app.py

import os

# Ensure environment variables are set before loading transformers
os.environ["HF_HOME"] = "/tmp/hf_cache"
os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"

from flask import Flask, request, jsonify
import requests
import torch
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration

app = Flask(__name__)

# Use a smaller model for faster CPU loading
model_id = "openai/whisper-base"
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

forced_decoder_ids = processor.get_decoder_prompt_ids(language="he", task="transcribe")

def transcribe_audio(audio_url):
    # 1) Download the file to /tmp
    response = requests.get(audio_url)
    audio_path = "/tmp/temp_audio.wav"
    with open(audio_path, "wb") as f:
        f.write(response.content)

    # 2) Load audio with librosa
    waveform, sr = librosa.load(audio_path, sr=16000)

    # 3) Truncate to 1 hour max
    max_duration_sec = 3600
    waveform = waveform[:sr * max_duration_sec]

    # 4) Split into 25-second chunks
    chunk_duration_sec = 25
    chunk_size = sr * chunk_duration_sec
    chunks = [waveform[i : i + chunk_size] for i in range(0, len(waveform), chunk_size)]

    partial_text = ""
    for chunk in chunks:
        # Preprocess chunk
        inputs = processor(chunk, sampling_rate=16000, return_tensors="pt", padding=True)
        input_features = inputs.input_features.to(device)

        # Transcribe chunk
        with torch.no_grad():
            predicted_ids = model.generate(
                input_features, 
                forced_decoder_ids=forced_decoder_ids
            )

        # Convert IDs back to text
        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
        partial_text += transcription + "\n"

    return partial_text.strip()

@app.route('/transcribe', methods=['POST'])
def transcribe_endpoint():
    data = request.get_json()
    audio_url = data.get('audio_url')
    if not audio_url:
        return jsonify({"error": "Missing 'audio_url' in request"}), 400

    # Perform transcription
    transcription = transcribe_audio(audio_url)
    return jsonify({"transcription": transcription})

if __name__ == '__main__':
    # Run the Flask app on port 7860
    app.run(host="0.0.0.0", port=7860)