kokoro grad.txt

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 'abefhijpz'}
pipelines['a'].g2p.lexicon.golds['kokoro'] = 'kˈOkəɹO'
pipelines['b'].g2p.lexicon.golds['kokoro'] = 'kˈQkəɹQ'

@spaces.GPU(duration=10)
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'):
    # Split the input text into words and return as a list of words (fix applied here)
    words = text.split()  # This splits the text into words based on spaces
    return words  # Return a list of words

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
    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()

random_texts = {}
for lang in ['en']:
    with open(f'{lang}.txt', 'r') as r:
        random_texts[lang] = [line.strip() for line in r]

def get_random_text(voice):
    lang = dict(a='en', b='en')[voice[0]]
    return random.choice(random_texts[lang])

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',
    
'🇪🇸 🚺 Dora': 'ef_dora',
    
'🇪🇸 🚹 Alex': 'em_alex',
'🇪🇸 🚹 Santa': 'em_santa',
    
'🇫🇷 🚺 Siwis': 'ff_siwis',
    
'🇮🇳 🚹 Alpha': 'hf_alpha',
'🇮🇳 🚹 Beta': 'hf_beta',
    
'🇮🇳 🚹 Omega': 'hm_omega',
'🇮🇳 🚹 Psi': 'hm_psi',
    
'🇮🇹 🚺 Sara': 'if_sara',
    
'🇮🇹 🚺 Nicola': 'im_nicola',
    
'🇯🇵 🚹 Alpha': 'jf_alpha',
'🇯🇵 🚹 Gongitsune': 'jf_gongitsune',
'🇯🇵 🚹 Nezumi': 'jf_nezumi',
'🇯🇵 🚹 Tebukuro': 'jf_tebukuro',
    
'🇯🇵 🚹 Kumo': 'jm_kumo',
    
'🇧🇷 🚺 Dora': 'pf_dora',
    
'🇧🇷 🚹 Alex': 'pm_alex',
'🇧🇷 🚹 Santa': 'pm_santa',
    
'🇨🇳 🚺 Xiaobei': 'zf_xiaobei',
'🇨🇳 🚺 Xiaoni': 'zf_xiaoni',
'🇨🇳 🚺 Xiaoxiao': 'zf_xiaoxiao',
'🇨🇳 🚺 Xiaoyi': 'zf_xiaoyi',

'🇨🇳 🚹 Yunjian': 'zm_yunjian',
'🇨🇳 🚹 Yunxi': 'zm_yunxi',
'🇨🇳 🚹 Yunxia': 'zm_yunxia',
'🇨🇳 🚹 Yunyang': 'zm_yunyang',
}
for v in CHOICES.values():
    pipelines[v[0]].load_voice(v)

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')
        predict_btn = gr.Button('Predict', variant='secondary', visible=False)

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 Text', variant='secondary')
        with gr.Column():
            gr.TabbedInterface([generate_tab], ['Generate'])
    random_btn.click(fn=get_random_text, inputs=[voice], 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)
    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)