helllo world

app.py

@@ -1,150 +1,21 @@
-# reference: https://huggingface.co/spaces/r3gm/Audio_separator
-import gradio as gr
-import shutil
-import numpy as np
-from pathlib import Path
 import os
-import time
-import torch
-from huggingface_hub import hf_hub_download
-from uvr_processing import process_uvr_task, run_mdx, get_model_params
-from utils import convert_to_stereo_and_wav
-import onnxruntime as ort
-import io
-
-
-MODEL_ID = "masszhou/mdxnet"
-MODELS_PATH = {
-    "bgm": Path(hf_hub_download(repo_id=MODEL_ID, filename="UVR-MDX-NET-Inst_HQ_3.onnx")), 
-    "basic_vocal": Path(hf_hub_download(repo_id=MODEL_ID, filename="UVR-MDX-NET-Voc_FT.onnx")),
-    "main_vocal": Path(hf_hub_download(repo_id=MODEL_ID, filename="UVR_MDXNET_KARA_2.onnx"))
-}
-
-def get_device_info():
-    if torch.cuda.is_available():
-        device = f"GPU ({torch.cuda.get_device_name(0)})"
-    else:
-        device = "CPU"
-    return f"Current running environment: {device}"
-
-
-def inference(audio_file: str, 
-              stem: str = "vocal",) -> list[str]:
-    # audio_file = '/private/var/folders/02/_9ymjkz12xq8m_xh5592pl840000gn/T/gradio/74c3de047a439ea3cfb8e2d1ee6e5a85ea999d3eb30537b88d386aac177902d0/Spare Zeit und Aufwand mit den Servicevertragen von Mercedes-Benz Trucks..m4a'
-
-    if not audio_file:
-        raise ValueError("The audio path is missing.")
-
-    if not stem:
-        raise ValueError("Please select 'vocal' or 'background' stem.")
-
-    audio_file = Path(audio_file)
-    output_dir = Path("./output")
-
-
-    outputs = []
-    start_time = time.time()
-    background_path, vocals_path = process_uvr_task(
-        input_file_path=audio_file,
-        output_dir=output_dir,
-        models_path=MODELS_PATH,
-        )
-    end_time = time.time()
-    execution_time = end_time - start_time
-    print(f"Execution time: {execution_time} seconds")
-
-    print(f"Background file: {background_path}")
-    print(f"Vocals file: {vocals_path}")
-    os.makedirs("static/results", exist_ok=True)
-
-    # shutil.copy(background_path, bg_dst)
-    # shutil.copy(vocals_path, vc_dst)
-
-    outputs.append(str(background_path))
-    outputs.append(str(vocals_path))
-
-    return outputs
-
-
-def inference_bgm(audio_file: str) -> list[str]:
-    mdx_model_params = get_model_params(Path("./mdx_models"))
-    audio_file = convert_to_stereo_and_wav(Path(audio_file))  # resampling at 44100 Hz
-    device_base = "cuda" if torch.cuda.is_available() else "cpu"
-    output_dir = Path("./output")
-    model_bgm_path = MODELS_PATH["bgm"]
-    background_path, _ = run_mdx(model_params=mdx_model_params,
-                                input_filename=audio_file,
-                                output_dir=output_dir,
-                                model_path=model_bgm_path,
-                                denoise=False,
-                                device_base=device_base,
-                                )
-
-    return [str(background_path)]
+import gradio as gr
+from scipy.io.wavfile import write
 
 
-def return_original_file(file):
-    # 读取原始文件名和内容
-    filename = os.path.basename(file.name)
-    with open(file.path, "rb") as f:
-        content = f.read()
+def inference(audio):
+  os.makedirs("out", exist_ok=True)
+  write('test.wav', audio[0], audio[1])
+  os.system("python3 -m demucs.separate -n htdemucs --two-stems=vocals test.wav -o out")
+  return "./out/htdemucs/test/vocals.wav","./out/htdemucs/test/no_vocals.wav"
     
-    return (filename, content)
-
-
-def get_gui(theme, title, description):
-    with gr.Blocks(theme=theme) as app:
-        # Add title and description
-        gr.Markdown(title)
-        gr.Markdown(description)
-        gr.Markdown(get_device_info())
-        
-        # audio_input = gr.Audio(label="Audio file", type="filepath") 
-        # download_button = gr.Button("Inference")
-        # file_output = gr.File(label="Result", file_count="multiple", interactive=False)
-        
-        # download_button.click(
-        #     inference,
-        #     inputs=[audio_input],
-        #     outputs=[file_output],
-        # )
-
-        audio_input = gr.File(file_types=[".mp3", ".wav"], label="上传音频")
-        download_btn = gr.DownloadData(return_original_file, audio_input)
-        
-    return app
-
-
-if __name__ == "__main__":
-    title = "<center><strong><font size='7'>Vocal BGM Separator</font></strong></center>"
-    description = "This demo uses the MDX-Net models to perform Ultimate Vocal Remover (uvr) task for vocal and background sound separation."
-    theme = "NoCrypt/miku"
-
-    model_id = "masszhou/mdxnet"
-    models_path = {
-        "bgm": Path(hf_hub_download(repo_id=model_id, filename="UVR-MDX-NET-Inst_HQ_3.onnx")),
-        "basic_vocal": Path(hf_hub_download(repo_id=model_id, filename="UVR-MDX-NET-Voc_FT.onnx")),
-        "main_vocal": Path(hf_hub_download(repo_id=model_id, filename="UVR_MDXNET_KARA_2.onnx"))
-    }
-    print(f"ort.get_available_providers(): {ort.get_available_providers()}")
-    print(gr.__version__)
-
-    # entry point for GUI
-    # predict(audio_file, api_name="/inference") -> result
-    app_gui = get_gui(theme, title, description)
-
-    # entry point for API
-    # predict(audio_file, api_name="/predict") -> output
-    app_api = gr.Interface(
-        fn=inference_bgm,
-        inputs=gr.Audio(type="filepath"),
-        outputs=gr.File(file_count="multiple"),
-    )
-
-    app = gr.TabbedInterface(
-        interface_list=[app_gui, app_api],
-        tab_names=["GUI", "API"]
-    )
-
-    app.queue(default_concurrency_limit=40)
-    app.launch()
+title = "Demucs Music Source Separation (v4)"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1911.13254' target='_blank'>Music Source Separation in the Waveform Domain</a> | <a href='https://github.com/facebookresearch/demucs' target='_blank'>Github Repo</a> | <a href='https://www.thafx.com' target='_blank'>//THAFX</a></p>"
+
+gr.Interface(
+    inference, 
+    gr.Audio(type="numpy", label="Input"),
+    [gr.Audio(type="filepath", label="Vocals"),gr.Audio(type="filepath", label="No Vocals / Instrumental")],
+    title=title,
+    article=article,
+    ).launch()

app_bak.py

@@ -0,0 +1,150 @@
+# reference: https://huggingface.co/spaces/r3gm/Audio_separator
+import gradio as gr
+import shutil
+import numpy as np
+from pathlib import Path
+import os
+import time
+import torch
+from huggingface_hub import hf_hub_download
+from uvr_processing import process_uvr_task, run_mdx, get_model_params
+from utils import convert_to_stereo_and_wav
+import onnxruntime as ort
+import io
+
+
+MODEL_ID = "masszhou/mdxnet"
+MODELS_PATH = {
+    "bgm": Path(hf_hub_download(repo_id=MODEL_ID, filename="UVR-MDX-NET-Inst_HQ_3.onnx")), 
+    "basic_vocal": Path(hf_hub_download(repo_id=MODEL_ID, filename="UVR-MDX-NET-Voc_FT.onnx")),
+    "main_vocal": Path(hf_hub_download(repo_id=MODEL_ID, filename="UVR_MDXNET_KARA_2.onnx"))
+}
+
+def get_device_info():
+    if torch.cuda.is_available():
+        device = f"GPU ({torch.cuda.get_device_name(0)})"
+    else:
+        device = "CPU"
+    return f"Current running environment: {device}"
+
+
+def inference(audio_file: str, 
+              stem: str = "vocal",) -> list[str]:
+    # audio_file = '/private/var/folders/02/_9ymjkz12xq8m_xh5592pl840000gn/T/gradio/74c3de047a439ea3cfb8e2d1ee6e5a85ea999d3eb30537b88d386aac177902d0/Spare Zeit und Aufwand mit den Servicevertragen von Mercedes-Benz Trucks..m4a'
+
+    if not audio_file:
+        raise ValueError("The audio path is missing.")
+
+    if not stem:
+        raise ValueError("Please select 'vocal' or 'background' stem.")
+
+    audio_file = Path(audio_file)
+    output_dir = Path("./output")
+
+
+    outputs = []
+    start_time = time.time()
+    background_path, vocals_path = process_uvr_task(
+        input_file_path=audio_file,
+        output_dir=output_dir,
+        models_path=MODELS_PATH,
+        )
+    end_time = time.time()
+    execution_time = end_time - start_time
+    print(f"Execution time: {execution_time} seconds")
+
+    print(f"Background file: {background_path}")
+    print(f"Vocals file: {vocals_path}")
+    os.makedirs("static/results", exist_ok=True)
+
+    # shutil.copy(background_path, bg_dst)
+    # shutil.copy(vocals_path, vc_dst)
+
+    outputs.append(str(background_path))
+    outputs.append(str(vocals_path))
+
+    return outputs
+
+
+def inference_bgm(audio_file: str) -> list[str]:
+    mdx_model_params = get_model_params(Path("./mdx_models"))
+    audio_file = convert_to_stereo_and_wav(Path(audio_file))  # resampling at 44100 Hz
+    device_base = "cuda" if torch.cuda.is_available() else "cpu"
+    output_dir = Path("./output")
+    model_bgm_path = MODELS_PATH["bgm"]
+    background_path, _ = run_mdx(model_params=mdx_model_params,
+                                input_filename=audio_file,
+                                output_dir=output_dir,
+                                model_path=model_bgm_path,
+                                denoise=False,
+                                device_base=device_base,
+                                )
+
+    return [str(background_path)]
+
+
+def return_original_file(file):
+    # 读取原始文件名和内容
+    filename = os.path.basename(file.name)
+    with open(file.path, "rb") as f:
+        content = f.read()
+    
+    return (filename, content)
+
+
+def get_gui(theme, title, description):
+    with gr.Blocks(theme=theme) as app:
+        # Add title and description
+        gr.Markdown(title)
+        gr.Markdown(description)
+        gr.Markdown(get_device_info())
+        
+        # audio_input = gr.Audio(label="Audio file", type="filepath") 
+        # download_button = gr.Button("Inference")
+        # file_output = gr.File(label="Result", file_count="multiple", interactive=False)
+        
+        # download_button.click(
+        #     inference,
+        #     inputs=[audio_input],
+        #     outputs=[file_output],
+        # )
+
+        audio_input = gr.File(file_types=[".mp3", ".wav"], label="上传音频")
+        download_btn = gr.DownloadData(return_original_file, audio_input)
+        
+    return app
+
+
+if __name__ == "__main__":
+    title = "<center><strong><font size='7'>Vocal BGM Separator</font></strong></center>"
+    description = "This demo uses the MDX-Net models to perform Ultimate Vocal Remover (uvr) task for vocal and background sound separation."
+    theme = "NoCrypt/miku"
+
+    model_id = "masszhou/mdxnet"
+    models_path = {
+        "bgm": Path(hf_hub_download(repo_id=model_id, filename="UVR-MDX-NET-Inst_HQ_3.onnx")),
+        "basic_vocal": Path(hf_hub_download(repo_id=model_id, filename="UVR-MDX-NET-Voc_FT.onnx")),
+        "main_vocal": Path(hf_hub_download(repo_id=model_id, filename="UVR_MDXNET_KARA_2.onnx"))
+    }
+    print(f"ort.get_available_providers(): {ort.get_available_providers()}")
+    print(gr.__version__)
+
+    # entry point for GUI
+    # predict(audio_file, api_name="/inference") -> result
+    app_gui = get_gui(theme, title, description)
+
+    # entry point for API
+    # predict(audio_file, api_name="/predict") -> output
+    app_api = gr.Interface(
+        fn=inference_bgm,
+        inputs=gr.Audio(type="filepath"),
+        outputs=gr.File(file_count="multiple"),
+    )
+
+    app = gr.TabbedInterface(
+        interface_list=[app_gui, app_api],
+        tab_names=["GUI", "API"]
+    )
+
+    app.queue(default_concurrency_limit=40)
+    app.launch()

requirements.txt

@@ -1,7 +1,9 @@
-gradio
-torch
-torchaudio
-librosa
-onnxruntime
-numpy
-tqdm
+gradio==5.23.0
+torch==2.6.0
+torchaudio==2.6.0
+librosa==0.11.0
+onnxruntime==onnxruntime
+numpy==2.2.4
+tqdm==4.67.1
+demucs==4.0.1
+scipy==1.15.2