forked from mrq/tortoise-tts
Added candidate selection for outputs, hide output elements (except for the main one) to only show one progress bar
This commit is contained in:
parent
a7330164ab
commit
6d06bcce05
118
webui.py
118
webui.py
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@ -43,7 +43,7 @@ def generate(
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repetition_penalty,
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cond_free_k,
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experimental_checkboxes,
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progress=gr.Progress(track_tqdm=True)
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progress=None
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):
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global args
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global tts
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@ -150,6 +150,10 @@ def generate(
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if idx:
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idx = idx + 1
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# reserve, if for whatever reason you manage to concurrently generate
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with open(f'{outdir}/input_{idx}.json', 'w', encoding="utf-8") as f:
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f.write(" ")
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def get_name(line=0, candidate=0, combined=False):
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name = f"{idx}"
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if combined:
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@ -204,8 +208,8 @@ def generate(
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output_voice = None
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output_voices = []
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if len(texts) > 1:
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for candidate in range(candidates):
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for candidate in range(candidates):
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if len(texts) > 1:
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audio_clips = []
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for line in range(len(texts)):
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name = get_name(line=line, candidate=candidate)
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@ -226,8 +230,7 @@ def generate(
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output_voices.append(f'{outdir}/{voice}_{name}.wav')
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if output_voice is None:
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output_voice = f'{outdir}/{voice}_{name}.wav'
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else:
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for candidate in range(candidates):
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else:
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name = get_name(candidate=candidate)
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output_voices.append(f'{outdir}/{voice}_{name}.wav')
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@ -266,14 +269,12 @@ def generate(
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# we could do this on the pieces before they get stiched up anyways to save some compute
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# but the stitching would need to read back from disk, defeating the point of caching the waveform
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for path in progress.tqdm(audio_cache, desc="Running voicefix..."):
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print("VoiceFix starting")
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voicefixer.restore(
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input=f'{outdir}/{voice}_{k}.wav',
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output=f'{outdir}/{voice}_{k}.wav',
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#cuda=False,
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#mode=mode,
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)
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print("VoiceFix finished")
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if args.embed_output_metadata:
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for path in progress.tqdm(audio_cache, desc="Embedding metadata..."):
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@ -289,25 +290,22 @@ def generate(
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if sample_voice is not None:
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sample_voice = (tts.input_sample_rate, sample_voice.numpy())
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if output_voice is None and len(output_voices):
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output_voice = output_voices[0]
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print(f"Generation took {info['time']} seconds, saved to '{output_voice}'\n")
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print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")
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info['seed'] = settings['use_deterministic_seed']
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del info['latents']
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with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(info, indent='\t') )
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results = [
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stats = [
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[ seed, "{:.3f}".format(info['time']) ]
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]
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return (
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sample_voice,
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output_voice,
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results,
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output_voices,
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stats,
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)
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def update_presets(value):
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@ -438,6 +436,7 @@ def check_for_updates():
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return False
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def reload_tts():
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del tts
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tts = setup_tortoise()
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def cancel_generate():
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@ -577,6 +576,8 @@ def setup_gradio():
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with gr.Row():
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with gr.Column():
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text = gr.Textbox(lines=4, label="Prompt")
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with gr.Row():
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with gr.Column():
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delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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emotion = gr.Radio(
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@ -640,12 +641,15 @@ def setup_gradio():
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],
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)
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with gr.Column():
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selected_voice = gr.Audio(label="Source Sample")
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output_audio = gr.Audio(label="Output")
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generation_results = gr.Dataframe(label="Results", headers=["Seed", "Time"])
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submit = gr.Button(value="Generate")
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stop = gr.Button(value="Stop")
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generation_results = gr.Dataframe(label="Results", headers=["Seed", "Time"], visible=False)
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source_sample = gr.Audio(label="Source Sample", visible=False)
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output_audio = gr.Audio(label="Output")
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candidates_list = gr.Dropdown(label="Candidates", type="value", visible=False)
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output_pick = gr.Button(value="Select Candidate", visible=False)
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with gr.Tab("History"):
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with gr.Row():
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with gr.Column():
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@ -810,11 +814,83 @@ def setup_gradio():
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experimental_checkboxes,
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]
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submit_event = submit.click(generate,
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inputs=input_settings,
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outputs=[selected_voice, output_audio, generation_results],
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# YUCK
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def run_generation(
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text,
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delimiter,
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emotion,
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prompt,
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voice,
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mic_audio,
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seed,
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candidates,
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num_autoregressive_samples,
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diffusion_iterations,
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temperature,
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diffusion_sampler,
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breathing_room,
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cvvp_weight,
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top_p,
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diffusion_temperature,
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length_penalty,
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repetition_penalty,
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cond_free_k,
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experimental_checkboxes,
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progress=gr.Progress(track_tqdm=True)
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):
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try:
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sample, outputs, stats = generate(
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text,
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delimiter,
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emotion,
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prompt,
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voice,
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mic_audio,
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seed,
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candidates,
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num_autoregressive_samples,
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diffusion_iterations,
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temperature,
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diffusion_sampler,
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breathing_room,
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cvvp_weight,
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top_p,
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diffusion_temperature,
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length_penalty,
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repetition_penalty,
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cond_free_k,
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experimental_checkboxes,
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progress
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)
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except Exception as e:
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raise gr.Error(e)
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return (
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outputs[0],
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gr.update(value=sample, visible=sample is not None),
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gr.update(choices=outputs, visible=len(outputs) > 1, interactive=True),
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gr.update(visible=len(outputs) > 1),
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gr.update(value=stats, visible=True),
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)
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output_pick.click(
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lambda x: x,
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inputs=candidates_list,
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outputs=output_audio,
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)
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submit.click(
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lambda: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)),
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outputs=[source_sample, candidates_list, output_pick, generation_results],
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)
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submit_event = submit.click(run_generation,
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inputs=input_settings,
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outputs=[output_audio, source_sample, candidates_list, output_pick, generation_results],
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)
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copy_button.click(import_generate_settings,
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inputs=audio_in, # JSON elements cannot be used as inputs
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outputs=input_settings
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