Support VITS VCTK models (#367)

* Support VITS VCTK models

* Release v1.8.1

scripts/vits/.gitignore

@@ -1 +1,2 @@
 tokens-ljs.txt
+tokens-vctk.txt

scripts/vits/export-onnx-ljs.py

@@ -191,6 +191,7 @@ def main():
         "comment": "ljspeech",
         "language": "English",
         "add_blank": int(hps.data.add_blank),
+        "n_speakers": int(hps.data.n_speakers),
         "sample_rate": hps.data.sampling_rate,
         "punctuation": " ".join(list(_punctuation)),
     }

scripts/vits/export-onnx-vctk.py

@@ -0,0 +1,222 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+This script converts vits models trained using the VCTK 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-vctk/tree/main
+
+wget https://huggingface.co/csukuangfj/vits-vctk/resolve/main/pretrained_vctk.pth
+
+(3) Run this file
+
+./export-onnx-vctk.py  \
+  --config ~/open-source//vits/configs/vctk_base.json \
+  --checkpoint ~/open-source/icefall-models/vits-vctk/pretrained_vctk.pth
+
+It will generate the following two files:
+
+$ ls -lh *.onnx
+-rw-r--r--  1 fangjun  staff    37M Oct 16 10:57 vits-vctk.int8.onnx
+-rw-r--r--  1 fangjun  staff   116M Oct 16 10:57 vits-vctk.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")  # noqa
+
+import argparse
+from pathlib import Path
+from typing import Dict, Any
+
+import commons
+import onnx
+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
+from text.symbols import _punctuation
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--config",
+        type=str,
+        required=True,
+        help="""Path to vctk_base.json.
+        You can find it at
+        https://huggingface.co/csukuangfj/vits-vctk/resolve/main/vctk_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-vctk/resolve/main/pretrained_vctk.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=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 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
+
+
+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)
+
+
+def generate_tokens():
+    with open("tokens-vctk.txt", "w", encoding="utf-8") as f:
+        for i, s in enumerate(symbols):
+            f.write(f"{s} {i}\n")
+    print("Generated tokens-vctk.txt")
+
+
+@torch.no_grad()
+def main():
+    args = get_args()
+    check_args(args)
+
+    generate_tokens()
+
+    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,
+        n_speakers=hps.data.n_speakers,
+        **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)
+    sid = torch.tensor([0], dtype=torch.int64)
+
+    model = OnnxModel(net_g)
+
+    opset_version = 13
+
+    filename = "vits-vctk.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": "vctk",
+        "language": "English",
+        "add_blank": int(hps.data.add_blank),
+        "n_speakers": int(hps.data.n_speakers),
+        "sample_rate": hps.data.sampling_rate,
+        "punctuation": " ".join(list(_punctuation)),
+    }
+    print("meta_data", meta_data)
+    add_meta_data(filename=filename, meta_data=meta_data)
+
+    print("Generate int8 quantization models")
+
+    filename_int8 = "vits-vctk.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()