Update onnx_inference_demo.py

onnx_inference_demo.py

@@ -1,20 +1,20 @@
-import soundfile
-from infer_pack.onnx_inference import OnnxRVC
-
-hop_size = 512
-sampling_rate = 40000  # 采样率
-f0_up_key = 0  # 升降调
-sid = 0  # 角色ID
-f0_method = "dio"  # F0提取算法
-model_path = "ShirohaRVC.onnx"  # 模型的完整路径
-vec_name = "vec-256-layer-9"  # 内部自动补齐为 f"pretrained/{vec_name}.onnx" 需要onnx的vec模型
-wav_path = "123.wav"  # 输入路径或ByteIO实例
-out_path = "out.wav"  # 输出路径或ByteIO实例
-
-model = OnnxRVC(
-    model_path, vec_path=vec_name, sr=sampling_rate, hop_size=hop_size, device="cuda"
-)
-
-audio = model.inference(wav_path, sid, f0_method=f0_method, f0_up_key=f0_up_key)
-
-soundfile.write(out_path, audio, sampling_rate)
+import soundfile
+from infer_pack.onnx_inference import OnnxRVC
+
+hop_size = 512
+sampling_rate = 40000  # Sampling rate
+f0_up_key = 0  # rising and falling tones
+sid = 0  # Role ID
+f0_method = "dio"  # F0 extraction algorithm
+model_path = "ShirohaRVC.onnx"  # full path to model
+vec_name = "vec-256-layer-9"  # The internal automatic completion is f"pretrained/{vec_name}.onnx" requires the onnx vec model
+wav_path = "123.wav"  # Input path or ByteIO instance
+out_path = "out.wav"  # Output path or ByteIO instance
+
+model = OnnxRVC(
+    model_path, vec_path=vec_name, sr=sampling_rate, hop_size=hop_size, device="cuda"
+)
+
+audio = model.inference(wav_path, sid, f0_method=f0_method, f0_up_key=f0_up_key)
+
+soundfile.write(out_path, audio, sampling_rate)