Create app.py

app.py

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+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/')
+CHAR_LIMIT = None if IS_DUPLICATE else 5000
+
+CUDA_AVAILABLE = torch.cuda.is_available()
+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)
+As of January 31st, 2025, Kokoro was the most-liked [**TTS model**](https://huggingface.co/models?pipeline_tag=text-to-speech&sort=likes) and the most-liked [**TTS space**](https://huggingface.co/spaces?sort=likes&search=tts) on Hugging Face.
+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)