Update app.py

app.py

@@ -1,720 +1,232 @@
-import os
-import json
-import requests
-import subprocess
-import shutil
-import time
-import sys
-import threading
-from typing import Dict, List, Optional, Any
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import JSONResponse
-import uvicorn
-import torch
-import librosa
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-
-# Fix Unicode encoding for Windows
-if sys.platform == 'win32':
-    import io
-    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
-
-# Initialize FastAPI app
-app = FastAPI(title="Audio Transcriber", description="Audio transcription and upload service")
-
-# ==== CONFIGURATION ====
-# The new backend URL for state management and transcription upload
-# It is now read from an environment variable, falling back to the default if not set.
-BACKEND_URL = os.environ.get("BACKEND_URL", "https://samfredoly-acp.hf.space")
-# The original Hugging Face repo IDs are still needed for fetching the audio files
-# and the reference file list, as the backend only handles transcription storage.
-SOURCE_REPO_ID = "Samfredoly/BG_Vid"  # Fetch audio files from here
-TARGET_REPO_ID = "samfred2/A_Text"  # Target repo ID is now a constant for the backend
-REFERENCE_REPO_ID = "Fred808/BG3"  # Reference repo to match audio filenames
-
-# Path Configuration
-DOWNLOAD_FOLDER = "downloads_audio"
-TRANSCRIPTIONS_FOLDER = "transcriptions"
-LOCAL_STATE_FOLDER = ".state_audio"
-
-os.makedirs(DOWNLOAD_FOLDER, exist_ok=True)
-os.makedirs(TRANSCRIPTIONS_FOLDER, exist_ok=True)
-os.makedirs(LOCAL_STATE_FOLDER, exist_ok=True)
-
-# Whisper Model Setup (using transformers)
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-TORCH_DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
-WHISPER_MODEL_ID = f"openai/whisper-small"
-
-# Global model cache
-_whisper_model = None
-_whisper_processor = None
-_whisper_pipeline = None
-
-def get_whisper_pipeline():
-    """Get or initialize the Whisper pipeline."""
-    global _whisper_model, _whisper_processor, _whisper_pipeline
-    
-    if _whisper_pipeline is not None:
-        return _whisper_pipeline
-    
-    try:
-        log_message(f"Loading Whisper model {WHISPER_MODEL_ID}...", "INFO")
-        
-        model = AutoModelForSpeechSeq2Seq.from_pretrained(
-            WHISPER_MODEL_ID,
-            torch_dtype=TORCH_DTYPE,
-            low_cpu_mem_usage=True,
-            use_safetensors=True
-        )
-        model = model.to(DEVICE)
-        
-        processor = AutoProcessor.from_pretrained(WHISPER_MODEL_ID)
-        
-        _whisper_pipeline = pipeline(
-            "automatic-speech-recognition",
-            model=model,
-            tokenizer=processor.tokenizer,
-            feature_extractor=processor.feature_extractor,
-            torch_dtype=TORCH_DTYPE,
-            device=DEVICE
-        )
-        
-        log_message(f"✅ Whisper model loaded successfully on {DEVICE.upper()}", "INFO")
-        return _whisper_pipeline
-        
-    except Exception as e:
-        log_message(f"❌ Failed to load Whisper model: {str(e)}", "ERROR")
-        raise
-
-# State Files
-FAILED_FILES_LOG = "failed_audio_files.log"
-HF_STATE_FILE = "processing_audio_state.json" # This is the filename the backend uses
-
-# Processing Parameters
-PROCESSING_DELAY = 2
-MAX_RETRIES = 3
-MIN_FREE_SPACE_GB = 1
-WHISPER_MODEL = "small"  # Whisper model size
-
-# NOTE: The Hugging Face API is still required for listing files in SOURCE_REPO_ID and REFERENCE_REPO_ID
-from huggingface_hub import HfApi, hf_hub_url
-HF_TOKEN = os.environ.get("HF_TOKEN", "")
-hf_api = HfApi(token=HF_TOKEN)
-
-# Global State
-processing_status = {
-    "is_running": False,
-    "current_file": None,
-    "total_files": 0,
-    "processed_files": 0,
-    "failed_files": 0,
-    "transcribed_files": 0,
-    "last_update": None,
-    "logs": []
-}
-
-def log_message(message: str, level: str = "INFO"):
-    """Log messages with timestamp"""
-    timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
-    log_entry = f"[{timestamp}] {level}: {message}"
-    print(log_entry)
-    processing_status["logs"].append(log_entry)
-    processing_status["last_update"] = timestamp
-    if len(processing_status["logs"]) > 100:
-        processing_status["logs"] = processing_status["logs"][-100:]
-
-def log_failed_file(filename: str, error: str):
-    """Log failed files to persistent file"""
-    with open(FAILED_FILES_LOG, "a") as f:
-        f.write(f"{time.strftime('%Y-%m-%d %H:%M:%S')} - {filename}: {error}\n")
-
-def get_disk_usage(path: str) -> Dict[str, float]:
-    """Get disk usage statistics in GB"""
-    statvfs = os.statvfs(path)
-    total = statvfs.f_frsize * statvfs.f_blocks / (1024**3)
-    free = statvfs.f_frsize * statvfs.f_bavail / (1024**3)
-    used = total - free
-    return {"total": total, "free": free, "used": used}
-
-def check_disk_space(path: str = ".") -> bool:
-    """Check if there's enough disk space"""
-    disk_info = get_disk_usage(path)
-    if disk_info["free"] < MIN_FREE_SPACE_GB:
-        log_message(f'⚠️ Low disk space: {disk_info["free"]:.2f}GB free, {disk_info["used"]:.2f}GB used')
-        return False
-    return True
-
-def cleanup_temp_files():
-    """Clean up temporary files to free space"""
-    log_message("🧹 Cleaning up temporary files...", "INFO")
-    
-    current_file = processing_status.get("current_file")
-    for file in os.listdir(DOWNLOAD_FOLDER):
-        if file != current_file and file.endswith((".wav", ".mp3")):
-            try:
-                os.remove(os.path.join(DOWNLOAD_FOLDER, file))
-                log_message(f"🗑️ Removed old download: {file}", "INFO")
-            except:
-                pass
-
-# Helper function to save state locally
-def save_json_state(file_path: str, data: Dict[str, Any]):
-    """Save state to JSON file"""
-    with open(file_path, "w") as f:
-        json.dump(data, f, indent=2)
-
-# --- NEW API FUNCTIONS FOR STATE MANAGEMENT AND UPLOAD ---
-
-def download_state_from_api() -> Dict[str, Any]:
-    """Downloads the state file from the backend API."""
-    url = f"{BACKEND_URL}/state/"
-    default_state = {"next_download_index": 0, "file_states": {}}
-    
-    try:
-        response = requests.get(url, timeout=10)
-        response.raise_for_status()
-        
-        # The API returns {"state": {...}}
-        state_data = response.json().get("state", default_state)
-        
-        # Ensure the structure is correct (migration logic from original load_json_state)
-        if "file_states" not in state_data or not isinstance(state_data["file_states"], dict):
-            state_data["file_states"] = {}
-        if "next_download_index" not in state_data:
-            state_data["next_download_index"] = 0
-        
-        log_message(f"✅ Downloaded state: next_download_index={state_data['next_download_index']}, processed_files={len([f for f,s in state_data['file_states'].items() if s=='processed'])}", "INFO")
-        return state_data
-        
-    except requests.exceptions.RequestException as e:
-        log_message(f"⚠️ Failed to download state from API ({url}): {str(e)}. Starting from default state.", "WARNING")
-        return default_state
-
-def upload_state_to_api(state: Dict[str, Any]) -> bool:
-    """
-    Saves the state locally and uploads it to the backend API's /upload/ endpoint.
-    This simulates the original HF state upload for locking/unlocking.
-    """
-    local_path = os.path.join(LOCAL_STATE_FOLDER, HF_STATE_FILE)
-    url = f"{BACKEND_URL}/upload/"
-    
-    try:
-        # 1. Save the current state locally
-        save_json_state(local_path, state)
-        
-        # 2. Upload the state file to the backend
-        with open(local_path, "rb") as f:
-            files = {'file': (HF_STATE_FILE, f, 'application/json')}
-            
-            response = requests.post(url, files=files, timeout=30)
-            response.raise_for_status()
-            
-            log_message(f"✅ Successfully uploaded state file to API: {HF_STATE_FILE}", "INFO")
-            return True
-            
-    except requests.exceptions.HTTPError as e:
-        if hasattr(e, 'response') and e.response.status_code == 409:
-            log_message(f"⚠️ State file already exists on server (409 Conflict) - Treating as success.", "INFO")
-            return True
-        log_message(f"❌ Failed to upload state file to API ({url}): {str(e)}", "ERROR")
-        return False
-    except requests.exceptions.RequestException as e:
-        log_message(f"❌ Failed to upload state file to API ({url}): {str(e)}", "ERROR")
-        return False
-    except Exception as e:
-        log_message(f"❌ An unexpected error occurred during API state upload: {str(e)}", "ERROR")
-        return False
-
-def upload_transcription_to_api(json_output_path: str, matched_filename: str) -> bool:
-    """Uploads the transcription JSON file to the backend API's /upload/ endpoint."""
-    url = f"{BACKEND_URL}/upload/"
-    
-    try:
-        with open(json_output_path, "rb") as f:
-            files = {'file': (os.path.basename(json_output_path), f, 'application/json')}
-            
-            response = requests.post(url, files=files, timeout=30)
-            response.raise_for_status()
-            
-            log_message(f"✅ Successfully uploaded transcription to API: {os.path.basename(json_output_path)}", "INFO")
-            return True
-            
-    except requests.exceptions.HTTPError as e:
-        if hasattr(e, 'response') and e.response.status_code == 409:
-            log_message(f"⚠️ File already exists on server (409 Conflict) - Treating as success.", "INFO")
-            return True
-        log_message(f"❌ Failed to upload transcription to API ({url}): {str(e)}", "ERROR")
-        return False
-    except requests.exceptions.RequestException as e:
-        log_message(f"❌ Failed to upload transcription to API ({url}): {str(e)}", "ERROR")
-        return False
-    except Exception as e:
-        log_message(f"❌ An unexpected error occurred during API upload: {str(e)}", "ERROR")
-        return False
-
-def lock_file_for_processing(wav_filename: str, state: Dict[str, Any]) -> bool:
-    """Marks a file as 'processing' in the state file and uploads the lock via API."""
-    log_message(f"🔒 Attempting to lock file: {wav_filename} (Marking as 'processing')", "INFO")
-    
-    state["file_states"][wav_filename] = "processing"
-    
-    if upload_state_to_api(state):
-        log_message(f"✅ Successfully locked file: {wav_filename} via API state upload", "INFO")
-        return True
-    else:
-        log_message(f"❌ Failed to upload lock for file: {wav_filename}. Aborting processing.", "ERROR")
-        # Revert local state change if upload fails
-        if wav_filename in state["file_states"]:
-            del state["file_states"][wav_filename]
-        return False
-
-def unlock_file_as_processed(wav_filename: str, state: Dict[str, Any], next_index: int) -> bool:
-    """Marks a file as 'processed', updates the index, and uploads the state via API."""
-    log_message(f"🔓 Attempting to unlock file: {wav_filename} (Marking as 'processed')", "INFO")
-    
-    state["file_states"][wav_filename] = "processed"
-    state["next_download_index"] = next_index
-    
-    if upload_state_to_api(state):
-        log_message(f"✅ Successfully unlocked and marked as processed: {wav_filename} via API state upload", "INFO")
-        return True
-    else:
-        log_message(f"❌ Failed to upload final state for file: {wav_filename}.", "ERROR")
-        return False
-
-# --- END NEW API FUNCTIONS ---
-
-def download_with_retry(url: str, dest_path: str, max_retries: int = 3) -> bool:
-    """Download file with retry logic and disk space checking"""
-    if not check_disk_space():
-        cleanup_temp_files()
-        if not check_disk_space():
-            log_message("❌ Insufficient disk space even after cleanup", "ERROR")
-            return False
-    
-    try:
-        os.makedirs(os.path.dirname(dest_path), exist_ok=True)
-    except Exception as e:
-        log_message(f"❌ Failed to create directory for download path {os.path.dirname(dest_path)}: {str(e)}", "ERROR")
-        return False
-        
-    # The original code used HF_TOKEN for authorization headers, which is only needed 
-    # if the source repo is private. We keep it for compatibility.
-    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-    for attempt in range(max_retries):
-        try:
-            with requests.get(url, headers=headers, stream=True) as r:
-                r.raise_for_status()
-                
-                with open(dest_path, "wb") as f:
-                    for chunk in r.iter_content(chunk_size=8192):
-                        if chunk:
-                            f.write(chunk)
-                
-                log_message(f"✅ Download successful: {os.path.basename(dest_path)}", "INFO")
-                return True
-        except requests.exceptions.RequestException as e:
-            log_message(f"⚠️ Download attempt {attempt + 1}/{max_retries} failed for {url}: {str(e)}", "WARNING")
-            if attempt < max_retries - 1:
-                time.sleep(2 ** attempt) # Exponential backoff
-            else:
-                log_message(f"❌ Download failed after {max_retries} attempts for {url}", "ERROR")
-                return False
-        except Exception as e:
-            log_message(f"❌ An unexpected error occurred during download: {str(e)}", "ERROR")
-            return False
-    return False
-
-def get_reference_map(reference_repo_id: str) -> Dict[str, str]:
-    """
-    Downloads the reference file list from the Hugging Face repo and creates a map
-    from audio filename (without extension) to the reference filename.
-    """
-    log_message(f"Fetching reference file list from {reference_repo_id}...", "INFO")
-    
-    # This is a placeholder for the actual logic to get the file list.
-    # Assuming the reference repo contains a list of files that match the audio files.
-    # In a real scenario, this would involve listing files in the repo.
-    # For now, we'll assume a simple list of files can be retrieved.
-    
-    try:
-        # Use HfApi to list files in the reference repo
-        repo_files = hf_api.list_repo_files(repo_id=reference_repo_id, repo_type="dataset")
-        
-        reference_map = {}
-        for file in repo_files:
-            # Assuming the reference files are named like 'audio_file_name.txt'
-            # and we want to map the audio file name (e.g., 'audio_file_name.wav') to it.
-            base_name, ext = os.path.splitext(file)
-            if ext.lower() in ['.txt', '.json']: # Only consider text/json files as reference
-                # The key is the audio file name without extension
-                reference_map[base_name] = file
-                
-        log_message(f"✅ Successfully created reference map with {len(reference_map)} entries.", "INFO")
-        return reference_map
-        
-    except Exception as e:
-        log_message(f"❌ Failed to fetch reference map from Hugging Face: {str(e)}", "ERROR")
-        return {}
-
-def find_matching_filename(audio_filename: str, reference_map: Dict[str, str]) -> Optional[str]:
-    """Finds the matching reference filename for a given audio filename."""
-    base_name, _ = os.path.splitext(audio_filename)
-    return reference_map.get(base_name)
-
-def get_next_file_to_process(source_repo_id: str, state: Dict[str, Any]) -> Optional[Dict[str, Any]]:
-    """
-    Determines the next file to process based on the current state and the file list
-    from the source Hugging Face repository.
-    """
-    log_message(f"Determining next file to process from {source_repo_id}...", "INFO")
-    
-    try:
-        # 1. Get the list of all files in the source repo
-        repo_files = hf_api.list_repo_files(repo_id=source_repo_id, repo_type="dataset")
-        
-        # Filter for audio files (e.g., .wav, .mp3)
-        audio_files = sorted([f for f in repo_files if f.lower().endswith(('.wav', '.mp3'))])
-        
-        processing_status["total_files"] = len(audio_files)
-        
-        if not audio_files:
-            log_message("No audio files found in the source repository.", "INFO")
-            return None
-            
-        # 2. Get the next index from the state
-        next_index = state.get("next_download_index", 0)
-        file_states = state.get("file_states", {})
-        
-        # 3. Skip forward past all processed and processing files starting from next_index
-        # This ensures we don't repeatedly find files that have already been handled
-        current_index = next_index
-        while current_index < len(audio_files):
-            filename = audio_files[current_index]
-            status = file_states.get(filename, "unprocessed")
-            
-            # If this file is processed or currently processing, skip it
-            if status in ["processed", "processing"]:
-                current_index += 1
-                continue
-            
-            # If this file failed, we can retry it, so return it
-            if status == "failed":
-                file_url = hf_hub_url(repo_id=source_repo_id, filename=filename, repo_type="dataset")
-                log_message(f"Found failed file for retry at index {current_index}: {filename}", "INFO")
-                return {
-                    "filename": filename,
-                    "url": file_url,
-                    "index": current_index
-                }
-            
-            # If this file is unprocessed, we found our next file
-            file_url = hf_hub_url(repo_id=source_repo_id, filename=filename, repo_type="dataset")
-            log_message(f"Found next file at index {current_index}: {filename}", "INFO")
-            return {
-                "filename": filename,
-                "url": file_url,
-                "index": current_index
-            }
-        
-        log_message("All files have been processed or are locked. Checking for any failed files from the start.", "INFO")
-        
-        # 4. If we've processed all files from next_index to end, check from beginning for failed files
-        for i in range(0, next_index):
-            filename = audio_files[i]
-            status = file_states.get(filename, "unprocessed")
-            
-            if status == "failed":
-                file_url = hf_hub_url(repo_id=source_repo_id, filename=filename, repo_type="dataset")
-                log_message(f"Found failed file for retry at index {i}: {filename}", "INFO")
-                return {
-                    "filename": filename,
-                    "url": file_url,
-                    "index": i
-                }
-        
-        log_message("All files have been processed. Waiting for new files...", "INFO")
-        return None
-        
-    except Exception as e:
-        log_message(f"❌ Failed to get next file to process: {str(e)}", "ERROR")
-        return None
-
-def run_whisper_transcription(audio_path: str, output_dir: str, model: str) -> Optional[str]:
-    """
-    Runs Whisper transcription using the transformers library.
-    Returns the path to the generated JSON file on success.
-    No ffmpeg dependency required.
-    """
-    log_message(f"🎙️ Starting transcription for {os.path.basename(audio_path)} with model {model}...", "INFO")
-    
-    try:
-        # Get the Whisper pipeline
-        pipe = get_whisper_pipeline()
-        
-        # Load audio using librosa
-        log_message(f"Loading audio file: {audio_path}", "INFO")
-        audio_data, sample_rate = librosa.load(audio_path, sr=16000)
-        
-        # Run transcription
-        log_message(f"Running transcription...", "INFO")
-        result = pipe(
-            audio_data,
-            chunk_length_s=30,
-            batch_size=8,
-            return_timestamps=True
-        )
-        
-        # Extract text and chunks
-        transcription_text = result.get("text", "")
-        chunks = result.get("chunks", [])
-        
-        log_message(f"✅ Transcription successful: {len(transcription_text)} characters", "INFO")
-        
-        # Prepare output JSON structure
-        output_json = {
-            "text": transcription_text,
-            "chunks": chunks,
-            "language": result.get("language", "en")
-        }
-        
-        # Save to JSON file
-        base_name, _ = os.path.splitext(os.path.basename(audio_path))
-        json_output_path = os.path.join(output_dir, f"{base_name}.json")
-        
-        with open(json_output_path, "w", encoding="utf-8") as f:
-            json.dump(output_json, f, indent=2, ensure_ascii=False)
-        
-        log_message(f"✅ Saved transcription to: {json_output_path}", "INFO")
-        return json_output_path
-        
-    except Exception as e:
-        log_message(f"❌ An error occurred during transcription: {str(e)}", "ERROR")
-        import traceback
-        log_message(f"Traceback: {traceback.format_exc()}", "ERROR")
-        return None
-
-def process_audio_file(audio_path: str, reference_map: Dict[str, str], output_filename: str) -> bool:
-    """
-    Transcribes the audio file, renames the output JSON to match the reference,
-    and uploads the result to the API.
-    """
-    
-    # 1. Run transcription
-    json_output_path = run_whisper_transcription(audio_path, TRANSCRIPTIONS_FOLDER, WHISPER_MODEL)
-    
-    if not json_output_path:
-        return False
-        
-    # 2. Rename the JSON file to the matched filename
-    # The output_filename already includes the correct extension (e.g., .txt or .json)
-    # We assume the reference map provides the full target filename.
-    
-    # The whisper output is a JSON file named after the audio file.
-    # We need to rename it to the target filename (which should be a JSON file for the backend).
-    
-    # The output_filename is the matched filename from the reference map (e.g., 'audio_file_name.txt')
-    # The backend expects a JSON file. Let's assume the matched filename should be used as the base
-    # but with a .json extension for the upload.
-    
-    # Let's stick to the original logic: the backend expects a JSON file with the name
-    # of the audio file (or the matched reference file) with a .json extension.
-    
-    # Since the whisper output is already a JSON file, we just need to rename it
-    # to the desired final name.
-    
-    # The output_filename passed here is the base name of the audio file or the matched reference file.
-    # If it's a reference file name (e.g., 'file.txt'), we should probably use 'file.json'.
-    
-    # For simplicity and to match the backend's expectation (which handles JSON),
-    # we will rename the whisper output JSON to the base name of the audio file
-    # and ensure it has a .json extension.
-    
-    base_name, _ = os.path.splitext(output_filename)
-    final_json_filename = f"{base_name}.json"
-    final_json_path = os.path.join(TRANSCRIPTIONS_FOLDER, final_json_filename)
-    
-    try:
-        if json_output_path != final_json_path:
-            shutil.move(json_output_path, final_json_path)
-            log_message(f"✅ Renamed transcription to: {final_json_filename}", "INFO")
-    except Exception as e:
-        log_message(f"❌ Failed to rename transcription file: {str(e)}", "ERROR")
-        return False
-        
-    # 3. Upload transcription to API
-    if upload_transcription_to_api(final_json_path, final_json_filename):
-        processing_status["transcribed_files"] += 1
-        # Clean up the local transcription file after successful upload
-        try:
-            os.remove(final_json_path)
-            log_message(f"🗑️ Cleaned up local transcription file: {final_json_path}", "INFO")
-        except Exception as e:
-            log_message(f"❌ Failed to clean up transcription file: {str(e)}", "ERROR")
-        return True
-    else:
-        log_message(f"❌ Failed to upload transcription to API: {final_json_filename}", "ERROR")
-        return False
-
-def main_processing_loop():
-    """The main loop that continuously checks for and processes new audio files."""
-    global processing_status
-    
-    if processing_status["is_running"]:
-        log_message("Processing loop is already running.", "WARNING")
-        return
-        
-    processing_status["is_running"] = True
-    log_message("🚀 Audio transcription processing loop started.", "INFO")
-    
-    # 1. Get the reference map once
-    reference_map = get_reference_map(REFERENCE_REPO_ID)
-    if not reference_map:
-        log_message("❌ Could not get reference map. Stopping loop.", "CRITICAL")
-        processing_status["is_running"] = False
-        return
-        
-    try:
-        while processing_status["is_running"]:
-            time.sleep(PROCESSING_DELAY)
-            
-            # 1. Download FRESH state from the API at the start of each iteration
-            # This ensures we respect the next_download_index that other workers may have set
-            current_state = download_state_from_api()
-            next_file_info = get_next_file_to_process(SOURCE_REPO_ID, current_state)
-            
-            if next_file_info is None:
-                log_message("💤 No new audio files to process. Sleeping for a while...", "INFO")
-                time.sleep(PROCESSING_DELAY * 5)
-                continue
-                
-            target_file = next_file_info['filename']
-            audio_url = next_file_info['url']
-            target_index = next_file_info['index']
-            
-            processing_status["current_file"] = target_file
-            success = False
-            matched_filename = None
-            
-            try:
-                # 2. Lock file by updating state on the API
-                # IMPORTANT: Update next_download_index when locking to prevent other workers from picking same file
-                old_index = current_state["next_download_index"]
-                current_state["next_download_index"] = target_index + 1
-                log_message(f"📍 Incrementing next_download_index from {old_index} to {current_state['next_download_index']}", "INFO")
-                
-                if not lock_file_for_processing(target_file, current_state):
-                    log_message(f"❌ Failed to lock file {target_file}. Skipping.", "ERROR")
-                    time.sleep(PROCESSING_DELAY)
-                    continue
-                    
-                local_wav_path = os.path.join(DOWNLOAD_FOLDER, os.path.basename(target_file))
-                log_message(f"⬇️ Downloading audio file: {target_file}", "INFO")
-                
-                if download_with_retry(audio_url, local_wav_path):
-                    
-                    # Extract base filename for matching
-                    base_filename = os.path.basename(target_file)
-                    matched_filename = find_matching_filename(base_filename, reference_map)
-                    
-                    # Use matched filename if found, otherwise use original filename
-                    output_filename = matched_filename if matched_filename else base_filename
-                    
-                    # 3. Process and Upload transcription to API
-                    if process_audio_file(local_wav_path, reference_map, output_filename):
-                        success = True
-                        log_message(f"✅ Finished processing: {target_file}", "INFO")
-                    else:
-                        log_message(f"❌ Processing failed for: {target_file}", "ERROR")
-                else:
-                    log_message(f"❌ Download failed for: {target_file}", "ERROR")
-                    
-            except Exception as e:
-                log_message(f"🔥 An unhandled error occurred while processing {target_file}: {str(e)}", "ERROR")
-                log_failed_file(target_file, str(e))
-                
-            finally:
-                # 4. Unlock/Mark as processed by updating state on the API
-                # IMPORTANT: Keep the incremented next_download_index from locking
-                
-                if success:
-                    # Mark as processed and keep the incremented index, then upload state
-                    unlock_file_as_processed(target_file, current_state, current_state["next_download_index"])
-                    processing_status["processed_files"] += 1
-                else:
-                    # Mark as failed but keep the incremented index so next worker can proceed
-                    log_message(f"⚠️ File {target_file} failed. Marking as 'failed' and updating state.", "WARNING")
-                    current_state["file_states"][target_file] = "failed"
-                    # Keep the incremented next_download_index - don't change it
-                    upload_state_to_api(current_state)
-                
-                # Clean up the downloaded audio file regardless of success
-                try:
-                    if os.path.exists(local_wav_path):
-                        os.remove(local_wav_path)
-                        log_message(f"🗑️ Cleaned up local audio file: {local_wav_path}", "INFO")
-                except Exception as e:
-                    log_message(f"❌ Failed to clean up audio file: {str(e)}", "ERROR")
-                
-                processing_status["current_file"] = None
-                time.sleep(PROCESSING_DELAY)
-                
-    except Exception as e:
-        log_message(f"🔥 Critical error in main processing loop: {str(e)}", "CRITICAL")
-        
-    finally:
-        processing_status["is_running"] = False
-        log_message("🛑 Audio transcription processing loop stopped.", "INFO")
-
-# --- FastAPI Endpoints (Unchanged) ---
-
-# Add to configuration section
-AUTO_START_PROCESSING = os.environ.get("AUTO_START_PROCESSING", "true").lower() == "true"
-
-@app.on_event("startup")
-async def startup_event():
-    """Conditionally start processing based on environment variable."""
-    if AUTO_START_PROCESSING:
-        log_message("🚀 AUTO_START_PROCESSING enabled - Starting processing loop...", "INFO")
-        thread = threading.Thread(target=main_processing_loop, daemon=True)
-        thread.start()
-        log_message("✅ Background processing thread started", "INFO")
-    else:
-        log_message("⏸️ AUTO_START_PROCESSING disabled - Use /start endpoint to begin", "INFO")
-
-@app.get("/")
-async def root():
-    """Root endpoint to check service status."""
-    return {"message": "Audio Transcriber Service is running", "status": processing_status}
-
-@app.get("/status")
-async def get_status():
-    """Get the current processing status."""
-    return processing_status
-
-@app.post("/start")
-async def start_processing():
-    """Start the background processing loop."""
-    if processing_status["is_running"]:
-        return JSONResponse(status_code=200, content={"message": "Processing already running."})
-    
-    thread = threading.Thread(target=main_processing_loop)
-    thread.start()
-    return JSONResponse(status_code=200, content={"message": "Processing started in background."})
-
-@app.post("/stop")
-async def stop_processing():
-    """Stop the background processing loop."""
-    if not processing_status["is_running"]:
-        return JSONResponse(status_code=200, content={"message": "Processing is not running."})
-    
-    processing_status["is_running"] = False
-    return JSONResponse(status_code=200, content={"message": "Processing stop requested. Will stop after current file."})
-
-# --- Main Execution ---
-
-if __name__ == "__main__":
-    # This block is for local testing and won't be used in the final sandbox execution
-    # but is good practice for a runnable script.
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+import os
+import json
+import torch
+import librosa
+import numpy as np
+from pathlib import Path
+from typing import Dict, Optional, List
+from datetime import datetime
+
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from pydantic import BaseModel
+from transformers import pipeline
+
+# --- Configuration ---
+WHISPER_MODEL = os.getenv("WHISPER_MODEL", "small")  # small, medium, large
+WHISPER_PORT = int(os.getenv("WHISPER_PORT", 8000))
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+TORCH_DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Global model cache
+_whisper_pipeline = None
+_model_info = {
+    "model_name": WHISPER_MODEL,
+    "device": DEVICE,
+    "dtype": str(TORCH_DTYPE),
+    "cuda_available": torch.cuda.is_available()
+}
+
+# --- Models ---
+class TranscriptionResponse(BaseModel):
+    text: str
+    language: str = "en"
+    confidence: Optional[float] = None
+    duration: float = 0.0
+    timestamp: str = ""
+
+# --- Utility Functions ---
+def get_whisper_pipeline():
+    """Get or initialize the Whisper pipeline (cached)."""
+    global _whisper_pipeline
+    if _whisper_pipeline is not None:
+        return _whisper_pipeline
+    
+    print(f"🔄 Loading Whisper model: {WHISPER_MODEL} on {DEVICE} with dtype {TORCH_DTYPE}")
+    
+    _whisper_pipeline = pipeline(
+        "automatic-speech-recognition",
+        model=f"openai/whisper-{WHISPER_MODEL}",
+        device=DEVICE,
+        torch_dtype=TORCH_DTYPE
+    )
+    
+    print(f"✅ Whisper model loaded successfully")
+    return _whisper_pipeline
+
+def load_and_resample_audio(audio_path: str, target_sr: int = 16000) -> tuple:
+    """Load audio file and resample to 16kHz (required by Whisper)."""
+    try:
+        # Load audio file with librosa (no ffmpeg needed)
+        audio, sr = librosa.load(audio_path, sr=target_sr, mono=True)
+        duration = librosa.get_duration(y=audio, sr=sr)
+        print(f"📁 Loaded audio: {Path(audio_path).name} | Duration: {duration:.2f}s | SR: {sr}Hz")
+        return audio, sr, duration
+    except Exception as e:
+        print(f"❌ Error loading audio: {e}")
+        raise
+
+async def transcribe_audio(audio_path: str) -> Dict:
+    """Transcribe audio file using Whisper."""
+    try:
+        # Load audio
+        audio, sr, duration = load_and_resample_audio(audio_path)
+        
+        # Get pipeline
+        pipeline_model = get_whisper_pipeline()
+        
+        print(f"🎤 Transcribing {Path(audio_path).name}...")
+        
+        # Transcribe
+        result = pipeline_model(
+            audio,
+            chunk_length_s=30,
+            stride_length_s=(4, 2),
+            language="en",  # Specify language or remove for auto-detection
+            batch_size=24 if torch.cuda.is_available() else 4
+        )
+        
+        print(f"✅ Transcription complete")
+        
+        return {
+            "text": result.get("text", "").strip(),
+            "language": "en",  # Whisper doesn't return language detection reliably
+            "confidence": None,  # Whisper doesn't provide per-segment confidence
+            "duration": duration,
+            "timestamp": datetime.now().isoformat()
+        }
+        
+    except Exception as e:
+        print(f"❌ Transcription error: {e}")
+        raise
+
+# --- FastAPI App ---
+app = FastAPI(
+    title="Whisper Transcription Server",
+    description="FastAPI server for audio transcription using OpenAI Whisper",
+    version="1.0.0"
+)
+
+@app.on_event("startup")
+async def startup():
+    print(f"🚀 Whisper Server starting on port {WHISPER_PORT}")
+    print(f"📊 Configuration:")
+    print(f"   - Model: {WHISPER_MODEL}")
+    print(f"   - Device: {DEVICE}")
+    print(f"   - CUDA Available: {torch.cuda.is_available()}")
+    print(f"   - Torch Dtype: {TORCH_DTYPE}")
+    
+    # Pre-load model
+    get_whisper_pipeline()
+
+@app.get("/health")
+async def health_check():
+    """Check server health and model status."""
+    return {
+        "status": "healthy",
+        "model_info": _model_info,
+        "cuda_available": torch.cuda.is_available(),
+        "device": DEVICE
+    }
+
+@app.get("/")
+async def root():
+    """Root endpoint with server info."""
+    return {
+        "server": "Whisper Transcription Backend",
+        "model": WHISPER_MODEL,
+        "device": DEVICE,
+        "endpoints": {
+            "/health": "Server health check",
+            "/transcribe": "POST - Transcribe audio file",
+            "/transcribe_file": "POST - Alternative transcribe endpoint"
+        }
+    }
+
+@app.post("/transcribe")
+async def transcribe(file: UploadFile = File(...)):
+    """
+    Transcribe an uploaded audio file.
+    Accepts: mp3, wav, m4a, flac, ogg, aac
+    """
+    if not file.filename:
+        raise HTTPException(status_code=400, detail="No file provided")
+    
+    # Check file extension
+    allowed_extensions = {'.mp3', '.wav', '.m4a', '.flac', '.ogg', '.aac'}
+    file_ext = Path(file.filename).suffix.lower()
+    
+    if file_ext not in allowed_extensions:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported file format: {file_ext}. Allowed: {allowed_extensions}"
+        )
+    
+    temp_file = None
+    try:
+        # Save uploaded file temporarily
+        temp_path = Path(f"temp_{file.filename}")
+        with open(temp_path, 'wb') as f:
+            content = await file.read()
+            f.write(content)
+        
+        temp_file = temp_path
+        
+        print(f"📤 Processing uploaded file: {file.filename} ({len(content)} bytes)")
+        
+        # Transcribe
+        result = await transcribe_audio(str(temp_path))
+        
+        return {
+            "audio_file": file.filename,
+            "text": result["text"],
+            "language": result["language"],
+            "duration": result["duration"],
+            "timestamp": result["timestamp"]
+        }
+        
+    except Exception as e:
+        print(f"❌ Transcription failed: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+    
+    finally:
+        # Cleanup
+        if temp_file and temp_file.exists():
+            temp_file.unlink()
+            print(f"🧹 Cleaned up temp file: {temp_file}")
+
+@app.post("/transcribe_file")
+async def transcribe_file(file: UploadFile = File(...)):
+    """Alternative endpoint name for transcription."""
+    return await transcribe(file)
+
+@app.post("/transcribe_batch")
+async def transcribe_batch(files: List[UploadFile] = File(...)):
+    """
+    Transcribe multiple audio files in parallel.
+    """
+    if not files:
+        raise HTTPException(status_code=400, detail="No files provided")
+    
+    results = []
+    for file in files:
+        try:
+            result = await transcribe(file)
+            results.append({
+                "status": "success",
+                "data": result
+            })
+        except Exception as e:
+            results.append({
+                "status": "error",
+                "filename": file.filename,
+                "error": str(e)
+            })
+    
+    return {
+        "total": len(files),
+        "results": results
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=WHISPER_PORT)