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| import spaces | |
| from kokoro import KModel, KPipeline | |
| import gradio as gr | |
| import os | |
| import random | |
| import torch | |
| from idn_phonemes import indo_to_ipa | |
| 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 'abefhip'} | |
| pipelines['a'].g2p.lexicon.golds['kokoro'] = 'kˈOkəɹO' | |
| pipelines['b'].g2p.lexicon.golds['kokoro'] = 'kˈQkəɹQ' | |
| 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 = f"[_](/{indo_to_ipa(text)}/)" | |
| text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT] | |
| print("[TEST]", text) | |
| pipeline = pipelines[voice[0]] | |
| pack = pipeline.load_voice(voice) | |
| use_gpu = use_gpu and CUDA_AVAILABLE | |
| print("[USE GPU]", use_gpu) | |
| pipeline_output = pipeline(text, voice, speed) | |
| print("[PIPELINE OUTPUT]", pipeline_output) | |
| for _, ps, _ in pipeline_output: | |
| print("[ENTER LOOPING]", ps) | |
| ref_s = pack[len(ps)-1] | |
| ps = text[5:-2] | |
| try: | |
| if use_gpu: | |
| audio = forward_gpu(ps, ref_s, speed) | |
| else: | |
| print("[STARTING MODEL RUN]") | |
| audio = models[False](ps, ref_s, speed) | |
| print("[MODEL FINISHED]") | |
| except gr.exceptions.Error as e: | |
| print("[ERROR DETECTED]", 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 | |
| print("[RETURN NONE]") | |
| 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 = f"[_](/{indo_to_ipa(text)}/)" | |
| 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] | |
| ps = text[5:-2] | |
| 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', | |
| 'Espanol':'ef_dora', | |
| 'Hindi':'hf_alpha', | |
| 'French':'ff_siwis', | |
| 'Italian':'if_sara', | |
| 'Portuguese':'pf_dora', | |
| } | |
| 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) | |
| ''' | |
| TOKEN_NOTE = "" | |
| 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 showcases Indonesian TTS with various accents. You can use the various languages accents | |
| ''' | |
| 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='Accent', 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) | |