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Running
on
T4
| from cached_path import cached_path | |
| # from dp.phonemizer import Phonemizer | |
| print("NLTK") | |
| import nltk | |
| nltk.download('punkt') | |
| print("SCIPY") | |
| from scipy.io.wavfile import write | |
| print("TORCH STUFF") | |
| import torch | |
| print("START") | |
| torch.manual_seed(0) | |
| torch.backends.cudnn.benchmark = False | |
| torch.backends.cudnn.deterministic = True | |
| import random | |
| random.seed(0) | |
| import numpy as np | |
| np.random.seed(0) | |
| # load packages | |
| import time | |
| import random | |
| import yaml | |
| from munch import Munch | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| import torchaudio | |
| import librosa | |
| from nltk.tokenize import word_tokenize | |
| from models import * | |
| from utils import * | |
| from text_utils import TextCleaner | |
| textclenaer = TextCleaner() | |
| to_mel = torchaudio.transforms.MelSpectrogram( | |
| n_mels=80, n_fft=2048, win_length=1200, hop_length=300) | |
| mean, std = -4, 4 | |
| def length_to_mask(lengths): | |
| mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) | |
| mask = torch.gt(mask+1, lengths.unsqueeze(1)) | |
| return mask | |
| def preprocess(wave): | |
| wave_tensor = torch.from_numpy(wave).float() | |
| mel_tensor = to_mel(wave_tensor) | |
| mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std | |
| return mel_tensor | |
| def compute_style(path): | |
| wave, sr = librosa.load(path, sr=24000) | |
| audio, index = librosa.effects.trim(wave, top_db=30) | |
| if sr != 24000: | |
| audio = librosa.resample(audio, sr, 24000) | |
| mel_tensor = preprocess(audio).to(device) | |
| with torch.no_grad(): | |
| ref_s = model.style_encoder(mel_tensor.unsqueeze(1)) | |
| ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1)) | |
| return torch.cat([ref_s, ref_p], dim=1) | |
| device = 'cpu' | |
| if torch.cuda.is_available(): | |
| device = 'cuda' | |
| elif torch.backends.mps.is_available(): | |
| print("MPS would be available but cannot be used rn") | |
| # device = 'mps' | |
| # config = yaml.safe_load(open("Models/LibriTTS/config.yml")) | |
| config = yaml.safe_load(open(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/config.yml")))) | |
| # load pretrained ASR model | |
| ASR_config = config.get('ASR_config', False) | |
| ASR_path = config.get('ASR_path', False) | |
| text_aligner = load_ASR_models(ASR_path, ASR_config) | |
| # load pretrained F0 model | |
| F0_path = config.get('F0_path', False) | |
| pitch_extractor = load_F0_models(F0_path) | |
| # load BERT model | |
| from Utils.PLBERT.util import load_plbert | |
| BERT_path = config.get('PLBERT_dir', False) | |
| plbert = load_plbert(BERT_path) | |
| model_params = recursive_munch(config['model_params']) | |
| model = build_model(model_params, text_aligner, pitch_extractor, plbert) | |
| _ = [model[key].eval() for key in model] | |
| _ = [model[key].to(device) for key in model] | |
| # params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu') | |
| params_whole = torch.load(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/epochs_2nd_00020.pth")), map_location='cpu') | |
| params = params_whole['net'] | |
| for key in model: | |
| if key in params: | |
| print('%s loaded' % key) | |
| try: | |
| model[key].load_state_dict(params[key]) | |
| except: | |
| from collections import OrderedDict | |
| state_dict = params[key] | |
| new_state_dict = OrderedDict() | |
| for k, v in state_dict.items(): | |
| name = k[7:] # remove `module.` | |
| new_state_dict[name] = v | |
| # load params | |
| model[key].load_state_dict(new_state_dict, strict=False) | |
| # except: | |
| # _load(params[key], model[key]) | |
| _ = [model[key].eval() for key in model] | |
| from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule | |
| sampler = DiffusionSampler( | |
| model.diffusion.diffusion, | |
| sampler=ADPM2Sampler(), | |
| sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters | |
| clamp=False | |
| ) | |
| voicelist = ['f-us-1', 'f-us-2', 'f-us-3', 'f-us-4', 'm-us-1', 'm-us-2', 'm-us-3', 'm-us-4'] | |
| voices = {} | |
| # todo: cache computed style, load using pickle | |
| for v in voicelist: | |
| print(f"Loading voice {v}") | |
| voices[v] = compute_style(f'voices/{v}.wav') | |
| import pickle | |
| with open('voices.pkl', 'wb') as f: | |
| pickle.dump(voices, f) |