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2025-12-29 04:12:44 -08:00
parent 208bc4fa2f
commit 29d040e19d
6 changed files with 24 additions and 18 deletions
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6
lib/classes/tts_engines/bark.py
View File

@@ -124,7 +124,6 @@ class Bark(TTSUtils, TTSRegistry, name='bark'):
return False
if self.engine:
device = devices['CUDA']['proc'] if self.session['device'] in ['cuda', 'jetson'] else self.session['device']
self.engine.to(device)
final_sentence_file = os.path.join(self.session['chapters_dir_sentences'], f'{sentence_index}.{default_audio_proc_format}')
if sentence == TTS_SML['break']:
silence_time = int(np.random.uniform(0.3, 0.6) * 100) / 100
@@ -187,6 +186,7 @@ class Bark(TTSUtils, TTSRegistry, name='bark'):
**fine_tuned_params
)
"""
self.engine.to(device)
audio_sentence = self.engine.tts(
text=sentence,
speaker=speaker,
@@ -195,8 +195,8 @@ class Bark(TTSUtils, TTSRegistry, name='bark'):
**fine_tuned_params
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
src_tensor = self._tensor_type(audio_sentence)
audio_tensor = src_tensor.clone().detach().unsqueeze(0).cpu()
if audio_tensor is not None and audio_tensor.numel() > 0:
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)

8
lib/classes/tts_engines/fairseq.py
View File

@@ -77,7 +77,6 @@ class Fairseq(TTSUtils, TTSRegistry, name='fairseq'):
return False
if self.engine:
device = devices['CUDA']['proc'] if self.session['device'] in ['cuda', 'jetson'] else self.session['device']
self.engine.to(device)
final_sentence_file = os.path.join(self.session['chapters_dir_sentences'], f'{sentence_index}.{default_audio_proc_format}')
if sentence == TTS_SML['break']:
silence_time = int(np.random.uniform(0.3, 0.6) * 100) / 100
@@ -102,6 +101,7 @@ class Fairseq(TTSUtils, TTSRegistry, name='fairseq'):
tmp_in_wav = os.path.join(proc_dir, f"{uuid.uuid4()}.wav")
tmp_out_wav = os.path.join(proc_dir, f"{uuid.uuid4()}.wav")
with torch.no_grad():
self.engine.to(device)
self.engine.tts_to_file(
text=re.sub(not_supported_punc_pattern, ' ', sentence),
file_path=tmp_in_wav,
@@ -145,6 +145,7 @@ class Fairseq(TTSUtils, TTSRegistry, name='fairseq'):
self.params['samplerate'] = TTS_VOICE_CONVERSION[self.tts_zs_key]['samplerate']
source_wav = self._resample_wav(tmp_out_wav, self.params['samplerate'])
target_wav = self._resample_wav(self.params['voice_path'], self.params['samplerate'])
self.engine_zs.to(device)
audio_sentence = self.engine_zs.voice_conversion(
source_wav=source_wav,
target_wav=target_wav
@@ -161,13 +162,14 @@ class Fairseq(TTSUtils, TTSRegistry, name='fairseq'):
os.remove(source_wav)
else:
with torch.no_grad():
self.engine.to(device)
audio_sentence = self.engine.tts(
text=re.sub(not_supported_punc_pattern, ' ', sentence),
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
src_tensor = self._tensor_type(audio_sentence)
audio_tensor = src_tensor.clone().detach().unsqueeze(0).cpu()
if audio_tensor is not None and audio_tensor.numel() > 0:
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)

8
lib/classes/tts_engines/tacotron.py
View File

@@ -102,7 +102,6 @@ class Tacotron2(TTSUtils, TTSRegistry, name='tacotron'):
return False
if self.engine:
device = devices['CUDA']['proc'] if self.session['device'] in ['cuda', 'jetson'] else self.session['device']
self.engine.to(device)
final_sentence_file = os.path.join(self.session['chapters_dir_sentences'], f'{sentence_index}.{default_audio_proc_format}')
if sentence == TTS_SML['break']:
silence_time = int(np.random.uniform(0.3, 0.6) * 100) / 100
@@ -130,6 +129,7 @@ class Tacotron2(TTSUtils, TTSRegistry, name='tacotron'):
tmp_in_wav = os.path.join(proc_dir, f"{uuid.uuid4()}.wav")
tmp_out_wav = os.path.join(proc_dir, f"{uuid.uuid4()}.wav")
with torch.no_grad():
self.engine.to(device)
self.engine.tts_to_file(
text=re.sub(not_supported_punc_pattern, ' ', sentence),
file_path=tmp_in_wav,
@@ -173,6 +173,7 @@ class Tacotron2(TTSUtils, TTSRegistry, name='tacotron'):
self.params['samplerate'] = TTS_VOICE_CONVERSION[self.tts_zs_key]['samplerate']
source_wav = self._resample_wav(tmp_out_wav, self.params['samplerate'])
target_wav = self._resample_wav(self.params['voice_path'], self.params['samplerate'])
self.engine_zs.to(device)
audio_sentence = self.engine_zs.voice_conversion(
source_wav=source_wav,
target_wav=target_wav
@@ -189,13 +190,14 @@ class Tacotron2(TTSUtils, TTSRegistry, name='tacotron'):
os.remove(source_wav)
else:
with torch.no_grad():
self.engine.to(device)
audio_sentence = self.engine.tts(
text=re.sub(not_supported_punc_pattern, ' ', sentence),
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
src_tensor = self._tensor_type(audio_sentence)
audio_tensor = src_tensor.clone().detach().unsqueeze(0).cpu()
if audio_tensor is not None and audio_tensor.numel() > 0:
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)

8
lib/classes/tts_engines/vits.py
View File

@@ -85,7 +85,6 @@ class Vits(TTSUtils, TTSRegistry, name='vits'):
return False
if self.engine:
device = devices['CUDA']['proc'] if self.session['device'] in ['cuda', 'jetson'] else self.session['device']
self.engine.to(device)
final_sentence_file = os.path.join(self.session['chapters_dir_sentences'], f'{sentence_index}.{default_audio_proc_format}')
if sentence == TTS_SML['break']:
silence_time = int(np.random.uniform(0.3, 0.6) * 100) / 100
@@ -115,6 +114,7 @@ class Vits(TTSUtils, TTSRegistry, name='vits'):
tmp_in_wav = os.path.join(proc_dir, f"{uuid.uuid4()}.wav")
tmp_out_wav = os.path.join(proc_dir, f"{uuid.uuid4()}.wav")
with torch.no_grad():
self.engine.to(device)
self.engine.tts_to_file(
text=sentence,
file_path=tmp_in_wav,
@@ -158,6 +158,7 @@ class Vits(TTSUtils, TTSRegistry, name='vits'):
self.params['samplerate'] = TTS_VOICE_CONVERSION[self.tts_zs_key]['samplerate']
source_wav = self._resample_wav(tmp_out_wav, self.params['samplerate'])
target_wav = self._resample_wav(self.params['voice_path'], self.params['samplerate'])
self.engine_zs.to(device)
audio_sentence = self.engine_zs.voice_conversion(
source_wav=source_wav,
target_wav=target_wav
@@ -174,13 +175,14 @@ class Vits(TTSUtils, TTSRegistry, name='vits'):
os.remove(source_wav)
else:
with torch.no_grad():
self.engine.to(device)
audio_sentence = self.engine.tts(
text=sentence,
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
src_tensor = self._tensor_type(audio_sentence)
audio_tensor = src_tensor.clone().detach().unsqueeze(0).cpu()
if audio_tensor is not None and audio_tensor.numel() > 0:
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)

6
lib/classes/tts_engines/xtts.py
View File

@@ -85,7 +85,6 @@ class XTTSv2(TTSUtils, TTSRegistry, name='xtts'):
return False
if self.engine:
device = devices['CUDA']['proc'] if self.session['device'] in ['cuda', 'jetson'] else self.session['device']
self.engine.to(device)
final_sentence_file = os.path.join(self.session['chapters_dir_sentences'], f'{sentence_index}.{default_audio_proc_format}')
if sentence == TTS_SML['break']:
silence_time = int(np.random.uniform(0.3, 0.6) * 100) / 100
@@ -132,6 +131,7 @@ class XTTSv2(TTSUtils, TTSRegistry, name='xtts'):
if self.session.get(key) is not None
}
with torch.no_grad():
self.engine.to(device)
result = self.engine.inference(
text=sentence,
language=self.session['language_iso1'],
@@ -141,8 +141,8 @@ class XTTSv2(TTSUtils, TTSRegistry, name='xtts'):
)
audio_sentence = result.get('wav')
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
src_tensor = self._tensor_type(audio_sentence)
audio_tensor = src_tensor.clone().detach().unsqueeze(0).cpu()
if audio_tensor is not None and audio_tensor.numel() > 0:
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)

6
lib/classes/tts_engines/yourtts.py
View File

@@ -76,7 +76,6 @@ class YourTTS(TTSUtils, TTSRegistry, name='yourtts'):
return False
if self.engine:
device = devices['CUDA']['proc'] if self.session['device'] in ['cuda', 'jetson'] else self.session['device']
self.engine.to(device)
final_sentence_file = os.path.join(self.session['chapters_dir_sentences'], f'{sentence_index}.{default_audio_proc_format}')
if sentence == TTS_SML['break']:
silence_time = int(np.random.uniform(0.3, 0.6) * 100) / 100
@@ -103,14 +102,15 @@ class YourTTS(TTSUtils, TTSRegistry, name='yourtts'):
voice_key = default_engine_settings[self.session['tts_engine']]['voices']['ElectroMale-2']
speaker_argument = {"speaker": voice_key}
with torch.no_grad():
self.engine.to(device)
audio_sentence = self.engine.tts(
text=re.sub(not_supported_punc_pattern, ' ', sentence),
language=language,
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
src_tensor = self._tensor_type(audio_sentence)
audio_tensor = src_tensor.clone().detach().unsqueeze(0).cpu()
if audio_tensor is not None and audio_tensor.numel() > 0:
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)