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README.md

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----
-title: Singing Segment Detector
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎤 Singing Segment Detector
+
+这是一个基于 Hugging Face + Gradio 的 AI 工具，
+可以自动识别长音频中的唱歌片段并输出时间戳。
+
+### 使用方法
+1. 上传从视频中提取的音频（例如 ffmpeg 抽取的 WAV）
+2. 点击“开始分析”
+3. 查看唱歌片段时间戳 JSON

app.py

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+import gradio as gr
+import librosa
+import numpy as np
+import torch
+from transformers import pipeline
+from tqdm import tqdm
+import tempfile
+import json
+import soundfile as sf
+
+# ==== 参数设置 ====
+SAMPLE_RATE = 8000        # 降采样，节省计算
+WINDOW = 5                 # 每个分析窗口长度（秒）
+STEP = 2                   # 滑动步长（秒）
+MUSIC_THRESHOLD = 0.4
+VOICE_THRESHOLD = 0.3
+MIN_SING_DURATION = 8      # 最短唱歌片段（秒）
+
+# ==== 初始化模型 ====
+music_pipe = pipeline("audio-classification", model="AI-Music-Detection/ai_music_detection_large_60s")
+voice_pipe = pipeline("audio-classification", model="superb/hubert-large-superb-sid")
+
+def detect_singing(audio_path):
+    """核心：检测唱歌时间戳"""
+    wav, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
+    duration = len(wav) / SAMPLE_RATE
+    results = []
+
+    for start in np.arange(0, duration - WINDOW, STEP):
+        end = start + WINDOW
+        snippet = wav[int(start * SAMPLE_RATE):int(end * SAMPLE_RATE)]
+
+        # 音乐概率
+        music_pred = music_pipe(snippet, sampling_rate=SAMPLE_RATE)
+        music_score = max([p['score'] for p in music_pred if 'music' in p['label'].lower()] or [0])
+
+        # 声音概率（有语音活动）
+        voice_pred = voice_pipe(snippet, sampling_rate=SAMPLE_RATE)
+        voice_score = max([p['score'] for p in voice_pred if 'speech' in p['label'].lower()] or [0])
+
+        if music_score > MUSIC_THRESHOLD and voice_score > VOICE_THRESHOLD:
+            results.append((float(start), float(end)))
+
+    # 合并连续区间
+    merged = []
+    for seg in results:
+        if not merged or seg[0] > merged[-1][1]:
+            merged.append(list(seg))
+        else:
+            merged[-1][1] = seg[1]
+
+    merged = [(s, e) for s, e in merged if e - s >= MIN_SING_DURATION]
+
+    return merged
+
+
+def analyze_audio(file):
+    """Gradio 接口函数"""
+    if file is None:
+        return "请上传音频文件", None
+
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        # 确保为wav格式
+        data, sr = librosa.load(file.name, sr=SAMPLE_RATE)
+        sf.write(tmp.name, data, sr)
+        segments = detect_singing(tmp.name)
+
+    if not segments:
+        return "未检测到明显唱歌片段", json.dumps([], indent=2)
+
+    json_output = json.dumps(
+        [{"start": s, "end": e, "duration": round(e - s, 2)} for s, e in segments],
+        indent=2
+    )
+    return f"检测到 {len(segments)} 段唱歌片段", json_output
+
+
+# ==== Gradio UI ====
+with gr.Blocks(title="🎵 Singing Segment Detector") as demo:
+    gr.Markdown("# 🎤 自动识别唱歌片段 (Hugging Face Space)\n上传音频文件，返回检测到的唱歌时间段 JSON。")
+    audio_in = gr.Audio(type="filepath", label="上传音频文件（从视频提取后）")
+    btn = gr.Button("开始分析")
+    status = gr.Textbox(label="分析结果", interactive=False)
+    json_out = gr.Code(label="唱歌片段时间戳（JSON）", language="json")
+    btn.click(fn=analyze_audio, inputs=[audio_in], outputs=[status, json_out])
+
+demo.launch()

requirements.txt

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+gradio
+librosa
+torch
+torchaudio
+transformers
+numpy
+tqdm
+soundfile