import os
import numpy as np
import random
import torch
import torch.utils.data
from vits.utils import load_wav_to_torch
def load_filepaths(filename, split="|"):
with open(filename, encoding='utf-8') as f:
filepaths = [line.strip().split(split) for line in f]
return filepaths
class TextAudioSpeakerSet(torch.utils.data.Dataset):
def __init__(self, filename, hparams):
self.items = load_filepaths(filename)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.segment_size = hparams.segment_size
self.hop_length = hparams.hop_length
self._filter()
print(f'----------{len(self.items)}----------')
def _filter(self):
lengths = []
items_new = []
items_min = int(self.segment_size / self.hop_length * 4) # 1 S
items_max = int(self.segment_size / self.hop_length * 16) # 4 S
for wavpath, spec, pitch, vec, ppg, spk in self.items:
if not os.path.isfile(wavpath):
continue
if not os.path.isfile(spec):
continue
if not os.path.isfile(pitch):
continue
if not os.path.isfile(vec):
continue
if not os.path.isfile(ppg):
continue
if not os.path.isfile(spk):
continue
temp = np.load(pitch)
usel = int(temp.shape[0] - 1) # useful length
if (usel < items_min):
continue
if (usel >= items_max):
usel = items_max
items_new.append([wavpath, spec, pitch, vec, ppg, spk, usel])
lengths.append(usel)
self.items = items_new
self.lengths = lengths
def read_wav(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
assert sampling_rate == self.sampling_rate, f"error: this sample rate of {filename} is {sampling_rate}"
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
return audio_norm
def __getitem__(self, index):
return self.my_getitem(index)
def __len__(self):
return len(self.items)
def my_getitem(self, idx):
item = self.items[idx]
# print(item)
wav = item[0]
spe = item[1]
pit = item[2]
vec = item[3]
ppg = item[4]
spk = item[5]
use = item[6]
wav = self.read_wav(wav)
spe = torch.load(spe)
pit = np.load(pit)
vec = np.load(vec)
vec = np.repeat(vec, 2, 0) # 320 PPG -> 160 * 2
ppg = np.load(ppg)
ppg = np.repeat(ppg, 2, 0) # 320 PPG -> 160 * 2
spk = np.load(spk)
pit = torch.FloatTensor(pit)
vec = torch.FloatTensor(vec)
ppg = torch.FloatTensor(ppg)
spk = torch.FloatTensor(spk)
len_pit = pit.size()[0]
len_vec = vec.size()[0] - 2 # for safe
len_ppg = ppg.size()[0] - 2 # for safe
len_min = min(len_pit, len_vec)
len_min = min(len_min, len_ppg)
len_wav = len_min * self.hop_length
pit = pit[:len_min]
vec = vec[:len_min, :]
ppg = ppg[:len_min, :]
spe = spe[:, :len_min]
wav = wav[:, :len_wav]
if len_min > use:
max_frame_start = ppg.size(0) - use - 1
frame_start = random.randint(0, max_frame_start)
frame_end = frame_start + use
pit = pit[frame_start:frame_end]
vec = vec[frame_start:frame_end, :]
ppg = ppg[frame_start:frame_end, :]
spe = spe[:, frame_start:frame_end]
wav_start = frame_start * self.hop_length
wav_end = frame_end * self.hop_length
wav = wav[:, wav_start:wav_end]
# print(spe.shape)
# print(wav.shape)
# print(ppg.shape)
# print(pit.shape)
# print(spk.shape)
return spe, wav, ppg, vec, pit, spk
class TextAudioSpeakerCollate:
"""Zero-pads model inputs and targets"""
def __call__(self, batch):
# Right zero-pad all one-hot text sequences to max input length
# mel: [freq, length]
# wav: [1, length]
# ppg: [len, 1024]
# pit: [len]
# spk: [256]
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spe_len = max([x[0].size(1) for x in batch])
max_wav_len = max([x[1].size(1) for x in batch])
spe_lengths = torch.LongTensor(len(batch))
wav_lengths = torch.LongTensor(len(batch))
spe_padded = torch.FloatTensor(
len(batch), batch[0][0].size(0), max_spe_len)
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
spe_padded.zero_()
wav_padded.zero_()
max_ppg_len = max([x[2].size(0) for x in batch])
ppg_lengths = torch.FloatTensor(len(batch))
ppg_padded = torch.FloatTensor(
len(batch), max_ppg_len, batch[0][2].size(1))
vec_padded = torch.FloatTensor(
len(batch), max_ppg_len, batch[0][3].size(1))
pit_padded = torch.FloatTensor(len(batch), max_ppg_len)
ppg_padded.zero_()
vec_padded.zero_()
pit_padded.zero_()
spk = torch.FloatTensor(len(batch), batch[0][5].size(0))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spe = row[0]
spe_padded[i, :, : spe.size(1)] = spe
spe_lengths[i] = spe.size(1)
wav = row[1]
wav_padded[i, :, : wav.size(1)] = wav
wav_lengths[i] = wav.size(1)
ppg = row[2]
ppg_padded[i, : ppg.size(0), :] = ppg
ppg_lengths[i] = ppg.size(0)
vec = row[3]
vec_padded[i, : vec.size(0), :] = vec
pit = row[4]
pit_padded[i, : pit.size(0)] = pit
spk[i] = row[5]
# print(ppg_padded.shape)
# print(ppg_lengths.shape)
# print(pit_padded.shape)
# print(spk.shape)
# print(spe_padded.shape)
# print(spe_lengths.shape)
# print(wav_padded.shape)
# print(wav_lengths.shape)
return (
ppg_padded,
ppg_lengths,
vec_padded,
pit_padded,
spk,
spe_padded,
spe_lengths,
wav_padded,
wav_lengths,
)
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)
if (len_bucket == 0):
continue
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