import os, threading, traceback
from huggingface_hub import HfApi
OUT="DriiftKing/contentvec-onnx"; TOK=os.environ.get("HF_TOKEN")
def job():
    api=HfApi(token=TOK)
    try:
        import torch, torch.nn as nn
        from transformers import HubertModel
        class CV(nn.Module):
            def __init__(s): super().__init__(); s.m=HubertModel.from_pretrained("lengyue233/content-vec-best")
            @torch.no_grad()
            def forward(s,w): return s.m(w,attention_mask=None,output_hidden_states=True).last_hidden_state
        enc=CV().eval(); d=torch.randn(1,16000); f=enc(d)
        assert f.shape[-1]==768, f"dim={tuple(f.shape)}"
        try:
            torch.onnx.export(enc,(d,),"contentvec.onnx",input_names=["wav_16k"],output_names=["feats"],
                dynamic_axes={"wav_16k":{1:"L"},"feats":{1:"T"}},opset_version=17,dynamo=False)
        except Exception:
            torch.onnx.export(enc,(d,),"contentvec.onnx",input_names=["wav_16k"],output_names=["feats"],
                dynamic_axes={"wav_16k":{1:"L"},"feats":{1:"T"}},opset_version=17)
        api.upload_file(path_or_fileobj="contentvec.onnx",path_in_repo="contentvec.onnx",repo_id=OUT,repo_type="dataset")
        print("UPLOADED OK", tuple(f.shape))
    except Exception:
        e=traceback.format_exc(); print(e)
        open("error.txt","w").write(e)
        try: api.upload_file(path_or_fileobj="error.txt",path_in_repo="error.txt",repo_id=OUT,repo_type="dataset")
        except Exception: pass
threading.Thread(target=job,daemon=True).start()
import gradio as gr
with gr.Blocks() as demo:
    gr.Markdown("Eksport contentvec.onnx leci w tle -> repo DriiftKing/contentvec-onnx")
demo.launch()