diff --git a/app.py b/app.py
index 875e4c8..a9a5dbc 100755
--- a/app.py
+++ b/app.py
@@ -130,7 +130,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, preset, seed, c
info = {
'text': text,
- 'delimiter': delimiter,
+ 'delimiter': '\\n' if delimiter == "\n" else delimiter,
'emotion': emotion,
'prompt': prompt,
'voice': voice,
@@ -185,89 +185,121 @@ def update_presets(value):
else:
return (gr.update(), gr.update())
+def read_metadata(file):
+ j = None
+ if file is not None:
+ metadata = music_tag.load_file(file.name)
+ if 'lyrics' in metadata:
+ j = json.loads(str(metadata['lyrics']))
+ print(j)
+ return j
+
+def copy_settings(file):
+ metadata = read_metadata(file)
+ if metadata is None:
+ return None
+
+ return (
+ metadata['text'],
+ metadata['delimiter'],
+ metadata['emotion'],
+ metadata['prompt'],
+ metadata['voice'],
+ metadata['mic_audio'],
+ metadata['preset'],
+ metadata['seed'],
+ metadata['candidates'],
+ metadata['num_autoregressive_samples'],
+ metadata['diffusion_iterations'],
+ metadata['temperature'],
+ metadata['diffusion_sampler'],
+ metadata['breathing_room'],
+ metadata['experimentals'],
+ )
+
def update_voices():
return gr.Dropdown.update(choices=os.listdir(os.path.join("tortoise", "voices")) + ["microphone"])
def main():
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- text = gr.Textbox(lines=4, label="Prompt")
- delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
+ with gr.Blocks() as webui:
+ with gr.Tab("Generate"):
+ with gr.Row():
+ with gr.Column():
+ text = gr.Textbox(lines=4, label="Prompt")
+ delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
- emotion = gr.Radio(
- ["None", "Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
- value="None",
- label="Emotion",
- type="value",
- interactive=True
- )
- prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
- voice = gr.Dropdown(
- os.listdir(os.path.join("tortoise", "voices")) + ["microphone"],
- label="Voice",
- type="value",
- )
- mic_audio = gr.Audio(
- label="Microphone Source",
- source="microphone",
- type="filepath",
- )
- refresh_voices = gr.Button(value="Refresh Voice List")
- refresh_voices.click(update_voices,
- inputs=None,
- outputs=voice
- )
-
- prompt.change(fn=lambda value: gr.update(value="Custom"),
- inputs=prompt,
- outputs=emotion
- )
- mic_audio.change(fn=lambda value: gr.update(value="microphone"),
- inputs=mic_audio,
- outputs=voice
- )
- with gr.Column():
- candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
- seed = gr.Number(value=0, precision=0, label="Seed")
+ emotion = gr.Radio(
+ ["None", "Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"],
+ value="None",
+ label="Emotion",
+ type="value",
+ interactive=True
+ )
+ prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
+ voice = gr.Dropdown(
+ os.listdir(os.path.join("tortoise", "voices")) + ["microphone"],
+ label="Voice",
+ type="value",
+ )
+ mic_audio = gr.Audio(
+ label="Microphone Source",
+ source="microphone",
+ type="filepath",
+ )
+ refresh_voices = gr.Button(value="Refresh Voice List")
+ refresh_voices.click(update_voices,
+ inputs=None,
+ outputs=voice
+ )
+
+ prompt.change(fn=lambda value: gr.update(value="Custom"),
+ inputs=prompt,
+ outputs=emotion
+ )
+ mic_audio.change(fn=lambda value: gr.update(value="microphone"),
+ inputs=mic_audio,
+ outputs=voice
+ )
+ with gr.Column():
+ candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
+ seed = gr.Number(value=0, precision=0, label="Seed")
- preset = gr.Radio(
- ["Ultra Fast", "Fast", "Standard", "High Quality", "None"],
- value="None",
- label="Preset",
- type="value",
- )
- num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
- diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
+ preset = gr.Radio(
+ ["Ultra Fast", "Fast", "Standard", "High Quality", "None"],
+ value="None",
+ label="Preset",
+ type="value",
+ )
+ num_autoregressive_samples = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Samples")
+ diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
- temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
- breathing_room = gr.Slider(value=12, minimum=1, maximum=32, step=1, label="Pause Size")
- diffusion_sampler = gr.Radio(
- ["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
- value="P",
- label="Diffusion Samplers",
- type="value",
- )
+ temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
+ breathing_room = gr.Slider(value=12, minimum=1, maximum=32, step=1, label="Pause Size")
+ diffusion_sampler = gr.Radio(
+ ["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
+ value="P",
+ label="Diffusion Samplers",
+ type="value",
+ )
- experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
+ experimentals = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
- preset.change(fn=update_presets,
- inputs=preset,
- outputs=[
- num_autoregressive_samples,
- diffusion_iterations,
- ],
- )
- with gr.Column():
- selected_voice = gr.Audio(label="Source Sample")
- output_audio = gr.Audio(label="Output")
- usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False)
-
- submit = gr.Button(value="Generate")
- #stop = gr.Button(value="Stop")
-
- submit_event = submit.click(generate,
- inputs=[
+ preset.change(fn=update_presets,
+ inputs=preset,
+ outputs=[
+ num_autoregressive_samples,
+ diffusion_iterations,
+ ],
+ )
+ with gr.Column():
+ selected_voice = gr.Audio(label="Source Sample")
+ output_audio = gr.Audio(label="Output")
+ usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False)
+
+ submit = gr.Button(value="Generate")
+ #stop = gr.Button(value="Stop")
+
+ input_settings = [
text,
delimiter,
emotion,
@@ -283,13 +315,34 @@ def main():
diffusion_sampler,
breathing_room,
experimentals,
- ],
- outputs=[selected_voice, output_audio, usedSeed],
- )
+ ]
- #stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
+ submit_event = submit.click(generate,
+ inputs=input_settings,
+ outputs=[selected_voice, output_audio, usedSeed],
+ )
- demo.queue().launch(share=args.share)
+ #stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event])
+ with gr.Tab("Utilities"):
+ with gr.Row():
+ with gr.Column():
+ audio_in = gr.File(type="file", label="Audio Input", file_types=["audio"])
+ copy_button = gr.Button(value="Copy Settings")
+ with gr.Column():
+ metadata_out = gr.JSON(label="Audio Metadata")
+
+ audio_in.upload(
+ fn=read_metadata,
+ inputs=audio_in,
+ outputs=metadata_out,
+ )
+
+ copy_button.click(copy_settings,
+ inputs=audio_in, # JSON elements cannt be used as inputs
+ outputs=input_settings
+ )
+
+ webui.queue().launch(share=args.share)
if __name__ == "__main__":
@@ -299,6 +352,7 @@ if __name__ == "__main__":
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")
args = parser.parse_args()
+ print("Initializating TorToiSe...")
tts = TextToSpeech(minor_optimizations=not args.low_vram)
main()
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