app.py

from flask import Flask, request, jsonify
from faster_whisper import WhisperModel
import torch
import io
import time
import datetime
from threading import Semaphore
import os
from werkzeug.utils import secure_filename
import tempfile

app = Flask(__name__)

# Configuration
MAX_CONCURRENT_REQUESTS = 2  # Adjust based on your server capacity
MAX_AUDIO_DURATION = 60 * 30  # 30 minutes maximum audio duration (adjust as needed)
TEMPORARY_FOLDER = tempfile.gettempdir()
ALLOWED_EXTENSIONS = {'mp3', 'wav', 'ogg', 'm4a', 'flac'}

# Device check for faster-whisper
device = "cuda" if torch.cuda.is_available() else "cpu"
compute_type = "float16" if device == "cuda" else "int8"
print(f"Using device: {device} with compute_type: {compute_type}")

# Faster Whisper setup with optimized parameters for long audio
beamsize = 5  # Slightly larger beam size can help with long-form accuracy
wmodel = WhisperModel(
    "guillaumekln/faster-whisper-small",
    device=device,
    compute_type=compute_type,
    download_root="./model_cache"  # Cache model to avoid re-downloading
)

# Concurrency control
request_semaphore = Semaphore(MAX_CONCURRENT_REQUESTS)
active_requests = 0

def allowed_file(filename):
    return '.' in filename and \
           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

def cleanup_temp_files(file_path):
    """Ensure temporary files are deleted after processing"""
    try:
        if os.path.exists(file_path):
            os.remove(file_path)
    except Exception as e:
        print(f"Error cleaning up temp file {file_path}: {str(e)}")

@app.route("/health", methods=["GET"])
def health_check():
    """Endpoint to check if API is running"""
    return jsonify({
        'status': 'API is running',
        'timestamp': datetime.datetime.now().isoformat(),
        'device': device,
        'compute_type': compute_type,
        'active_requests': active_requests,
        'max_duration_supported': MAX_AUDIO_DURATION
    })

@app.route("/status/busy", methods=["GET"])
def server_busy():
    """Endpoint to check if server is busy"""
    is_busy = active_requests >= MAX_CONCURRENT_REQUESTS
    return jsonify({
        'is_busy': is_busy,
        'active_requests': active_requests,
        'max_capacity': MAX_CONCURRENT_REQUESTS
    })

@app.route("/whisper_transcribe", methods=["POST"])
def whisper_transcribe():
    global active_requests
    
    if not request_semaphore.acquire(blocking=False):
        return jsonify({
            'status': 'Server busy',
            'message': f'Currently processing {active_requests} requests',
            'suggestion': 'Please try again shortly'
        }), 503
    
    active_requests += 1
    print(f"Starting transcription (Active requests: {active_requests})")
    
    temp_file_path = None
    
    try:
        if 'audio' not in request.files:
            return jsonify({'error': 'No file provided'}), 400

        audio_file = request.files['audio']
        if not (audio_file and allowed_file(audio_file.filename)):
            return jsonify({'error': 'Invalid file format'}), 400

        # Save to temporary file for large audio processing
        temp_file_path = os.path.join(TEMPORARY_FOLDER, secure_filename(audio_file.filename))
        audio_file.save(temp_file_path)
        
        # Get processing parameters from request
        language = request.form.get('language', None)
        task = request.form.get('task', 'transcribe')  # 'transcribe' or 'translate'
        vad_filter = request.form.get('vad_filter', 'true').lower() == 'true'
        word_timestamps = request.form.get('word_timestamps', 'false').lower() == 'true'
        
        try:
            start_time = time.time()
            
            # Process in chunks with VAD for long audio
            segments, info = wmodel.transcribe(
                temp_file_path,
                beam_size=beamsize,
                language=language,
                task=task,
                vad_filter=vad_filter,
                word_timestamps=word_timestamps,
                chunk_length=30  # Process in 30-second chunks
            )
            
            # Stream results as they become available
            results = []
            for segment in segments:
                if time.time() - start_time > MAX_AUDIO_DURATION:
                    raise TimeoutError(f"Transcription exceeded maximum allowed duration of {MAX_AUDIO_DURATION} seconds")
                
                result = {
                    'text': segment.text,
                    'start': segment.start,
                    'end': segment.end
                }
                
                if word_timestamps and segment.words:
                    result['words'] = [{
                        'word': word.word,
                        'start': word.start,
                        'end': word.end,
                        'probability': word.probability
                    } for word in segment.words]
                
                results.append(result)
            
            processing_time = time.time() - start_time
            print(f"Transcription completed in {processing_time:.2f} seconds")
            
            return jsonify({
                'segments': results,
                'summary': {
                    'processing_time': processing_time,
                    'language': info.language,
                    'language_probability': info.language_probability,
                    'duration': sum(seg.end - seg.start for seg in results if hasattr(seg, 'end'))
                }
            })
            
        except TimeoutError as te:
            print(f"Transcription timeout: {str(te)}")
            return jsonify({'error': str(te)}), 504
        except Exception as e:
            print(f"Transcription error: {str(e)}")
            return jsonify({'error': 'Transcription failed', 'details': str(e)}), 500
            
    finally:
        if temp_file_path:
            cleanup_temp_files(temp_file_path)
        active_requests -= 1
        request_semaphore.release()
        print(f"Request completed (Active requests: {active_requests})")

if __name__ == "__main__":
    # Create temporary folder if it doesn't exist
    if not os.path.exists(TEMPORARY_FOLDER):
        os.makedirs(TEMPORARY_FOLDER)
    
    app.run(host="0.0.0.0", port=7860, threaded=True)