convert wespeaker models to sherpa-onnx (#475)

+name: export-wespeaker-to-onnx
+
+on:
+  workflow_dispatch:
+
+concurrency:
+  group: export-wespeaker-to-onnx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  export-wespeaker-to-onnx:
+    if: github.repository_owner == 'k2-fsa' || github.repository_owner == 'csukuangfj'
+    name: export wespeaker
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.8"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Python dependencies
+        shell: bash
+        run: |
+          pip install kaldi-native-fbank numpy onnx onnxruntime
+
+      - name: Run
+        shell: bash
+        run: |
+          cd scripts/wespeaker
+          ./run.sh
+
+          mv -v *.onnx ../..
+
+      - name: Release
+        uses: svenstaro/upload-release-action@v2
+        with:
+          file_glob: true
+          file: ./*.onnx
+          overwrite: true
+          repo_name: k2-fsa/sherpa-onnx
+          repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
+          tag: speaker-recongition-models

+# Introduction
+
+This folder contains script for adding meta data to onnx models from
+https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md
+
+You can use the models with metadata in sherpa-onnx.
+
+
+**Caution**: You have to add model meta data to `*.onnx` since we plan
+to support models from different frameworks.

+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+This script adds meta data to a model so that it can be used in sherpa-onnx.
+
+Usage:
+./add_meta_data.py --model ./voxceleb_resnet34.onnx  --language English
+"""
+
+import argparse
+from pathlib import Path
+from typing import Dict
+
+import onnx
+import onnxruntime
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help="Path to the input onnx model. Example value: model.onnx",
+    )
+
+    parser.add_argument(
+        "--language",
+        type=str,
+        required=True,
+        help="""Supported language of the input model.
+        Example value: Chinese, English.
+        """,
+    )
+
+    parser.add_argument(
+        "--url",
+        type=str,
+        default="https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md",
+        help="Where the model is downloaded",
+    )
+
+    parser.add_argument(
+        "--comment",
+        type=str,
+        default="no comment",
+        help="Comment about the model",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="Sample rate expected by the model",
+    )
+
+    return parser.parse_args()
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """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 get_output_dim(filename) -> int:
+    filename = str(filename)
+    session_opts = onnxruntime.SessionOptions()
+    session_opts.log_severity_level = 3  # error level
+    sess = onnxruntime.InferenceSession(filename, session_opts)
+
+    for i in sess.get_inputs():
+        print(i)
+
+    print("----------")
+
+    for o in sess.get_outputs():
+        print(o)
+
+    print("----------")
+
+    assert len(sess.get_inputs()) == 1
+    assert len(sess.get_outputs()) == 1
+
+    i = sess.get_inputs()[0]
+    o = sess.get_outputs()[0]
+
+    assert i.shape[:2] == ["B", "T"], i.shape
+    assert o.shape[0] == "B"
+
+    assert i.shape[2] == 80, i.shape
+
+    return o.shape[1]
+
+
+def main():
+    args = get_args()
+    model = Path(args.model)
+    language = args.language
+    url = args.url
+    comment = args.comment
+    sample_rate = args.sample_rate
+
+    if not model.is_file():
+        raise ValueError(f"{model} does not exist")
+
+    assert len(language) > 0, len(language)
+    assert len(url) > 0, len(url)
+
+    output_dim = get_output_dim(model)
+
+    # all models from wespeaker expect input samples in the range
+    # [-32768, 32767]
+    normalize_features = 0
+
+    meta_data = {
+        "framework": "wespeaker",
+        "language": language,
+        "url": url,
+        "comment": comment,
+        "sample_rate": sample_rate,
+        "output_dim": output_dim,
+        "normalize_features": normalize_features,
+    }
+    print(meta_data)
+    add_meta_data(filename=str(model), meta_data=meta_data)
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env bash
+
+set -ex
+
+echo "Downloading models"
+export GIT_LFS_SKIP_SMUDGE=1
+git clone https://huggingface.co/openspeech/wespeaker-models
+cd wespeaker-models
+git lfs pull --include "*.onnx"
+ls -lh
+cd ..
+mv wespeaker-models/*.onnx .
+ls -lh
+
+./add_meta_data.py \
+  --model ./voxceleb_resnet34.onnx \
+  --language English \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet34.onnx
+./test.py  --model ./voxceleb_resnet34.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00024_spk1.wav \
+
+./test.py  --model ./voxceleb_resnet34.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00010_spk2.wav
+
+mv voxceleb_resnet34.onnx en_voxceleb_resnet34.onnx
+
+./add_meta_data.py \
+  --model ./voxceleb_resnet34_LM.onnx \
+  --language English \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet34_LM.onnx
+./test.py  --model ./voxceleb_resnet34_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00024_spk1.wav \
+
+./test.py  --model ./voxceleb_resnet34_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00010_spk2.wav
+
+mv voxceleb_resnet34_LM.onnx en_voxceleb_resnet34_LM.onnx
+
+./add_meta_data.py \
+  --model ./voxceleb_resnet152_LM.onnx \
+  --language English \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet152_LM.onnx
+
+./test.py  --model ./voxceleb_resnet152_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00024_spk1.wav \
+
+./test.py  --model ./voxceleb_resnet152_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00010_spk2.wav
+
+mv voxceleb_resnet152_LM.onnx en_voxceleb_resnet152_LM.onnx
+
+./add_meta_data.py \
+  --model ./voxceleb_resnet221_LM.onnx \
+  --language English \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet221_LM.onnx
+
+./test.py  --model ./voxceleb_resnet221_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00024_spk1.wav \
+
+./test.py  --model ./voxceleb_resnet221_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00010_spk2.wav
+
+mv voxceleb_resnet221_LM.onnx en_voxceleb_resnet221_LM.onnx
+
+./add_meta_data.py \
+  --model ./voxceleb_resnet293_LM.onnx \
+  --language English \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet293_LM.onnx
+
+./test.py  --model ./voxceleb_resnet293_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00024_spk1.wav \
+
+./test.py  --model ./voxceleb_resnet293_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00010_spk2.wav
+
+mv voxceleb_resnet293_LM.onnx en_voxceleb_resnet293_LM.onnx
+
+./add_meta_data.py \
+  --model ./voxceleb_CAM++.onnx \
+  --language English \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_CAM++.onnx
+
+./test.py  --model ./voxceleb_CAM++.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00024_spk1.wav \
+
+./test.py  --model ./voxceleb_CAM++.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00010_spk2.wav
+
+mv voxceleb_CAM++.onnx en_voxceleb_CAM++.onnx
+
+./add_meta_data.py \
+  --model ./voxceleb_CAM++_LM.onnx \
+  --language English \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_CAM++_LM.onnx
+
+./test.py  --model ./voxceleb_CAM++_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00024_spk1.wav \
+
+./test.py  --model ./voxceleb_CAM++_LM.onnx \
+  --file1 ./wespeaker-models/test_wavs/00001_spk1.wav \
+  --file2 ./wespeaker-models/test_wavs/00010_spk2.wav
+
+mv voxceleb_CAM++_LM.onnx en_voxceleb_CAM++_LM.onnx
+
+./add_meta_data.py \
+  --model ./cnceleb_resnet34.onnx \
+  --language Chinese \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/cnceleb/cnceleb_resnet34.onnx
+
+mv cnceleb_resnet34.onnx zh_cnceleb_resnet34.onnx
+
+./add_meta_data.py \
+  --model ./cnceleb_resnet34_LM.onnx \
+  --language Chinese \
+  --url https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/cnceleb/cnceleb_resnet34_LM.onnx
+
+mv cnceleb_resnet34_LM.onnx zh_cnceleb_resnet34_LM.onnx
+
+ls -lh

+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+This script computes speaker similarity score in the range [0-1]
+of two wave files using a speaker recognition model.
+"""
+import argparse
+import wave
+from pathlib import Path
+
+import kaldi_native_fbank as knf
+import numpy as np
+import onnxruntime as ort
+from numpy.linalg import norm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help="Path to the input onnx model. Example value: model.onnx",
+    )
+
+    parser.add_argument(
+        "--file1",
+        type=str,
+        required=True,
+        help="Input wave 1",
+    )
+
+    parser.add_argument(
+        "--file2",
+        type=str,
+        required=True,
+        help="Input wave 2",
+    )
+
+    return parser.parse_args()
+
+
+def read_wavefile(filename, expected_sample_rate: int = 16000) -> np.ndarray:
+    """
+    Args:
+      filename:
+        Path to a wave file, which must be of 16-bit and 16kHz.
+     expected_sample_rate:
+       Expected sample rate of the wave file.
+    Returns:
+      Return a 1-D float32 array containing audio samples. Each sample is in
+      the range [-1, 1].
+    """
+    filename = str(filename)
+    with wave.open(filename) as f:
+        # Note: If wave_file_sample_rate is different from
+        # recognizer.sample_rate, we will do resampling inside sherpa-ncnn
+        wave_file_sample_rate = f.getframerate()
+        assert wave_file_sample_rate == expected_sample_rate, (
+            wave_file_sample_rate,
+            expected_sample_rate,
+        )
+
+        num_channels = f.getnchannels()
+        assert f.getsampwidth() == 2, f.getsampwidth()  # it is in bytes
+        num_samples = f.getnframes()
+        samples = f.readframes(num_samples)
+        samples_int16 = np.frombuffer(samples, dtype=np.int16)
+        samples_int16 = samples_int16.reshape(-1, num_channels)[:, 0]
+        samples_float32 = samples_int16.astype(np.float32)
+
+        samples_float32 = samples_float32 / 32768
+
+        return samples_float32
+
+
+def compute_features(samples: np.ndarray, sample_rate: int) -> np.ndarray:
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.samp_freq = sample_rate
+    opts.frame_opts.snip_edges = False
+
+    opts.mel_opts.num_bins = 80
+    opts.mel_opts.debug_mel = False
+
+    fbank = knf.OnlineFbank(opts)
+    fbank.accept_waveform(sample_rate, samples)
+    fbank.input_finished()
+
+    features = []
+    for i in range(fbank.num_frames_ready):
+        f = fbank.get_frame(i)
+        features.append(f)
+    features = np.stack(features, axis=0)
+
+    return features
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 4
+
+        self.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+        )
+
+        meta = self.model.get_modelmeta().custom_metadata_map
+        self.normalize_features = int(meta["normalize_features"])
+        self.sample_rate = int(meta["sample_rate"])
+        self.output_dim = int(meta["output_dim"])
+
+    def __call__(self, x: np.ndarray) -> np.ndarray:
+        """
+        Args:
+          x:
+            A 2-D float32 tensor of shape (T, C).
+          y:
+            A 1-D float32 tensor containing model output.
+        """
+        x = np.expand_dims(x, axis=0)
+
+        return self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x,
+            },
+        )[0][0]
+
+
+def main():
+    args = get_args()
+    filename = Path(args.model)
+    file1 = Path(args.file1)
+    file2 = Path(args.file2)
+    assert filename.is_file(), filename
+    assert file1.is_file(), file1
+    assert file2.is_file(), file2
+
+    model = OnnxModel(filename)
+    wave1 = read_wavefile(file1, model.sample_rate)
+    wave2 = read_wavefile(file2, model.sample_rate)
+
+    if not model.normalize_features:
+        wave1 = wave1 * 32768
+        wave2 = wave2 * 32768
+
+    features1 = compute_features(wave1, model.sample_rate)
+    features2 = compute_features(wave2, model.sample_rate)
+
+    output1 = model(features1)
+    output2 = model(features2)
+
+    print(output1.shape)
+    print(output2.shape)
+    similarity = np.dot(output1, output2) / (norm(output1) * norm(output2))
+    print(f"similarity in the range [0-1]: {similarity}")
+
+
+if __name__ == "__main__":
+    main()