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@ -490,6 +490,17 @@ def stop_training():
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training_process.kill()
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training_process.kill()
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return "Training cancelled"
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return "Training cancelled"
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def convert_to_halfp():
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autoregressive_model_path = get_model_path('autoregressive.pth')
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model = torch.load(autoregressive_model_path)
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for k in model:
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if re.findall(r'\.weight$', k):
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print(f"Converting: {k}")
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model[k] = model[k].half()
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torch.save(model, './models/tortoise/autoregressive_half.pth')
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print('Converted model to half precision: ./models/tortoise/autoregressive_half.pth')
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def prepare_dataset( files, outdir, language=None, progress=None ):
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def prepare_dataset( files, outdir, language=None, progress=None ):
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unload_tts()
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unload_tts()
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@ -961,6 +972,7 @@ def read_generate_settings(file, read_latents=True):
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if isinstance(file, list) and len(file) == 1:
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if isinstance(file, list) and len(file) == 1:
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file = file[0]
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file = file[0]
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try:
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if file is not None:
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if file is not None:
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if hasattr(file, 'name'):
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if hasattr(file, 'name'):
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file = file.name
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file = file.name
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@ -972,6 +984,8 @@ def read_generate_settings(file, read_latents=True):
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elif file[-5:] == ".json":
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elif file[-5:] == ".json":
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with open(file, 'r') as f:
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with open(file, 'r') as f:
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j = json.load(f)
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j = json.load(f)
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except Exception as e:
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pass
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if j is None:
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if j is None:
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print("No metadata found in audio file to read")
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print("No metadata found in audio file to read")
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