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mrq 2023-03-07 13:40:41 +00:00
parent df5ba634c0
commit 166d491a98

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