app.py

import gradio as gr
import torch
import yaml
import os
from huggingface_hub import hf_hub_download
# Assuming these are available in your Space's environment
# from seed_vc_wrapper import SeedVCWrapper 
# from modules.v2.vc_wrapper import VoiceConversionWrapper 

# --- CONFIGURATION (UPDATE YOUR_USERNAME HERE) ---
# Replace 'YOUR_USERNAME' with your actual Hugging Face username
MODEL_REPO_ID = "Bajiyo/dhanush_seedvc" 
CFM_FILE = "CFM_epoch_00651_step_21500.pth"
AR_FILE = "AR_epoch_00651_step_21500.pth"
# -----------------------------------------------

if torch.cuda.is_available():
    device = torch.device("cuda")
elif torch.backends.mps.is_available():
    device = torch.device("mps")
else:
    device = torch.device("cpu")

dtype = torch.float16

def load_models(args):
    """
    Loads models, handling checkpoint download from Hugging Face Hub.
    """
    # 1. Setup local directory and download checkpoints
    LOCAL_CHECKPOINTS_DIR = "downloaded_checkpoints"
    os.makedirs(LOCAL_CHECKPOINTS_DIR, exist_ok=True)
    print(f"Downloading checkpoints from {MODEL_REPO_ID}...")

    # Download CFM
    cfm_local_path = hf_hub_download(
        repo_id=MODEL_REPO_ID, 
        filename=CFM_FILE, 
        local_dir=LOCAL_CHECKPOINTS_DIR,
        local_dir_use_symlinks=False
    )
    print(f"CFM checkpoint downloaded to: {cfm_local_path}")
    
    # Download AR
    ar_local_path = hf_hub_download(
        repo_id=MODEL_REPO_ID, 
        filename=AR_FILE, 
        local_dir=LOCAL_CHECKPOINTS_DIR,
        local_dir_use_symlinks=False
    )
    print(f"AR checkpoint downloaded to: {ar_local_path}")

    # 2. Instantiate and load models
    from hydra.utils import instantiate
    from omegaconf import DictConfig
    
    # Assuming 'configs/v2/vc_wrapper.yaml' is present in the Space repo
    cfg = DictConfig(yaml.safe_load(open("configs/v2/vc_wrapper.yaml", "r")))
    vc_wrapper = instantiate(cfg)
    
    # Load the downloaded checkpoints
    vc_wrapper.load_checkpoints(
        ar_checkpoint_path=ar_local_path,
        cfm_checkpoint_path=cfm_local_path
    )
    vc_wrapper.to(device)
    vc_wrapper.eval()

    vc_wrapper.setup_ar_caches(max_batch_size=1, max_seq_len=4096, dtype=dtype, device=device)

    if args.compile:
        # Standard torch compile settings
        torch._inductor.config.coordinate_descent_tuning = True
        torch._inductor.config.triton.unique_kernel_names = True

        if hasattr(torch._inductor.config, "fx_graph_cache"):
            torch._inductor.config.fx_graph_cache = True
        vc_wrapper.compile_ar()
        # vc_wrapper.compile_cfm()

    return vc_wrapper

def main(args):
    # load_models handles the download and initialization now
    vc_wrapper = load_models(args)
    
    # Define wrapper function for Gradio to ensure arguments are handled correctly
    @gr.Gradio()
    @spaces.GPU # Ensures conversion runs on the specified GPU if available
    def convert_voice_wrapper(source_audio_path, target_audio_path, diffusion_steps,
                              length_adjust, intelligibility_cfg_rate, similarity_cfg_rate,
                              top_p, temperature, repetition_penalty, convert_style, 
                              anonymization_only, stream_output=True):
        """
        Wrapper function for vc_wrapper.convert_voice_with_streaming that can be decorated.
        """
        # Ensure correct type for the stream_output argument if needed, 
        # though the main function is now calling convert_voice_with_streaming directly
        yield from vc_wrapper.convert_voice_with_streaming(
            source_audio_path=source_audio_path,
            target_audio_path=target_audio_path,
            diffusion_steps=diffusion_steps,
            length_adjust=length_adjust,
            intelligebility_cfg_rate=intelligibility_cfg_rate,
            similarity_cfg_rate=similarity_cfg_rate,
            top_p=top_p,
            temperature=temperature,
            repetition_penalty=repetition_penalty,
            convert_style=convert_style,
            anonymization_only=anonymization_only,
            device=device,
            dtype=dtype,
            stream_output=stream_output
        )

    # Set up Gradio interface
    description = ("Zero-shot voice conversion with in-context learning. For local deployment please check [GitHub repository](https://github.com/Plachtaa/seed-vc) "
                   "for details and updates.<br>Note that any reference audio will be forcefully clipped to 25s if beyond this length.<br> "
                   "If total duration of source and reference audio exceeds 30s, source audio will be processed in chunks.<br> "
                   "无需训练的 zero-shot 语音/歌声转换模型，若需本地部署查看[GitHub页面](https://github.com/Plachtaa/seed-vc]<br>"
                   "请注意，参考音频若超过 25 秒，则会被自动裁剪至此长度。<br>若源音频和参考音频的总时长超过 30 秒，源音频将被分段处理。")
    
    inputs = [
        gr.Audio(type="filepath", label="Source Audio / 源音频"),
        gr.Audio(type="filepath", label="Reference Audio / 参考音频"),
        gr.Slider(minimum=1, maximum=200, value=30, step=1, label="Diffusion Steps / 扩散步数", 
                 info="30 by default, 50~100 for best quality / 默认为 30，50~100 为最佳质量"),
        gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Adjust / 长度调整", 
                 info="<1.0 for speed-up speech, >1.0 for slow-down speech / <1.0 加速语速，>1.0 减慢语速"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.5, label="Intelligibility CFG Rate",
                 info="controls pronunciation intelligibility / 控制发音清晰度"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.5, label="Similarity CFG Rate",
                  info="controls similarity to reference audio / 控制与参考音频的相似度"),
        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.9, label="Top-p",
                 info="Controls diversity of generated audio / 控制生成音频的多样性"),
        gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=1.0, label="Temperature",
                 info="Controls randomness of generated audio / 控制生成音频的随机性"),
        gr.Slider(minimum=1.0, maximum=3.0, step=0.1, value=1.0, label="Repetition Penalty",
                 info="Penalizes repetition in generated audio / 惩罚生成音频中的重复"),
        gr.Checkbox(label="convert style", value=False),
        gr.Checkbox(label="anonymization only", value=False),
    ]
    
    examples = [
        ["examples/source/yae_0.wav", "examples/reference/dingzhen_0.wav", 50, 1.0, 0.5, 0.5, 0.9, 1.0, 1.0, False, False],
        ["examples/source/jay_0.wav", "examples/reference/azuma_0.wav", 50, 1.0, 0.5, 0.5, 0.9, 1.0, 1.0, False, False],
    ]
    
    outputs = [
        gr.Audio(label="Stream Output Audio / 流式输出", streaming=True, format='mp3'),
        gr.Audio(label="Full Output Audio / 完整输出", streaming=False, format='wav')
    ]
    
    # Launch the Gradio interface
    gr.Interface(
        fn=convert_voice_wrapper,
        description=description,
        inputs=inputs,
        outputs=outputs,
        title="Seed Voice Conversion V2",
        examples=examples,
        cache_examples=False,
    ).queue().launch(share=False) # Changed share=True to share=False for Spaces deployment

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--compile", action="store_true", help="Compile the model using torch.compile")
    # These arguments are now effectively ignored/not needed since we download the models
    # but we keep them to maintain compatibility with the original script structure.
    parser.add_argument("--ar-checkpoint-path", type=str, default=None,
                        help="Path to custom checkpoint file (overridden by HF download in Space)")
    parser.add_argument("--cfm-checkpoint-path", type=str, default=None,
                        help="Path to custom checkpoint file (overridden by HF download in Space)")
    args = parser.parse_args()
    main(args)