import time
import os
import random
import numpy as np
import torch
import torch.utils.data
import torchaudio
import commons
from mel_processing import spectrogram_torch
from utils import load_wav_to_torch, load_filepaths_and_text
from text import text_to_sequence, cleaned_text_to_sequence
"""Multi speaker version"""
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
1) loads audio, speaker_id, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths_sid_text, hparams, symbols):
self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
self.text_cleaners = hparams.text_cleaners
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.cleaned_text = getattr(hparams, "cleaned_text", False)
self.add_blank = hparams.add_blank
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 190)
self.symbols = symbols
random.seed(1234)
random.shuffle(self.audiopaths_sid_text)
self._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_sid_text_new = []
lengths = []
for audiopath, sid, text in self.audiopaths_sid_text:
# audiopath = "./user_voice/" + audiopath
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_sid_text_new.append([audiopath, sid, text])
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
self.audiopaths_sid_text = audiopaths_sid_text_new
self.lengths = lengths
def get_audio_text_speaker_pair(self, audiopath_sid_text):
# separate filename, speaker_id and text
audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
text = self.get_text(text)
spec, wav = self.get_audio(audiopath)
sid = self.get_sid(sid)
return (text, spec, wav, sid)
def get_audio(self, filename):
# audio, sampling_rate = load_wav_to_torch(filename)
# if sampling_rate != self.sampling_rate:
# raise ValueError("{} {} SR doesn't match target {} SR".format(
# sampling_rate, self.sampling_rate))
# audio_norm = audio / self.max_wav_value if audio.max() > 10 else audio
# audio_norm = audio_norm.unsqueeze(0)
audio_norm, sampling_rate = torchaudio.load(filename, frame_offset=0, num_frames=-1, normalize=True, channels_first=True)
# spec_filename = filename.replace(".wav", ".spec.pt")
# if os.path.exists(spec_filename):
# spec = torch.load(spec_filename)
# else:
# try:
spec = spectrogram_torch(audio_norm, self.filter_length,
self.sampling_rate, self.hop_length, self.win_length,
center=False)
spec = spec.squeeze(0)
# except NotImplementedError:
# print("?")
# spec = torch.squeeze(spec, 0)
# torch.save(spec, spec_filename)
return spec, audio_norm
def get_text(self, text):
if self.cleaned_text:
text_norm = cleaned_text_to_sequence(text, self.symbols)
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):
self.return_ids = return_ids
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)
try:
for i in range(len(buckets) - 1, 0, -1):
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)
assert all(len(bucket) > 0 for bucket in buckets)
# When one bucket is not traversed
except Exception as e:
print('Bucket warning ', e)
for i in range(len(buckets) - 1, -1, -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)):
bucket = self.buckets[i]
len_bucket = len(bucket)
ids_bucket = indices[i]
num_samples_bucket = self.num_samples_per_bucket[i]
# 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