v1: svd prototype — single-file singing detector

HuggingFace matthijs/svd (torch.jit) model + ffmpeg audio extract + tkinter file picker. Proves the concept in one script.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

.gitignore

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+__pycache__/
+*.pyc
+*.wav
+*.mp4
+.DS_Store
+venv/
+.venv/

sing_detect.py

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+import subprocess
+import os
+import torch
+import librosa
+import numpy as np
+from huggingface_hub import hf_hub_download
+import tkinter as tk
+from tkinter import filedialog
+
+# ------------------------
+# 1. Extract audio from video
+# ------------------------
+def extract_audio(input_video, output_wav):
+    cmd = [
+        "ffmpeg", "-y",
+        "-i", input_video,
+        "-vn",          # no video
+        "-ac", "1",     # mono
+        "-ar", "16000", # 16kHz
+        output_wav
+    ]
+    subprocess.run(cmd, check=True)
+
+# ------------------------
+# 2. Load pretrained model
+# ------------------------
+def load_model():
+    repo_id = "matthijs/svd"  # HuggingFace singing voice detection model
+    filename = hf_hub_download(repo_id=repo_id, filename="model.pt")
+    model = torch.jit.load(filename)
+    model.eval()
+    return model
+
+# ------------------------
+# 3. Run inference on audio
+# ------------------------
+def detect_singing(audio_path, model, hop_length=512, threshold=0.5, min_duration=10.0):
+    y, sr = librosa.load(audio_path, sr=16000)
+    x = torch.tensor(y).float().unsqueeze(0)
+
+    with torch.no_grad():
+        pred = model(x).squeeze().numpy()
+
+    times = librosa.frames_to_time(np.arange(len(pred)), sr=sr, hop_length=hop_length)
+
+    segments = []
+    in_segment = False
+    seg_start = None
+
+    for t, p in zip(times, pred):
+        if p > threshold and not in_segment:
+            in_segment = True
+            seg_start = t
+        elif p <= threshold and in_segment:
+            in_segment = False
+            seg_end = t
+            if seg_end - seg_start >= min_duration:
+                segments.append((seg_start, seg_end))
+
+    if in_segment:
+        seg_end = times[-1]
+        if seg_end - seg_start >= min_duration:
+            segments.append((seg_start, seg_end))
+
+    return segments
+
+# ------------------------
+# 4. Export CSV
+# ------------------------
+def export_csv(segments, out_file="segments.csv"):
+    with open(out_file, "w", encoding="utf-8") as f:
+        f.write("start_time,end_time\n")
+        for s, e in segments:
+            f.write(f"{s:.2f},{e:.2f}\n")
+    print(f"[+] Saved: {out_file}")
+
+# ------------------------
+# MAIN
+# ------------------------
+if __name__ == "__main__":
+    # Tkinter GUI to choose file
+    root = tk.Tk()
+    root.withdraw()
+    input_video = filedialog.askopenfilename(
+        title="Choose your video file",
+        filetypes=[("Video files", "*.mp4 *.mkv *.flv *.mov *.avi"), ("All files", "*.*")]
+    )
+    if not input_video:
+        print("No file selected. Exiting.")
+        exit()
+
+    audio_file = "audio.wav"
+
+    print(f"[+] Processing: {input_video}")
+
+    # Step 1: extract audio
+    extract_audio(input_video, audio_file)
+
+    # Step 2: load model
+    model = load_model()
+
+    # Step 3: run detection
+    segments = detect_singing(audio_file, model)
+
+    # Step 4: save results
+    export_csv(segments, "segments.csv")
+
+    print("[+] Done! Detected singing segments have been saved to segments.csv")