Delete app.py

app.py

@@ -1,202 +0,0 @@
-import spaces
-from kokoro import KModel, KPipeline
-import gradio as gr
-import os
-import random
-import torch
-
-IS_DUPLICATE = not os.getenv('SPACE_ID', '').startswith('hexgrad/')
-CUDA_AVAILABLE = torch.cuda.is_available()
-if not IS_DUPLICATE:
-    import kokoro
-    import misaki
-    print('DEBUG', kokoro.__version__, CUDA_AVAILABLE, misaki.__version__)
-
-CHAR_LIMIT = None if IS_DUPLICATE else 5000
-models = {gpu: KModel().to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
-pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False) for lang_code in 'ab'}
-pipelines['a'].g2p.lexicon.golds['kokoro'] = 'kˈOkəɹO'
-pipelines['b'].g2p.lexicon.golds['kokoro'] = 'kˈQkəɹQ'
-
-@spaces.GPU(duration=30)
-def forward_gpu(ps, ref_s, speed):
-    return models[True](ps, ref_s, speed)
-
-def generate_first(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
-    text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
-    pipeline = pipelines[voice[0]]
-    pack = pipeline.load_voice(voice)
-    use_gpu = use_gpu and CUDA_AVAILABLE
-    for _, ps, _ in pipeline(text, voice, speed):
-        ref_s = pack[len(ps)-1]
-        try:
-            if use_gpu:
-                audio = forward_gpu(ps, ref_s, speed)
-            else:
-                audio = models[False](ps, ref_s, speed)
-        except gr.exceptions.Error as e:
-            if use_gpu:
-                gr.Warning(str(e))
-                gr.Info('Retrying with CPU. To avoid this error, change Hardware to CPU.')
-                audio = models[False](ps, ref_s, speed)
-            else:
-                raise gr.Error(e)
-        return (24000, audio.numpy()), ps
-    return None, ''
-
-# Arena API
-def predict(text, voice='af_heart', speed=1):
-    return generate_first(text, voice, speed, use_gpu=False)[0]
-
-def tokenize_first(text, voice='af_heart'):
-    pipeline = pipelines[voice[0]]
-    for _, ps, _ in pipeline(text, voice):
-        return ps
-    return ''
-
-def generate_all(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
-    text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
-    pipeline = pipelines[voice[0]]
-    pack = pipeline.load_voice(voice)
-    use_gpu = use_gpu and CUDA_AVAILABLE
-    first = True
-    for _, ps, _ in pipeline(text, voice, speed):
-        ref_s = pack[len(ps)-1]
-        try:
-            if use_gpu:
-                audio = forward_gpu(ps, ref_s, speed)
-            else:
-                audio = models[False](ps, ref_s, speed)
-        except gr.exceptions.Error as e:
-            if use_gpu:
-                gr.Warning(str(e))
-                gr.Info('Switching to CPU')
-                audio = models[False](ps, ref_s, speed)
-            else:
-                raise gr.Error(e)
-        yield 24000, audio.numpy()
-        if first:
-            first = False
-            yield 24000, torch.zeros(1).numpy()
-
-with open('en.txt', 'r') as r:
-    random_quotes = [line.strip() for line in r]
-
-def get_random_quote():
-    return random.choice(random_quotes)
-
-def get_gatsby():
-    with open('gatsby5k.md', 'r') as r:
-        return r.read().strip()
-
-def get_frankenstein():
-    with open('frankenstein5k.md', 'r') as r:
-        return r.read().strip()
-
-CHOICES = {
-'🇺🇸 🚺 Heart ❤️': 'af_heart',
-'🇺🇸 🚺 Bella 🔥': 'af_bella',
-'🇺🇸 🚺 Nicole 🎧': 'af_nicole',
-'🇺🇸 🚺 Aoede': 'af_aoede',
-'🇺🇸 🚺 Kore': 'af_kore',
-'🇺🇸 🚺 Sarah': 'af_sarah',
-'🇺🇸 🚺 Nova': 'af_nova',
-'🇺🇸 🚺 Sky': 'af_sky',
-'🇺🇸 🚺 Alloy': 'af_alloy',
-'🇺🇸 🚺 Jessica': 'af_jessica',
-'🇺🇸 🚺 River': 'af_river',
-'🇺🇸 🚹 Michael': 'am_michael',
-'🇺🇸 🚹 Fenrir': 'am_fenrir',
-'🇺🇸 🚹 Puck': 'am_puck',
-'🇺🇸 🚹 Echo': 'am_echo',
-'🇺🇸 🚹 Eric': 'am_eric',
-'🇺🇸 🚹 Liam': 'am_liam',
-'🇺🇸 🚹 Onyx': 'am_onyx',
-'🇺🇸 🚹 Santa': 'am_santa',
-'🇺🇸 🚹 Adam': 'am_adam',
-'🇬🇧 🚺 Emma': 'bf_emma',
-'🇬🇧 🚺 Isabella': 'bf_isabella',
-'🇬🇧 🚺 Alice': 'bf_alice',
-'🇬🇧 🚺 Lily': 'bf_lily',
-'🇬🇧 🚹 George': 'bm_george',
-'🇬🇧 🚹 Fable': 'bm_fable',
-'🇬🇧 🚹 Lewis': 'bm_lewis',
-'🇬🇧 🚹 Daniel': 'bm_daniel',
-}
-for v in CHOICES.values():
-    pipelines[v[0]].load_voice(v)
-
-TOKEN_NOTE = '''
-💡 Customize pronunciation with Markdown link syntax and /slashes/ like `[Kokoro](/kˈOkəɹO/)`
-
-💬 To adjust intonation, try punctuation `;:,.!?—…"()“”` or stress `ˈ` and `ˌ`
-
-⬇️ Lower stress `[1 level](-1)` or `[2 levels](-2)`
-
-⬆️ Raise stress 1 level `[or](+2)` 2 levels (only works on less stressed, usually short words)
-'''
-
-with gr.Blocks() as generate_tab:
-    out_audio = gr.Audio(label='Output Audio', interactive=False, streaming=False, autoplay=True)
-    generate_btn = gr.Button('Generate', variant='primary')
-    with gr.Accordion('Output Tokens', open=True):
-        out_ps = gr.Textbox(interactive=False, show_label=False, info='Tokens used to generate the audio, up to 510 context length.')
-        tokenize_btn = gr.Button('Tokenize', variant='secondary')
-        gr.Markdown(TOKEN_NOTE)
-        predict_btn = gr.Button('Predict', variant='secondary', visible=False)
-
-STREAM_NOTE = ['⚠️ There is an unknown Gradio bug that might yield no audio the first time you click `Stream`.']
-if CHAR_LIMIT is not None:
-    STREAM_NOTE.append(f'✂️ Each stream is capped at {CHAR_LIMIT} characters.')
-    STREAM_NOTE.append('🚀 Want more characters? You can [use Kokoro directly](https://huggingface.co/hexgrad/Kokoro-82M#usage) or duplicate this space:')
-STREAM_NOTE = '\n\n'.join(STREAM_NOTE)
-
-with gr.Blocks() as stream_tab:
-    out_stream = gr.Audio(label='Output Audio Stream', interactive=False, streaming=True, autoplay=True)
-    with gr.Row():
-        stream_btn = gr.Button('Stream', variant='primary')
-        stop_btn = gr.Button('Stop', variant='stop')
-    with gr.Accordion('Note', open=True):
-        gr.Markdown(STREAM_NOTE)
-        gr.DuplicateButton()
-
-BANNER_TEXT = '''
-[***Kokoro*** **is an open-weight TTS model with 82 million parameters.**](https://huggingface.co/hexgrad/Kokoro-82M)
-
-This demo only showcases English, but you can directly use the model to access other languages.
-'''
-API_OPEN = os.getenv('SPACE_ID') != 'hexgrad/Kokoro-TTS'
-API_NAME = None if API_OPEN else False
-with gr.Blocks() as app:
-    with gr.Row():
-        gr.Markdown(BANNER_TEXT, container=True)
-    with gr.Row():
-        with gr.Column():
-            text = gr.Textbox(label='Input Text', info=f"Up to ~500 characters per Generate, or {'∞' if CHAR_LIMIT is None else CHAR_LIMIT} characters per Stream")
-            with gr.Row():
-                voice = gr.Dropdown(list(CHOICES.items()), value='af_heart', label='Voice', info='Quality and availability vary by language')
-                use_gpu = gr.Dropdown(
-                    [('ZeroGPU 🚀', True), ('CPU 🐌', False)],
-                    value=CUDA_AVAILABLE,
-                    label='Hardware',
-                    info='GPU is usually faster, but has a usage quota',
-                    interactive=CUDA_AVAILABLE
-                )
-            speed = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.1, label='Speed')
-            random_btn = gr.Button('🎲 Random Quote 💬', variant='secondary')
-            with gr.Row():
-                gatsby_btn = gr.Button('🥂 Gatsby 📕', variant='secondary')
-                frankenstein_btn = gr.Button('💀 Frankenstein 📗', variant='secondary')
-        with gr.Column():
-            gr.TabbedInterface([generate_tab, stream_tab], ['Generate', 'Stream'])
-    random_btn.click(fn=get_random_quote, inputs=[], outputs=[text], api_name=API_NAME)
-    gatsby_btn.click(fn=get_gatsby, inputs=[], outputs=[text], api_name=API_NAME)
-    frankenstein_btn.click(fn=get_frankenstein, inputs=[], outputs=[text], api_name=API_NAME)
-    generate_btn.click(fn=generate_first, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps], api_name=API_NAME)
-    tokenize_btn.click(fn=tokenize_first, inputs=[text, voice], outputs=[out_ps], api_name=API_NAME)
-    stream_event = stream_btn.click(fn=generate_all, inputs=[text, voice, speed, use_gpu], outputs=[out_stream], api_name=API_NAME)
-    stop_btn.click(fn=None, cancels=stream_event)
-    predict_btn.click(fn=predict, inputs=[text, voice, speed], outputs=[out_audio], api_name=API_NAME)
-
-if __name__ == '__main__':
-    app.queue(api_open=API_OPEN).launch(show_api=API_OPEN, ssr_mode=True)