142 lines
5.4 KiB
Python
142 lines
5.4 KiB
Python
import os
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import pathlib
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import random
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import audio2numpy
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import numpy as np
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import torch
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import torch.utils.data
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import torch.nn.functional as F
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from tqdm import tqdm
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import models.tacotron2.layers as layers
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from data.audio.nv_tacotron_dataset import load_mozilla_cv, load_voxpopuli
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from models.tacotron2.taco_utils import load_wav_to_torch, load_filepaths_and_text
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from models.tacotron2.text import text_to_sequence
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from utils.util import opt_get
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def get_similar_files_libritts(filename):
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filedir = os.path.dirname(filename)
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return list(pathlib.Path(filedir).glob('*.wav'))
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class StopPredictionDataset(torch.utils.data.Dataset):
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"""
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1) loads audio,text pairs
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2) normalizes text and converts them to sequences of one-hot vectors
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3) computes mel-spectrograms from audio files.
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"""
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def __init__(self, hparams):
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self.path = hparams['path']
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if not isinstance(self.path, list):
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self.path = [self.path]
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fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
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if not isinstance(fetcher_mode, list):
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fetcher_mode = [fetcher_mode]
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assert len(self.path) == len(fetcher_mode)
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self.audiopaths_and_text = []
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for p, fm in zip(self.path, fetcher_mode):
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if fm == 'lj' or fm == 'libritts':
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fetcher_fn = load_filepaths_and_text
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self.get_similar_files = get_similar_files_libritts
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elif fm == 'voxpopuli':
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fetcher_fn = load_voxpopuli
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self.get_similar_files = None # TODO: Fix.
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else:
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raise NotImplementedError()
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self.audiopaths_and_text.extend(fetcher_fn(p))
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self.sampling_rate = hparams.sampling_rate
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self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
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self.stft = layers.TacotronSTFT(
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hparams.filter_length, hparams.hop_length, hparams.win_length,
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hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
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hparams.mel_fmax)
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random.seed(hparams.seed)
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random.shuffle(self.audiopaths_and_text)
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self.max_mel_len = opt_get(hparams, ['max_mel_length'], None)
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self.max_text_len = opt_get(hparams, ['max_text_length'], None)
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def get_mel(self, filename):
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filename = str(filename)
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if filename.endswith('.wav'):
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audio, sampling_rate = load_wav_to_torch(filename)
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else:
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audio, sampling_rate = audio2numpy.audio_from_file(filename)
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audio = torch.tensor(audio)
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if sampling_rate != self.input_sample_rate:
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if sampling_rate < self.input_sample_rate:
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print(f'{filename} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.input_sample_rate}. This is not a good idea.')
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audio_norm = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.input_sample_rate/sampling_rate, mode='nearest', recompute_scale_factor=False).squeeze()
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else:
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audio_norm = audio
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if audio_norm.std() > 1:
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print(f"Something is very wrong with the given audio. std_dev={audio_norm.std()}. file={filename}")
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return None
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audio_norm.clip_(-1, 1)
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audio_norm = audio_norm.unsqueeze(0)
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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if self.input_sample_rate != self.sampling_rate:
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ratio = self.sampling_rate / self.input_sample_rate
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audio_norm = torch.nn.functional.interpolate(audio_norm.unsqueeze(0), scale_factor=ratio, mode='area').squeeze(0)
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melspec = self.stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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return melspec
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def __getitem__(self, index):
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path = self.audiopaths_and_text[index][0]
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similar_files = self.get_similar_files(path)
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mel = self.get_mel(path)
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terms = torch.zeros(mel.shape[1])
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terms[-1] = 1
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while mel.shape[-1] < self.max_mel_len:
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another_file = random.choice(similar_files)
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another_mel = self.get_mel(another_file)
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oterms = torch.zeros(another_mel.shape[1])
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oterms[-1] = 1
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mel = torch.cat([mel, another_mel], dim=-1)
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terms = torch.cat([terms, oterms], dim=-1)
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mel = mel[:, :self.max_mel_len]
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terms = terms[:self.max_mel_len]
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return {
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'padded_mel': mel,
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'termination_mask': terms,
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}
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def __len__(self):
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return len(self.audiopaths_and_text)
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if __name__ == '__main__':
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params = {
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'mode': 'stop_prediction',
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'path': 'E:\\audio\\LibriTTS\\train-clean-360_list.txt',
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'phase': 'train',
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'n_workers': 0,
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'batch_size': 16,
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'fetcher_mode': 'libritts',
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'max_mel_length': 800,
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#'return_wavs': True,
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#'input_sample_rate': 22050,
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#'sampling_rate': 8000
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}
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from data import create_dataset, create_dataloader
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ds, c = create_dataset(params, return_collate=True)
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dl = create_dataloader(ds, params, collate_fn=c, shuffle=True)
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i = 0
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m = None
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for k in range(1000):
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for i, b in tqdm(enumerate(dl)):
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continue
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pm = b['padded_mel']
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pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
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m = pm if m is None else torch.cat([m, pm], dim=0)
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print(m.mean(), m.std()) |