mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-03 08:43:57 +03:00
493 lines
19 KiB
Python
493 lines
19 KiB
Python
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import time
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import os
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import random
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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 tqdm
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import commons
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from mel_processing import spectrogram_torch
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from utils import load_wav_to_torch, load_filepaths_and_text
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from text import text_to_sequence, cleaned_text_to_sequence
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#add
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from retry import retry
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import random
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import torchaudio
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class TextAudioLoader(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 integers
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3) computes spectrograms from audio files.
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"""
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def __init__(self, audiopaths_and_text, hparams, use_test = True):
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self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
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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.filter_length = hparams.filter_length
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self.hop_length = hparams.hop_length
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self.win_length = hparams.win_length
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self.sampling_rate = hparams.sampling_rate
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self.use_test = use_test
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self.cleaned_text = getattr(hparams, "cleaned_text", False)
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self.add_blank = hparams.add_blank
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self.min_text_len = getattr(hparams, "min_text_len", 1)
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self.max_text_len = getattr(hparams, "max_text_len", 190)
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random.seed(1234)
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random.shuffle(self.audiopaths_and_text)
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self._filter()
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def _filter(self):
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"""
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Filter text & store spec lengths
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"""
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# Store spectrogram lengths for Bucketing
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# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
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# spec_length = wav_length // hop_length
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audiopaths_and_text_new = []
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lengths = []
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for audiopath, text in self.audiopaths_and_text:
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if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
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audiopaths_and_text_new.append([audiopath, text])
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lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
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self.audiopaths_and_text = audiopaths_and_text_new
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self.lengths = lengths
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def get_audio_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 = self.get_text(text)
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if self.use_test != True:
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text = torch.as_tensor("a")
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spec, wav = self.get_audio(audiopath)
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return (text, spec, wav)
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def get_audio(self, filename):
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != self.sampling_rate:
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raise ValueError("{} {} SR doesn't match target {} SR".format(
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sampling_rate, self.sampling_rate))
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audio_norm = audio / self.max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
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spec_filename = filename.replace(".wav", ".spec.pt")
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if os.path.exists(spec_filename):
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spec = torch.load(spec_filename)
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else:
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spec = spectrogram_torch(audio_norm, self.filter_length,
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self.sampling_rate, self.hop_length, self.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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torch.save(spec, spec_filename)
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return spec, audio_norm
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def get_text(self, text):
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if self.cleaned_text:
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text_norm = cleaned_text_to_sequence(text)
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else:
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text_norm = text_to_sequence(text, self.text_cleaners)
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if self.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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text_norm = torch.LongTensor(text_norm)
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return text_norm
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def __getitem__(self, index):
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return self.get_audio_text_pair(self.audiopaths_and_text[index])
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def __len__(self):
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return len(self.audiopaths_and_text)
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class TextAudioCollate():
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""" Zero-pads model inputs and targets
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"""
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def __init__(self, return_ids=False):
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self.return_ids = return_ids
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def __call__(self, batch):
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"""Collate's training batch from normalized text and aduio
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PARAMS
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------
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batch: [text_normalized, spec_normalized, wav_normalized]
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"""
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# Right zero-pad all one-hot text sequences to max input length
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_, ids_sorted_decreasing = torch.sort(
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torch.LongTensor([x[1].size(1) for x in batch]),
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dim=0, descending=True)
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max_text_len = max([len(x[0]) for x in batch])
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max_spec_len = max([x[1].size(1) for x in batch])
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max_wav_len = max([x[2].size(1) for x in batch])
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text_lengths = torch.LongTensor(len(batch))
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spec_lengths = torch.LongTensor(len(batch))
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wav_lengths = torch.LongTensor(len(batch))
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text_padded = torch.LongTensor(len(batch), max_text_len)
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spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
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wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
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text_padded.zero_()
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spec_padded.zero_()
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wav_padded.zero_()
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for i in range(len(ids_sorted_decreasing)):
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row = batch[ids_sorted_decreasing[i]]
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text = row[0]
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text_padded[i, :text.size(0)] = text
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text_lengths[i] = text.size(0)
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spec = row[1]
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spec_padded[i, :, :spec.size(1)] = spec
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spec_lengths[i] = spec.size(1)
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wav = row[2]
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wav_padded[i, :, :wav.size(1)] = wav
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wav_lengths[i] = wav.size(1)
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if self.return_ids:
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return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
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return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
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"""Multi speaker version"""
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class TextAudioSpeakerLoader(torch.utils.data.Dataset):
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"""
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1) loads audio, speaker_id, text pairs
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2) normalizes text and converts them to sequences of integers
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3) computes spectrograms from audio files.
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"""
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def __init__(self, audiopaths_sid_text, hparams, no_text=False, augmentation=False, augmentation_params=None, no_use_textfile = False):
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if no_use_textfile:
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self.audiopaths_sid_text = list()
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else:
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self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
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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.filter_length = hparams.filter_length
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self.hop_length = hparams.hop_length
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self.win_length = hparams.win_length
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self.sampling_rate = hparams.sampling_rate
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self.no_text = no_text
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self.augmentation = augmentation
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if augmentation :
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self.gain_p = augmentation_params.gain_p
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self.min_gain_in_db = augmentation_params.min_gain_in_db
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self.max_gain_in_db = augmentation_params.max_gain_in_db
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self.time_stretch_p = augmentation_params.time_stretch_p
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self.min_rate = augmentation_params.min_rate
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self.max_rate = augmentation_params.max_rate
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self.pitch_shift_p = augmentation_params.pitch_shift_p
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self.min_semitones = augmentation_params.min_semitones
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self.max_semitones = augmentation_params.max_semitones
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self.add_gaussian_noise_p = augmentation_params.add_gaussian_noise_p
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self.min_amplitude = augmentation_params.min_amplitude
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self.max_amplitude = augmentation_params.max_amplitude
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self.frequency_mask_p = augmentation_params.frequency_mask_p
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self.cleaned_text = getattr(hparams, "cleaned_text", False)
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self.add_blank = hparams.add_blank
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self.min_text_len = getattr(hparams, "min_text_len", 1)
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self.max_text_len = getattr(hparams, "max_text_len", 1000)
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random.seed(1234)
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random.shuffle(self.audiopaths_sid_text)
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self._filter()
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@retry(tries=30, delay=10)
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def _filter(self):
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"""
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Filter text & store spec lengths
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"""
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# Store spectrogram lengths for Bucketing
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# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
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# spec_length = wav_length // hop_length
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audiopaths_sid_text_new = []
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lengths = []
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for audiopath, sid, text in tqdm.tqdm(self.audiopaths_sid_text):
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if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
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audiopaths_sid_text_new.append([audiopath, sid, text])
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lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
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self.audiopaths_sid_text = audiopaths_sid_text_new
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self.lengths = lengths
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def get_audio_text_speaker_pair(self, audiopath_sid_text):
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# separate filename, speaker_id and text
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audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
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text = self.get_text(text)
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if self.no_text:
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text = self.get_text("a")
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spec, wav = self.get_audio(audiopath)
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sid = self.get_sid(sid)
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return (text, spec, wav, sid)
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@retry(exceptions=(PermissionError), tries=100, delay=10)
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def get_audio(self, filename):
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# 音声データは±1.0内に正規化したtorchベクトルでunsqueeze(0)で外側1次元くるんだものを扱う
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audio, sampling_rate = load_wav_to_torch(filename)
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try:
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if sampling_rate != self.sampling_rate:
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raise ValueError("[Error] Exception: source {} SR doesn't match target {} SR".format(
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sampling_rate, self.sampling_rate))
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except ValueError as e:
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print(e)
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exit()
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audio_norm = self.get_normalized_audio(audio, self.max_wav_value)
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if self.augmentation:
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audio_augmented = self.add_augmentation(audio_norm, sampling_rate)
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audio_noised = self.add_noise(audio_augmented, sampling_rate)
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# ノーマライズ後のaugmentationとnoise付加で範囲外になったところを削る
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audio_augmented = torch.clamp(audio_augmented, -1, 1)
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audio_noised = torch.clamp(audio_noised, -1, 1)
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# audio(音声波形)は教師信号となるのでノイズは含まずaugmentationのみしたものを使用
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audio_norm = audio_augmented
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# spec(スペクトログラム)は入力信号となるのでaugmentationしてさらにノイズを付加したものを使用
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spec = spectrogram_torch(audio_noised, self.filter_length,
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self.sampling_rate, self.hop_length, self.win_length,
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center=False)
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spec_noised = self.add_spectrogram_noise(spec)
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spec = torch.squeeze(spec_noised, 0)
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else:
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spec = spectrogram_torch(audio_norm, self.filter_length,
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self.sampling_rate, self.hop_length, self.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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return spec, audio_norm
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def add_augmentation(self, audio, sampling_rate):
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gain_in_db = 0.0
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if random.random() <= self.gain_p:
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gain_in_db = random.uniform(self.min_gain_in_db, self.max_gain_in_db)
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time_stretch_rate = 1.0
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if random.random() <= self.time_stretch_p:
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time_stretch_rate = random.uniform(self.min_rate, self.max_rate)
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pitch_shift_semitones = 0
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if random.random() <= self.pitch_shift_p:
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pitch_shift_semitones = random.uniform(self.min_semitones, self.max_semitones) * 100 # 1/100 semitone 単位指定のため
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augmentation_effects = [
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["gain", f"{gain_in_db}"],
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["tempo", f"{time_stretch_rate}"],
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["pitch", f"{pitch_shift_semitones}"],
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["rate", f"{sampling_rate}"]
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]
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audio_augmented, _ = torchaudio.sox_effects.apply_effects_tensor(audio, sampling_rate, augmentation_effects)
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return audio_augmented
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def add_noise(self, audio, sampling_rate):
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# AddGaussianNoise
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audio = self.add_gaussian_noise(audio)
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return audio
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def add_gaussian_noise(self, audio):
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assert self.min_amplitude >= 0.0
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assert self.max_amplitude >= 0.0
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assert self.max_amplitude >= self.min_amplitude
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if random.random() > self.add_gaussian_noise_p:
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return audio
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amplitude = random.uniform(self.min_amplitude, self.max_amplitude)
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noise = torch.randn(audio.size())
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noised_audio = audio + amplitude * noise
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return noised_audio
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def add_spectrogram_noise(self, spec):
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# FrequencyMask
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masking = torchaudio.transforms.FrequencyMasking(freq_mask_param=80)
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masked = masking(spec)
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return masked
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def get_normalized_audio(self, audio, max_wav_value):
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audio_norm = audio / max_wav_value
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audio_norm = audio_norm.unsqueeze(0)
|
|||
|
|
return audio_norm
|
|||
|
|
|
|||
|
|
def get_text(self, text):
|
|||
|
|
if self.cleaned_text:
|
|||
|
|
text_norm = cleaned_text_to_sequence(text)
|
|||
|
|
else:
|
|||
|
|
text_norm = text_to_sequence(text, self.text_cleaners)
|
|||
|
|
if self.add_blank:
|
|||
|
|
text_norm = commons.intersperse(text_norm, 0)
|
|||
|
|
text_norm = torch.LongTensor(text_norm)
|
|||
|
|
return text_norm
|
|||
|
|
|
|||
|
|
def get_sid(self, sid):
|
|||
|
|
sid = torch.LongTensor([int(sid)])
|
|||
|
|
return sid
|
|||
|
|
|
|||
|
|
def __getitem__(self, index):
|
|||
|
|
return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
|
|||
|
|
|
|||
|
|
def __len__(self):
|
|||
|
|
return len(self.audiopaths_sid_text)
|
|||
|
|
|
|||
|
|
|
|||
|
|
class TextAudioSpeakerCollate():
|
|||
|
|
""" Zero-pads model inputs and targets
|
|||
|
|
"""
|
|||
|
|
def __init__(self, return_ids=False, no_text = False):
|
|||
|
|
self.return_ids = return_ids
|
|||
|
|
self.no_text = no_text
|
|||
|
|
|
|||
|
|
def __call__(self, batch):
|
|||
|
|
"""Collate's training batch from normalized text, audio and speaker identities
|
|||
|
|
PARAMS
|
|||
|
|
------
|
|||
|
|
batch: [text_normalized, spec_normalized, wav_normalized, sid]
|
|||
|
|
"""
|
|||
|
|
# Right zero-pad all one-hot text sequences to max input length
|
|||
|
|
_, ids_sorted_decreasing = torch.sort(
|
|||
|
|
torch.LongTensor([x[1].size(1) for x in batch]),
|
|||
|
|
dim=0, descending=True)
|
|||
|
|
|
|||
|
|
max_text_len = max([len(x[0]) for x in batch])
|
|||
|
|
max_spec_len = max([x[1].size(1) for x in batch])
|
|||
|
|
max_wav_len = max([x[2].size(1) for x in batch])
|
|||
|
|
|
|||
|
|
text_lengths = torch.LongTensor(len(batch))
|
|||
|
|
spec_lengths = torch.LongTensor(len(batch))
|
|||
|
|
wav_lengths = torch.LongTensor(len(batch))
|
|||
|
|
sid = torch.LongTensor(len(batch))
|
|||
|
|
|
|||
|
|
text_padded = torch.LongTensor(len(batch), max_text_len)
|
|||
|
|
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
|||
|
|
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
|||
|
|
text_padded.zero_()
|
|||
|
|
spec_padded.zero_()
|
|||
|
|
wav_padded.zero_()
|
|||
|
|
for i in range(len(ids_sorted_decreasing)):
|
|||
|
|
row = batch[ids_sorted_decreasing[i]]
|
|||
|
|
|
|||
|
|
text = row[0]
|
|||
|
|
text_padded[i, :text.size(0)] = text
|
|||
|
|
text_lengths[i] = text.size(0)
|
|||
|
|
|
|||
|
|
spec = row[1]
|
|||
|
|
spec_padded[i, :, :spec.size(1)] = spec
|
|||
|
|
spec_lengths[i] = spec.size(1)
|
|||
|
|
|
|||
|
|
wav = row[2]
|
|||
|
|
wav_padded[i, :, :wav.size(1)] = wav
|
|||
|
|
wav_lengths[i] = wav.size(1)
|
|||
|
|
|
|||
|
|
sid[i] = row[3]
|
|||
|
|
|
|||
|
|
if self.return_ids:
|
|||
|
|
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
|
|||
|
|
return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
|
|||
|
|
|
|||
|
|
|
|||
|
|
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
|
|||
|
|
"""
|
|||
|
|
Maintain similar input lengths in a batch.
|
|||
|
|
Length groups are specified by boundaries.
|
|||
|
|
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
|
|||
|
|
|
|||
|
|
It removes samples which are not included in the boundaries.
|
|||
|
|
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
|
|||
|
|
"""
|
|||
|
|
def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
|
|||
|
|
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
|
|||
|
|
self.lengths = dataset.lengths
|
|||
|
|
self.batch_size = batch_size
|
|||
|
|
self.boundaries = boundaries
|
|||
|
|
|
|||
|
|
self.buckets, self.num_samples_per_bucket = self._create_buckets()
|
|||
|
|
self.total_size = sum(self.num_samples_per_bucket)
|
|||
|
|
self.num_samples = self.total_size // self.num_replicas
|
|||
|
|
|
|||
|
|
def _create_buckets(self):
|
|||
|
|
buckets = [[] for _ in range(len(self.boundaries) - 1)]
|
|||
|
|
for i in range(len(self.lengths)):
|
|||
|
|
length = self.lengths[i]
|
|||
|
|
idx_bucket = self._bisect(length)
|
|||
|
|
if idx_bucket != -1:
|
|||
|
|
buckets[idx_bucket].append(i)
|
|||
|
|
|
|||
|
|
for i in range(len(buckets) - 1, 0, -1):
|
|||
|
|
if len(buckets[i]) == 0:
|
|||
|
|
buckets.pop(i)
|
|||
|
|
self.boundaries.pop(i+1)
|
|||
|
|
|
|||
|
|
num_samples_per_bucket = []
|
|||
|
|
for i in range(len(buckets)):
|
|||
|
|
len_bucket = len(buckets[i])
|
|||
|
|
total_batch_size = self.num_replicas * self.batch_size
|
|||
|
|
rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
|
|||
|
|
num_samples_per_bucket.append(len_bucket + rem)
|
|||
|
|
return buckets, num_samples_per_bucket
|
|||
|
|
|
|||
|
|
def __iter__(self):
|
|||
|
|
# deterministically shuffle based on epoch
|
|||
|
|
g = torch.Generator()
|
|||
|
|
g.manual_seed(self.epoch)
|
|||
|
|
|
|||
|
|
indices = []
|
|||
|
|
if self.shuffle:
|
|||
|
|
for bucket in self.buckets:
|
|||
|
|
indices.append(torch.randperm(len(bucket), generator=g).tolist())
|
|||
|
|
else:
|
|||
|
|
for bucket in self.buckets:
|
|||
|
|
indices.append(list(range(len(bucket))))
|
|||
|
|
|
|||
|
|
batches = []
|
|||
|
|
for i in range(len(self.buckets)):
|
|||
|
|
next_bucket = (i+1) % len(self.buckets)
|
|||
|
|
bucket = self.buckets[i]
|
|||
|
|
len_bucket = len(bucket)
|
|||
|
|
ids_bucket = indices[i]
|
|||
|
|
num_samples_bucket = self.num_samples_per_bucket[i]
|
|||
|
|
|
|||
|
|
if len_bucket == 0:
|
|||
|
|
print("[Warn] Exception: length of buckets {} is 0. ID:{} Skip.".format(i,i))
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# add extra samples to make it evenly divisible
|
|||
|
|
rem = num_samples_bucket - len_bucket
|
|||
|
|
ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
|
|||
|
|
|
|||
|
|
# subsample
|
|||
|
|
ids_bucket = ids_bucket[self.rank::self.num_replicas]
|
|||
|
|
|
|||
|
|
# batching
|
|||
|
|
for j in range(len(ids_bucket) // self.batch_size):
|
|||
|
|
batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
|
|||
|
|
batches.append(batch)
|
|||
|
|
|
|||
|
|
if self.shuffle:
|
|||
|
|
batch_ids = torch.randperm(len(batches), generator=g).tolist()
|
|||
|
|
batches = [batches[i] for i in batch_ids]
|
|||
|
|
self.batches = batches
|
|||
|
|
|
|||
|
|
assert len(self.batches) * self.batch_size == self.num_samples
|
|||
|
|
return iter(self.batches)
|
|||
|
|
|
|||
|
|
def _bisect(self, x, lo=0, hi=None):
|
|||
|
|
if hi is None:
|
|||
|
|
hi = len(self.boundaries) - 1
|
|||
|
|
|
|||
|
|
if hi > lo:
|
|||
|
|
mid = (hi + lo) // 2
|
|||
|
|
if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
|
|||
|
|
return mid
|
|||
|
|
elif x <= self.boundaries[mid]:
|
|||
|
|
return self._bisect(x, lo, mid)
|
|||
|
|
else:
|
|||
|
|
return self._bisect(x, mid + 1, hi)
|
|||
|
|
else:
|
|||
|
|
return -1
|
|||
|
|
|
|||
|
|
def __len__(self):
|
|||
|
|
return self.num_samples // self.batch_size
|