forked from mrq/ai-voice-cloning
fixes
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@ -42,7 +42,7 @@ WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v2"]
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WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
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WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band'] #, 'bigvgan_24khz_100band']
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
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EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
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@ -184,7 +184,7 @@ def generate(
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settings['sample_batch_size'] = num_autoregressive_samples
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if settings['conditioning_latents'] is not None and len(settings['conditioning_latents']) == 2 and settings['cvvp_amount'] > 0:
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print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
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print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents with 'Slimmer voice latents' unchecked.")
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settings['cvvp_amount'] = 0
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return settings
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@ -317,7 +317,7 @@ def generate(
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if emotion == "Custom":
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if prompt and prompt.strip() != "":
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cut_text = f"[{prompt},] {cut_text}"
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elif emotion != "None":
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elif emotion != "None" and emotion:
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cut_text = f"[I am really {emotion.lower()},] {cut_text}"
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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