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Cleanup and verbose argument

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dscripka 2023-08-06 11:09:39 -04:00
parent a1f84c5ec3
commit 8b4b20b712
1 changed files with 13 additions and 14 deletions
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27
generate_samples.py
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@ -35,10 +35,11 @@ def generate_samples(
noise_scales: List[float] = [0.667],
noise_scale_ws: List[float] = [0.8],
max_speakers: float = None,
verbose: bool = False,
**kwargs
) -> None:
"""
Generate synthetic speech clips, saving the clips to the specified outpur directory.
Generate synthetic speech clips, saving the clips to the specified output directory.
Args:
text (List[str]): The text to convert into speech. Can be either a
@ -54,6 +55,7 @@ def generate_samples(
noise_scales (List[float]): A parameter for overall variability of the generated speech.
noise_scale_ws (List[float]): A parameter for the stochastic duration of words/phonemes.
max_speakers (int): The maximum speaker number to use, if the model is multi-speaker.
verbose (bool): Enable or disable more detailed logging messages (default: False).
Returns:
None
@ -130,17 +132,13 @@ def generate_samples(
break
batch_size = len(speakers_batch)
# slerp_weight, length_scale, noise_scale, noise_scale_w = next(settings_iter)
slerp_weight = np.random.choice(slerp_weights)
length_scale = np.random.choice(length_scales)
noise_scale = np.random.choice(noise_scales)
noise_scale_w = np.random.choice(noise_scale_ws)
slerp_weight, length_scale, noise_scale, noise_scale_w = next(settings_iter)
with torch.no_grad():
speaker_1 = torch.LongTensor([s[0] for s in speakers_batch])
speaker_2 = torch.LongTensor([s[1] for s in speakers_batch])
phoneme_ids = [get_phonemes(phonemizer, config, next(texts))]*batch_size
phoneme_ids = [get_phonemes(phonemizer, config, next(texts), verbose)]*batch_size
audio = generate_audio(model, speaker_1, speaker_2, phoneme_ids, slerp_weight, noise_scale, noise_scale_w, length_scale, max_len)
# Resample audio
@ -165,18 +163,18 @@ def generate_samples(
is_done = True
break
print(f"Batch {batch_idx +1}/{max_samples//batch_size} complete", " "*200, end='\r')
# print(f"Batch {batch_idx +1}/{max_samples//batch_size} complete", " "*200, end='\r')
# Next batch
# _LOGGER.debug("Batch %s complete", batch_idx + 1)
_LOGGER.debug(f"Batch {batch_idx +1}/{max_samples//batch_size} complete")
speakers_batch = list(it.islice(speakers_iter, 0, batch_size))
batch_idx += 1
# _LOGGER.info("Done")
_LOGGER.info("Done")
def generate_audio(model, speaker_1, speaker_2, phoneme_ids, slerp_weight, noise_scale, noise_scale_w, length_scale, max_len):
x = torch.LongTensor(phoneme_ids)#.repeat((batch_size, 1))
x_lengths = torch.LongTensor([len(i) for i in phoneme_ids])#.repeat(batch_size)
x = torch.LongTensor(phoneme_ids)
x_lengths = torch.LongTensor([len(i) for i in phoneme_ids])
if torch.cuda.is_available():
speaker_1 = speaker_1.cuda()
@ -218,10 +216,11 @@ def generate_audio(model, speaker_1, speaker_2, phoneme_ids, slerp_weight, noise
audio = o
return audio
def get_phonemes(phonemizer, config, text):
def get_phonemes(phonemizer, config, text, verbose):
phonemes_str = phonemizer.phonemize(text)
phonemes = list(unicodedata.normalize("NFD", phonemes_str))
# _LOGGER.debug("Phonemes: %s", phonemes)
if verbose is True:
_LOGGER.debug("Phonemes: %s", phonemes)
id_map = config["phoneme_id_map"]
phoneme_ids = list(id_map["^"])