app.py

import json
import os
import subprocess
from pathlib import Path

import gradio as gr
import librosa
import numpy as np
import torch
from demucs.apply import apply_model
from demucs.pretrained import DEFAULT_MODEL, get_model
from huggingface_hub import hf_hub_download, list_repo_files

from so_vits_svc_fork.hparams import HParams
from so_vits_svc_fork.inference.core import Svc

###################################################################
# REPLACE THESE VALUES TO CHANGE THE MODEL REPO/CKPT NAME/SETTINGS
###################################################################
# The Hugging Face Hub repo IDs - 在这里修改repo_id，可替换成任何已经训练好的模型！
repo_ids = ["nijisakai/sunyanzi", "kevinwang676/jay"]

# If None, Uses latest ckpt in the repo
ckpt_name = None

# If None, Uses "kmeans.pt" if it exists in the repo
cluster_model_name = None

# Set the default f0 type to use - use the one it was trained on.
# The default for so-vits-svc-fork is "dio".
# Options: "crepe", "crepe-tiny", "parselmouth", "dio", "harvest"
default_f0_method = "crepe"

# The default ratio of cluster inference to SVC inference.
# If cluster_model_name is not found in the repo, this is set to 0.
default_cluster_infer_ratio = 0.5

# Limit on duration of audio at inference time. increase if you can
# In this parent app, we set the limit with an env var to 30 seconds
# If you didnt set env var + you go OOM try changing 9e9 to <=300ish
duration_limit = int(os.environ.get("MAX_DURATION_SECONDS", 9e9))
###################################################################

interfaces = []
for repo_id in repo_ids:
    # Figure out the latest generator by taking highest value one.
    # Ex. if the repo has: G_0.pth, G_100.pth, G_200.pth, we'd use G_200.pth
    if ckpt_name is None:
        latest_id = sorted(
            [
                int(Path(x).stem.split("_")[1])
                for x in list_repo_files(repo_id)
                if x.startswith("G_") and x.endswith(".pth")
            ]
        )[-1]
        ckpt_name = f"G_{latest_id}.pth"

    cluster_model_name = cluster_model_name or "kmeans.pt"
    if cluster_model_name in list_repo_files(repo_id):
        print(f"Found Cluster model - Downloading {cluster_model_name} from {repo_id}")
        cluster_model_path = hf_hub_download(repo_id, cluster_model_name)
    else:
        print(f"Could not find {cluster_model_name} in {repo_id}. Using None")
        cluster_model_path = None
    default_cluster_infer_ratio = default_cluster_infer_ratio if cluster_model_path else 0

    generator_path = hf_hub_download(repo_id, ckpt_name)
    config_path = hf_hub_download(repo_id, "config.json")
    hparams = HParams(**json.loads(Path(config_path).read_text()))
    speakers = list(hparams.spk.keys())
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = Svc(net_g_path=generator_path, config_path=config_path, device=device, cluster_model_path=cluster_model_path)
    demucs_model = get_model(DEFAULT_MODEL)

    # ... (same code as before to define the functions)

    interface = gr.Interface(
        predict,
        inputs=[
            gr.Dropdown(speakers, label="🎤AI歌手选择🎶"),
            gr.Audio(type="filepath", source="microphone", label="请用麦克风上传您想转换的歌曲"),
            # ... (same inputs as before)
        ],
        outputs="audio",
        cache_examples=False,
        title=f"🌊💕🎶 - 滔滔AI+音乐：可从B站直接上传素材，无需分离背景音 ({repo_id})",
        description=description,
        article=article,
    )
    interfaces.append(interface)

# Combine the interfaces using a TabbedInterface
interface = gr.TabbedInterface(interfaces, [f"Model {i+1}" for i in range(len(interfaces))])

if __name__ == "__main__":
    interface.launch(show_error=True)