forked from mrq/tortoise-tts
Added tab to read and copy settings from a voice clip (in the future, I'll see about enmbedding the latent used to generate the voice)
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parent
edb6a173d3
commit
e25ec325fe
214
app.py
214
app.py
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@ -130,7 +130,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, preset, seed, c
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info = {
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'text': text,
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'delimiter': delimiter,
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'delimiter': '\\n' if delimiter == "\n" else delimiter,
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'emotion': emotion,
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'prompt': prompt,
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'voice': voice,
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@ -185,89 +185,121 @@ def update_presets(value):
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else:
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return (gr.update(), gr.update())
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def read_metadata(file):
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j = None
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if file is not None:
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metadata = music_tag.load_file(file.name)
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if 'lyrics' in metadata:
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j = json.loads(str(metadata['lyrics']))
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print(j)
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return j
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def copy_settings(file):
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metadata = read_metadata(file)
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if metadata is None:
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return None
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return (
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metadata['text'],
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metadata['delimiter'],
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metadata['emotion'],
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metadata['prompt'],
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metadata['voice'],
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metadata['mic_audio'],
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metadata['preset'],
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metadata['seed'],
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metadata['candidates'],
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metadata['num_autoregressive_samples'],
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metadata['diffusion_iterations'],
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metadata['temperature'],
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metadata['diffusion_sampler'],
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metadata['breathing_room'],
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metadata['experimentals'],
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)
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def update_voices():
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return gr.Dropdown.update(choices=os.listdir(os.path.join("tortoise", "voices")) + ["microphone"])
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def main():
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with gr.Blocks() as demo:
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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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delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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with gr.Blocks() as webui:
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with gr.Tab("Generate"):
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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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delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
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emotion = gr.Radio(
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["None", "Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
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value="None",
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label="Emotion",
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type="value",
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interactive=True
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)
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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voice = gr.Dropdown(
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os.listdir(os.path.join("tortoise", "voices")) + ["microphone"],
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label="Voice",
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type="value",
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)
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mic_audio = gr.Audio(
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label="Microphone Source",
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source="microphone",
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type="filepath",
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)
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refresh_voices = gr.Button(value="Refresh Voice List")
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refresh_voices.click(update_voices,
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inputs=None,
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outputs=voice
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)
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prompt.change(fn=lambda value: gr.update(value="Custom"),
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inputs=prompt,
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outputs=emotion
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)
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mic_audio.change(fn=lambda value: gr.update(value="microphone"),
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inputs=mic_audio,
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outputs=voice
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)
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with gr.Column():
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candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
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seed = gr.Number(value=0, precision=0, label="Seed")
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emotion = gr.Radio(
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["None", "Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
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value="None",
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label="Emotion",
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type="value",
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interactive=True
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)
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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voice = gr.Dropdown(
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os.listdir(os.path.join("tortoise", "voices")) + ["microphone"],
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label="Voice",
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type="value",
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)
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mic_audio = gr.Audio(
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label="Microphone Source",
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source="microphone",
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type="filepath",
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)
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refresh_voices = gr.Button(value="Refresh Voice List")
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refresh_voices.click(update_voices,
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inputs=None,
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outputs=voice
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)
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prompt.change(fn=lambda value: gr.update(value="Custom"),
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inputs=prompt,
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outputs=emotion
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)
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mic_audio.change(fn=lambda value: gr.update(value="microphone"),
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inputs=mic_audio,
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outputs=voice
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)
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with gr.Column():
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candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
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seed = gr.Number(value=0, precision=0, label="Seed")
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preset = gr.Radio(
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["Ultra Fast", "Fast", "Standard", "High Quality", "None"],
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value="None",
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label="Preset",
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type="value",
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)
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num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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preset = gr.Radio(
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["Ultra Fast", "Fast", "Standard", "High Quality", "None"],
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value="None",
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label="Preset",
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type="value",
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)
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num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
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diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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breathing_room = gr.Slider(value=12, minimum=1, maximum=32, step=1, label="Pause Size")
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diffusion_sampler = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="P",
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label="Diffusion Samplers",
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type="value",
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)
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temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
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breathing_room = gr.Slider(value=12, minimum=1, maximum=32, step=1, label="Pause Size")
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diffusion_sampler = gr.Radio(
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["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
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value="P",
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label="Diffusion Samplers",
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type="value",
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)
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experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
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preset.change(fn=update_presets,
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inputs=preset,
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outputs=[
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num_autoregressive_samples,
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diffusion_iterations,
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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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usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False)
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submit = gr.Button(value="Generate")
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#stop = gr.Button(value="Stop")
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submit_event = submit.click(generate,
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inputs=[
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preset.change(fn=update_presets,
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inputs=preset,
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outputs=[
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num_autoregressive_samples,
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diffusion_iterations,
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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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usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False)
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submit = gr.Button(value="Generate")
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#stop = gr.Button(value="Stop")
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input_settings = [
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text,
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delimiter,
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emotion,
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@ -283,13 +315,34 @@ def main():
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diffusion_sampler,
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breathing_room,
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experimentals,
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],
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outputs=[selected_voice, output_audio, usedSeed],
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)
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]
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#stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
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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, usedSeed],
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)
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demo.queue().launch(share=args.share)
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#stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
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with gr.Tab("Utilities"):
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with gr.Row():
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with gr.Column():
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audio_in = gr.File(type="file", label="Audio Input", file_types=["audio"])
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copy_button = gr.Button(value="Copy Settings")
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with gr.Column():
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metadata_out = gr.JSON(label="Audio Metadata")
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audio_in.upload(
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fn=read_metadata,
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inputs=audio_in,
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outputs=metadata_out,
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)
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copy_button.click(copy_settings,
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inputs=audio_in, # JSON elements cannt be used as inputs
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outputs=input_settings
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)
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webui.queue().launch(share=args.share)
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if __name__ == "__main__":
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@ -299,6 +352,7 @@ if __name__ == "__main__":
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parser.add_argument("--cond-latent-max-chunk-size", type=int, default=1000000, help="Sets an upper limit to audio chunk size when computing conditioning latents")
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args = parser.parse_args()
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print("Initializating TorToiSe...")
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tts = TextToSpeech(minor_optimizations=not args.low_vram)
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main()
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