update

export-onnx-zh-hf-fanchen-models.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+
+import sys
+
+sys.path.insert(0, "VITS-fast-fine-tuning")
+
+import os
+from pathlib import Path
+from typing import Any, Dict
+
+import onnx
+import torch
+import utils
+from models import SynthesizerTrn
+
+
+class OnnxModel(torch.nn.Module):
+    def __init__(self, model: SynthesizerTrn):
+        super().__init__()
+        self.model = model
+
+    def forward(
+        self,
+        x,
+        x_lengths,
+        noise_scale=1,
+        length_scale=1,
+        noise_scale_w=1.0,
+        sid=0,
+        max_len=None,
+    ):
+        return self.model.infer(
+            x=x,
+            x_lengths=x_lengths,
+            sid=sid,
+            noise_scale=noise_scale,
+            length_scale=length_scale,
+            noise_scale_w=noise_scale_w,
+            max_len=max_len,
+        )[0]
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, Any]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+
+    onnx.save(model, filename)
+
+
+@torch.no_grad()
+def main():
+    name = os.environ.get("NAME", None)
+    if not name:
+        print("Please provide the environment variable NAME")
+        return
+
+    print("name", name)
+
+    if name == "C":
+        model_path = "G_C.pth"
+        config_path = "G_C.json"
+    elif name == "ZhiHuiLaoZhe":
+        model_path = "G_lkz_lao_new_new1_latest.pth"
+        config_path = "G_lkz_lao_new_new1_latest.json"
+    elif name == "ZhiHuiLaoZhe_new":
+        model_path = "G_lkz_unity_onnx_new1_latest.pth"
+        config_path = "G_lkz_unity_onnx_new1_latest.json"
+    else:
+        model_path = f"G_{name}_latest.pth"
+        config_path = f"G_{name}_latest.json"
+
+    print(name, model_path, config_path)
+    hps = utils.get_hparams_from_file(config_path)
+    net_g = SynthesizerTrn(
+        len(hps.symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    )
+    _ = net_g.eval()
+    _ = utils.load_checkpoint(model_path, net_g, None)
+
+    x = torch.randint(low=1, high=50, size=(50,), dtype=torch.int64)
+    x = x.unsqueeze(0)
+
+    x_length = torch.tensor([x.shape[1]], dtype=torch.int64)
+    noise_scale = torch.tensor([1], dtype=torch.float32)
+    length_scale = torch.tensor([1], dtype=torch.float32)
+    noise_scale_w = torch.tensor([1], dtype=torch.float32)
+    sid = torch.tensor([0], dtype=torch.int64)
+
+    model = OnnxModel(net_g)
+
+    opset_version = 13
+
+    filename = f"vits-zh-hf-fanchen-{name}.onnx"
+
+    torch.onnx.export(
+        model,
+        (x, x_length, noise_scale, length_scale, noise_scale_w, sid),
+        filename,
+        opset_version=opset_version,
+        input_names=[
+            "x",
+            "x_length",
+            "noise_scale",
+            "length_scale",
+            "noise_scale_w",
+            "sid",
+        ],
+        output_names=["y"],
+        dynamic_axes={
+            "x": {0: "N", 1: "L"},  # n_audio is also known as batch_size
+            "x_length": {0: "N"},
+            "y": {0: "N", 2: "L"},
+        },
+    )
+    meta_data = {
+        "model_type": "vits",
+        "comment": f"hf-vits-models-fanchen-{name}",
+        "language": "Chinese",
+        "add_blank": int(hps.data.add_blank),
+        "n_speakers": int(hps.data.n_speakers),
+        "sample_rate": hps.data.sampling_rate,
+        "punctuation": ", . : ; ! ? ， 。 ： ； ！ ？ 、",
+    }
+    print("meta_data", meta_data)
+    add_meta_data(filename=filename, meta_data=meta_data)
+
+
+if __name__ == "__main__":
+    main()

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@@ -1,3 +1,3 @@
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