HarkonCollider/tortoise-tts
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fixes

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mrq 2023-02-15 15:33:08 +00:00
parent 729b292515
commit dcc5c140e6
1 changed files with 14 additions and 6 deletions
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20
webui.py
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@ -548,10 +548,14 @@ def cancel_generate():
tortoise.api.STOP_SIGNAL = True tortoise.api.STOP_SIGNAL = True
def get_voice_list(dir=get_voice_dir()): def get_voice_list(dir=get_voice_dir()):
os.makedirs(dir, exist_ok=True)
return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"] return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"]
def update_voices(): def update_voices():
return gr.Dropdown.update(choices=get_voice_list()) return (
gr.Dropdown.update(choices=get_voice_list()),
gr.Dropdown.update(choices=get_voice_list("./results/")),
)
def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, sample_batch_size, concurrency_count, output_sample_rate, output_volume ): def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
args.listen = listen args.listen = listen
@ -662,7 +666,7 @@ def setup_tortoise(restart=False):
voicefixer = VoiceFixer() voicefixer = VoiceFixer()
print("initialized voice-fixer") print("initialized voice-fixer")
except Exception as e: except Exception as e:
pass print(f"Error occurred while tring to initialize voicefixer: {e}")
print("Initializating TorToiSe...") print("Initializating TorToiSe...")
tts = TextToSpeech(minor_optimizations=not args.low_vram) tts = TextToSpeech(minor_optimizations=not args.low_vram)
@ -716,10 +720,6 @@ def setup_gradio():
type="filepath", type="filepath",
) )
refresh_voices = gr.Button(value="Refresh Voice List") refresh_voices = gr.Button(value="Refresh Voice List")
refresh_voices.click(update_voices,
inputs=None,
outputs=voice
)
voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1) voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1)
recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents") recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
recompute_voice_latents.click(compute_latents, recompute_voice_latents.click(compute_latents,
@ -1040,6 +1040,14 @@ def setup_gradio():
gr.update(value=stats, visible=True), gr.update(value=stats, visible=True),
) )
refresh_voices.click(update_voices,
inputs=None,
outputs=[
voice,
history_voices
]
)
output_pick.click( output_pick.click(
lambda x: x, lambda x: x,
inputs=candidates_list, inputs=candidates_list,