import os
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 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 load_mozilla_cv(filename):
with open(filename, encoding='utf-8') as f:
components = [line.strip().split('\t') for line in f][1:] # First line is the header
base = os.path.dirname(filename)
filepaths_and_text = [[os.path.join(base, f'clips/{component[1]}'), component[2]] for component in components]
return filepaths_and_text
def load_voxpopuli(filename):
with open(filename, encoding='utf-8') as f:
lines = [line.strip().split('\t') for line in f][1:] # First line is the header
base = os.path.dirname(filename)
filepaths_and_text = []
for line in lines:
if len(line) == 0:
continue
file, raw_text, norm_text, speaker_id, split, gender = line
year = file[:4]
filepaths_and_text.append([os.path.join(base, year, f'{file}.ogg'), raw_text])
return filepaths_and_text
class TextMelLoader(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
elif fm == 'mozilla_cv':
fetcher_fn = load_mozilla_cv
elif fm == 'voxpopuli':
fetcher_fn = load_voxpopuli
else:
raise NotImplementedError()
self.audiopaths_and_text.extend(fetcher_fn(p))
self.text_cleaners = hparams.text_cleaners
self.sampling_rate = hparams.sampling_rate
self.load_mel_from_disk = opt_get(hparams, ['load_mel_from_disk'], False)
self.return_wavs = opt_get(hparams, ['return_wavs'], False)
self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
assert not (self.load_mel_from_disk and self.return_wavs)
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)
# If needs_collate=False, all outputs will be aligned and padded at maximum length.
self.needs_collate = opt_get(hparams, ['needs_collate'], True)
if not self.needs_collate:
assert self.max_mel_len is not None and self.max_text_len is not None
def get_mel_text_pair(self, audiopath_and_text):
# separate filename and text
audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
text_seq = self.get_text(text)
mel = self.get_mel(audiopath)
return (text_seq, mel, text, audiopath_and_text[0])
def get_mel(self, filename):
if self.load_mel_from_disk and os.path.exists(f'{filename}_mel.npy'):
melspec = torch.from_numpy(np.load(f'{filename}_mel.npy'))
assert melspec.size(0) == self.stft.n_mel_channels, (
'Mel dimension mismatch: given {}, expected {}'.format(melspec.size(0), self.stft.n_mel_channels))
else:
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)
if self.return_wavs:
melspec = audio_norm
else:
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
return melspec
def get_text(self, text):
text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
return text_norm
def __getitem__(self, index):
tseq, mel, text, path = self.get_mel_text_pair(self.audiopaths_and_text[index])
if mel is None or \
(self.max_mel_len is not None and mel.shape[-1] > self.max_mel_len) or \
(self.max_text_len is not None and tseq.shape[0] > self.max_text_len):
#if mel is not None:
# print(f"Exception {index} mel_len:{mel.shape[-1]} text_len:{tseq.shape[0]} fname: {path}")
# It's hard to handle this situation properly. Best bet is to return the a random valid token and skew the dataset somewhat as a result.
rv = random.randint(0,len(self)-1)
return self[rv]
orig_output = mel.shape[-1]
orig_text_len = tseq.shape[0]
if not self.needs_collate:
if mel.shape[-1] != self.max_mel_len:
mel = F.pad(mel, (0, self.max_mel_len - mel.shape[-1]))
if tseq.shape[0] != self.max_text_len:
tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
return {
'real_text': text,
'padded_text': tseq,
'input_lengths': torch.tensor(orig_text_len, dtype=torch.long),
'padded_mel': mel,
'output_lengths': torch.tensor(orig_output, dtype=torch.long),
'filenames': path
}
return tseq, mel, path, text
def __len__(self):
return len(self.audiopaths_and_text)
class TextMelCollate():
""" Zero-pads model inputs and targets based on number of frames per setep
"""
def __init__(self, n_frames_per_step):
self.n_frames_per_step = n_frames_per_step
def __call__(self, batch):
"""Collate's training batch from normalized text and mel-spectrogram
PARAMS
------
batch: [text_normalized, mel_normalized, filename]
"""
# Right zero-pad all one-hot text sequences to max input length
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x[0]) for x in batch]),
dim=0, descending=True)
max_input_len = input_lengths[0]
text_padded = torch.LongTensor(len(batch), max_input_len)
text_padded.zero_()
filenames = []
real_text = []
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]][0]
text_padded[i, :text.size(0)] = text
filenames.append(batch[ids_sorted_decreasing[i]][2])
real_text.append(batch[ids_sorted_decreasing[i]][3])
# Right zero-pad mel-spec
num_mels = batch[0][1].size(0)
max_target_len = max([x[1].size(1) for x in batch])
if max_target_len % self.n_frames_per_step != 0:
max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
assert max_target_len % self.n_frames_per_step == 0
# include mel padded and gate padded
mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
mel_padded.zero_()
gate_padded = torch.FloatTensor(len(batch), max_target_len)
gate_padded.zero_()
output_lengths = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]][1]
mel_padded[i, :, :mel.size(1)] = mel
gate_padded[i, mel.size(1)-1:] = 1
output_lengths[i] = mel.size(1)
return {
'padded_text': text_padded,
'input_lengths': input_lengths,
'padded_mel': mel_padded,
'padded_gate': gate_padded,
'output_lengths': output_lengths,
'filenames': filenames,
'real_text': real_text,
}
def save_mel_buffer_to_file(mel, path):
np.save(path, mel.cpu().numpy())
def dump_mels_to_disk():
params = {
'mode': 'nv_tacotron',
'path': ['Z:\\voxpopuli\\audio\\transcribed_data\\en\\asr_test.tsv'],
'fetcher_mode': ['voxpopuli'],
'phase': 'train',
'n_workers': 0,
'batch_size': 1,
'needs_collate': True,
'max_mel_length': 4000,
'max_text_length': 600,
#'return_wavs': True,
#'input_sample_rate': 22050,
#'sampling_rate': 8000
}
output_path = 'D:\\dlas\\results\\mozcv_mels'
os.makedirs(os.path.join(output_path, 'clips'), exist_ok=True)
from data import create_dataset, create_dataloader
ds, c = create_dataset(params, return_collate=True)
dl = create_dataloader(ds, params, collate_fn=c)
for i, b in tqdm(enumerate(dl)):
mels = b['padded_mel']
fnames = b['filenames']
for j, fname in enumerate(fnames):
save_mel_buffer_to_file(mels[j], f'{os.path.join(output_path, fname)}_mel.npy')
if __name__ == '__main__':
dump_mels_to_disk()
'''
params = {
'mode': 'nv_tacotron',
'path': 'E:\\audio\\MozillaCommonVoice\\en\\train.tsv',
'phase': 'train',
'n_workers': 12,
'batch_size': 32,
'fetcher_mode': 'mozilla_cv',
'needs_collate': False,
'max_mel_length': 800,
'max_text_length': 200,
#'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)
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())
'''