import os import requests import tempfile import uvicorn from fastapi import FastAPI, HTTPException, Query from faster_whisper import WhisperModel # 1. Inisialisasi FastAPI app = FastAPI(title="Open Whisper API (No Limits)") # 2. Inisialisasi Model (Base sudah cukup akurat dan cepat) # Gunakan device="cuda" jika server punya GPU NVIDIA model_size = "base" print(f"Loading Whisper model '{model_size}'...") model = WhisperModel(model_size, device="cpu", compute_type="int8") @app.get("/") def home(): return {"status": "API Active", "usage": "/generate?url=YOUR_AUDIO_URL"} @app.get("/generate") async def generate_transcription(url: str = Query(..., description="URL file audio (mp3, wav, m4a, dll)")): """ Menerima URL audio, mendownloadnya, dan mengembalikan teks hasil transkripsi. """ tmp_path = None try: # 3. Download file dari URL ke folder temp suffix = ".wav" # Default suffix if "." in url: potential_ext = "." + url.split(".")[-1].split("?")[0] if len(potential_ext) <= 5: suffix = potential_ext with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp: response = requests.get(url, stream=True, timeout=30) response.raise_for_status() # Cek jika download gagal for chunk in response.iter_content(chunk_size=8192): tmp.write(chunk) tmp_path = tmp.name # 4. Proses Transkripsi print(f"Processing: {url}") segments, info = model.transcribe(tmp_path, beam_size=5) full_text = " ".join([segment.text for segment in segments]).strip() # 5. Kembalikan Response JSON return { "success": True, "language": info.language, "language_probability": info.language_probability, "text": full_text, "url_processed": url } except requests.exceptions.RequestException as e: raise HTTPException(status_code=400, detail=f"Gagal mendownload file: {str(e)}") except Exception as e: raise HTTPException(status_code=500, detail=f"Internal Error: {str(e)}") finally: # 6. Bersihkan file sementara if tmp_path and os.path.exists(tmp_path): os.remove(tmp_path) if __name__ == "__main__": # Jalankan server uvicorn.run(app, host="0.0.0.0", port=7860)