513 lines
18 KiB
Python
513 lines
18 KiB
Python
import os, traceback
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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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from infer.lib.train.mel_processing import spectrogram_torch
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from infer.lib.train.utils import load_wav_to_torch, load_filepaths_and_text
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class TextAudioLoaderMultiNSFsid(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):
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self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
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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.min_text_len = getattr(hparams, "min_text_len", 1)
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self.max_text_len = getattr(hparams, "max_text_len", 5000)
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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, pitch, pitchf, dv 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, pitch, pitchf, dv])
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lengths.append(os.path.getsize(audiopath) // (3 * 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_sid(self, sid):
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sid = torch.LongTensor([int(sid)])
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return sid
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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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file = audiopath_and_text[0]
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phone = audiopath_and_text[1]
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pitch = audiopath_and_text[2]
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pitchf = audiopath_and_text[3]
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dv = audiopath_and_text[4]
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phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
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spec, wav = self.get_audio(file)
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dv = self.get_sid(dv)
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len_phone = phone.size()[0]
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len_spec = spec.size()[-1]
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# print(123,phone.shape,pitch.shape,spec.shape)
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if len_phone != len_spec:
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len_min = min(len_phone, len_spec)
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# amor
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len_wav = len_min * self.hop_length
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spec = spec[:, :len_min]
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wav = wav[:, :len_wav]
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phone = phone[:len_min, :]
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pitch = pitch[:len_min]
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pitchf = pitchf[:len_min]
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return (spec, wav, phone, pitch, pitchf, dv)
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def get_labels(self, phone, pitch, pitchf):
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phone = np.load(phone)
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phone = np.repeat(phone, 2, axis=0)
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pitch = np.load(pitch)
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pitchf = np.load(pitchf)
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n_num = min(phone.shape[0], 900) # DistributedBucketSampler
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# print(234,phone.shape,pitch.shape)
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phone = phone[:n_num, :]
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pitch = pitch[:n_num]
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pitchf = pitchf[:n_num]
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phone = torch.FloatTensor(phone)
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pitch = torch.LongTensor(pitch)
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pitchf = torch.FloatTensor(pitchf)
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return phone, pitch, pitchf
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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(
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"{} SR doesn't match target {} SR".format(
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sampling_rate, self.sampling_rate
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)
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)
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audio_norm = audio
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# audio_norm = audio / self.max_wav_value
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# audio_norm = audio / np.abs(audio).max()
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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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try:
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spec = torch.load(spec_filename)
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except:
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print(spec_filename, traceback.format_exc())
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spec = spectrogram_torch(
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audio_norm,
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self.filter_length,
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self.sampling_rate,
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self.hop_length,
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self.win_length,
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center=False,
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)
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spec = torch.squeeze(spec, 0)
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torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
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else:
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spec = spectrogram_torch(
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audio_norm,
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self.filter_length,
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self.sampling_rate,
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self.hop_length,
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self.win_length,
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center=False,
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)
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spec = torch.squeeze(spec, 0)
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torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
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return spec, audio_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 TextAudioCollateMultiNSFsid:
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"""Zero-pads model inputs and targets"""
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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[0].size(1) for x in batch]), dim=0, descending=True
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)
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max_spec_len = max([x[0].size(1) for x in batch])
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max_wave_len = max([x[1].size(1) for x in batch])
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spec_lengths = torch.LongTensor(len(batch))
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wave_lengths = torch.LongTensor(len(batch))
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spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
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wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
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spec_padded.zero_()
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wave_padded.zero_()
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max_phone_len = max([x[2].size(0) for x in batch])
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phone_lengths = torch.LongTensor(len(batch))
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phone_padded = torch.FloatTensor(
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len(batch), max_phone_len, batch[0][2].shape[1]
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) # (spec, wav, phone, pitch)
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pitch_padded = torch.LongTensor(len(batch), max_phone_len)
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pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
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phone_padded.zero_()
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pitch_padded.zero_()
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pitchf_padded.zero_()
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# dv = torch.FloatTensor(len(batch), 256)#gin=256
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sid = torch.LongTensor(len(batch))
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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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spec = row[0]
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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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wave = row[1]
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wave_padded[i, :, : wave.size(1)] = wave
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wave_lengths[i] = wave.size(1)
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phone = row[2]
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phone_padded[i, : phone.size(0), :] = phone
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phone_lengths[i] = phone.size(0)
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pitch = row[3]
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pitch_padded[i, : pitch.size(0)] = pitch
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pitchf = row[4]
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pitchf_padded[i, : pitchf.size(0)] = pitchf
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# dv[i] = row[5]
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sid[i] = row[5]
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return (
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phone_padded,
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phone_lengths,
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pitch_padded,
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pitchf_padded,
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spec_padded,
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spec_lengths,
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wave_padded,
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wave_lengths,
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# dv
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sid,
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)
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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):
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self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
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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.min_text_len = getattr(hparams, "min_text_len", 1)
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self.max_text_len = getattr(hparams, "max_text_len", 5000)
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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, dv 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, dv])
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lengths.append(os.path.getsize(audiopath) // (3 * 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_sid(self, sid):
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sid = torch.LongTensor([int(sid)])
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return sid
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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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file = audiopath_and_text[0]
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phone = audiopath_and_text[1]
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dv = audiopath_and_text[2]
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phone = self.get_labels(phone)
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spec, wav = self.get_audio(file)
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dv = self.get_sid(dv)
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len_phone = phone.size()[0]
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len_spec = spec.size()[-1]
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if len_phone != len_spec:
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len_min = min(len_phone, len_spec)
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len_wav = len_min * self.hop_length
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spec = spec[:, :len_min]
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wav = wav[:, :len_wav]
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phone = phone[:len_min, :]
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return (spec, wav, phone, dv)
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def get_labels(self, phone):
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phone = np.load(phone)
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phone = np.repeat(phone, 2, axis=0)
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n_num = min(phone.shape[0], 900) # DistributedBucketSampler
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phone = phone[:n_num, :]
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phone = torch.FloatTensor(phone)
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return phone
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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(
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"{} SR doesn't match target {} SR".format(
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sampling_rate, self.sampling_rate
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)
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)
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audio_norm = audio
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# audio_norm = audio / self.max_wav_value
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# audio_norm = audio / np.abs(audio).max()
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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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try:
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spec = torch.load(spec_filename)
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except:
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print(spec_filename, traceback.format_exc())
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spec = spectrogram_torch(
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audio_norm,
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self.filter_length,
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self.sampling_rate,
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self.hop_length,
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self.win_length,
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center=False,
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)
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spec = torch.squeeze(spec, 0)
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torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
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else:
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spec = spectrogram_torch(
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audio_norm,
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self.filter_length,
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self.sampling_rate,
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self.hop_length,
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self.win_length,
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center=False,
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)
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spec = torch.squeeze(spec, 0)
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torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
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return spec, audio_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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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[0].size(1) for x in batch]), dim=0, descending=True
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)
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max_spec_len = max([x[0].size(1) for x in batch])
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max_wave_len = max([x[1].size(1) for x in batch])
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spec_lengths = torch.LongTensor(len(batch))
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wave_lengths = torch.LongTensor(len(batch))
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spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
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wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
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spec_padded.zero_()
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wave_padded.zero_()
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max_phone_len = max([x[2].size(0) for x in batch])
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phone_lengths = torch.LongTensor(len(batch))
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phone_padded = torch.FloatTensor(
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len(batch), max_phone_len, batch[0][2].shape[1]
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)
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phone_padded.zero_()
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sid = torch.LongTensor(len(batch))
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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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spec = row[0]
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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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wave = row[1]
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wave_padded[i, :, : wave.size(1)] = wave
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wave_lengths[i] = wave.size(1)
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phone = row[2]
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phone_padded[i, : phone.size(0), :] = phone
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phone_lengths[i] = phone.size(0)
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sid[i] = row[3]
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return (
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phone_padded,
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phone_lengths,
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spec_padded,
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spec_lengths,
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wave_padded,
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wave_lengths,
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sid,
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)
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class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
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"""
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Maintain similar input lengths in a batch.
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Length groups are specified by boundaries.
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Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
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It removes samples which are not included in the boundaries.
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Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
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"""
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def __init__(
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self,
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dataset,
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batch_size,
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boundaries,
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num_replicas=None,
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rank=None,
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shuffle=True,
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):
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super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
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self.lengths = dataset.lengths
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self.batch_size = batch_size
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self.boundaries = boundaries
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self.buckets, self.num_samples_per_bucket = self._create_buckets()
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self.total_size = sum(self.num_samples_per_bucket)
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self.num_samples = self.total_size // self.num_replicas
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def _create_buckets(self):
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buckets = [[] for _ in range(len(self.boundaries) - 1)]
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for i in range(len(self.lengths)):
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length = self.lengths[i]
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idx_bucket = self._bisect(length)
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if idx_bucket != -1:
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buckets[idx_bucket].append(i)
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for i in range(len(buckets) - 1, -1, -1): #
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if len(buckets[i]) == 0:
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buckets.pop(i)
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self.boundaries.pop(i + 1)
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num_samples_per_bucket = []
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for i in range(len(buckets)):
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len_bucket = len(buckets[i])
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total_batch_size = self.num_replicas * self.batch_size
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rem = (
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total_batch_size - (len_bucket % total_batch_size)
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) % total_batch_size
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num_samples_per_bucket.append(len_bucket + rem)
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return buckets, num_samples_per_bucket
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def __iter__(self):
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# deterministically shuffle based on epoch
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g = torch.Generator()
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g.manual_seed(self.epoch)
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indices = []
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if self.shuffle:
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for bucket in self.buckets:
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indices.append(torch.randperm(len(bucket), generator=g).tolist())
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else:
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for bucket in self.buckets:
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indices.append(list(range(len(bucket))))
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batches = []
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for i in range(len(self.buckets)):
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bucket = self.buckets[i]
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len_bucket = len(bucket)
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ids_bucket = indices[i]
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num_samples_bucket = self.num_samples_per_bucket[i]
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# add extra samples to make it evenly divisible
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rem = num_samples_bucket - len_bucket
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ids_bucket = (
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ids_bucket
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+ ids_bucket * (rem // len_bucket)
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+ ids_bucket[: (rem % len_bucket)]
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)
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# subsample
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ids_bucket = ids_bucket[self.rank :: self.num_replicas]
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# batching
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for j in range(len(ids_bucket) // self.batch_size):
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batch = [
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bucket[idx]
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for idx in ids_bucket[
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j * self.batch_size : (j + 1) * self.batch_size
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|
]
|
|
]
|
|
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
|