.model.diffusion.diffusion, | |
| sampler=ADPM2Sampler(), | |
| sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), | |
| clamp=False | |
| ) | |
| def length_to_mask(self, lengths): | |
| mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) | |
| mask = torch.gt(mask + 1, lengths.unsqueeze(1)) | |
| return mask | |
| def preprocess(self, wave): | |
| wave_tensor = torch.from_numpy(wave).float() | |
| mel_tensor = self.to_mel(wave_tensor) | |
| mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - self.mean) / self.std | |
| return mel_tensor | |
| def compute_style(self, path): | |
| wave, sr = librosa.load(path, sr=24000) | |
| audio, index = librosa.effects.trim(wave, top_db=30) | |
| if sr != 24000: | |
| audio = librosa.resample(audio, sr, 24000) | |
| mel_tensor = self.preprocess(audio).to(self.device) | |
| with torch.no_grad(): | |
| ref_s = self.model.style_encoder(mel_tensor.unsqueeze(1)) | |
| ref_p = self.model.predictor_encoder(mel_tensor.unsqueeze(1)) | |
| return torch.cat([ref_s, ref_p], dim=1) | |
| def inference(self, text, ref_s, alpha=0.3, beta=0.7, diffusion_steps=5, embedding_scale=1): | |
| text = text.strip() | |
| ps = self.global_phonemizer.phonemize([text]) | |
| ps = word_tokenize(ps[0]) | |
| ps = ' '.join(ps) | |
| tokens = self.textcleaner(ps) | |
| tokens.insert(0, 0) | |
| tokens = torch.LongTensor(tokens).to(self.device).unsqueeze(0) | |
| print("tokens:", tokens) | |
| with torch.no_grad(): | |
| input_lengths = torch.LongTensor([tokens.shape[-1]]).to(self.device) | |
| text_mask = self.length_to_mask(input_lengths).to(self.device) | |
| t_en = self.model.text_encoder(tokens, input_lengths, text_mask) | |
| bert_dur = self.model.bert(tokens, attention_mask=(~text_mask).int()) | |
| d_en = self.model.bert_encoder(bert_dur).transpose(-1, -2) | |
| s_pred = self.sampler( | |
| noise=torch.randn((1, 256)).unsqueeze(1).to(self.device), | |
| embedding=bert_dur, | |
| embedding_scale=embedding_scale, | |
| features=ref_s, | |
| num_steps=diffusion_steps | |
| ).squeeze(1) | |
| s = s_pred[:, 128:] | |
| ref = s_pred[:, :128] | |
| ref = alpha * ref + (1 - alpha) * ref_s[:, :128] | |
| s = beta * s + (1 - beta) * ref_s[:, 128:] | |
| d = self.model.predictor.text_encoder(d_en, s, input_lengths, text_mask) | |
| x, _ = self.model.predictor.lstm(d) | |
| duration = self.model.predictor.duration_proj(x) | |
| duration = torch.sigmoid(duration).sum(axis=-1) | |
| pred_dur = torch.round(duration.squeeze()).clamp(min=1) | |
| pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data)) | |
| c_frame = 0 | |
| for i in range(pred_aln_trg.size(0)): | |
| pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1 | |
| c_frame += int(pred_dur[i].data) | |
| en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(self.device)) | |
| if self.model_params.decoder.type == "hifigan": | |
| asr_new = torch.zeros_like(en) | |
| asr_new[:, :, 0] = en[:, :, 0] | |
| asr_new[:, :, 1:] = en[:, :, 0:-1] | |
| en = asr_new | |
| F0_pred, N_pred = self.model.predictor.F0Ntrain(en, s) | |
| asr = (t_en @ pred_aln_trg.unsqueeze(0).to(self.device)) | |
| if self.model_params.decoder.type == "hifigan": | |
| asr_new = torch.zeros_like(asr) | |
| asr_new[:, :, 0] = asr[:, :, 0] | |
| asr_new[:, :, 1:] = asr[:, :, 0:-1] | |
| asr = asr_new | |
| out = self.model.decoder(asr, F0_pred, N_pred, ref.squeeze().unsqueeze(0)) | |
| return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed laterclass STTS2: | |
| def length_to_mask(self, lengths): | |
| mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) | |
| mask = torch.gt(mask + 1, lengths.unsqueeze(1)) | |
| return mask | |
| def preprocess(self, wave): | |
| wave_tensor = torch.from_numpy(wave).float() | |
| mel_tensor = self.to_mel(wave_tensor) | |
| mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - self.mean) / self.std | |
| return mel_tensor | |
| def compute_style(self, path): | |
| wave, sr = librosa.load(path, sr=24000) | |
| audio, index = librosa.effects.trim(wave, top_db=30) | |
| if sr != 24000: | |
| audio = librosa.resample(audio, sr, 24000) | |
| mel_tensor = self.preprocess(audio).to(self.device) | |
| with torch.no_grad(): | |
| ref_s = self.model.style_encoder(mel_tensor.unsqueeze(1)) | |
| ref_p = self.model.predictor_encoder(mel_tensor.unsqueeze(1)) | |
| return torch.cat([ref_s, ref_p], dim=1) | |
| def inference(self, text, ref_s, alpha=0.3, beta=0.7, diffusion_steps=5, embedding_scale=1): | |
| text = text.strip() | |
| ps = self.global_phonemizer.phonemize([text]) | |
| ps[0] = ps[0].replace("(en)", "").replace("(fr)", "") | |
| ps = word_tokenize(ps[0]) | |
| ps = ' '.join(ps) | |
| tokens = self.textcleaner(ps) | |
| tokens.insert(0, 0) | |
| tokens = torch.LongTensor(tokens).to(self.device).unsqueeze(0) | |
| with torch.no_grad(): | |
| input_lengths = torch.LongTensor([tokens.shape[-1]]).to(self.device) | |
| text_mask = self.length_to_mask(input_lengths).to(self.device) | |
| t_en = self.model.text_encoder(tokens, input_lengths, text_mask) | |
| bert_dur = self.model.bert(tokens, attention_mask=(~text_mask).int()) | |
| d_en = self.model.bert_encoder(bert_dur).transpose(-1, -2) | |
| s_pred = self.sampler( | |
| noise=torch.randn((1, 256)).unsqueeze(1).to(self.device), | |
| embedding=bert_dur, | |
| embedding_scale=embedding_scale, | |
| features=ref_s, | |
| num_steps=diffusion_steps | |
| ).squeeze(1) | |
| s = s_pred[:, 128:] | |
| ref = s_pred[:, :128] | |
| ref = alpha * ref + (1 - alpha) * ref_s[:, :128] | |
| s = beta * s + (1 - beta) * ref_s[:, 128:] | |
| d = self.model.predictor.text_encoder(d_en, s, input_lengths, text_mask) | |
| x, _ = self.model.predictor.lstm(d) | |
| duration = self.model.predictor.duration_proj(x) | |
| duration = torch.sigmoid(duration).sum(axis=-1) | |
| pred_dur = torch.round(duration.squeeze()).clamp(min=1) | |
| pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data)) | |
| c_frame = 0 | |
| for i in range(pred_aln_trg.size(0)): | |
| pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1 | |
| c_frame += int(pred_dur[i].data) | |
| en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(self.device)) | |
| if self.model_params.decoder.type == "hifigan": | |
| asr_new = torch.zeros_like(en) | |
| asr_new[:, :, 0] = en[:, :, 0] | |
| asr_new[:, :, 1:] = en[:, :, 0:-1] | |
| en = asr_new | |
| F0_pred, N_pred = self.model.predictor.F0Ntrain(en, s) | |
| asr = (t_en @ pred_aln_trg.unsqueeze(0).to(self.device)) | |
| if self.model_params.decoder.type == "hifigan": | |
| asr_new = torch.zeros_like(asr) | |
| asr_new[:, :, 0] = asr[:, :, 0] | |
| asr_new[:, :, 1:] = asr[:, :, 0:-1] | |
| asr = asr_new | |
| out = self.model.decoder(asr, F0_pred, N_pred, ref.squeeze().unsqueeze(0)) | |
| return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later | |