Port HuggingFace Space to plain Gradio (#81)
* Port HuggingFace Space to plain Gradio * Update app.py Remove BANNER_TEXT --------- Co-authored-by: Careless Parsley <carelessparsley@gmail.com> Co-authored-by: hexgrad <166769057+hexgrad@users.noreply.github.com>
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CarelessParsley
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demo/app.py
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182
demo/app.py
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import spaces
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from kokoro import KModel, KPipeline
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import gradio as gr
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import os
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import random
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import torch
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CUDA_AVAILABLE = torch.cuda.is_available()
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models = {gpu: KModel().to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
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pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False) for lang_code in 'ab'}
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pipelines['a'].g2p.lexicon.golds['kokoro'] = 'kˈOkəɹO'
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pipelines['b'].g2p.lexicon.golds['kokoro'] = 'kˈQkəɹQ'
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@spaces.GPU(duration=30)
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def forward_gpu(ps, ref_s, speed):
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return models[True](ps, ref_s, speed)
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def generate_first(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
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pipeline = pipelines[voice[0]]
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pack = pipeline.load_voice(voice)
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use_gpu = use_gpu and CUDA_AVAILABLE
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for _, ps, _ in pipeline(text, voice, speed):
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ref_s = pack[len(ps)-1]
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try:
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if use_gpu:
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audio = forward_gpu(ps, ref_s, speed)
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else:
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audio = models[False](ps, ref_s, speed)
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except gr.exceptions.Error as e:
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if use_gpu:
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gr.Warning(str(e))
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gr.Info('Retrying with CPU. To avoid this error, change Hardware to CPU.')
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audio = models[False](ps, ref_s, speed)
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else:
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raise gr.Error(e)
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return (24000, audio.numpy()), ps
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return None, ''
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# Arena API
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def predict(text, voice='af_heart', speed=1):
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return generate_first(text, voice, speed, use_gpu=False)[0]
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def tokenize_first(text, voice='af_heart'):
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pipeline = pipelines[voice[0]]
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for _, ps, _ in pipeline(text, voice):
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return ps
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return ''
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def generate_all(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
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pipeline = pipelines[voice[0]]
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pack = pipeline.load_voice(voice)
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use_gpu = use_gpu and CUDA_AVAILABLE
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first = True
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for _, ps, _ in pipeline(text, voice, speed):
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ref_s = pack[len(ps)-1]
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try:
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if use_gpu:
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audio = forward_gpu(ps, ref_s, speed)
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else:
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audio = models[False](ps, ref_s, speed)
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except gr.exceptions.Error as e:
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if use_gpu:
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gr.Warning(str(e))
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gr.Info('Switching to CPU')
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audio = models[False](ps, ref_s, speed)
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else:
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raise gr.Error(e)
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yield 24000, audio.numpy()
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if first:
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first = False
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yield 24000, torch.zeros(1).numpy()
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with open('en.txt', 'r') as r:
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random_quotes = [line.strip() for line in r]
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def get_random_quote():
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return random.choice(random_quotes)
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def get_gatsby():
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with open('gatsby5k.md', 'r') as r:
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return r.read().strip()
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def get_frankenstein():
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with open('frankenstein5k.md', 'r') as r:
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return r.read().strip()
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CHOICES = {
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'🇺🇸 🚺 Heart ❤️': 'af_heart',
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'🇺🇸 🚺 Bella 🔥': 'af_bella',
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'🇺🇸 🚺 Nicole 🎧': 'af_nicole',
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'🇺🇸 🚺 Aoede': 'af_aoede',
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'🇺🇸 🚺 Kore': 'af_kore',
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'🇺🇸 🚺 Sarah': 'af_sarah',
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'🇺🇸 🚺 Nova': 'af_nova',
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'🇺🇸 🚺 Sky': 'af_sky',
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'🇺🇸 🚺 Alloy': 'af_alloy',
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'🇺🇸 🚺 Jessica': 'af_jessica',
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'🇺🇸 🚺 River': 'af_river',
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'🇺🇸 🚹 Michael': 'am_michael',
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'🇺🇸 🚹 Fenrir': 'am_fenrir',
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'🇺🇸 🚹 Puck': 'am_puck',
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'🇺🇸 🚹 Echo': 'am_echo',
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'🇺🇸 🚹 Eric': 'am_eric',
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'🇺🇸 🚹 Liam': 'am_liam',
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'🇺🇸 🚹 Onyx': 'am_onyx',
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'🇺🇸 🚹 Santa': 'am_santa',
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'🇺🇸 🚹 Adam': 'am_adam',
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'🇬🇧 🚺 Emma': 'bf_emma',
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'🇬🇧 🚺 Isabella': 'bf_isabella',
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'🇬🇧 🚺 Alice': 'bf_alice',
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'🇬🇧 🚺 Lily': 'bf_lily',
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'🇬🇧 🚹 George': 'bm_george',
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'🇬🇧 🚹 Fable': 'bm_fable',
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'🇬🇧 🚹 Lewis': 'bm_lewis',
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'🇬🇧 🚹 Daniel': 'bm_daniel',
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}
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for v in CHOICES.values():
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pipelines[v[0]].load_voice(v)
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TOKEN_NOTE = '''
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💡 Customize pronunciation with Markdown link syntax and /slashes/ like `[Kokoro](/kˈOkəɹO/)`
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💬 To adjust intonation, try punctuation `;:,.!?—…"()“”` or stress `ˈ` and `ˌ`
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⬇️ Lower stress `[1 level](-1)` or `[2 levels](-2)`
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⬆️ Raise stress 1 level `[or](+2)` 2 levels (only works on less stressed, usually short words)
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'''
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with gr.Blocks() as generate_tab:
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out_audio = gr.Audio(label='Output Audio', interactive=False, streaming=False, autoplay=True)
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generate_btn = gr.Button('Generate', variant='primary')
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with gr.Accordion('Output Tokens', open=True):
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out_ps = gr.Textbox(interactive=False, show_label=False, info='Tokens used to generate the audio, up to 510 context length.')
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tokenize_btn = gr.Button('Tokenize', variant='secondary')
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gr.Markdown(TOKEN_NOTE)
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predict_btn = gr.Button('Predict', variant='secondary', visible=False)
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STREAM_NOTE = ['⚠️ There is an unknown Gradio bug that might yield no audio the first time you click `Stream`.']
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STREAM_NOTE = '\n\n'.join(STREAM_NOTE)
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with gr.Blocks() as stream_tab:
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out_stream = gr.Audio(label='Output Audio Stream', interactive=False, streaming=True, autoplay=True)
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with gr.Row():
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stream_btn = gr.Button('Stream', variant='primary')
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stop_btn = gr.Button('Stop', variant='stop')
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with gr.Accordion('Note', open=True):
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gr.Markdown(STREAM_NOTE)
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gr.DuplicateButton()
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API_OPEN = True
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with gr.Blocks() as app:
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with gr.Row():
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with gr.Column():
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text = gr.Textbox(label='Input Text', info=f"Arbitrarily many characters supported")
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with gr.Row():
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voice = gr.Dropdown(list(CHOICES.items()), value='af_heart', label='Voice', info='Quality and availability vary by language')
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use_gpu = gr.Dropdown(
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[('ZeroGPU 🚀', True), ('CPU 🐌', False)],
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value=CUDA_AVAILABLE,
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label='Hardware',
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info='GPU is usually faster, but has a usage quota',
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interactive=CUDA_AVAILABLE
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)
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speed = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.1, label='Speed')
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random_btn = gr.Button('🎲 Random Quote 💬', variant='secondary')
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with gr.Row():
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gatsby_btn = gr.Button('🥂 Gatsby 📕', variant='secondary')
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frankenstein_btn = gr.Button('💀 Frankenstein 📗', variant='secondary')
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with gr.Column():
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gr.TabbedInterface([generate_tab, stream_tab], ['Generate', 'Stream'])
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random_btn.click(fn=get_random_quote, inputs=[], outputs=[text])
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gatsby_btn.click(fn=get_gatsby, inputs=[], outputs=[text])
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frankenstein_btn.click(fn=get_frankenstein, inputs=[], outputs=[text])
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generate_btn.click(fn=generate_first, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps])
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tokenize_btn.click(fn=tokenize_first, inputs=[text, voice], outputs=[out_ps])
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stream_event = stream_btn.click(fn=generate_all, inputs=[text, voice, speed, use_gpu], outputs=[out_stream])
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stop_btn.click(fn=None, cancels=stream_event)
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predict_btn.click(fn=predict, inputs=[text, voice, speed], outputs=[out_audio])
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if __name__ == '__main__':
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app.queue(api_open=API_OPEN).launch(server_name="0.0.0.0", server_port=40001, show_api=API_OPEN)
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