Create app.py

app.py

@@ -0,0 +1,79 @@
+import os
+import tempfile
+from typing import List, Optional
+
+from fastapi import FastAPI, File, Form, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from faster_whisper import WhisperModel
+
+APP_PORT = int(os.environ.get("PORT", "7860"))
+
+_models = {}
+def get_model(name: str):
+    if name not in _models:
+        _models[name] = WhisperModel(
+            name, compute_type="int8", cpu_threads=os.cpu_count() or 2
+        )
+    return _models[name]
+
+class Segment(BaseModel):
+    start: float
+    end: float
+    text: str
+
+class TranscribeOut(BaseModel):
+    text: str
+    segments: List[Segment]
+    duration_sec: Optional[float] = None
+    words: Optional[int] = None
+    wpm: Optional[float] = None
+    model: str
+
+app = FastAPI(title="Nuvia Free Transcriber")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], allow_credentials=True,
+    allow_methods=["*"], allow_headers=["*"],
+)
+
+@app.get("/health")
+def health():
+    return {"ok": True}
+
+@app.post("/transcribe", response_model=TranscribeOut)
+def transcribe(file: UploadFile = File(...), model: str = Form("base.en")):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
+        tmp.write(file.file.read())
+        tmp_path = tmp.name
+
+    try:
+        m = get_model(model)
+        segments, info = m.transcribe(tmp_path, vad_filter=True)
+
+        segs = []
+        total_words = 0
+        for s in segments:
+            txt = s.text.strip()
+            segs.append(Segment(start=float(s.start), end=float(s.end), text=txt))
+            total_words += len(txt.split())
+
+        dur = float(info.duration) if getattr(info, "duration", None) else None
+        wpm = None
+        if dur and dur > 0:
+            wpm = round(total_words / (dur / 60.0), 2)
+
+        full_text = " ".join([s.text for s in segs]).strip()
+        return TranscribeOut(
+            text=full_text,
+            segments=segs,
+            duration_sec=dur,
+            words=total_words,
+            wpm=wpm,
+            model=model
+        )
+    finally:
+        try:
+            os.remove(tmp_path)
+        except Exception:
+            pass