DL-Art-School/codes/data/audio/stop_prediction_dataset.py

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2021-08-17 04:51:53 +00:00
import os
import pathlib
import random
import audio2numpy
import numpy as np
import torch
import torch.utils.data
import torch.nn.functional as F
from tqdm import tqdm
import models.tacotron2.layers as layers
from data.audio.nv_tacotron_dataset import load_mozilla_cv, load_voxpopuli
from models.tacotron2.taco_utils import load_wav_to_torch, load_filepaths_and_text
from models.tacotron2.text import text_to_sequence
from utils.util import opt_get
def get_similar_files_libritts(filename):
filedir = os.path.dirname(filename)
return list(pathlib.Path(filedir).glob('*.wav'))
class StopPredictionDataset(torch.utils.data.Dataset):
"""
1) loads audio,text pairs
2) normalizes text and converts them to sequences of one-hot vectors
3) computes mel-spectrograms from audio files.
"""
def __init__(self, hparams):
self.path = hparams['path']
if not isinstance(self.path, list):
self.path = [self.path]
fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
if not isinstance(fetcher_mode, list):
fetcher_mode = [fetcher_mode]
assert len(self.path) == len(fetcher_mode)
self.audiopaths_and_text = []
for p, fm in zip(self.path, fetcher_mode):
if fm == 'lj' or fm == 'libritts':
fetcher_fn = load_filepaths_and_text
self.get_similar_files = get_similar_files_libritts
elif fm == 'voxpopuli':
fetcher_fn = load_voxpopuli
self.get_similar_files = None # TODO: Fix.
else:
raise NotImplementedError()
self.audiopaths_and_text.extend(fetcher_fn(p))
self.sampling_rate = hparams.sampling_rate
self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
self.stft = layers.TacotronSTFT(
hparams.filter_length, hparams.hop_length, hparams.win_length,
hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
hparams.mel_fmax)
random.seed(hparams.seed)
random.shuffle(self.audiopaths_and_text)
self.max_mel_len = opt_get(hparams, ['max_mel_length'], None)
self.max_text_len = opt_get(hparams, ['max_text_length'], None)
def get_mel(self, filename):
filename = str(filename)
if filename.endswith('.wav'):
audio, sampling_rate = load_wav_to_torch(filename)
else:
audio, sampling_rate = audio2numpy.audio_from_file(filename)
audio = torch.tensor(audio)
if sampling_rate != self.input_sample_rate:
if sampling_rate < self.input_sample_rate:
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.')
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()
else:
audio_norm = audio
if audio_norm.std() > 1:
print(f"Something is very wrong with the given audio. std_dev={audio_norm.std()}. file={filename}")
return None
audio_norm.clip_(-1, 1)
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
if self.input_sample_rate != self.sampling_rate:
ratio = self.sampling_rate / self.input_sample_rate
audio_norm = torch.nn.functional.interpolate(audio_norm.unsqueeze(0), scale_factor=ratio, mode='area').squeeze(0)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
return melspec
def __getitem__(self, index):
path = self.audiopaths_and_text[index][0]
similar_files = self.get_similar_files(path)
mel = self.get_mel(path)
terms = torch.zeros(mel.shape[1])
terms[-1] = 1
while mel.shape[-1] < self.max_mel_len:
another_file = random.choice(similar_files)
another_mel = self.get_mel(another_file)
oterms = torch.zeros(another_mel.shape[1])
oterms[-1] = 1
mel = torch.cat([mel, another_mel], dim=-1)
terms = torch.cat([terms, oterms], dim=-1)
mel = mel[:, :self.max_mel_len]
terms = terms[:self.max_mel_len]
return {
'padded_mel': mel,
'termination_mask': terms,
}
def __len__(self):
return len(self.audiopaths_and_text)
if __name__ == '__main__':
params = {
'mode': 'stop_prediction',
'path': 'E:\\audio\\LibriTTS\\train-clean-360_list.txt',
'phase': 'train',
'n_workers': 0,
'batch_size': 16,
'fetcher_mode': 'libritts',
'max_mel_length': 800,
#'return_wavs': True,
#'input_sample_rate': 22050,
#'sampling_rate': 8000
}
from data import create_dataset, create_dataloader
ds, c = create_dataset(params, return_collate=True)
dl = create_dataloader(ds, params, collate_fn=c, shuffle=True)
i = 0
m = None
for k in range(1000):
for i, b in tqdm(enumerate(dl)):
continue
pm = b['padded_mel']
pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
m = pm if m is None else torch.cat([m, pm], dim=0)
print(m.mean(), m.std())