Help Needed: Out of Memory Errors with Flask and Whisper Timestamped Library

I’m encountering a memory-related issue while trying to transcribe audio files using Flask and the whisper_timestamped (GitHub - linto-ai/whisper-timestamped: Multilingual Automatic Speech Recognition with word-level timestamps and confidence) library. I’ve deployed my app on Render and I’m using a /transcribe endpoint to handle audio transcription requests. However, I’m consistently running into the following error:

[ERROR] Worker (pid:67) was sent SIGKILL! Perhaps out of memory?

I’ve provided the relevant code snippet below:

from flask import Flask, request, jsonify
import whisper_timestamped
import tempfile
import os

app = Flask(__name__)

def hello():
    name = "Hello World"
    return name

def transcribe_audio(audio_file_path):
    model = whisper_timestamped.load_model("base")
    audio = whisper_timestamped.load_audio(audio_file_path)
    result = whisper_timestamped.transcribe(model, audio, language="en")
    transcribed_text = result["text"]
    word_timestamps = []
    for segment in result["segments"]:
        for word in segment["words"]:
            word_timestamps.append({"word": word["text"], "startTime": word["start"], "endTime": word["end"]})

    return {"transcribedText": transcribed_text, "wordTimestamps": word_timestamps}

@app.route('/transcribe', methods=['POST'])
def transcribe():
    if 'audio' not in request.files:
        return jsonify({'error': 'No audio file provided.'}), 400

    audio_file = request.files['audio']

    # Create a temporary directory to store the audio file
    temp_dir = tempfile.mkdtemp()

    # Save the audio file temporarily to the temporary directory
    temp_audio_path = os.path.join(temp_dir, "temp_audio.wav")

    result = transcribe_audio(temp_audio_path)

    # Remove the temporary audio file and directory after transcription

    return jsonify(result), 200

if __name__ == "__main__":

I’m using the whisper_timestamped library to transcribe audio and obtain word timestamps. The error occurs when the worker process handling the request is terminated due to memory issues. I suspect that the library or model might be memory-intensive, causing the app to exceed its memory allocation.

Can anyone offer suggestions on how to diagnose and resolve this memory-related error? Are there any strategies or best practices I can follow to prevent my app from running out of memory during audio transcription?

Any help or insights would be greatly appreciated.


I’ve replied to the ticket you also opened. Let’s keep the conversation in one place (on the ticket). Then you can update this post when we get to the solution.


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