update

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

@@ -1,144 +0,0 @@
-#!/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()