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# app.py
import os
import tempfile
import subprocess
from pathlib import Path
import torch
torch.set_num_threads(1)
import torchaudio
import soundfile as sf
import numpy as np
from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, HTMLResponse
# NOTE: we lazy-load these inside get_model()
processor = None
model = None
TARGET_SR = 16000 # wav2vec2 expects 16 kHz
def get_model():
"""
Lazily load processor and model on first call and cache them globally.
Uses a custom HF cache dir to avoid permission issues on Hugging Face Spaces.
"""
global processor, model
if processor is None or model is None:
print("🔁 Loading HF processor & model (this may take 10–60s on first request)...")
from transformers import Wav2Vec2Processor, AutoModelForAudioClassification
cache_dir = os.getenv("HF_HOME", "/app/hf_cache")
processor = Wav2Vec2Processor.from_pretrained(
"facebook/wav2vec2-base-960h",
cache_dir=cache_dir
)
model = AutoModelForAudioClassification.from_pretrained(
"prithivMLmods/Common-Voice-Gender-Detection",
cache_dir=cache_dir
)
model.eval()
print("✅ Model & processor loaded.")
return processor, model
app = FastAPI(title="Gender Detection API (lazy model load)")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/", response_class=HTMLResponse)
async def home():
return """
<html>
<body>
<h2>Upload Audio for Gender Detection</h2>
<form action="/predict" enctype="multipart/form-data" method="post">
<input name="file" type="file" accept=".wav,.mp3,.flac,.ogg" />
<input type="submit" value="Upload" />
</form>
<p>POST /predict (multipart form-data, field name "file")</p>
</body>
</html>
"""
@app.get("/health")
async def health():
return {"status": "ok"}
@app.get("/labels")
async def labels():
proc, mdl = get_model()
return mdl.config.id2label
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
proc, mdl = get_model()
# Save upload to a temporary file
suffix = Path(file.filename or "").suffix or ".wav"
with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
raw = await file.read()
tmp.write(raw)
tmp_path = tmp.name
try:
# Try to read using soundfile (libsndfile)
try:
waveform_np, sr = sf.read(tmp_path, dtype="float32")
except Exception as e:
# If soundfile fails, convert with ffmpeg then read
print("⚠️ soundfile could not read directly, trying ffmpeg conversion:", e)
converted = tmp_path + ".converted.wav"
ffmpeg_cmd = [
"ffmpeg", "-y", "-i", tmp_path,
"-ar", str(TARGET_SR), "-ac", "1", converted
]
subprocess.run(ffmpeg_cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=False)
waveform_np, sr = sf.read(converted, dtype="float32")
try:
os.unlink(converted)
except Exception:
pass
finally:
try:
os.unlink(tmp_path)
except Exception:
pass
if waveform_np.ndim > 1:
waveform_np = waveform_np.mean(axis=1)
waveform = torch.tensor(waveform_np, dtype=torch.float32).unsqueeze(0)
if sr != TARGET_SR:
resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=TARGET_SR)
waveform = resampler(waveform)
sr = TARGET_SR
inputs = proc(
waveform.squeeze().numpy(),
sampling_rate=sr,
return_tensors="pt",
padding=True,
)
with torch.no_grad():
logits = mdl(**inputs).logits
probs = torch.softmax(logits, dim=-1).cpu().numpy()[0]
labels_map = mdl.config.id2label
result = {labels_map[i]: float(probs[i]) for i in range(len(labels_map))}
top_idx = int(probs.argmax())
return JSONResponse(content={"top": labels_map[top_idx], "scores": result})
except Exception as e:
import traceback
print("🔥 Error in /predict:", e)
traceback.print_exc()
return JSONResponse(status_code=400, content={"error": str(e)})
if __name__ == "__main__":
import uvicorn
port = int(os.environ.get("PORT", 8000))
print(f"🚀 Starting app on port {port}")
uvicorn.run(app, host="0.0.0.0", port=port)
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