app.py

import random
import os
import numpy as np
import torch
import gradio as gr
import spaces
from chatterbox.tts_turbo import ChatterboxTurboTTS


MODEL = ChatterboxTurboTTS.from_pretrained("cuda" )


CUSTOM_CSS = """
.tag-container {
    display: flex !important;
    flex-wrap: wrap !important;
    gap: 8px !important;
    margin-top: 5px !important;
    margin-bottom: 10px !important;
    border: none !important;
    background: transparent !important;
}

.tag-btn {
    min-width: fit-content !important;
    width: auto !important;
    height: 32px !important;
    font-size: 13px !important;
    background: #eef2ff !important;
    border: 1px solid #c7d2fe !important;
    color: #3730a3 !important;
    border-radius: 6px !important;
    padding: 0 10px !important;
    margin: 0 !important;
    box-shadow: none !important;
}

.tag-btn:hover {
    background: #c7d2fe !important;
    transform: translateY(-1px);
}
"""

INSERT_TAG_JS = """
(tag_val, current_text) => {
    const textarea = document.querySelector('#main_textbox textarea');
    if (!textarea) return current_text + " " + tag_val;

    const start = textarea.selectionStart;
    const end = textarea.selectionEnd;

    let prefix = " ";
    let suffix = " ";

    if (start === 0) prefix = "";
    else if (current_text[start - 1] === ' ') prefix = "";

    if (end < current_text.length && current_text[end] === ' ') suffix = "";

    return current_text.slice(0, start) + prefix + tag_val + suffix + current_text.slice(end);
}
"""

def set_seed(seed: int):
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    random.seed(seed)
    np.random.seed(seed)

@spaces.GPU
def generate(
        text,
        audio_prompt_path,
        temperature,
        seed_num,
        top_p,
        top_k,
        repetition_penalty,
        norm_loudness
):
    if seed_num != 0:
        set_seed(int(seed_num))

    wav = MODEL.generate(
        text,
        audio_prompt_path=audio_prompt_path,
        temperature=temperature,
        top_p=top_p,
        top_k=int(top_k),
        repetition_penalty=repetition_penalty,
        norm_loudness=norm_loudness,
    )

    return (MODEL.sr, wav.squeeze(0).cpu().numpy())


VOICE_OPTIONS = {
    "mic": "samples/mic_trimmed.wav",
    "nic": "samples/nic_trimmed.wav"
}

def update_ref_audio(voice_name):
    return VOICE_OPTIONS.get(voice_name, list(VOICE_OPTIONS.values())[0])

with gr.Blocks(title="Chatterbox Turbo") as demo:
    gr.Markdown(
        """
        # ⚡ Røst v3 Chatterbox 350m
        Generate high-quality danish speech from text with reference audio styling. This is model was developed as part of the CoRal project, and is a finetuned version of Chatterbox Multilingual.
        """)
    


    with gr.Row():
        with gr.Column():
            text = gr.Textbox(
                value="København er Danmarks hovedstad og ligger på øerne Sjælland og Amager, hvor mange turister besøger de smukke kanaler og historiske bygninger.",
                label="Text to synthesize (max chars 300)",
                max_lines=5,
                elem_id="main_textbox"
            )

            voice = gr.Dropdown(
                choices=list(VOICE_OPTIONS.keys()),
                value="mic",
                label="Voice Selection",
                info="Choose a voice or upload your own below"
            )

            ref_wav = gr.Audio(
                sources=["upload", "microphone"],
                type="filepath",
                label="Reference Audio File",
                value=VOICE_OPTIONS["mic"],
            )

            run_btn = gr.Button("Generate ⚡", variant="primary")

        with gr.Column():
            audio_output = gr.Audio(label="Output Audio")

            with gr.Accordion("Advanced Options", open=False):
                seed_num = gr.Number(value=0, label="Random seed (0 for random)")
                temp = gr.Slider(0.05, 2.0, step=.05, label="Temperature", value=0.7)
                top_p = gr.Slider(0.00, 1.00, step=0.01, label="Top P", value=0.95)
                top_k = gr.Slider(0, 1000, step=10, label="Top K", value=600)
                repetition_penalty = gr.Slider(1.00, 2.00, step=0.05, label="Repetition Penalty", value=1.2)
                norm_loudness = gr.Checkbox(value=True, label="Normalize Loudness (-27 LUFS)")

    voice.change(
        fn=update_ref_audio,
        inputs=[voice],
        outputs=[ref_wav],
    )

    run_btn.click(
        fn=generate,
        inputs=[
            text,
            ref_wav,
            temp,
            seed_num,
            top_p,
            top_k,
            repetition_penalty,
            norm_loudness,
        ],
        outputs=audio_output,
    )

if __name__ == "__main__":
    demo.queue().launch(
        mcp_server=True,
        css=CUSTOM_CSS,
        ssr_mode=False
    )