app.py

import random
import numpy as np
import torch
from chatterbox.src.chatterbox.tts import ChatterboxTTS
import gradio as gr
import spaces

# ─── Global patch to fix CUDA deserialization error on CPU ───
# This forces map_location='cpu' on all torch.load calls when CUDA is unavailable
original_torch_load = torch.load

def patched_torch_load(*args, **kwargs):
    if 'map_location' not in kwargs and not torch.cuda.is_available():
        kwargs['map_location'] = torch.device('cpu')
    return original_torch_load(*args, **kwargs)

torch.load = patched_torch_load
# ─────────────────────────────────────────────────────────────

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"🚀 Running on device: {DEVICE}")

# --- Global Model Initialization ---
MODEL = None

def get_or_load_model():
    """Loads the ChatterboxTTS model if it hasn't been loaded already,
    and ensures it's on the correct device."""
    global MODEL
    if MODEL is None:
        print("Model not loaded, initializing...")
        try:
            MODEL = ChatterboxTTS.from_pretrained(DEVICE)
            # On CPU, .to(DEVICE) is usually redundant after loading with map_location
            # but we keep it for safety / future GPU support
            if hasattr(MODEL, 'to') and str(MODEL.device) != DEVICE:
                MODEL.to(DEVICE)
            print(f"Model loaded successfully. Internal device: {getattr(MODEL, 'device', 'N/A')}")
        except Exception as e:
            print(f"Error loading model: {e}")
            raise
    return MODEL

# Attempt to load the model at startup (helps catch errors early in logs)
try:
    get_or_load_model()
except Exception as e:
    print(f"CRITICAL: Failed to load model on startup. Application may not function. Error: {e}")

def set_seed(seed: int):
    """Sets the random seed for reproducibility across torch, numpy, and random."""
    torch.manual_seed(seed)
    if DEVICE == "cuda":
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
    random.seed(seed)
    np.random.seed(seed)

@spaces.GPU  # harmless on CPU, ignored by HF when no GPU is allocated
def generate_tts_audio(
    text_input: str,
    audio_prompt_path_input: str = None,
    exaggeration_input: float = 0.5,
    temperature_input: float = 0.8,
    seed_num_input: int = 0,
    cfgw_input: float = 0.5,
    vad_trim_input: bool = False,
) -> tuple[int, np.ndarray]:
    """
    Generate high-quality speech audio from text using ChatterboxTTS model with optional reference audio styling.
    """
    current_model = get_or_load_model()

    if current_model is None:
        raise RuntimeError("TTS model is not loaded.")

    if seed_num_input != 0:
        set_seed(int(seed_num_input))

    print(f"Generating audio for text: '{text_input[:50]}...'")

    # Handle optional audio prompt
    generate_kwargs = {
        "exaggeration": exaggeration_input,
        "temperature": temperature_input,
        "cfg_weight": cfgw_input,
        "vad_trim": vad_trim_input,
    }

    if audio_prompt_path_input:
        generate_kwargs["audio_prompt_path"] = audio_prompt_path_input

    wav = current_model.generate(
        text_input[:300],  # Truncate text to max chars
        **generate_kwargs
    )
    print("Audio generation complete.")
    return (current_model.sr, wav.squeeze(0).numpy())

with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Chatterbox TTS Demo
        Generate high-quality speech from text with reference audio styling.
        """
    )
    with gr.Row():
        with gr.Column():
            text = gr.Textbox(
                value="Now let's make my mum's favourite. So three mars bars into the pan. Then we add the tuna and just stir for a bit, just let the chocolate and fish infuse. A sprinkle of olive oil and some tomato ketchup. Now smell that. Oh boy this is going to be incredible.",
                label="Text to synthesize (max chars 300)",
                max_lines=5
            )
            ref_wav = gr.Audio(
                sources=["upload", "microphone"],
                type="filepath",
                label="Reference Audio File (Optional)",
                value="https://storage.googleapis.com/chatterbox-demo-samples/prompts/female_shadowheart4.flac"
            )
            exaggeration = gr.Slider(
                0.25, 2, step=.05, label="Exaggeration (Neutral = 0.5, extreme values can be unstable)", value=.5
            )
            cfg_weight = gr.Slider(
                0.2, 1, step=.05, label="CFG/Pace", value=0.5
            )

            with gr.Accordion("More options", open=False):
                seed_num = gr.Number(value=0, label="Random seed (0 for random)")
                temp = gr.Slider(0.05, 5, step=.05, label="Temperature", value=.8)
                vad_trim = gr.Checkbox(label="Ref VAD trimming", value=False)

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

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

    run_btn.click(
        fn=generate_tts_audio,
        inputs=[
            text,
            ref_wav,
            exaggeration,
            temp,
            seed_num,
            cfg_weight,
            vad_trim,
        ],
        outputs=[audio_output],
    )

demo.launch(mcp_server=True)