app.py

from flask import Flask, request, jsonify
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer, pipeline
from pydub import AudioSegment
import torch
import torchaudio
from datetime import datetime, time
import pytz

app = Flask(__name__)

# Load speech recognition model and tokenizer
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

# Load translation model explicitly
translation_model = "Helsinki-NLP/opus-mt-en-hi"
translation_pipeline = pipeline("translation", model=translation_model)

# Function to preprocess audio
def preprocess_audio(audio_file):
    audio = AudioSegment.from_file(audio_file)
    audio = audio.set_frame_rate(16000)
    audio.export("processed.wav", format="wav")
    waveform, sample_rate = torchaudio.load("processed.wav")
    return waveform

# Function to check if the current time is after 6 PM IST
def is_after_6pm_ist():
    ist = pytz.timezone('Asia/Kolkata')
    current_time = datetime.now(ist).time()
    return current_time >= time(18, 0)

@app.route('/translate', methods=['POST'])
def translate():
    if not is_after_6pm_ist():
        return jsonify({"error": "Service is available only after 6 PM IST"}), 403

    if 'audio' not in request.files:
        return jsonify({"error": "No audio file provided"}), 400

    audio_file = request.files['audio']
    waveform = preprocess_audio(audio_file)
    
    input_values = tokenizer(waveform.squeeze().numpy(), return_tensors="pt").input_values
    logits = model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = tokenizer.batch_decode(predicted_ids)[0]

    translation = translation_pipeline(transcription)
    translated_text = translation[0]['translation_text']

    return jsonify({"transcription": transcription, "translation": translated_text})

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=8080)