From 1553ea8e837e5113cf87742ed9436a6c1c5a7b45 Mon Sep 17 00:00:00 2001 From: Luxf23 <你的GitHub邮箱> Date: Fri, 19 Sep 2025 18:20:18 +1000 Subject: [PATCH] feat: overlapping sound detection API + Docker image & docs --- Docker | 13 ++++++ Dockerfile | 13 ++++++ app/main.py | 30 +++++++++++++ app/overlap_detector.py | 99 +++++++++++++++++++++++++++++++++++++++++ requirements.txt | 13 +++--- 5 files changed, 162 insertions(+), 6 deletions(-) create mode 100644 Docker create mode 100644 Dockerfile create mode 100644 app/main.py create mode 100644 app/overlap_detector.py diff --git a/Docker b/Docker new file mode 100644 index 000000000..1172a53ff --- /dev/null +++ b/Docker @@ -0,0 +1,13 @@ +FROM python:3.11-slim + +RUN apt-get update && apt-get install -y --no-install-recommends \ + libsndfile1 ffmpeg \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY app ./app +EXPOSE 8000 +CMD ["uvicorn", "app.main:app", "--host","0.0.0.0","--port","8000"] diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 000000000..1172a53ff --- /dev/null +++ b/Dockerfile @@ -0,0 +1,13 @@ +FROM python:3.11-slim + +RUN apt-get update && apt-get install -y --no-install-recommends \ + libsndfile1 ffmpeg \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY app ./app +EXPOSE 8000 +CMD ["uvicorn", "app.main:app", "--host","0.0.0.0","--port","8000"] diff --git a/app/main.py b/app/main.py new file mode 100644 index 000000000..c36dfb442 --- /dev/null +++ b/app/main.py @@ -0,0 +1,30 @@ +# app/main.py +from fastapi import FastAPI, UploadFile, File, HTTPException +from pydantic import BaseModel +from .overlap_detector import run_from_bytes + +app = FastAPI(title="EchoNet Overlap Detector", version="0.2.0") + +class OverlapEvent(BaseModel): + labels: list[str] + start: float + end: float + confidence: float + +class PredictResponse(BaseModel): + events: list[OverlapEvent] + meta: dict + +@app.get("/health") +async def health(): + return {"status": "ok"} + +@app.post("/predict", response_model=PredictResponse) +async def predict(file: UploadFile = File(...)): + if not file.content_type or not file.content_type.startswith("audio/"): + raise HTTPException(status_code=400, detail="File must be audio/*") + data = await file.read() + if not data: + raise HTTPException(status_code=400, detail="Empty file") + events, meta = run_from_bytes(data) + return {"events": events, "meta": meta} diff --git a/app/overlap_detector.py b/app/overlap_detector.py new file mode 100644 index 000000000..14cf7ce36 --- /dev/null +++ b/app/overlap_detector.py @@ -0,0 +1,99 @@ +# app/overlap_detector.py +import io +import numpy as np +import librosa +import soundfile as sf +from scipy.signal import medfilt + +SR = 16000 +N_MELS = 64 +N_FFT = 1024 +HOP = 160 # 10ms +WIN = 400 # 25ms +BANDS = [(0, 250), (250, 2000), (2000, 8000)] # 低/中/高 频段(Hz) +BAND_NAMES = ["low", "mid", "high"] + +def load_audio_from_bytes(b: bytes, sr=SR): + data, file_sr = sf.read(io.BytesIO(b), dtype="float32", always_2d=False) + if data.ndim > 1: + data = np.mean(data, axis=1) + if file_sr != sr: + data = librosa.resample(y=data, orig_sr=file_sr, target_sr=sr) + return data + +def logmel(y, sr=SR): + S = librosa.feature.melspectrogram( + y=y, sr=sr, n_fft=N_FFT, hop_length=HOP, win_length=WIN, n_mels=N_MELS, power=2.0 + ) + S_db = librosa.power_to_db(S + 1e-10) + return S_db # [n_mels, T] + +def band_flux(S_db, sr=SR): + """按频段计算谱通量(帧间差分>0)，作为“事件活跃度”基线。""" + n_mels, T = S_db.shape + mel_freqs = librosa.mel_frequencies(n_mels=n_mels, fmin=0, fmax=sr//2) + # 计算每帧对前一帧的正增量（谱通量） + d = np.maximum(0.0, np.diff(S_db, axis=1)) + d = np.pad(d, ((0,0),(1,0))) # 对齐到T + band_fluxes = [] + for (lo, hi) in BANDS: + mask = (mel_freqs >= lo) & (mel_freqs < hi) + # 频段内取均值 + bf = d[mask].mean(axis=0) if np.any(mask) else np.zeros(T, dtype=np.float32) + band_fluxes.append(bf) + band_fluxes = np.stack(band_fluxes, axis=1) # [T, B] + # 中值滤波去毛刺 + band_fluxes = medfilt(band_fluxes, kernel_size=(5,1)) + # 0-1 归一化（逐频段） + eps = 1e-6 + mn = band_fluxes.min(axis=0, keepdims=True) + mx = band_fluxes.max(axis=0, keepdims=True) + norm = (band_fluxes - mn) / (mx - mn + eps) + return norm # [T, B] + +def detect_overlaps(y, sr=SR, th=0.45, min_dur=0.15): + """ + 返回可能的“重叠事件”： + - 规则：同一帧内，≥2个频段活跃视为重叠 + - 输出：事件的起止时间、活跃频段标签、置信度（该段内的平均活跃度） + """ + S_db = logmel(y, sr) + bf = band_flux(S_db, sr) # [T, B] + active = (bf >= th).astype(np.int32) # [T, B] + multi = active.sum(axis=1) >= 2 # [T] 是否重叠 + events = [] + T = bf.shape[0] + i = 0 + frame_sec = HOP / sr + while i < T: + if multi[i]: + j = i + while j < T and multi[j]: + j += 1 + dur = (j - i) * frame_sec + if dur >= min_dur: + seg = bf[i:j] # [L, B] + mean_flux = seg.mean(axis=0) # [B] + hot_bands = [BAND_NAMES[k] for k,v in enumerate(mean_flux) if v >= th] + conf = float(mean_flux.mean()) + events.append({ + "labels": hot_bands if hot_bands else ["overlap"], + "start": round(i * frame_sec, 3), + "end": round(j * frame_sec, 3), + "confidence": round(conf, 3) + }) + i = j + else: + i += 1 + # 统计信息，便于HMI可视化 + meta = { + "fps": round(1.0 / frame_sec, 2), + "frames": T, + "bands": BAND_NAMES, + "threshold": th + } + return events, meta + +def run_from_bytes(b: bytes, th=0.45, min_dur=0.15): + y = load_audio_from_bytes(b, sr=SR) + return detect_overlaps(y, sr=SR, th=th, min_dur=min_dur) diff --git a/requirements.txt b/requirements.txt index a0b0dcb42..dec1ee222 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,8 @@ +fastapi +uvicorn[standard] +python-multipart +pydantic +librosa +soundfile +scipy numpy -ffmpeg-python==0.2.0 -keras -tensorflow -tensorflow-io -tfimm -pydub \ No newline at end of file