first version

LICENSE

@@ -0,0 +1,202 @@
+
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app.py

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+#!/usr/bin/env python3
+#
+# Copyright      2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# References:
+# https://gradio.app/docs/#dropdown
+
+import logging
+import shutil
+import tempfile
+import time
+import urllib.request
+from datetime import datetime
+import soundfile as sf
+
+import gradio as gr
+import numpy as np
+
+from separate import get_file, load_audio, load_model, model_list
+
+examples = [
+    "yesterday-once-more-Carpenters.mp3",
+    "das-beste-Silbermond.mp3",
+    "hotel-in-california.mp3",
+    "起风了.mp3",
+]
+
+for name in examples:
+    filename = get_file(
+        "csukuangfj/spleeter-torch",
+        name,
+        subfolder="test_wavs",
+    )
+
+    shutil.copyfile(filename, name)
+
+
+def build_html_output(s: str, style: str = "result_item_success"):
+    return f"""
+    <div class='result'>
+        <div class='result_item {style}'>
+          {s}
+        </div>
+    </div>
+    """
+
+
+def process_url(model_name: str, url: str):
+    logging.info(f"Processing URL: {url}")
+    with tempfile.NamedTemporaryFile() as f:
+        try:
+            urllib.request.urlretrieve(url, f.name)
+            return process(model_name, in_filename=f.name)
+        except Exception as e:
+            logging.info(str(e))
+            return "", build_html_output(str(e), "result_item_error")
+
+
+def process_uploaded_file(model_name: str, in_filename: str):
+    if in_filename is None or in_filename == "":
+        return "", build_html_output(
+            "Please first upload a file and then click "
+            'the button "submit for separation"',
+            "result_item_error",
+        )
+
+    logging.info(f"Processing uploaded file: {in_filename}")
+    try:
+        return process(model_name, in_filename=in_filename)
+    except Exception as e:
+        logging.info(str(e))
+        return "", build_html_output(str(e), "result_item_error")
+
+
+def process_microphone(model_name: str, in_filename: str):
+    if in_filename is None or in_filename == "":
+        return "", build_html_output(
+            "Please first click 'Record from microphone', speak, "
+            "click 'Stop recording', and then "
+            "click the button 'submit for separation'",
+            "result_item_error",
+        )
+
+    logging.info(f"Processing microphone: {in_filename}")
+    try:
+        return process(model_name, in_filename=in_filename)
+    except Exception as e:
+        logging.info(str(e))
+        return "", build_html_output(str(e), "result_item_error")
+
+
+def process(model_name, in_filename: str):
+    logging.info(f"model_name: {model_name}")
+    logging.info(f"in_filename: {in_filename}")
+
+    waveform = load_audio(in_filename)
+    waveform = np.transpose(waveform)
+    waveform = np.ascontiguousarray(waveform)
+
+    duration = waveform.shape[1] / 44100  # in seconds
+
+    sp = load_model(model_name)
+
+    now = datetime.now()
+    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
+    logging.info(f"Started at {date_time}")
+
+    start = time.time()
+
+    output = sp.process(sample_rate=44100, samples=waveform)
+
+    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
+    end = time.time()
+
+    vocals = output.stems[0].data
+    non_vocals = output.stems[1].data
+    # vocals.shape (num_channels, num_samples)
+
+    vocals = np.transpose(vocals)
+    non_vocals = np.transpose(non_vocals)
+
+    vocals_filename = in_filename + "-vocals.mp3"
+    non_vocals_filename = in_filename + "-non-vocals.mp3"
+
+    sf.write(vocals_filename, vocals, samplerate=output.sample_rate)
+    sf.write(non_vocals_filename, non_vocals, samplerate=output.sample_rate)
+
+    rtf = (end - start) / duration
+
+    logging.info(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s")
+
+    info = f"""
+    Input duration  : {duration: .3f} s <br/>
+    Processing time: {end - start: .3f} s <br/>
+    RTF: {end - start: .3f}/{duration: .3f} = {rtf:.3f} <br/>
+    """
+    logging.info(info)
+
+    return vocals_filename, non_vocals_filename, build_html_output(info)
+
+
+title = "# Source separation with Next-gen Kaldi"
+
+# css style is copied from
+# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
+css = """
+.result {display:flex;flex-direction:column}
+.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
+.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
+.result_item_error {background-color:#ff7070;color:white;align-self:start}
+"""
+
+
+demo = gr.Blocks(css=css)
+
+
+with demo:
+    gr.Markdown(title)
+
+    model_dropdown = gr.Dropdown(
+        choices=model_list[model_list[0]],
+        label="Select a model",
+        value=model_list[model_list[0]],
+    )
+
+    with gr.Tabs():
+        with gr.TabItem("Upload from disk"):
+            uploaded_file = gr.Audio(
+                sources=["upload"],  # Choose between "microphone", "upload"
+                type="filepath",
+                label="Upload from disk",
+            )
+            upload_button = gr.Button("Submit for separation")
+            uploaded_html_info = gr.HTML(label="Info")
+
+            uploaded_vocals = gr.Audio(label="vocals")
+            uploaded_non_vocals = gr.Audio(label="non_vocals")
+
+            gr.Examples(
+                examples=examples,
+                inputs=[model_dropdown, uploaded_file],
+                outputs=[uploaded_vocals, uploaded_non_vocals, uploaded_html_info],
+                fn=process_uploaded_file,
+            )
+
+        with gr.TabItem("Record from microphone"):
+            microphone = gr.Audio(
+                sources=["microphone"],  # Choose between "microphone", "upload"
+                type="filepath",
+                label="Record from microphone",
+            )
+
+            record_button = gr.Button("Submit for separation")
+            recorded_html_info = gr.HTML(label="Info")
+
+            recorded_vocals = gr.Audio(label="vocals")
+            recorded_non_vocals = gr.Audio(label="non-vocals")
+
+            gr.Examples(
+                examples=examples,
+                inputs=[model_dropdown, microphone],
+                outputs=[recorded_vocals, recorded_non_vocals, recorded_html_info],
+                fn=process_microphone,
+            )
+
+        with gr.TabItem("From URL"):
+            url_textbox = gr.Textbox(
+                max_lines=1,
+                placeholder="URL to an audio file",
+                label="URL",
+                interactive=True,
+            )
+
+            url_button = gr.Button("Submit for separation")
+            url_html_info = gr.HTML(label="Info")
+
+            url_vocals = gr.Audio(label="vocals")
+            url_non_vocals = gr.Audio(label="non-vocals")
+
+            gr.Examples(
+                examples=[
+                    "https://huggingface.co/csukuangfj/spleeter-torch/resolve/main/test_wavs/yesterday-once-more-Carpenters.mp3",
+                    "https://huggingface.co/csukuangfj/spleeter-torch/resolve/main/test_wavs/das-beste-Silbermond.mp3",
+                    "https://huggingface.co/csukuangfj/spleeter-torch/resolve/main/test_wavs/hotel-in-california.mp3",
+                ],
+                inputs=[model_dropdown, url_textbox],
+                outputs=[url_vocals, url_non_vocals, recorded_html_info],
+                fn=process_url,
+            )
+
+        upload_button.click(
+            process_uploaded_file,
+            inputs=[model_dropdown, uploaded_file],
+            outputs=[uploaded_vocals, uploaded_non_vocals, uploaded_html_info],
+        )
+
+        record_button.click(
+            process_microphone,
+            inputs=[model_dropdown, microphone],
+            outputs=[recorded_vocals, recorded_non_vocals, recorded_html_info],
+        )
+
+        url_button.click(
+            process_url,
+            inputs=[model_dropdown, url_textbox],
+            outputs=[url_vocals, url_non_vocals, url_html_info],
+        )
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+numpy<2
+soundfile
+
+huggingface_hub
+
+https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/cpu/1.12.1/sherpa_onnx-1.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+
+#sherpa-onnx>=1.12.1

separate.py

@@ -0,0 +1,138 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+from functools import lru_cache
+
+import ffmpeg
+import numpy as np
+from huggingface_hub import hf_hub_download
+import sherpa_onnx
+
+
+sample_rate = 44100
+
+
+def load_audio(filename):
+    probe = ffmpeg.probe(filename)
+    if "streams" not in probe or len(probe["streams"]) == 0:
+        raise ValueError("No stream was found with ffprobe")
+
+    metadata = next(
+        stream for stream in probe["streams"] if stream["codec_type"] == "audio"
+    )
+    n_channels = metadata["channels"]
+
+    process = (
+        ffmpeg.input(filename)
+        .output("pipe:", format="f32le", ar=sample_rate)
+        .run_async(pipe_stdout=True, pipe_stderr=True)
+    )
+    buffer, _ = process.communicate()
+    waveform = np.frombuffer(buffer, dtype="<f4").reshape(-1, n_channels)
+
+    if n_channels > 2:
+        waveform = waveform[:, :2]
+
+    return waveform
+
+
+@lru_cache(maxsize=10)
+def get_file(
+    repo_id: str,
+    filename: str,
+    subfolder: str = "2stems",
+) -> str:
+    nn_model_filename = hf_hub_download(
+        repo_id=repo_id,
+        filename=filename,
+        subfolder=subfolder,
+    )
+    return nn_model_filename
+
+
+@lru_cache(maxsize=30)
+def load_model(name: str):
+    if "spleeter" in name:
+        return load_spleeter_model(name)
+    elif "UVR" in name:
+        return load_uvr_model(name)
+
+    raise ValueError(f"Unsupported model name {name}")
+
+
+def load_uvr_model(name: str):
+    model = get_file(
+        repo_id="k2-fsa/sherpa-onnx-models",
+        subfolder="source-separation-models",
+        filename=name,
+    )
+
+    config = sherpa_onnx.OfflineSourceSeparationConfig(
+        model=sherpa_onnx.OfflineSourceSeparationModelConfig(
+            uvr=sherpa_onnx.OfflineSourceSeparationUvrModelConfig(
+                model=model,
+            ),
+            num_threads=2,
+            debug=False,
+            provider="cpu",
+        )
+    )
+    if not config.validate():
+        raise ValueError("Please check your config.")
+
+    return sherpa_onnx.OfflineSourceSeparation(config)
+
+
+def load_spleeter_model(name: str):
+    if "fp16" in name:
+        suffix = "fp16.onnx"
+    elif "int8" in name:
+        suffix = "int8.onnx"
+    else:
+        suffix = ".onnx"
+
+    vocals = get_file(repo_id=f"csukuangfj/{name}", filename=f"vocals.{suffix}")
+    accompaniment = get_file(
+        repo_id=f"csukuangfj/{name}", filename=f"accompaniment.{suffix}"
+    )
+
+    config = sherpa_onnx.OfflineSourceSeparationConfig(
+        model=sherpa_onnx.OfflineSourceSeparationModelConfig(
+            spleeter=sherpa_onnx.OfflineSourceSeparationSpleeterModelConfig(
+                vocals=vocals,
+                accompaniment=accompaniment,
+            ),
+            num_threads=2,
+            debug=False,
+            provider="cpu",
+        )
+    )
+    if not config.validate():
+        raise ValueError("Please check your config.")
+
+    return sherpa_onnx.OfflineSourceSeparation(config)
+
+
+model_list = [
+    "sherpa-onnx-spleeter-2stems-fp16",
+    "sherpa-onnx-spleeter-2stems-int8",
+    "sherpa-onnx-spleeter-2stems",
+    "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",
+]