forked from mrq/DL-Art-School
273 lines
11 KiB
Python
273 lines
11 KiB
Python
import os
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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 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 load_mozilla_cv(filename):
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with open(filename, encoding='utf-8') as f:
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components = [line.strip().split('\t') for line in f][1:] # First line is the header
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base = os.path.dirname(filename)
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filepaths_and_text = [[os.path.join(base, f'clips/{component[1]}'), component[2]] for component in components]
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return filepaths_and_text
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class TextMelLoader(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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elif fm == 'mozilla_cv':
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fetcher_fn = load_mozilla_cv
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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.text_cleaners = hparams.text_cleaners
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self.max_wav_value = hparams.max_wav_value
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self.sampling_rate = hparams.sampling_rate
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self.load_mel_from_disk = hparams.load_mel_from_disk
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self.return_wavs = opt_get(hparams, ['return_wavs'], False)
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self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
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assert not (self.load_mel_from_disk and self.return_wavs)
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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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# If needs_collate=False, all outputs will be aligned and padded at maximum length.
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self.needs_collate = opt_get(hparams, ['needs_collate'], True)
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if not self.needs_collate:
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assert self.max_mel_len is not None and self.max_text_len is not None
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def get_mel_text_pair(self, audiopath_and_text):
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# separate filename and text
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audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
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text_seq = self.get_text(text)
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mel = self.get_mel(audiopath)
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return (text_seq, mel, text, audiopath_and_text[0])
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def get_mel(self, filename):
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if not self.load_mel_from_disk:
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if filename.endswith('.wav'):
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audio, sampling_rate = load_wav_to_torch(filename)
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audio = (audio / self.max_wav_value)
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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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if self.return_wavs:
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melspec = audio_norm
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else:
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melspec = self.stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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else:
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melspec = torch.from_numpy(np.load(filename))
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assert melspec.size(0) == self.stft.n_mel_channels, (
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'Mel dimension mismatch: given {}, expected {}'.format(melspec.size(0), self.stft.n_mel_channels))
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return melspec
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def get_text(self, text):
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text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
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return text_norm
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def __getitem__(self, index):
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tseq, mel, text, path = self.get_mel_text_pair(self.audiopaths_and_text[index])
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if mel is None or \
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(self.max_mel_len is not None and mel.shape[-1] > self.max_mel_len) or \
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(self.max_text_len is not None and tseq.shape[0] > self.max_text_len):
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#if mel is not None:
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# print(f"Exception {index} mel_len:{mel.shape[-1]} text_len:{tseq.shape[0]} fname: {path}")
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# 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.
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rv = random.randint(0,len(self)-1)
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return self[rv]
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orig_output = mel.shape[-1]
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orig_text_len = tseq.shape[0]
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if not self.needs_collate:
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if mel.shape[-1] != self.max_mel_len:
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mel = F.pad(mel, (0, self.max_mel_len - mel.shape[-1]))
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if tseq.shape[0] != self.max_text_len:
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tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
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return {
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'real_text': text,
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'padded_text': tseq,
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'input_lengths': torch.tensor(orig_text_len, dtype=torch.long),
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'padded_mel': mel,
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'output_lengths': torch.tensor(orig_output, dtype=torch.long),
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'filenames': path
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}
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return tseq, mel, path, text
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def __len__(self):
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return len(self.audiopaths_and_text)
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class TextMelCollate():
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""" Zero-pads model inputs and targets based on number of frames per setep
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"""
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def __init__(self, n_frames_per_step):
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self.n_frames_per_step = n_frames_per_step
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def __call__(self, batch):
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"""Collate's training batch from normalized text and mel-spectrogram
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PARAMS
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------
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batch: [text_normalized, mel_normalized, filename]
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"""
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# Right zero-pad all one-hot text sequences to max input length
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input_lengths, ids_sorted_decreasing = torch.sort(
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torch.LongTensor([len(x[0]) for x in batch]),
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dim=0, descending=True)
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max_input_len = input_lengths[0]
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text_padded = torch.LongTensor(len(batch), max_input_len)
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text_padded.zero_()
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filenames = []
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real_text = []
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for i in range(len(ids_sorted_decreasing)):
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text = batch[ids_sorted_decreasing[i]][0]
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text_padded[i, :text.size(0)] = text
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filenames.append(batch[ids_sorted_decreasing[i]][2])
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real_text.append(batch[ids_sorted_decreasing[i]][3])
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# Right zero-pad mel-spec
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num_mels = batch[0][1].size(0)
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max_target_len = max([x[1].size(1) for x in batch])
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if max_target_len % self.n_frames_per_step != 0:
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max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
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assert max_target_len % self.n_frames_per_step == 0
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# include mel padded and gate padded
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mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
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mel_padded.zero_()
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gate_padded = torch.FloatTensor(len(batch), max_target_len)
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gate_padded.zero_()
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output_lengths = torch.LongTensor(len(batch))
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for i in range(len(ids_sorted_decreasing)):
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mel = batch[ids_sorted_decreasing[i]][1]
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mel_padded[i, :, :mel.size(1)] = mel
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gate_padded[i, mel.size(1)-1:] = 1
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output_lengths[i] = mel.size(1)
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return {
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'padded_text': text_padded,
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'input_lengths': input_lengths,
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'padded_mel': mel_padded,
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'padded_gate': gate_padded,
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'output_lengths': output_lengths,
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'filenames': filenames,
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'real_text': real_text,
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}
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def save_mel_buffer_to_file(mel, path):
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np.save(path, mel.numpy())
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def load_mel_buffer_from_file(path):
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return torch.tensor(np.load(path))
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def dump_mels_to_disk():
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params = {
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'mode': 'nv_tacotron',
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'path': ['E:\\audio\\MozillaCommonVoice\\en\\test.tsv', 'E:\\audio\\LibriTTS\\train-other-500_list.txt'],
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'fetcher_mode': ['mozilla_cv', 'libritts'],
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'phase': 'train',
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'n_workers': 0,
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'batch_size': 1,
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'needs_collate': True,
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'max_mel_length': 1000,
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'max_text_length': 200,
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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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output_path = 'D:\\dlas\\results\\mozcv_mels'
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os.makedirs(os.path.join(output_path, 'clips'), exist_ok=True)
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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)
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for i, b in tqdm(enumerate(dl)):
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mels = b['padded_mel']
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fnames = b['filenames']
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for j, fname in enumerate(fnames):
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save_mel_buffer_to_file(mels[j], f'{os.path.join(output_path, fname)}_mel.npy')
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if __name__ == '__main__':
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dump_mels_to_disk()
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'''
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params = {
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'mode': 'nv_tacotron',
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'path': 'E:\\audio\\MozillaCommonVoice\\en\\train.tsv',
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'phase': 'train',
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'n_workers': 12,
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'batch_size': 32,
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'fetcher_mode': 'mozilla_cv',
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'needs_collate': False,
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'max_mel_length': 800,
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'max_text_length': 200,
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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)
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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())
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''' |