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import time
import os
import random
import numpy as np
import torch
import torch.utils.data
import commons
from mel_processing import spectrogram_torch, spec_to_mel_torch
from utils import load_wav_to_torch, load_filepaths_and_text, transform
#import h5py
"""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, hparams):
self.audiopaths = load_filepaths_and_text(audiopaths)
self.max_wav_value = hparams.data.max_wav_value
self.sampling_rate = hparams.data.sampling_rate
self.filter_length = hparams.data.filter_length
self.hop_length = hparams.data.hop_length
self.win_length = hparams.data.win_length
self.sampling_rate = hparams.data.sampling_rate
self.use_sr = hparams.train.use_sr
self.use_spk = hparams.model.use_spk
self.spec_len = hparams.train.max_speclen
random.seed(1234)
random.shuffle(self.audiopaths)
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
lengths = []
for audiopath in self.audiopaths:
lengths.append(os.path.getsize(audiopath[0]) // (2 * self.hop_length))
self.lengths = lengths
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
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
spec = torch.load(spec_filename)
else:
spec = spectrogram_torch(audio_norm, self.filter_length,
self.sampling_rate, self.hop_length, self.win_length,
center=False)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename)
if self.use_spk:
spk_filename = filename.replace(".wav", ".npy")
spk_filename = spk_filename.replace("DUMMY", "dataset/spk")
spk = torch.from_numpy(np.load(spk_filename))
if not self.use_sr:
c_filename = filename.replace(".wav", ".pt")
c_filename = c_filename.replace("DUMMY", "dataset/hubert")
c = torch.load(c_filename).squeeze(0)
else:
i = random.randint(68,92)
'''
basename = os.path.basename(filename)[:-4]
spkname = basename[:4]
#print(basename, spkname)
with h5py.File(f"dataset/rs/wavlm/{spkname}/{i}.hdf5","r") as f:
c = torch.from_numpy(f[basename][()]).squeeze(0)
#print(c)
'''
c_filename = filename.replace(".wav", f"_{i}.pt")
c_filename = c_filename.replace("DUMMY", "dataset/sr/hubert")
c = torch.load(c_filename).squeeze(0)
# 2023.01.10 update: code below can deteriorate model performance
# I added these code during cleaning up, thinking that it can offer better performance than my provided checkpoints, but actually it does the opposite.
# What an act of 'adding legs to a snake'!
'''
lmin = min(c.size(-1), spec.size(-1))
spec, c = spec[:, :lmin], c[:, :lmin]
audio_norm = audio_norm[:, :lmin*self.hop_length]
_spec, _c, _audio_norm = spec, c, audio_norm
while spec.size(-1) < self.spec_len:
spec = torch.cat((spec, _spec), -1)
c = torch.cat((c, _c), -1)
audio_norm = torch.cat((audio_norm, _audio_norm), -1)
start = random.randint(0, spec.size(-1) - self.spec_len)
end = start + self.spec_len
spec = spec[:, start:end]
c = c[:, start:end]
audio_norm = audio_norm[:, start*self.hop_length:end*self.hop_length]
'''
if self.use_spk:
return c, spec, audio_norm, spk
else:
return c, spec, audio_norm
def __getitem__(self, index):
return self.get_audio(self.audiopaths[index][0])
def __len__(self):
return len(self.audiopaths)
class TextAudioSpeakerCollate():
""" Zero-pads model inputs and targets
"""
def __init__(self, hps):
self.hps = hps
self.use_sr = hps.train.use_sr
self.use_spk = hps.model.use_spk
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[0].size(1) for x in batch]),
dim=0, descending=True)
max_spec_len = max([x[1].size(1) for x in batch])
max_wav_len = max([x[2].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wav_lengths = torch.LongTensor(len(batch))
if self.use_spk:
spks = torch.FloatTensor(len(batch), batch[0][3].size(0))
else:
spks = None
c_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_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)
c_padded.zero_()
spec_padded.zero_()
wav_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
c = row[0]
c_padded[i, :, :c.size(1)] = c
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)
if self.use_spk:
spks[i] = row[3]
spec_seglen = spec_lengths[-1] if spec_lengths[-1] < self.hps.train.max_speclen + 1 else self.hps.train.max_speclen + 1
wav_seglen = spec_seglen * self.hps.data.hop_length
spec_padded, ids_slice = commons.rand_spec_segments(spec_padded, spec_lengths, spec_seglen)
wav_padded = commons.slice_segments(wav_padded, ids_slice * self.hps.data.hop_length, wav_seglen)
c_padded = commons.slice_segments(c_padded, ids_slice, spec_seglen)[:,:,:-1]
spec_padded = spec_padded[:,:,:-1]
wav_padded = wav_padded[:,:,:-self.hps.data.hop_length]
if self.use_spk:
return c_padded, spec_padded, wav_padded, spks
else:
return c_padded, spec_padded, wav_padded
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)):
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
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