import time, logging
import os
import random, traceback
import numpy as np
import torch
import torch.utils.data
from tqdm import tqdm
from module import commons
from module.mel_processing import spectrogram_torch
from text import cleaned_text_to_sequence
from utils import load_wav_to_torch, load_filepaths_and_text
import torch.nn.functional as F
from functools import lru_cache
import torch
import requests
from scipy.io import wavfile
from io import BytesIO
# from config import exp_dir
from my_utils import load_audio
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, hparams, val=False):
exp_dir = hparams.exp_dir
self.path2 = "%s/2-name2text.txt" % exp_dir
self.path4 = "%s/4-cnhubert" % exp_dir
self.path5 = "%s/5-wav32k" % exp_dir
assert os.path.exists(self.path2)
assert os.path.exists(self.path4)
assert os.path.exists(self.path5)
names4 = set([name[:-3] for name in list(os.listdir(self.path4))]) # 去除.pt后缀
names5 = set(os.listdir(self.path5))
self.phoneme_data = {}
with open(self.path2, "r", encoding="utf8") as f:
lines = f.read().strip("\n").split("\n")
for line in lines:
tmp = line.split("\t")
if len(tmp) != 4:
continue
self.phoneme_data[tmp[0]] = [tmp[1]]
self.audiopaths_sid_text = list(set(self.phoneme_data) & names4 & names5)
tmp = self.audiopaths_sid_text
leng = len(tmp)
min_num = 100
if leng < min_num:
self.audiopaths_sid_text = []
for _ in range(max(2, int(min_num / leng))):
self.audiopaths_sid_text += tmp
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.val = val
random.seed(1234)
random.shuffle(self.audiopaths_sid_text)
print("phoneme_data_len:", len(self.phoneme_data.keys()))
print("wav_data_len:", len(self.audiopaths_sid_text))
audiopaths_sid_text_new = []
lengths = []
skipped_phone = 0
skipped_dur = 0
for audiopath in tqdm(self.audiopaths_sid_text):
try:
phoneme = self.phoneme_data[audiopath][0]
phoneme = phoneme.split(" ")
phoneme_ids = cleaned_text_to_sequence(phoneme)
except Exception:
print(f"{audiopath} not in self.phoneme_data !")
skipped_phone += 1
continue
size = os.path.getsize("%s/%s" % (self.path5, audiopath))
duration = size / self.sampling_rate / 2
if 54 > duration > 0.6 or self.val:
audiopaths_sid_text_new.append([audiopath, phoneme_ids])
lengths.append(size // (2 * self.hop_length))
else:
skipped_dur += 1
continue
print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur)
print("total left: ", len(audiopaths_sid_text_new))
assert len(audiopaths_sid_text_new) > 1 # 至少能凑够batch size，这里todo
self.audiopaths_sid_text = audiopaths_sid_text_new
self.lengths = lengths
def get_audio_text_speaker_pair(self, audiopath_sid_text):
audiopath, phoneme_ids = audiopath_sid_text
text = torch.FloatTensor(phoneme_ids)
try:
spec, wav = self.get_audio("%s/%s" % (self.path5, audiopath))
with torch.no_grad():
ssl = torch.load(
"%s/%s.pt" % (self.path4, audiopath), map_location="cpu"
)
if ssl.shape[-1] != spec.shape[-1]:
typee = ssl.dtype
ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee)
ssl.requires_grad = False
except:
traceback.print_exc()
spec = torch.zeros(1025, 100)
wav = torch.zeros(1, 100 * self.hop_length)
ssl = torch.zeros(1, 768, 100)
text = text[-1:]
print("load audio or ssl error!!!!!!", audiopath)
# print(ssl.requires_grad,spec.requires_grad,wav.requires_grad,text.requires_grad)
return (ssl, spec, wav, text)
def get_audio(self, filename):
audio_array = load_audio(
filename, self.sampling_rate
) # load_audio的方法是已经归一化到-1~1之间的，不用再/32768
# print(filename,audio_array.max(),audio_array.min(),audio_array.mean())
audio = torch.FloatTensor(audio_array) # /32768
audio_norm = audio
audio_norm = audio_norm.unsqueeze(0)
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
return spec, audio_norm
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
def __getitem__(self, index):
# with torch.no_grad():
return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
def __len__(self):
return len(self.audiopaths_sid_text)
def random_slice(self, ssl, wav, mel):
assert abs(ssl.shape[-1] - wav.shape[-1] // self.hop_length) < 3, (
"first",
ssl.shape,
wav.shape,
)
len_mel = mel.shape[1]
if self.val:
reference_mel = mel[:, : len_mel // 3]
return reference_mel, ssl, wav, mel
dir = random.randint(0, 1)
sep_point = random.randint(int(len_mel // 3), int(len_mel // 3 * 2))
if dir == 0:
reference_mel = mel[:, :sep_point]
ssl = ssl[:, :, sep_point:]
wav2 = wav[:, sep_point * self.hop_length :]
mel = mel[:, sep_point:]
else:
reference_mel = mel[:, sep_point:]
ssl = ssl[:, :, :sep_point]
wav2 = wav[:, : sep_point * self.hop_length]
mel = mel[:, :sep_point]
assert abs(ssl.shape[-1] - wav2.shape[-1] // self.hop_length) < 3, (
ssl.shape,
wav.shape,
wav2.shape,
mel.shape,
sep_point,
self.hop_length,
sep_point * self.hop_length,
dir,
)
return reference_mel, ssl, wav2, mel
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_ssl_len = max([x[0].size(2) for x in batch])
max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
max_spec_len = max([x[1].size(1) for x in batch])
max_spec_len = int(2 * ((max_spec_len // 2) + 1))
max_wav_len = max([x[2].size(1) for x in batch])
max_text_len = max([x[3].size(0) for x in batch])
ssl_lengths = torch.LongTensor(len(batch))
spec_lengths = torch.LongTensor(len(batch))
wav_lengths = torch.LongTensor(len(batch))
text_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len)
text_padded = torch.LongTensor(len(batch), max_text_len)
spec_padded.zero_()
wav_padded.zero_()
ssl_padded.zero_()
text_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
ssl = row[0]
ssl_padded[i, :, : ssl.size(2)] = ssl[0, :, :]
ssl_lengths[i] = ssl.size(2)
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)
text = row[3]
text_padded[i, : text.size(0)] = text
text_lengths[i] = text.size(0)
return (
ssl_padded,
ssl_lengths,
spec_padded,
spec_lengths,
wav_padded,
wav_lengths,
text_padded,
text_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
# print(233333333333333,self.lengths,dir(dataset))
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):
# 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