diff --git a/infer-web.py b/infer-web.py index a3bac6d..e96fbb0 100644 --- a/infer-web.py +++ b/infer-web.py @@ -1119,12 +1119,12 @@ def change_info_(ckpt_path): from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM -def export_onnx(ModelPath, ExportedPath, MoeVS=True): +def export_onnx(ModelPath, ExportedPath): cpt = torch.load(ModelPath, map_location="cpu") - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2] # hidden_channels,为768Vec做准备 + cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] + vec_channels = 256 if cpt.get("version","v1")=="v1"else 768 - test_phone = torch.rand(1, 200, hidden_channels) # hidden unit + test_phone = torch.rand(1, 200, vec_channels) # hidden unit test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度(貌似没啥用) test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频(单位赫兹) test_pitchf = torch.rand(1, 200) # nsf基频 @@ -1160,7 +1160,7 @@ def export_onnx(ModelPath, ExportedPath, MoeVS=True): "rnd": [2], }, do_constant_folding=False, - opset_version=16, + opset_version=13, verbose=False, input_names=input_names, output_names=output_names, @@ -1835,11 +1835,10 @@ with gr.Blocks() as app: label=i18n("Onnx输出路径"), value="", interactive=True ) with gr.Row(): - moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=False,visible=False) infoOnnx = gr.Label(label="info") with gr.Row(): butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") - butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx) + butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) tab_faq = i18n("常见问题解答") with gr.TabItem(tab_faq): diff --git a/infer_pack/onnx_inference.py b/infer_pack/onnx_inference.py index 09a4ed2..7502543 100644 --- a/infer_pack/onnx_inference.py +++ b/infer_pack/onnx_inference.py @@ -3,7 +3,6 @@ import librosa import numpy as np import soundfile - class ContentVec: def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None): print("load model(s) from {}".format(vec_path)) @@ -11,6 +10,8 @@ class ContentVec: providers = ["CPUExecutionProvider"] elif device == "cuda": providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] + elif device == "dml": + providers = ["DmlExecutionProvider"] else: raise RuntimeError("Unsportted Device") self.model = onnxruntime.InferenceSession(vec_path, providers=providers) @@ -68,6 +69,8 @@ class OnnxRVC: providers = ["CPUExecutionProvider"] elif device == "cuda": providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] + elif device == "dml": + providers = ["DmlExecutionProvider"] else: raise RuntimeError("Unsportted Device") self.model = onnxruntime.InferenceSession(model_path, providers=providers)