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+FROM python:3.11-slim-bullseye
+
+# Install system dependencies
+RUN sed -i 's/main/main contrib non-free/' /etc/apt/sources.list && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends \
+    unrar \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+    
+WORKDIR /app
+
+# Upgrade pip and install core dependencies first
+RUN pip install --no-cache-dir --upgrade pip setuptools wheel packaging
+
+# Install CPU-only PyTorch first
+
+# Copy requirements and install with special handling for flash_attn
+COPY requirements.txt .
+RUN pip install --no-cache-dir \
+    -r requirements.txt \
+    --find-links https://download.pytorch.org/whl/cpu \
+    --extra-index-url https://pypi.org/simple && \
+# Install remaining packages that might have been skipped
+    pip install --no-cache-dir \
+    accelerate \
+    transformers==4.36.2 \
+    timm==0.9.12 \
+    einops==0.7.0
+
+# Copy application code
+COPY . .
+
+# Create non-root user
+RUN useradd -m -u 1000 user && \
+    chown -R user:user /app
+
+USER user
+
+# Environment variables to suppress warnings
+ENV HF_HUB_DISABLE_PROGRESS=1
+ENV TF_CPP_MIN_LOG_LEVEL=3
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+import json
+import requests
+import subprocess
+import shutil
+import time
+import sys
+import threading
+from typing import Dict, List, Optional, Any
+from huggingface_hub import HfApi, hf_hub_url
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import JSONResponse
+import uvicorn
+
+# 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 ====
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
+SOURCE_REPO_ID = "Samfredoly/BG_Vid"  # Fetch audio files from here
+TARGET_REPO_ID = "samfred2/A_Text"  # Upload transcriptions here
+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)
+
+# State Files
+FAILED_FILES_LOG = "failed_audio_files.log"
+HF_STATE_FILE = "processing_audio_state.json"
+
+# Processing Parameters
+PROCESSING_DELAY = 2
+MAX_RETRIES = 3
+MIN_FREE_SPACE_GB = 1
+WHISPER_MODEL = "small"  # Whisper model size
+
+# Initialize HF API
+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
+
+def load_json_state(file_path: str, default_value: Dict[str, Any]) -> Dict[str, Any]:
+    """Load state from JSON file with migration logic for new structure."""
+    if os.path.exists(file_path):
+        try:
+            with open(file_path, "r") as f:
+                data = json.load(f)
+                
+                if "file_states" not in data or not isinstance(data["file_states"], dict):
+                    log_message("ℹ️ Initializing 'file_states' dictionary.", "INFO")
+                    data["file_states"] = {}
+                    
+                if "next_download_index" not in data:
+                    data["next_download_index"] = 0
+                    
+                return data
+        except json.JSONDecodeError:
+            log_message(f"⚠️ Corrupted state file: {file_path}", "WARNING")
+    return default_value
+
+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)
+
+def download_hf_state(repo_id: str, filename: str) -> Dict[str, Any]:
+    """Downloads the state file from Hugging Face or returns a default state."""
+    local_path = os.path.join(LOCAL_STATE_FOLDER, filename)
+    default_state = {"next_download_index": 0, "file_states": {}}
+    
+    try:
+        files = hf_api.list_repo_files(repo_id=repo_id, repo_type="dataset")
+        if filename not in files:
+            log_message(f"ℹ️ State file {filename} not found in {repo_id}. Starting from default state.", "INFO")
+            return default_state
+
+        from huggingface_hub import hf_hub_download
+        hf_hub_download(
+            repo_id=repo_id,
+            filename=filename,
+            repo_type="dataset",
+            local_dir=LOCAL_STATE_FOLDER,
+            local_dir_use_symlinks=False
+        )
+        
+        log_message(f"✅ Successfully downloaded state file from {repo_id}.", "INFO")
+        return load_json_state(local_path, default_state)
+        
+    except Exception as e:
+        log_message(f"⚠️ Failed to download state file from Hugging Face: {str(e)}. Starting from default state.", "WARNING")
+        return default_state
+
+def upload_hf_state(repo_id: str, filename: str, state: Dict[str, Any]) -> bool:
+    """Uploads the state file to Hugging Face."""
+    local_path = os.path.join(LOCAL_STATE_FOLDER, filename)
+    
+    try:
+        save_json_state(local_path, state)
+        
+        hf_api.upload_file(
+            path_or_fileobj=local_path,
+            path_in_repo=filename,
+            repo_id=repo_id,
+            repo_type="dataset",
+            commit_message=f"Update audio processing state: next_index={state['next_download_index']}"
+        )
+        log_message(f"✅ Successfully uploaded updated state file to {repo_id}", "INFO")
+        return True
+    except Exception as e:
+        log_message(f"❌ Failed to upload state file to Hugging Face: {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."""
+    log_message(f"🔒 Attempting to lock file: {wav_filename} (Marking as 'processing')", "INFO")
+    
+    state["file_states"][wav_filename] = "processing"
+    
+    if upload_hf_state(TARGET_REPO_ID, HF_STATE_FILE, state):
+        log_message(f"✅ Successfully locked file: {wav_filename}", "INFO")
+        return True
+    else:
+        log_message(f"❌ Failed to upload lock for file: {wav_filename}. Aborting processing.", "ERROR")
+        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."""
+    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_hf_state(TARGET_REPO_ID, HF_STATE_FILE, state):
+        log_message(f"✅ Successfully unlocked and marked as processed: {wav_filename}", "INFO")
+        return True
+    else:
+        log_message(f"❌ Failed to upload final state for file: {wav_filename}.", "ERROR")
+        return False
+
+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
+        
+    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: {dest_path}", "INFO")
+                return True
+            
+        except requests.exceptions.RequestException as e:
+            log_message(f"❌ Download attempt {attempt + 1} failed for {url}: {str(e)}", "WARNING")
+            time.sleep(PROCESSING_DELAY)
+        except Exception as e:
+            log_message(f"❌ An unexpected error occurred during download: {str(e)}", "ERROR")
+            return False
+            
+    log_message(f"❌ Failed to download {url} after {max_retries} attempts.", "ERROR")
+    return False
+
+def fetch_reference_files(repo_id: str) -> Dict[str, str]:
+    """Fetch all files from Fred808/BG3 repo to match with audio filenames."""
+    log_message(f"📋 Fetching file list from {repo_id}...", "INFO")
+    
+    try:
+        files_list = hf_api.list_repo_files(repo_id=repo_id, repo_type="dataset")
+        
+        # Include all file types (zip, rar, wav, mp3, etc.)
+        all_files = [f for f in files_list]
+        
+        # Create a mapping of base filename (without extension) to full path
+        filename_map = {}
+        for file_path in all_files:
+            base_name = os.path.splitext(os.path.basename(file_path))[0]
+            filename_map[base_name] = file_path
+        
+        log_message(f"✅ Found {len(filename_map)} files in reference repo", "INFO")
+        return filename_map
+        
+    except Exception as e:
+        log_message(f"❌ Failed to fetch reference files: {str(e)}", "ERROR")
+        return {}
+
+def find_matching_filename(transcribed_filename: str, reference_map: Dict[str, str]) -> Optional[str]:
+    """Find matching filename in reference map from Fred808/BG3."""
+    base_name = os.path.splitext(transcribed_filename)[0]
+    
+    # Exact match first
+    if base_name in reference_map:
+        full_path = reference_map[base_name]
+        print(f"\n✅ MATCH FOUND:")
+        print(f"   Audio: {transcribed_filename}")
+        print(f"   File:  {full_path}")
+        log_message(f"✅ Found exact match: {transcribed_filename} -> {full_path}", "INFO")
+        return full_path
+    
+    # Partial/fuzzy match (check if reference contains transcribed as substring)
+    matches = []
+    for ref_base, ref_full_path in reference_map.items():
+        if base_name.lower() in ref_base.lower() or ref_base.lower() in base_name.lower():
+            matches.append((ref_base, ref_full_path))
+    
+    # Return first partial match if found
+    if matches:
+        ref_base, ref_full_path = matches[0]
+        print(f"\n✅ PARTIAL MATCH FOUND:")
+        print(f"   Audio: {transcribed_filename}")
+        print(f"   File:  {ref_full_path}")
+        log_message(f"✅ Found partial match: {transcribed_filename} -> {ref_full_path}", "INFO")
+        return ref_full_path
+    
+    print(f"\n❌ NO MATCH FOUND:")
+    print(f"   Audio: {transcribed_filename}")
+    log_message(f"⚠️ No matching filename found for: {transcribed_filename}", "WARNING")
+    return None
+
+def transcribe_audio(wav_path: str) -> Optional[Dict[str, Any]]:
+    """Transcribe audio file using Whisper from Transformers."""
+    log_message(f"🎤 Transcribing audio file: {wav_path}", "INFO")
+    
+    try:
+        from transformers import pipeline
+        import librosa
+        
+        # Load audio with librosa
+        log_message(f"Loading audio file: {wav_path}", "INFO")
+        audio, sr = librosa.load(wav_path, sr=16000)
+        
+        # Initialize Whisper pipeline
+        log_message(f"Loading Whisper {WHISPER_MODEL} model from Transformers...", "INFO")
+        pipe = pipeline(
+            "automatic-speech-recognition",
+            model=f"openai/whisper-{WHISPER_MODEL}",
+            device=0 if __import__('torch').cuda.is_available() else -1  # GPU if available, else CPU
+        )
+        
+        # Transcribe
+        log_message("Transcribing audio...", "INFO")
+        result = pipe(audio)
+        
+        # Format result to match openai-whisper format
+        formatted_result = {
+            "text": result["text"],
+            "segments": [{"text": result["text"]}]
+        }
+        
+        log_message(f"✅ Successfully transcribed: {wav_path}", "INFO")
+        return formatted_result
+        
+    except ImportError as e:
+        missing_lib = str(e)
+        log_message(f"❌ Missing library. Install with: pip install transformers librosa torch torchaudio", "ERROR")
+        log_message(f"   Error: {missing_lib}", "ERROR")
+        return None
+    except Exception as e:
+        log_message(f"❌ Failed to transcribe {wav_path}: {str(e)}", "ERROR")
+        return None
+
+def process_audio_file(wav_path: str, reference_map: Dict[str, str], matched_filename: str) -> bool:
+    """
+    Main processing logic for a single audio file:
+    1. Transcribe using Whisper
+    2. Save transcription as JSON
+    3. Upload to HF dataset
+    4. Clean up local files
+    """
+    wav_filename = os.path.basename(wav_path)
+    
+    # 1. Transcribe audio
+    transcription = transcribe_audio(wav_path)
+    if transcription is None:
+        log_failed_file(wav_filename, "Transcription failed")
+        return False
+    
+    # 2. Save transcription as JSON
+    json_filename = os.path.splitext(matched_filename)[0] + "_transcription.json"
+    json_output_path = os.path.join(TRANSCRIPTIONS_FOLDER, json_filename)
+    
+    try:
+        os.makedirs(os.path.dirname(json_output_path), exist_ok=True)
+        
+        with open(json_output_path, "w", encoding="utf-8") as f:
+            json.dump(transcription, f, indent=2, ensure_ascii=False)
+        
+        log_message(f"✅ Saved transcription: {json_output_path}", "INFO")
+        
+    except Exception as e:
+        log_message(f"❌ Failed to save transcription JSON: {str(e)}", "ERROR")
+        log_failed_file(wav_filename, f"Failed to save JSON: {str(e)}")
+        return False
+    
+    # 3. Upload to HF dataset
+    try:
+        path_in_repo = f"transcriptions/{json_filename}"
+        commit_message = f"Add transcription for: {matched_filename}"
+        
+        hf_api.upload_file(
+            path_or_fileobj=json_output_path,
+            path_in_repo=path_in_repo,
+            repo_id=TARGET_REPO_ID,
+            repo_type="dataset",
+            commit_message=commit_message
+        )
+        log_message(f"✅ Successfully uploaded transcription: {json_filename}", "INFO")
+        processing_status["transcribed_files"] += 1
+        
+    except Exception as e:
+        log_message(f"❌ Failed to upload transcription to HF: {str(e)}", "ERROR")
+        log_failed_file(wav_filename, f"Failed to upload: {str(e)}")
+        return False
+    
+    # 4. Clean up local files
+    try:
+        os.remove(json_output_path)
+        log_message(f"🗑️ Cleaned up local transcription file: {json_output_path}", "INFO")
+    except:
+        pass
+    
+    return True
+
+def get_next_file_to_process(repo_id: str, state: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """
+    Finds the next audio file to process from the source repo in reverse order (oldest to newest).
+    Returns: { 'filename': str, 'url': str, 'index': int } or None
+    """
+    log_message(f"🔍 Searching for next audio file to process in {repo_id}", "INFO")
+    
+    try:
+        files_list = hf_api.list_repo_files(repo_id=repo_id, repo_type="dataset")
+        
+        # Filter for audio files and sort in reverse order (descending)
+        audio_files = sorted([f for f in files_list if f.endswith(('.wav', '.mp3'))], reverse=True)
+        
+        if not audio_files:
+            log_message("ℹ️ No audio files found in the source repository.", "INFO")
+            return None
+            
+        processing_status["total_files"] = len(audio_files)
+        
+        start_index = state.get("next_download_index", 0)
+        
+        for index in range(start_index, len(audio_files)):
+            filename = audio_files[index]
+            file_state = state["file_states"].get(filename)
+            
+            if file_state is None or file_state == "failed":
+                url = hf_hub_url(repo_id=repo_id, filename=filename, repo_type="dataset", subfolder=None)
+                
+                log_message(f"✅ Found next audio file: {filename} at index {index}", "INFO")
+                return {
+                    'filename': filename,
+                    'url': url,
+                    'index': index
+                }
+                
+            elif file_state == "processing":
+                log_message(f"⚠️ File {filename} is currently marked as 'processing'. Skipping for now.", "WARNING")
+                
+            elif file_state == "processed":
+                log_message(f"ℹ️ File {filename} already processed. Skipping.", "INFO")
+                
+        log_message("ℹ️ All files up to the current index have been processed or skipped.", "INFO")
+        
+        if start_index >= len(audio_files):
+            log_message("ℹ️ Reached end of file list. Resetting index to 0 for next loop.", "INFO")
+            state["next_download_index"] = 0
+            upload_hf_state(TARGET_REPO_ID, HF_STATE_FILE, state)
+            
+        return None
+        
+    except Exception as e:
+        log_message(f"❌ Failed to list files from Hugging Face: {str(e)}", "ERROR")
+        return None
+
+def main_processing_loop():
+    """The main loop that orchestrates the download, transcription, and upload cycle."""
+    
+    if processing_status["is_running"]:
+        log_message("⚠️ Processing loop is already running.", "WARNING")
+        return
+        
+    processing_status["is_running"] = True
+    
+    try:
+        log_message("🚀 Starting audio transcription processing loop...", "INFO")
+        
+        # Fetch reference files from BG_Vid repo once at the start
+        reference_map = fetch_reference_files(REFERENCE_REPO_ID)
+        
+        if not reference_map:
+            log_message("❌ No reference files found. Cannot proceed.", "ERROR")
+            return
+        
+        while processing_status["is_running"]:
+            
+            current_state = download_hf_state(TARGET_REPO_ID, HF_STATE_FILE)
+            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:
+                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)
+                    
+                    if matched_filename:
+                        if process_audio_file(local_wav_path, reference_map, matched_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"❌ No matching filename found for: {base_filename}", "ERROR")
+                        log_failed_file(target_file, "No matching reference filename")
+                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:
+                next_index_to_save = target_index + 1
+                current_state = download_hf_state(TARGET_REPO_ID, HF_STATE_FILE)
+                
+                if success:
+                    unlock_file_as_processed(target_file, current_state, next_index_to_save)
+                    processing_status["processed_files"] += 1
+                else:
+                    log_message(f"⚠️ Processing failed for {target_file}. Marking as 'failed' and advancing index.", "WARNING")
+                    current_state["file_states"][target_file] = "failed"
+                    current_state["next_download_index"] = next_index_to_save
+                    upload_hf_state(TARGET_REPO_ID, HF_STATE_FILE, current_state)
+                    processing_status["failed_files"] += 1
+                    
+                if os.path.exists(local_wav_path):
+                    os.remove(local_wav_path)
+                    log_message(f"🗑️ Cleaned up local file: {local_wav_path}", "INFO")
+
+            time.sleep(PROCESSING_DELAY)
+
+        log_message("🎉 Processing complete!", "INFO")
+        log_message(f"📊 Final stats: {processing_status['transcribed_files']} audio files transcribed, {processing_status['processed_files']} files processed", "INFO")
+        
+    except KeyboardInterrupt:
+        log_message("⏹️ Processing interrupted by user", "WARNING")
+    except Exception as e:
+        log_message(f"❌ Fatal error: {str(e)}", "ERROR")
+    finally:
+        processing_status["is_running"] = False
+        cleanup_temp_files()
+
+if __name__ == "__main__":
+    main_processing_loop()
+
+# ===== FASTAPI ENDPOINTS =====
+
+@app.get("/")
+async def root():
+    """Root endpoint with service info"""
+    return {
+        "service": "Audio Transcriber",
+        "status": "running",
+        "version": "1.0.0",
+        "endpoints": {
+            "status": "/status",
+            "start": "/start",
+            "stop": "/stop",
+            "process": "/process/{filename}",
+            "logs": "/logs"
+        }
+    }
+
+@app.get("/status")
+async def get_status():
+    """Get current processing status"""
+    return {
+        "is_running": processing_status["is_running"],
+        "current_file": processing_status["current_file"],
+        "total_files": processing_status["total_files"],
+        "processed_files": processing_status["processed_files"],
+        "transcribed_files": processing_status["transcribed_files"],
+        "failed_files": processing_status["failed_files"],
+        "last_update": processing_status["last_update"],
+        "recent_logs": processing_status["logs"][-10:]
+    }
+
+@app.post("/start")
+async def start_processing():
+    """Start the main processing loop"""
+    if processing_status["is_running"]:
+        raise HTTPException(status_code=400, detail="Processing already running")
+    
+    # Start processing in a separate thread
+    thread = threading.Thread(target=main_processing_loop, daemon=True)
+    thread.start()
+    
+    return {
+        "message": "Processing started",
+        "status": "started"
+    }
+
+@app.post("/stop")
+async def stop_processing():
+    """Stop the main processing loop"""
+    if not processing_status["is_running"]:
+        raise HTTPException(status_code=400, detail="Processing not running")
+    
+    processing_status["is_running"] = False
+    
+    return {
+        "message": "Processing stopped",
+        "status": "stopped"
+    }
+
+@app.get("/logs")
+async def get_logs(limit: int = 50):
+    """Get recent logs"""
+    logs = processing_status["logs"][-limit:]
+    return {
+        "total_logs": len(processing_status["logs"]),
+        "recent_logs": logs
+    }
+
+@app.post("/process/{filename}")
+async def process_single_file(filename: str):
+    """Process a single audio file manually"""
+    try:
+        log_message(f"🎯 Manual processing requested for: {filename}", "INFO")
+        
+        # Download and process the file
+        reference_map = fetch_reference_files(REFERENCE_REPO_ID)
+        if not reference_map:
+            raise HTTPException(status_code=500, detail="Could not fetch reference files")
+        
+        # Get file URL
+        audio_url = hf_hub_url(repo_id=SOURCE_REPO_ID, filename=filename, repo_type="dataset", subfolder=None)
+        local_wav_path = os.path.join(DOWNLOAD_FOLDER, os.path.basename(filename))
+        
+        # Download
+        if not download_with_retry(audio_url, local_wav_path):
+            raise HTTPException(status_code=500, detail="Failed to download file")
+        
+        # Find match
+        base_filename = os.path.basename(filename)
+        matched_filename = find_matching_filename(base_filename, reference_map)
+        
+        if not matched_filename:
+            os.remove(local_wav_path)
+            raise HTTPException(status_code=404, detail="No matching filename found")
+        
+        # Process
+        if process_audio_file(local_wav_path, reference_map, matched_filename):
+            processing_status["transcribed_files"] += 1
+            
+            if os.path.exists(local_wav_path):
+                os.remove(local_wav_path)
+            
+            return {
+                "status": "success",
+                "file": filename,
+                "matched": matched_filename,
+                "message": "Audio transcribed and uploaded successfully"
+            }
+        else:
+            if os.path.exists(local_wav_path):
+                os.remove(local_wav_path)
+            raise HTTPException(status_code=500, detail="Processing failed")
+            
+    except Exception as e:
+        log_message(f"❌ Manual processing error: {str(e)}", "ERROR")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.on_event("startup")
+async def startup_event():
+    """Auto-start processing when server starts"""
+    log_message("🚀 Server startup: Checking dependencies...", "INFO")
+    
+    try:
+        import transformers
+        log_message("✅ Transformers found", "INFO")
+    except ImportError:
+        log_message("⚠️ WARNING: Transformers not installed!", "WARNING")
+        log_message("   Install with: pip install transformers librosa torch torchaudio", "WARNING")
+    
+    log_message("🚀 Server startup: Auto-starting processing loop", "INFO")
+    
+    # Start processing in a separate thread
+    thread = threading.Thread(target=main_processing_loop, daemon=True)
+    thread.start()
+
+def run_api(host: str = "0.0.0.0", port: int = 8000):
+    """Run the FastAPI server"""
+    log_message(f"🚀 Starting FastAPI server on {host}:{port}", "INFO")
+    uvicorn.run(app, host=host, port=port)
+
+if __name__ == "__main__":
+    # Run API server (processing will auto-start via startup event)
+    run_api()

requirements.txt

@@ -0,0 +1,17 @@
+
+accelerate
+fastapi
+uvicorn
+opencv-python-headless
+numpy
+pathlib
+huggingface_hub
+pillow
+rarfile
+python-multipart
+openai-whisper
+ffmpeg-python
+transformers 
+librosa 
+torch 
+torchaudio