| import gradio as gr |
| import librosa |
| import numpy as np |
| import soundfile as sf |
| import tempfile |
| import subprocess |
| import os |
|
|
| def extract_audio_if_needed(audio_path): |
| if audio_path.endswith(".mp4"): |
| temp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False) |
| |
| subprocess.run([ |
| "ffmpeg", "-y", "-i", audio_path, |
| "-vn", "-acodec", "pcm_s16le", "-ar", "44100", temp_wav.name |
| ], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) |
| return temp_wav.name |
| return audio_path |
|
|
| def summarize_audio(audio): |
| if isinstance(audio, tuple): |
| audio_path = audio[0] |
| else: |
| audio_path = audio |
|
|
| audio_path = extract_audio_if_needed(audio_path) |
|
|
| y, sr = librosa.load(audio_path, sr=None) |
| chroma = librosa.feature.chroma_cqt(y=y, sr=sr) |
| similarity = librosa.segment.recurrence_matrix(chroma, mode='affinity', sym=True) |
| segments = librosa.segment.agglomerative(similarity, k=4) |
| boundaries = librosa.frames_to_time(np.where(np.diff(segments))[0], hop_length=512, sr=sr) |
|
|
| summary_audio = [] |
| for i in range(min(3, len(boundaries))): |
| start = int(boundaries[i] * sr) |
| end = start + int(15 * sr) |
| summary_audio.append(y[start:end]) |
|
|
| combined = np.concatenate(summary_audio) |
| tmp_out = tempfile.NamedTemporaryFile(suffix=".wav", delete=False) |
| sf.write(tmp_out.name, combined, sr) |
| return tmp_out.name |
|
|
| demo = gr.Interface( |
| fn=summarize_audio, |
| inputs=gr.Audio(type="filepath"), |
| outputs=gr.Audio(type="filepath"), |
| title="Jam Summarizer", |
| description="Upload a long jam session or song. This tool finds key musical sections and outputs a ~1 min highlight reel." |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
