281 lines
10 KiB
Python
281 lines
10 KiB
Python
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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from utils.audio_utils import spectrogram, load_wav
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from utils.util import intersperse
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from models.synthesizer.utils.text import text_to_sequence
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"""Multi speaker version"""
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class VitsDataset(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, audio_file_path, hparams):
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with open(audio_file_path, encoding='utf-8') as f:
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self.audio_metadata = [line.strip().split('|') for line in f]
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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.cleaned_text = getattr(hparams, "cleaned_text", False)
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self.add_blank = hparams.add_blank
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self.datasets_root = hparams.datasets_root
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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.audio_metadata)
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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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audio_metadata_new = []
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lengths = []
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# for audiopath, sid, text in self.audio_metadata:
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sid = 0
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spk_to_sid = {}
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for wav_fpath, mel_fpath, embed_path, wav_length, mel_frames, text in self.audio_metadata:
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if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
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# TODO: for magic data only
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speaker_name = wav_fpath.split("_")[1]
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if speaker_name not in spk_to_sid:
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sid += 1
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spk_to_sid[speaker_name] = sid
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audio_metadata_new.append([wav_fpath, mel_fpath, embed_path, wav_length, mel_frames, text, spk_to_sid[speaker_name]])
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lengths.append(os.path.getsize(f'{self.datasets_root}{os.sep}audio{os.sep}{wav_fpath}') // (2 * self.hop_length))
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print("found sid:%d", sid)
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self.audio_metadata = audio_metadata_new
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self.lengths = lengths
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def get_audio_text_speaker_pair(self, audio_metadata):
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# separate filename, speaker_id and text
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wav_fpath, text, sid = audio_metadata[0], audio_metadata[5], audio_metadata[6]
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text = self.get_text(text)
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spec, wav = self.get_audio(f'{self.datasets_root}{os.sep}audio{os.sep}{wav_fpath}')
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sid = self.get_sid(sid)
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emo = torch.FloatTensor(np.load(f'{self.datasets_root}{os.sep}emo{os.sep}{wav_fpath.replace("audio", "emo")}'))
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return (text, spec, wav, sid, emo)
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def get_audio(self, filename):
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# audio, sampling_rate = load_wav(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 = torch.load(filename)
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audio = torch.FloatTensor(np.load(filename).astype(np.float32))
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audio = audio.unsqueeze(0)
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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(audio, 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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spec = spectrogram(audio, self.filter_length, 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
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def get_text(self, text):
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if self.cleaned_text:
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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 = 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 get_sid(self, sid):
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sid = torch.LongTensor([int(sid)])
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return sid
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def __getitem__(self, index):
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return self.get_audio_text_speaker_pair(self.audio_metadata[index])
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def __len__(self):
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return len(self.audio_metadata)
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class VitsDatasetCollate():
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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, audio and speaker identities
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PARAMS
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------
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batch: [text_normalized, spec_normalized, wav_normalized, sid]
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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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sid = 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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emo = torch.FloatTensor(len(batch), 1024)
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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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emo.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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sid[i] = row[3]
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emo[i, :] = row[4]
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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, sid, ids_sorted_decreasing
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return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, emo
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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__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
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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, 0, -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 = (total_batch_size - (len_bucket % total_batch_size)) % 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 = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
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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 = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
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batches.append(batch)
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if self.shuffle:
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batch_ids = torch.randperm(len(batches), generator=g).tolist()
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batches = [batches[i] for i in batch_ids]
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self.batches = batches
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assert len(self.batches) * self.batch_size == self.num_samples
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return iter(self.batches)
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def _bisect(self, x, lo=0, hi=None):
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if hi is None:
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hi = len(self.boundaries) - 1
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if hi > lo:
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mid = (hi + lo) // 2
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if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
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return mid
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elif x <= self.boundaries[mid]:
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return self._bisect(x, lo, mid)
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else:
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return self._bisect(x, mid + 1, hi)
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else:
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return -1
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def __len__(self):
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return self.num_samples // self.batch_size
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