Spaces:
Configuration error
Configuration error
File size: 7,524 Bytes
ed1cdd1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
import matplotlib
matplotlib.use('Agg')
import glob
import importlib
from utils.cwt import get_lf0_cwt
import os
import torch.optim
import torch.utils.data
from utils.indexed_datasets import IndexedDataset
from utils.pitch_utils import norm_interp_f0
import numpy as np
from training.dataset.base_dataset import BaseDataset
import torch
import torch.optim
import torch.utils.data
import utils
import torch.distributions
from utils.hparams import hparams
class FastSpeechDataset(BaseDataset):
def __init__(self, prefix, shuffle=False):
super().__init__(shuffle)
self.data_dir = hparams['binary_data_dir']
self.prefix = prefix
self.hparams = hparams
self.sizes = np.load(f'{self.data_dir}/{self.prefix}_lengths.npy')
self.indexed_ds = None
# self.name2spk_id={}
# pitch stats
f0_stats_fn = f'{self.data_dir}/train_f0s_mean_std.npy'
if os.path.exists(f0_stats_fn):
hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = np.load(f0_stats_fn)
hparams['f0_mean'] = float(hparams['f0_mean'])
hparams['f0_std'] = float(hparams['f0_std'])
else:
hparams['f0_mean'], hparams['f0_std'] = self.f0_mean, self.f0_std = None, None
if prefix == 'test':
if hparams['test_input_dir'] != '':
self.indexed_ds, self.sizes = self.load_test_inputs(hparams['test_input_dir'])
else:
if hparams['num_test_samples'] > 0:
self.avail_idxs = list(range(hparams['num_test_samples'])) + hparams['test_ids']
self.sizes = [self.sizes[i] for i in self.avail_idxs]
if hparams['pitch_type'] == 'cwt':
_, hparams['cwt_scales'] = get_lf0_cwt(np.ones(10))
def _get_item(self, index):
if hasattr(self, 'avail_idxs') and self.avail_idxs is not None:
index = self.avail_idxs[index]
if self.indexed_ds is None:
self.indexed_ds = IndexedDataset(f'{self.data_dir}/{self.prefix}')
return self.indexed_ds[index]
def __getitem__(self, index):
hparams = self.hparams
item = self._get_item(index)
max_frames = hparams['max_frames']
spec = torch.Tensor(item['mel'])[:max_frames]
energy = (spec.exp() ** 2).sum(-1).sqrt()
mel2ph = torch.LongTensor(item['mel2ph'])[:max_frames] if 'mel2ph' in item else None
f0, uv = norm_interp_f0(item["f0"][:max_frames], hparams)
#phone = torch.LongTensor(item['phone'][:hparams['max_input_tokens']])
hubert=torch.Tensor(item['hubert'][:hparams['max_input_tokens']])
pitch = torch.LongTensor(item.get("pitch"))[:max_frames]
# print(item.keys(), item['mel'].shape, spec.shape)
sample = {
"id": index,
"item_name": item['item_name'],
# "text": item['txt'],
# "txt_token": phone,
"hubert":hubert,
"mel": spec,
"pitch": pitch,
"energy": energy,
"f0": f0,
"uv": uv,
"mel2ph": mel2ph,
"mel_nonpadding": spec.abs().sum(-1) > 0,
}
if self.hparams['use_spk_embed']:
sample["spk_embed"] = torch.Tensor(item['spk_embed'])
if self.hparams['use_spk_id']:
sample["spk_id"] = item['spk_id']
# sample['spk_id'] = 0
# for key in self.name2spk_id.keys():
# if key in item['item_name']:
# sample['spk_id'] = self.name2spk_id[key]
# break
#======not used==========
# if self.hparams['pitch_type'] == 'cwt':
# cwt_spec = torch.Tensor(item['cwt_spec'])[:max_frames]
# f0_mean = item.get('f0_mean', item.get('cwt_mean'))
# f0_std = item.get('f0_std', item.get('cwt_std'))
# sample.update({"cwt_spec": cwt_spec, "f0_mean": f0_mean, "f0_std": f0_std})
# elif self.hparams['pitch_type'] == 'ph':
# f0_phlevel_sum = torch.zeros_like(phone).float().scatter_add(0, mel2ph - 1, f0)
# f0_phlevel_num = torch.zeros_like(phone).float().scatter_add(
# 0, mel2ph - 1, torch.ones_like(f0)).clamp_min(1)
# sample["f0_ph"] = f0_phlevel_sum / f0_phlevel_num
return sample
def collater(self, samples):
if len(samples) == 0:
return {}
id = torch.LongTensor([s['id'] for s in samples])
item_names = [s['item_name'] for s in samples]
text = [s['text'] for s in samples]
txt_tokens = utils.collate_1d([s['txt_token'] for s in samples], 0)
f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
pitch = utils.collate_1d([s['pitch'] for s in samples],1)
uv = utils.collate_1d([s['uv'] for s in samples])
energy = utils.collate_1d([s['energy'] for s in samples], 0.0)
mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
if samples[0]['mel2ph'] is not None else None
mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
txt_lengths = torch.LongTensor([s['txt_token'].numel() for s in samples])
mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
batch = {
'id': id,
'item_name': item_names,
'nsamples': len(samples),
'text': text,
'txt_tokens': txt_tokens,
'txt_lengths': txt_lengths,
'mels': mels,
'mel_lengths': mel_lengths,
'mel2ph': mel2ph,
'energy': energy,
'pitch': pitch,
'f0': f0,
'uv': uv,
}
if self.hparams['use_spk_embed']:
spk_embed = torch.stack([s['spk_embed'] for s in samples])
batch['spk_embed'] = spk_embed
if self.hparams['use_spk_id']:
spk_ids = torch.LongTensor([s['spk_id'] for s in samples])
batch['spk_ids'] = spk_ids
if self.hparams['pitch_type'] == 'cwt':
cwt_spec = utils.collate_2d([s['cwt_spec'] for s in samples])
f0_mean = torch.Tensor([s['f0_mean'] for s in samples])
f0_std = torch.Tensor([s['f0_std'] for s in samples])
batch.update({'cwt_spec': cwt_spec, 'f0_mean': f0_mean, 'f0_std': f0_std})
elif self.hparams['pitch_type'] == 'ph':
batch['f0'] = utils.collate_1d([s['f0_ph'] for s in samples])
return batch
def load_test_inputs(self, test_input_dir, spk_id=0):
inp_wav_paths = glob.glob(f'{test_input_dir}/*.wav') + glob.glob(f'{test_input_dir}/*.mp3')
sizes = []
items = []
binarizer_cls = hparams.get("binarizer_cls", 'basics.base_binarizer.BaseBinarizer')
pkg = ".".join(binarizer_cls.split(".")[:-1])
cls_name = binarizer_cls.split(".")[-1]
binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
binarization_args = hparams['binarization_args']
from preprocessing.hubertinfer import Hubertencoder
for wav_fn in inp_wav_paths:
item_name = os.path.basename(wav_fn)
ph = txt = tg_fn = ''
wav_fn = wav_fn
encoder = Hubertencoder(hparams['hubert_path'])
item = binarizer_cls.process_item(item_name, {'wav_fn':wav_fn}, encoder, binarization_args)
print(item)
items.append(item)
sizes.append(item['len'])
return items, sizes
|