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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 tasks.base_task 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']])
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,
"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
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])
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", 'data_gen.tts.base_binarizerr.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']
for wav_fn in inp_wav_paths:
item_name = os.path.basename(wav_fn)
ph = txt = tg_fn = ''
wav_fn = wav_fn
encoder = None
item = binarizer_cls.process_item(item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args)
items.append(item)
sizes.append(item['len'])
return items, sizes
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