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v25.12.25

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ROBERT MCDOWELL
2025-12-25 16:54:57 -08:00
committed by GitHub
parent 9cb0316fbf ca9f4073b6
commit 2a571f04b7
8 changed files with 100 additions and 18 deletions
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17
lib/classes/tts_engines/bark.py
View File

@@ -165,6 +165,7 @@ class Bark(TTSUtils, TTSRegistry, name='bark'):
"""
pth_voice_dir = os.path.join(bark_dir, speaker)
pth_voice_file = os.path.join(bark_dir, speaker, f'{speaker}.pth')
self.engine.synthesizer.voice_dir = pth_voice_dir
tts_dyn_params = {}
if not os.path.exists(pth_voice_file) or speaker not in self.engine.speakers:
tts_dyn_params['speaker_wav'] = self.params['voice_path']
@@ -197,8 +198,16 @@ class Bark(TTSUtils, TTSRegistry, name='bark'):
#if is_audio_data_valid(audio_sentence):
# audio_sentence = audio_sentence.tolist()
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
if isinstance(audio_sentence, torch.Tensor):
audio_tensor = audio_sentence.detach().cpu().unsqueeze(0)
elif isinstance(audio_sentence, np.ndarray):
audio_tensor = torch.from_numpy(audio_sentence).unsqueeze(0)
elif isinstance(audio_sentence, (list, tuple)):
audio_tensor = torch.tensor(audio_sentence, dtype=torch.float32).unsqueeze(0)
else:
error = f"Unsupported Bark wav type: {type(audio_sentence)}"
print(error)
return False
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)
if audio_tensor is not None and audio_tensor.numel() > 0:
@@ -231,6 +240,10 @@ class Bark(TTSUtils, TTSRegistry, name='bark'):
error = f"Cannot create {final_sentence_file}"
print(error)
return False
else:
error = f"audio_tensor not valid"
print(error)
return False
else:
error = f"audio_sentence not valid"
print(error)

11
lib/classes/tts_engines/common/utils.py
View File

@@ -207,7 +207,16 @@ class TTSUtils:
speaker_embedding=speaker_embedding,
**fine_tuned_params,
)
audio_sentence = result.get('wav') if isinstance(result, dict) else None
if isinstance(audio_sentence, torch.Tensor):
audio_tensor = audio_sentence.detach().cpu().unsqueeze(0)
elif isinstance(audio_sentence, np.ndarray):
audio_tensor = torch.from_numpy(audio_sentence).unsqueeze(0)
elif isinstance(audio_sentence, (list, tuple)):
audio_tensor = torch.tensor(audio_sentence, dtype=torch.float32).unsqueeze(0)
else:
error = f"Unsupported XTTSv2 wav type: {type(audio_sentence)}"
print(error)
return False
if audio_sentence is not None:
audio_sentence = audio_sentence.tolist()
sourceTensor = self._tensor_type(audio_sentence)

16
lib/classes/tts_engines/fairseq.py
View File

@@ -166,8 +166,16 @@ class Fairseq(TTSUtils, TTSRegistry, name='fairseq'):
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
if isinstance(audio_sentence, torch.Tensor):
audio_tensor = audio_sentence.detach().cpu().unsqueeze(0)
elif isinstance(audio_sentence, np.ndarray):
audio_tensor = torch.from_numpy(audio_sentence).unsqueeze(0)
elif isinstance(audio_sentence, (list, tuple)):
audio_tensor = torch.tensor(audio_sentence, dtype=torch.float32).unsqueeze(0)
else:
error = f"Unsupported Fairseq wav type: {type(audio_sentence)}"
print(error)
return False
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)
if audio_tensor is not None and audio_tensor.numel() > 0:
@@ -200,6 +208,10 @@ class Fairseq(TTSUtils, TTSRegistry, name='fairseq'):
error = f"Cannot create {final_sentence_file}"
print(error)
return False
else:
error = f"audio_tensor not valid"
print(error)
return False
else:
error = f"audio_sentence not valid"
print(error)

16
lib/classes/tts_engines/tacotron.py
View File

@@ -194,8 +194,16 @@ class Tacotron2(TTSUtils, TTSRegistry, name='tacotron'):
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
if isinstance(audio_sentence, torch.Tensor):
audio_tensor = audio_sentence.detach().cpu().unsqueeze(0)
elif isinstance(audio_sentence, np.ndarray):
audio_tensor = torch.from_numpy(audio_sentence).unsqueeze(0)
elif isinstance(audio_sentence, (list, tuple)):
audio_tensor = torch.tensor(audio_sentence, dtype=torch.float32).unsqueeze(0)
else:
error = f"Unsupported Tacotron2 wav type: {type(audio_sentence)}"
print(error)
return False
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)
if audio_tensor is not None and audio_tensor.numel() > 0:
@@ -228,6 +236,10 @@ class Tacotron2(TTSUtils, TTSRegistry, name='tacotron'):
error = f"Cannot create {final_sentence_file}"
print(error)
return False
else:
error = f"audio_tensor not valid"
print(error)
return False
else:
error = f"audio_sentence not valid"
print(error)

16
lib/classes/tts_engines/vits.py
View File

@@ -179,8 +179,16 @@ class Vits(TTSUtils, TTSRegistry, name='vits'):
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
if isinstance(audio_sentence, torch.Tensor):
audio_tensor = audio_sentence.detach().cpu().unsqueeze(0)
elif isinstance(audio_sentence, np.ndarray):
audio_tensor = torch.from_numpy(audio_sentence).unsqueeze(0)
elif isinstance(audio_sentence, (list, tuple)):
audio_tensor = torch.tensor(audio_sentence, dtype=torch.float32).unsqueeze(0)
else:
error = f"Unsupported Vits wav type: {type(audio_sentence)}"
print(error)
return False
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)
if audio_tensor is not None and audio_tensor.numel() > 0:
@@ -213,6 +221,10 @@ class Vits(TTSUtils, TTSRegistry, name='vits'):
error = f"Cannot create {final_sentence_file}"
print(error)
return False
else:
error = f"audio_tensor not valid"
print(error)
return False
else:
error = f"audio_sentence not valid"
print(error)

18
lib/classes/tts_engines/xtts.py
View File

@@ -141,10 +141,16 @@ class XTTSv2(TTSUtils, TTSRegistry, name='xtts'):
)
audio_sentence = result.get('wav')
if is_audio_data_valid(audio_sentence):
audio_sentence = audio_sentence.tolist()
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
if isinstance(audio_sentence, torch.Tensor):
audio_tensor = audio_sentence.detach().cpu().unsqueeze(0)
elif isinstance(audio_sentence, np.ndarray):
audio_tensor = torch.from_numpy(audio_sentence).unsqueeze(0)
elif isinstance(audio_sentence, (list, tuple)):
audio_tensor = torch.tensor(audio_sentence, dtype=torch.float32).unsqueeze(0)
else:
error = f"Unsupported XTTSv2 wav type: {type(audio_sentence)}"
print(error)
return False
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)
if audio_tensor is not None and audio_tensor.numel() > 0:
@@ -177,6 +183,10 @@ class XTTSv2(TTSUtils, TTSRegistry, name='xtts'):
error = f"Cannot create {final_sentence_file}"
print(error)
return False
else:
error = f"audio_tensor not valid"
print(error)
return False
else:
error = f"audio_sentence not valid"
print(error)

16
lib/classes/tts_engines/yourtts.py
View File

@@ -109,8 +109,16 @@ class YourTTS(TTSUtils, TTSRegistry, name='yourtts'):
**speaker_argument
)
if is_audio_data_valid(audio_sentence):
sourceTensor = self._tensor_type(audio_sentence)
audio_tensor = sourceTensor.clone().detach().unsqueeze(0).cpu()
if isinstance(audio_sentence, torch.Tensor):
audio_tensor = audio_sentence.detach().cpu().unsqueeze(0)
elif isinstance(audio_sentence, np.ndarray):
audio_tensor = torch.from_numpy(audio_sentence).unsqueeze(0)
elif isinstance(audio_sentence, (list, tuple)):
audio_tensor = torch.tensor(audio_sentence, dtype=torch.float32).unsqueeze(0)
else:
error = f"Unsupported YourTTS wav type: {type(audio_sentence)}"
print(error)
return False
if sentence[-1].isalnum() or sentence[-1] == '—':
audio_tensor = trim_audio(audio_tensor.squeeze(), self.params['samplerate'], 0.001, trim_audio_buffer).unsqueeze(0)
if audio_tensor is not None and audio_tensor.numel() > 0:
@@ -143,6 +151,10 @@ class YourTTS(TTSUtils, TTSRegistry, name='yourtts'):
error = f"Cannot create {final_sentence_file}"
print(error)
return False
else:
error = f"audio_tensor not valid"
print(error)
return False
else:
error = f"audio_sentence not valid"
print(error)

8
lib/conf.py
View File

@@ -45,14 +45,16 @@ os.environ['MPLCONFIGDIR'] = f'{models_dir}/matplotlib'
os.environ['TESSDATA_PREFIX'] = f'{models_dir}/tessdata'
os.environ['STANZA_RESOURCES_DIR'] = os.path.join(models_dir, 'stanza')
os.environ['ARGOS_TRANSLATE_PACKAGE_PATH'] = os.path.join(models_dir, 'argostranslate')
os.environ['MallocStackLogging'] = '0'
os.environ['MallocStackLoggingNoCompact'] = '0'
os.environ['TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD'] = '1'
os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
os.environ['PYTORCH_NO_CUDA_MEMORY_CACHING'] = '1'
os.environ['TORCH_CUDA_ENABLE_CUDA_GRAPH'] = '0'
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128,garbage_collection_threshold:0.6,expandable_segments:True'
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
os.environ["CUDA_CACHE_MAXSIZE"] = "2147483648"
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
os.environ['CUDA_CACHE_MAXSIZE'] = '2147483648'
os.environ['SUNO_OFFLOAD_CPU'] = 'False'
os.environ['SUNO_USE_SMALL_MODELS'] = 'False'
if platform.system() == 'Windows':