Whisper-based speech recognition functionality as a Flask endpoint (Research)

Check out all on 'Automatic Speech Recognition'

Part 1

Dated: March 2025

I want to run a Speech Recognition app as a flask endpoint?

Below is an example of how to expose your Whisper-based speech recognition functionality as a Flask endpoint. In this example, the server accepts an audio file via a POST request, processes it with the Whisper model, and returns the transcription as JSON.

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Example: app.py

    
from flask import Flask, request, jsonify
import whisper
import os
import uuid
import soundfile as sf

app = Flask(__name__)

# Load the Whisper model (choose "base", "small", "medium", or "large")
model = whisper.load_model("base")

@app.route("/transcribe", methods=["POST"])
def transcribe():
    # Check if an audio file is included in the request
    if "audio" not in request.files:
        return jsonify({"error": "No audio file provided."}), 400

    audio_file = request.files["audio"]
    # Generate a unique filename to temporarily store the audio file
    temp_filename = f"temp_{uuid.uuid4().hex}.wav"
    audio_file.save(temp_filename)

    try:
        # Optional: Ensure the audio is 16kHz. 
        # This reads the file and, if needed, rewrites it with the correct sample rate.
        audio_data, samplerate = sf.read(temp_filename)
        if samplerate != 16000:
            sf.write(temp_filename, audio_data, 16000)
    except Exception as e:
        os.remove(temp_filename)
        return jsonify({"error": "Failed to process audio file", "details": str(e)}), 500

    try:
        # Run transcription using Whisper
        result = model.transcribe(temp_filename)
        transcription = result.get("text", "")
    except Exception as e:
        os.remove(temp_filename)
        return jsonify({"error": "Transcription failed", "details": str(e)}), 500

    # Clean up the temporary file
    os.remove(temp_filename)
    return jsonify({"transcription": transcription})

if __name__ == "__main__":
    # Run the Flask server on 0.0.0.0 so it's accessible on your network
    app.run(debug=True, host="0.0.0.0", port=5000)