mirror of https://github.com/coqui-ai/TTS.git
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
"""Search a good noise schedule for WaveGrad for a given number of inferece iterations"""
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import argparse
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from itertools import product as cartesian_product
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.io import load_config
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from TTS.vocoder.datasets.preprocess import load_wav_data
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from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset
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from TTS.vocoder.utils.generic_utils import setup_generator
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_path', type=str, help='Path to model checkpoint.')
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parser.add_argument('--config_path', type=str, help='Path to model config file.')
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parser.add_argument('--data_path', type=str, help='Path to data directory.')
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parser.add_argument('--output_path', type=str, help='path for output file including file name and extension.')
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parser.add_argument('--num_iter', type=int, help='Number of model inference iterations that you like to optimize noise schedule for.')
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parser.add_argument('--use_cuda', type=bool, help='enable/disable CUDA.')
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parser.add_argument('--num_samples', type=int, default=1, help='Number of datasamples used for inference.')
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parser.add_argument('--search_depth', type=int, default=3, help='Search granularity. Increasing this increases the run-time exponentially.')
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# load config
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args = parser.parse_args()
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config = load_config(args.config_path)
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# setup audio processor
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ap = AudioProcessor(**config.audio)
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# load dataset
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_, train_data = load_wav_data(args.data_path, 0)
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train_data = train_data[:args.num_samples]
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dataset = WaveGradDataset(ap=ap,
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items=train_data,
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seq_len=-1,
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hop_len=ap.hop_length,
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pad_short=config.pad_short,
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conv_pad=config.conv_pad,
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is_training=True,
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return_segments=False,
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use_noise_augment=False,
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use_cache=False,
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verbose=True)
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loader = DataLoader(
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dataset,
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batch_size=1,
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shuffle=False,
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collate_fn=dataset.collate_full_clips,
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drop_last=False,
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num_workers=config.num_loader_workers,
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pin_memory=False)
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# setup the model
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model = setup_generator(config)
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if args.use_cuda:
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model.cuda()
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# setup optimization parameters
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base_values = sorted(10 * np.random.uniform(size=args.search_depth))
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print(base_values)
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exponents = 10 ** np.linspace(-6, -1, num=args.num_iter)
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best_error = float('inf')
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best_schedule = None
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total_search_iter = len(base_values)**args.num_iter
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for base in tqdm(cartesian_product(base_values, repeat=args.num_iter), total=total_search_iter):
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beta = exponents * base
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model.compute_noise_level(beta)
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for data in loader:
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mel, audio = data
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y_hat = model.inference(mel.cuda() if args.use_cuda else mel)
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if args.use_cuda:
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y_hat = y_hat.cpu()
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y_hat = y_hat.numpy()
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mel_hat = []
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for i in range(y_hat.shape[0]):
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m = ap.melspectrogram(y_hat[i, 0])[:, :-1]
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mel_hat.append(torch.from_numpy(m))
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mel_hat = torch.stack(mel_hat)
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mse = torch.sum((mel - mel_hat) ** 2).mean()
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if mse.item() < best_error:
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best_error = mse.item()
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best_schedule = {'beta': beta}
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print(f" > Found a better schedule. - MSE: {mse.item()}")
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np.save(args.output_path, best_schedule)
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