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app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from transformers import HubertModel
+
+
+class ContentVec768(nn.Module):
+    def __init__(self, model_id="lengyue233/content-vec-best"):
+        super().__init__()
+        self.m = HubertModel.from_pretrained(model_id)
+
+    @torch.no_grad()
+    def forward(self, wav_16k):
+        out = self.m(wav_16k, attention_mask=None, output_hidden_states=True)
+        return out.last_hidden_state          # warstwa 12 -> [1, T, 768]
+
+
+def export():
+    enc = ContentVec768().eval()
+    dummy = torch.randn(1, 16000)
+    feats = enc(dummy)
+    if feats.shape[-1] != 768:
+        return None, f"BLAD: feats={tuple(feats.shape)} (oczekiwane 768)"
+    torch.onnx.export(
+        enc, (dummy,), "contentvec.onnx",
+        input_names=["wav_16k"], output_names=["feats"],
+        dynamic_axes={"wav_16k": {1: "L"}, "feats": {1: "T"}},
+        opset_version=17, dynamo=False,
+    )
+    return "contentvec.onnx", f"OK: feats={tuple(feats.shape)} -> contentvec.onnx gotowy do pobrania"
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## contentvec.onnx (768-wym, RVC v2)\nKliknij i pobierz plik dla aplikacji VoiceClone.")
+    btn = gr.Button("Eksportuj contentvec.onnx", variant="primary")
+    status = gr.Textbox(label="Status")
+    out = gr.File(label="Pobierz contentvec.onnx")
+    btn.click(export, outputs=[out, status])
+
+demo.launch()