def _text_to_batch(lines,
device,
symbol_set="english_basic",
text_cleaners=["english_cleaners"],
batch_size=128):
columns = ['text']
fields = [lines]
fields = {c: f for c, f in zip(columns, fields)}
tp = TextProcessing(symbol_set, text_cleaners)
fields['text'] = [
torch.LongTensor(tp.encode_text(text)) for text in fields['text']
]
order = np.argsort([-t.size(0) for t in fields['text']])
fields['text'] = [fields['text'][i] for i in order]
fields['text_lens'] = torch.LongTensor([t.size(0) for t in fields['text']])
# cut into batches & pad
batches = []
for b in range(0, len(order), batch_size):
batch = {f: values[b:b + batch_size] for f, values in fields.items()}
for f in batch:
if f == 'text':
batch[f] = pad_sequence(batch[f], batch_first=True)
if type(batch[f]) is torch.Tensor:
batch[f] = batch[f].to(device)
batches.append(batch)
return batches[0]
def prepare_input_sequence(fields,
device,
symbol_set,
text_cleaners,
batch_size=128,
dataset=None,
load_mels=False,
load_pitch=False,
p_arpabet=0.0):
tp = TextProcessing(symbol_set, text_cleaners, p_arpabet=p_arpabet)
fields['text'] = [
torch.LongTensor(tp.encode_text(text)) for text in fields['text']
]
order = np.argsort([-t.size(0) for t in fields['text']])
fields['text'] = [fields['text'][i] for i in order]
fields['text_lens'] = torch.LongTensor([t.size(0) for t in fields['text']])
for t in fields['text']:
print(tp.sequence_to_text(t.numpy()))
if load_mels:
assert 'mel' in fields
fields['mel'] = [
torch.load(Path(dataset, fields['mel'][i])).t() for i in order
]
fields['mel_lens'] = torch.LongTensor(
[t.size(0) for t in fields['mel']])
if load_pitch:
assert 'pitch' in fields
fields['pitch'] = [
torch.load(Path(dataset, fields['pitch'][i])) for i in order
]
fields['pitch_lens'] = torch.LongTensor(
[t.size(0) for t in fields['pitch']])
if 'output' in fields:
fields['output'] = [fields['output'][i] for i in order]
# cut into batches & pad
batches = []
for b in range(0, len(order), batch_size):
batch = {f: values[b:b + batch_size] for f, values in fields.items()}
for f in batch:
if f == 'text':
batch[f] = pad_sequence(batch[f], batch_first=True)
elif f == 'mel' and load_mels:
batch[f] = pad_sequence(batch[f],
batch_first=True).permute(0, 2, 1)
elif f == 'pitch' and load_pitch:
batch[f] = pad_sequence(batch[f], batch_first=True)
if type(batch[f]) is torch.Tensor:
batch[f] = batch[f].to(device)
batches.append(batch)
return batches
def __init__(self, filenames, **kwargs):
# dict_args = vars(args)
kwargs['audiopaths_and_text'] = kwargs['wav_text_filelist']
kwargs['load_mel_from_disk'] = False
super(FilenamedLoader, self).__init__(**kwargs)
self.tp = TextProcessing(kwargs['symbol_set'], kwargs['text_cleaners'])
self.filenames = filenames
def __init__(self, **kwargs):
super(TextMelAliLoader, self).__init__(**kwargs)
self.tp = TextProcessing(kwargs['symbol_set'], kwargs['text_cleaners'])
self.n_speakers = kwargs['n_speakers']
if len(self.audiopaths_and_text[0]) != 4 + (kwargs['n_speakers'] > 1):
raise ValueError(
'Expected four columns in audiopaths file for single speaker model. \n'
'For multispeaker model, the filelist format is '
'<mel>|<dur>|<pitch>|<text>|<speaker_id>')
def __init__(self, filenames, *args, **kwargs):
super(FilenamedLoader, self).__init__(*args, **kwargs)
self.tp = TextProcessing(args[-1].symbol_set, args[-1].text_cleaners)
self.filenames = filenames
def __init__(
self,
dataset_path,
audiopaths_and_text,
text_cleaners,
n_mel_channels,
symbol_set='english_basic',
p_arpabet=1.0,
n_speakers=1,
load_mel_from_disk=True,
load_pitch_from_disk=True,
pitch_mean=214.72203, # LJSpeech defaults
pitch_std=65.72038,
max_wav_value=None,
sampling_rate=None,
filter_length=None,
hop_length=None,
win_length=None,
mel_fmin=None,
mel_fmax=None,
prepend_space_to_text=False,
append_space_to_text=False,
pitch_online_dir=None,
betabinomial_online_dir=None,
use_betabinomial_interpolator=True,
pitch_online_method='pyin',
**ignored):
# Expect a list of filenames
if type(audiopaths_and_text) is str:
audiopaths_and_text = [audiopaths_and_text]
self.dataset_path = dataset_path
self.audiopaths_and_text = load_filepaths_and_text(
dataset_path, audiopaths_and_text, has_speakers=(n_speakers > 1))
self.load_mel_from_disk = load_mel_from_disk
if not load_mel_from_disk:
self.max_wav_value = max_wav_value
self.sampling_rate = sampling_rate
self.stft = layers.TacotronSTFT(filter_length, hop_length,
win_length, n_mel_channels,
sampling_rate, mel_fmin, mel_fmax)
self.load_pitch_from_disk = load_pitch_from_disk
self.prepend_space_to_text = prepend_space_to_text
self.append_space_to_text = append_space_to_text
assert p_arpabet == 0.0 or p_arpabet == 1.0, (
'Only 0.0 and 1.0 p_arpabet is currently supported. '
'Variable probability breaks caching of betabinomial matrices.')
self.tp = TextProcessing(symbol_set,
text_cleaners,
p_arpabet=p_arpabet)
self.n_speakers = n_speakers
self.pitch_tmp_dir = pitch_online_dir
self.f0_method = pitch_online_method
self.betabinomial_tmp_dir = betabinomial_online_dir
self.use_betabinomial_interpolator = use_betabinomial_interpolator
if use_betabinomial_interpolator:
self.betabinomial_interpolator = BetaBinomialInterpolator()
expected_columns = (2 + int(load_pitch_from_disk) + (n_speakers > 1))
assert not (load_pitch_from_disk and self.pitch_tmp_dir is not None)
if len(self.audiopaths_and_text[0]) < expected_columns:
raise ValueError(
f'Expected {expected_columns} columns in audiopaths file. '
'The format is <mel_or_wav>|[<pitch>|]<text>[|<speaker_id>]')
if len(self.audiopaths_and_text[0]) > expected_columns:
print('WARNING: Audiopaths file has more columns than expected')
to_tensor = lambda x: torch.Tensor([x]) if type(x) is float else x
self.pitch_mean = to_tensor(pitch_mean)
self.pitch_std = to_tensor(pitch_std)
class TTSDataset(torch.utils.data.Dataset):
"""
1) loads audio,text pairs
2) normalizes text and converts them to sequences of one-hot vectors
3) computes mel-spectrograms from audio files.
"""
def __init__(
self,
dataset_path,
audiopaths_and_text,
text_cleaners,
n_mel_channels,
symbol_set='english_basic',
p_arpabet=1.0,
n_speakers=1,
load_mel_from_disk=True,
load_pitch_from_disk=True,
pitch_mean=214.72203, # LJSpeech defaults
pitch_std=65.72038,
max_wav_value=None,
sampling_rate=None,
filter_length=None,
hop_length=None,
win_length=None,
mel_fmin=None,
mel_fmax=None,
prepend_space_to_text=False,
append_space_to_text=False,
pitch_online_dir=None,
betabinomial_online_dir=None,
use_betabinomial_interpolator=True,
pitch_online_method='pyin',
**ignored):
# Expect a list of filenames
if type(audiopaths_and_text) is str:
audiopaths_and_text = [audiopaths_and_text]
self.dataset_path = dataset_path
self.audiopaths_and_text = load_filepaths_and_text(
dataset_path, audiopaths_and_text, has_speakers=(n_speakers > 1))
self.load_mel_from_disk = load_mel_from_disk
if not load_mel_from_disk:
self.max_wav_value = max_wav_value
self.sampling_rate = sampling_rate
self.stft = layers.TacotronSTFT(filter_length, hop_length,
win_length, n_mel_channels,
sampling_rate, mel_fmin, mel_fmax)
self.load_pitch_from_disk = load_pitch_from_disk
self.prepend_space_to_text = prepend_space_to_text
self.append_space_to_text = append_space_to_text
assert p_arpabet == 0.0 or p_arpabet == 1.0, (
'Only 0.0 and 1.0 p_arpabet is currently supported. '
'Variable probability breaks caching of betabinomial matrices.')
self.tp = TextProcessing(symbol_set,
text_cleaners,
p_arpabet=p_arpabet)
self.n_speakers = n_speakers
self.pitch_tmp_dir = pitch_online_dir
self.f0_method = pitch_online_method
self.betabinomial_tmp_dir = betabinomial_online_dir
self.use_betabinomial_interpolator = use_betabinomial_interpolator
if use_betabinomial_interpolator:
self.betabinomial_interpolator = BetaBinomialInterpolator()
expected_columns = (2 + int(load_pitch_from_disk) + (n_speakers > 1))
assert not (load_pitch_from_disk and self.pitch_tmp_dir is not None)
if len(self.audiopaths_and_text[0]) < expected_columns:
raise ValueError(
f'Expected {expected_columns} columns in audiopaths file. '
'The format is <mel_or_wav>|[<pitch>|]<text>[|<speaker_id>]')
if len(self.audiopaths_and_text[0]) > expected_columns:
print('WARNING: Audiopaths file has more columns than expected')
to_tensor = lambda x: torch.Tensor([x]) if type(x) is float else x
self.pitch_mean = to_tensor(pitch_mean)
self.pitch_std = to_tensor(pitch_std)
def __getitem__(self, index):
# Separate filename and text
if self.n_speakers > 1:
audiopath, *extra, text, speaker = self.audiopaths_and_text[index]
speaker = int(speaker)
else:
audiopath, *extra, text = self.audiopaths_and_text[index]
speaker = None
mel = self.get_mel(audiopath)
text = self.get_text(text)
pitch = self.get_pitch(index, mel.size(-1))
energy = torch.norm(mel.float(), dim=0, p=2)
attn_prior = self.get_prior(index, mel.shape[1], text.shape[0])
assert pitch.size(-1) == mel.size(-1)
# No higher formants?
if len(pitch.size()) == 1:
pitch = pitch[None, :]
return (text, mel, len(text), pitch, energy, speaker, attn_prior,
audiopath)
def __len__(self):
return len(self.audiopaths_and_text)
def get_mel(self, filename):
if not self.load_mel_from_disk:
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.stft.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.stft.sampling_rate))
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm,
requires_grad=False)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
else:
melspec = torch.load(filename)
# assert melspec.size(0) == self.stft.n_mel_channels, (
# 'Mel dimension mismatch: given {}, expected {}'.format(
# melspec.size(0), self.stft.n_mel_channels))
return melspec
def get_text(self, text):
text = self.tp.encode_text(text)
space = [self.tp.encode_text("A A")[1]]
if self.prepend_space_to_text:
text = space + text
if self.append_space_to_text:
text = text + space
return torch.LongTensor(text)
def get_prior(self, index, mel_len, text_len):
if self.use_betabinomial_interpolator:
return torch.from_numpy(
self.betabinomial_interpolator(mel_len, text_len))
if self.betabinomial_tmp_dir is not None:
audiopath, *_ = self.audiopaths_and_text[index]
fname = Path(audiopath).relative_to(self.dataset_path)
fname = fname.with_suffix('.pt')
cached_fpath = Path(self.betabinomial_tmp_dir, fname)
if cached_fpath.is_file():
return torch.load(cached_fpath)
attn_prior = beta_binomial_prior_distribution(text_len, mel_len)
if self.betabinomial_tmp_dir is not None:
cached_fpath.parent.mkdir(parents=True, exist_ok=True)
torch.save(attn_prior, cached_fpath)
return attn_prior
def get_pitch(self, index, mel_len=None):
audiopath, *fields = self.audiopaths_and_text[index]
if self.n_speakers > 1:
spk = int(fields[-1])
else:
spk = 0
if self.load_pitch_from_disk:
pitchpath = fields[0]
pitch = torch.load(pitchpath)
if self.pitch_mean is not None:
assert self.pitch_std is not None
pitch = normalize_pitch(pitch, self.pitch_mean, self.pitch_std)
return pitch
if self.pitch_tmp_dir is not None:
fname = Path(audiopath).relative_to(self.dataset_path)
fname_method = fname.with_suffix('.pt')
cached_fpath = Path(self.pitch_tmp_dir, fname_method)
if cached_fpath.is_file():
return torch.load(cached_fpath)
# No luck so far - calculate
wav = audiopath
if not wav.endswith('.wav'):
wav = re.sub('/mels/', '/wavs/', wav)
wav = re.sub('.pt$', '.wav', wav)
pitch_mel = estimate_pitch(wav, mel_len, self.f0_method,
self.pitch_mean, self.pitch_std)
if self.pitch_tmp_dir is not None and not cached_fpath.is_file():
cached_fpath.parent.mkdir(parents=True, exist_ok=True)
torch.save(pitch_mel, cached_fpath)
return pitch_mel