forked from mrq/ai-voice-cloning
favor existing arguments from parameters (kwargs) over global (args)
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@ -1015,7 +1015,7 @@ def generate_tortoise(**kwargs):
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'cond_free_k': float(parameters['cond_free_k']),
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'num_autoregressive_samples': parameters['num_autoregressive_samples'],
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'sample_batch_size': args.sample_batch_size,
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'sample_batch_size': parameters['sample_batch_size'] if parameters.get('sample_batch_size') else args.sample_batch_size,
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'diffusion_iterations': parameters['diffusion_iterations'],
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'voice_samples': None,
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@ -1030,9 +1030,9 @@ def generate_tortoise(**kwargs):
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'cond_free': "Conditioning-Free" in parameters['experimentals'],
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'cvvp_amount': parameters['cvvp_weight'],
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'autoregressive_model': args.autoregressive_model,
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'diffusion_model': args.diffusion_model,
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'tokenizer_json': args.tokenizer_json,
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'autoregressive_model': parameters['autoregressive_model'] if parameters.get('autoregressive_model') else args.autoregressive_model,
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'diffusion_model': parameters['diffusion_model'] if parameters.get('diffusion_model') else args.diffusion_model,
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'tokenizer_json': parameters['tokenizer_json'] if parameters.get('tokenizer_json') else args.tokenizer_json,
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}
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# could be better to just do a ternary on everything above, but i am not a professional
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@ -1046,6 +1046,7 @@ def generate_tortoise(**kwargs):
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continue
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settings[k] = override[k]
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if settings['autoregressive_model'] is not None:
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if settings['autoregressive_model'] == "auto":
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settings['autoregressive_model'] = deduce_autoregressive_model(selected_voice)
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