Add UVR models for source separation. (#2266)

@@ -4,9 +4,6 @@ on:
   push:
   push:
     branches:
     branches:
       - master
       - master
-  pull_request:
-    branches:
-      - master
 
 
   workflow_dispatch:
   workflow_dispatch:
 
 

+name: export-uvr-to-onnx
+
+on:
+  push:
+    branches:
+      - uvr
+  workflow_dispatch:
+
+concurrency:
+  group: export-uvr-to-onnx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  export-uvr-to-onnx:
+    if: github.repository_owner == 'k2-fsa' || github.repository_owner == 'csukuangfj'
+    name: export UVR to ONNX
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos-latest]
+        python-version: ["3.10"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          pip install "numpy<2" onnx==1.17.0 onnxruntime==1.17.1 onnxmltools kaldi-native-fbank librosa soundfile
+
+      - name: Run
+        shell: bash
+        run: |
+          cd scripts/uvr_mdx
+          curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/source-separation-models/audio_example.wav
+          ls -lh audio_example.wav
+          ./run.sh
+
+      - name: Collect mp3 files
+        shell: bash
+        run: |
+          mv -v scripts/uvr_mdx/*.mp3 ./
+          ls -lh *.mp3
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: generated-mp3
+          path: ./*.mp3
+
+      - name: Collect models
+        shell: bash
+        run: |
+          mv -v scripts/uvr_mdx/models/*.onnx ./
+          ls -lh *.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: source-separation-models
+
+      - name: Publish to huggingface
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v3
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            export GIT_LFS_SKIP_SMUDGE=1
+            export GIT_CLONE_PROTECTION_ACTIVE=false
+
+            rm -rf huggingface
+            git clone https://huggingface.co/k2-fsa/sherpa-onnx-models huggingface
+            cd huggingface
+            mkdir -p source-separation-models
+            cp -av ../*.onnx ./source-separation-models
+            git lfs track "*.onnx"
+            git status
+            git add .
+            ls -lh
+            git status
+            git commit -m "add source separation models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/k2-fsa/sherpa-onnx-models main

+# Introduction
+
+This folder contains scripts for converting models from
+https://github.com/TRvlvr/model_repo/releases/tag/all_public_uvr_models
+to sherpa-onnx.

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import argparse
+from pathlib import Path
+
+import onnx
+import onnxmltools
+import onnxruntime
+from onnxmltools.utils.float16_converter import convert_float_to_float16
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--filename",
+        type=str,
+        required=True,
+        help="Path to onnx model",
+    )
+
+    return parser.parse_args()
+
+
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def validate(model: onnxruntime.InferenceSession):
+    for i in model.get_inputs():
+        print(i)
+
+    print("-----")
+
+    for i in model.get_outputs():
+        print(i)
+
+    assert len(model.get_inputs()) == 1, len(model.get_inputs())
+    assert len(model.get_outputs()) == 1, len(model.get_outputs())
+
+    inp = model.get_inputs()[0]
+    outp = model.get_outputs()[0]
+
+    assert len(inp.shape) == 4, inp.shape
+    assert len(outp.shape) == 4, outp.shape
+
+    assert inp.shape[1:] == outp.shape[1:], (inp.shape, outp.shape)
+
+
+def add_meta_data(filename, meta_data):
+    model = onnx.load(filename)
+
+    print(model.metadata_props)
+
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+    print("--------------------")
+
+    print(model.metadata_props)
+
+    onnx.save(model, filename)
+
+
+def main():
+    args = get_args()
+    filename = Path(args.filename)
+    if not filename.is_file():
+        raise ValueError(f"{filename} does not exist")
+
+    name = filename.stem
+    print("name", name)
+
+    model = onnx.load(str(filename))
+
+    session_opts = onnxruntime.SessionOptions()
+    session_opts.log_severity_level = 3
+    sess = onnxruntime.InferenceSession(
+        str(filename), session_opts, providers=["CPUExecutionProvider"]
+    )
+    validate(sess)
+
+    inp = sess.get_inputs()[0]
+    outp = sess.get_outputs()[0]
+
+    meta_data = {
+        "model_type": "UVR",
+        "model_name": name,
+        "sample_rate": 44100,
+        "comment": "This model is downloaded from https://github.com/TRvlvr/model_repo/releases",
+        "n_fft": inp.shape[2] * 2,
+        "center": 1,
+        "window_type": "hann",
+        "win_length": inp.shape[2] * 2,
+        "hop_length": 1024,
+        "dim_t": inp.shape[3],
+        "dim_f": inp.shape[2],
+        "dim_c": inp.shape[1],
+        "stems": 2,
+    }
+    add_meta_data(str(filename), meta_data)
+
+    filename_fp16 = f"./{name}.fp16.onnx"
+    export_onnx_fp16(filename, filename_fp16)
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env bash
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+set -ex
+
+
+# Please see https://github.com/TRvlvr/model_repo/releases/tag/all_public_uvr_models
+models=(
+UVR-MDX-NET-Inst_1.onnx
+UVR-MDX-NET-Inst_2.onnx
+UVR-MDX-NET-Inst_3.onnx
+UVR-MDX-NET-Inst_HQ_1.onnx
+UVR-MDX-NET-Inst_HQ_2.onnx
+UVR-MDX-NET-Inst_HQ_3.onnx
+UVR-MDX-NET-Inst_HQ_4.onnx
+UVR-MDX-NET-Inst_HQ_5.onnx
+UVR-MDX-NET-Inst_Main.onnx
+UVR-MDX-NET-Voc_FT.onnx
+UVR-MDX-NET_Crowd_HQ_1.onnx
+UVR_MDXNET_1_9703.onnx
+UVR_MDXNET_2_9682.onnx
+UVR_MDXNET_3_9662.onnx
+UVR_MDXNET_9482.onnx
+UVR_MDXNET_KARA.onnx
+UVR_MDXNET_KARA_2.onnx
+UVR_MDXNET_Main.onnx
+)
+
+mkdir -p models
+for m in ${models[@]}; do
+  if [ ! -f models/$m ]; then
+    curl -SL --output models/$m https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/$m
+  fi
+done
+
+ls -lh models
+
+for m in ${models[@]}; do
+  echo "----------$m----------"
+  python3 ./add_meta_data_and_quantize.py --filename models/$m
+
+  ls -lh models/
+done
+
+if [ -f ./audio_example.wav ]; then
+  for m in ${models[@]}; do
+    ./test.py  --model-filename ./models/$m --audio-filename ./audio_example.wav
+    name=$(basename -s .onnx $m)
+    mv -v vocals.mp3 ${name}_vocals.mp3
+    mv -v non_vocals.mp3 ${name}_non_vocals.mp3
+  done
+
+  ls -lh *.mp3
+fi

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import onnxruntime
+import onnx
+
+"""
+[]
+NodeArg(name='input', type='tensor(float)', shape=['batch_size', 4, 3072, 256])
+-----
+NodeArg(name='output', type='tensor(float)', shape=['batch_size', 4, 3072, 256])
+"""
+
+
+def show(filename):
+    model = onnx.load(filename)
+    print(model.metadata_props)
+
+    session_opts = onnxruntime.SessionOptions()
+    session_opts.log_severity_level = 3
+    sess = onnxruntime.InferenceSession(
+        filename, session_opts, providers=["CPUExecutionProvider"]
+    )
+    for i in sess.get_inputs():
+        print(i)
+
+    print("-----")
+
+    for i in sess.get_outputs():
+        print(i)
+
+
+def main():
+    #  show("./UVR-MDX-NET-Voc_FT.onnx")
+    show("./UVR_MDXNET_1_9703.onnx")
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import time
+
+import argparse
+import kaldi_native_fbank as knf
+import librosa
+import numpy as np
+import onnxruntime as ort
+import soundfile as sf
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model-filename",
+        type=str,
+        required=True,
+        help="Path to onnx model",
+    )
+
+    parser.add_argument(
+        "--audio-filename",
+        type=str,
+        required=True,
+        help="Path to input audio file",
+    )
+
+    return parser.parse_args()
+
+
+class OnnxModel:
+    def __init__(self, filename):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 4
+        session_opts.intra_op_num_threads = 4
+
+        self.session_opts = session_opts
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+
+        self.dim_t = self.model.get_outputs()[0].shape[3]
+
+        self.dim_f = self.model.get_outputs()[0].shape[2]
+
+        self.n_fft = self.dim_f * 2
+
+        self.dim_c = self.model.get_outputs()[0].shape[1]
+        assert self.dim_c == 4, self.dim_c
+
+        self.hop = 1024
+        self.n_bins = self.n_fft // 2 + 1
+        self.chunk_size = self.hop * (self.dim_t - 1)
+
+        self.freq_pad = np.zeros([1, self.dim_c, self.n_bins - self.dim_f, self.dim_t])
+
+        print(f"----------inputs for {filename}----------")
+        for i in self.model.get_inputs():
+            print(i)
+
+        print(f"----------outputs for {filename}----------")
+
+        for i in self.model.get_outputs():
+            print(i)
+            print(i.shape)
+        print("--------------------")
+
+    def __call__(self, x):
+        """
+        Args:
+          x: (batch_size, 4, self.dim_f, self.dim_t)
+        Returns:
+          spec: (batch_size, 4, self.dim_f, self.dim_t)
+        """
+        spec = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x,
+            },
+        )[0]
+
+        return spec
+
+
+def main():
+    args = get_args()
+    m = OnnxModel(args.model_filename)
+
+    stft_config = knf.StftConfig(
+        n_fft=m.n_fft,
+        hop_length=m.hop,
+        win_length=m.n_fft,
+        center=True,
+        window_type="hann",
+    )
+    knf_stft = knf.Stft(stft_config)
+    knf_istft = knf.IStft(stft_config)
+
+    sample_rate = 44100
+
+    samples, rate = librosa.load(args.audio_filename, mono=False, sr=sample_rate)
+
+    start_time = time.time()
+
+    assert rate == sample_rate, (rate, sample_rate)
+
+    # samples: (2, 479832) , (num_channels, num_samples), 44100, 10.88
+    print("samples", samples.shape, rate, samples.shape[1] / rate)
+
+    assert samples.ndim == 2, samples.shape
+    assert samples.shape[0] == 2, samples.shape
+
+    margin = sample_rate
+
+    num_chunks = 15
+    chunk_size = num_chunks * sample_rate
+
+    # if they are too few samples, reset chunk_size
+    if samples.shape[1] < chunk_size:
+        chunk_size = samples.shape[1]
+
+    if margin > chunk_size:
+        margin = chunk_size
+
+    segments = []
+    for skip in range(0, samples.shape[1], chunk_size):
+        start = max(0, skip - margin)
+        end = min(skip + chunk_size + margin, samples.shape[1])
+        segments.append(samples[:, start:end])
+        if end == samples.shape[1]:
+            break
+
+    sources = []
+    for kk, s in enumerate(segments):
+        num_samples = s.shape[1]
+        trim = m.n_fft // 2
+        gen_size = m.chunk_size - 2 * trim
+        pad = gen_size - s.shape[1] % gen_size
+        mix_p = np.concatenate(
+            (
+                np.zeros((2, trim)),
+                s,
+                np.zeros((2, pad)),
+                np.zeros((2, trim)),
+            ),
+            axis=1,
+        )
+
+        chunk_list = []
+        i = 0
+        while i < s.shape[1] + pad:
+            chunk_list.append(mix_p[:, i : i + m.chunk_size])
+            i += gen_size
+
+        mix_waves = np.array(chunk_list)
+
+        mix_waves_reshaped = mix_waves.reshape(-1, m.chunk_size)
+        stft_results = []
+        for w in mix_waves_reshaped:
+            stft = knf_stft(w)
+            stft_results.append(stft)
+        real = np.array(
+            [np.array(s.real).reshape(s.num_frames, -1) for s in stft_results],
+            dtype=np.float32,
+        )[:, :, :-1]
+        # real: (6, 256, 3072)
+
+        real = real.transpose(0, 2, 1)
+        # real: (6, 3072, 256)
+
+        imag = np.array(
+            [np.array(s.imag).reshape(s.num_frames, -1) for s in stft_results],
+            dtype=np.float32,
+        )[:, :, :-1]
+        imag = imag.transpose(0, 2, 1)
+        # imag: (6, 3072, 256)
+
+        x = np.stack([real, imag], axis=1)
+        # x: (6, 2, 3072, 256) -> (batch_size, real_imag, 3072, 256)
+        x = x.reshape(-1, m.dim_c, m.dim_f, m.dim_t)
+        # x: (3, 4, 3072, 256)
+        spec = m(x)
+
+        freq_pad = np.repeat(m.freq_pad, spec.shape[0], axis=0)
+
+        x = np.concatenate([spec, freq_pad], axis=2)
+        # x: (3, 4, 3073, 256)
+        x = x.reshape(-1, 2, m.n_bins, m.dim_t)
+        # x: (6, 2, 3073, 256)
+        x = x.transpose(0, 1, 3, 2)
+        # x: (6, 2, 256, 3073)
+        num_frames = x.shape[2]
+
+        x = x.reshape(x.shape[0], x.shape[1], -1)
+        wav_list = []
+        for k in range(x.shape[0]):
+            istft_result = knf.StftResult(
+                real=x[k, 0].reshape(-1).tolist(),
+                imag=x[k, 1].reshape(-1).tolist(),
+                num_frames=num_frames,
+            )
+            wav = knf_istft(istft_result)
+            wav_list.append(wav)
+        wav = np.array(wav_list, dtype=np.float32)
+        # wav: (6, 261120)
+
+        wav = wav.reshape(-1, 2, wav.shape[-1])
+        # wav: (3, 2, 261120)
+
+        wav = wav[:, :, trim:-trim]
+        # wav: (3, 2, 254976)
+
+        wav = wav.transpose(1, 0, 2)
+        # wav: (2, 3, 254976)
+
+        wav = wav.reshape(2, -1)
+        # wav: (2, 764928)
+
+        wav = wav[:, :-pad]
+        # wav: 2, 705600)
+        if kk == 0:
+            start = 0
+        else:
+            start = margin
+
+        if kk == len(segments) - 1:
+            end = None
+        else:
+            end = -margin
+
+        sources.append(wav[:, start:end])
+
+    sources = np.concatenate(sources, axis=-1)
+
+    vocals = sources
+    non_vocals = samples - vocals
+    end_time = time.time()
+    elapsed_seconds = end_time - start_time
+    print(f"Elapsed seconds: {elapsed_seconds:.3f}")
+
+    audio_duration = samples.shape[1] / sample_rate
+    real_time_factor = elapsed_seconds / audio_duration
+    print(f"Elapsed seconds: {elapsed_seconds:.3f}")
+    print(f"Audio duration in seconds: {audio_duration:.3f}")
+    print(f"RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {real_time_factor:.3f}")
+
+    sf.write(f"./vocals.mp3", np.transpose(vocals), sample_rate)
+    sf.write(f"./non_vocals.mp3", np.transpose(non_vocals), sample_rate)
+
+
+if __name__ == "__main__":
+    main()