Add script to convert vits models

scripts/vits/export-onnx-ljs.py

@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+
+"""
+This script converts vits models trained using the LJ Speech dataset.
+
+Usage:
+
+(1) Download vits
+
+cd /Users/fangjun/open-source
+git clone https://github.com/jaywalnut310/vits
+
+(2) Download pre-trained models from
+https://huggingface.co/csukuangfj/vits-ljs/tree/main
+
+wget https://huggingface.co/csukuangfj/vits-ljs/resolve/main/pretrained_ljs.pth
+
+(3) Run this file
+
+./export-onnx-ljs.py  \
+  --config ~/open-source//vits/configs/ljs_base.json \
+  --checkpoint ~/open-source/icefall-models/vits-ljs/pretrained_ljs.pth
+
+It will generate the following two files:
+
+$ ls -lh *.onnx
+-rw-r--r--  1 fangjun  staff    36M Oct 10 20:48 vits-ljs.int8.onnx
+-rw-r--r--  1 fangjun  staff   109M Oct 10 20:48 vits-ljs.onnx
+"""
+import sys
+
+# Please change this line to point to the vits directory.
+# You can download vits from
+# https://github.com/jaywalnut310/vits
+sys.path.insert(0, "/Users/fangjun/open-source/vits")
+
+import argparse
+from pathlib import Path
+
+import commons
+import torch
+import utils
+from models import SynthesizerTrn
+from onnxruntime.quantization import QuantType, quantize_dynamic
+from text import text_to_sequence
+from text.symbols import symbols
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--config",
+        type=str,
+        required=True,
+        help="""Path to ljs_base.json.
+        You can find it at
+        https://huggingface.co/csukuangfj/vits-ljs/resolve/main/ljs_base.json
+        """,
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="""Path to the checkpoint file.
+        You can find it at
+        https://huggingface.co/csukuangfj/vits-ljs/resolve/main/pretrained_ljs.pth
+
+        """,
+    )
+
+    return parser.parse_args()
+
+
+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=None,
+        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 get_text(text, hps):
+    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+
+def check_args(args):
+    assert Path(args.config).is_file(), args.config
+    assert Path(args.checkpoint).is_file(), args.checkpoint
+
+
+@torch.no_grad()
+def main():
+    args = get_args()
+    hps = utils.get_hparams_from_file(args.config)
+
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        **hps.model,
+    )
+    _ = net_g.eval()
+
+    _ = utils.load_checkpoint(args.checkpoint, net_g, None)
+
+    x = get_text("Liliana is the most beautiful assistant", hps)
+    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)
+
+    model = OnnxModel(net_g)
+
+    opset_version = 13
+
+    filename = "vits-ljs.onnx"
+
+    torch.onnx.export(
+        model,
+        (x, x_length, noise_scale, length_scale, noise_scale_w),
+        filename,
+        opset_version=opset_version,
+        input_names=["x", "x_length", "noise_scale", "length_scale", "noise_scale_w"],
+        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"},
+        },
+    )
+
+    print("Generate int8 quantization models")
+
+    filename_int8 = "vits-ljs.int8.onnx"
+    quantize_dynamic(
+        model_input=filename,
+        model_output=filename_int8,
+        weight_type=QuantType.QUInt8,
+    )
+
+    print(f"Saved to {filename} and {filename_int8}")
+
+
+if __name__ == "__main__":
+    main()