Worker hangs for really long time, performance is not close to what it should be

async def async_transcribe_wfiles(
        audio_file, 
        filename: str, 
        file_path:str,
        ):
    # Create timestamp
    ic(type(audio_file))
    loop = asyncio.get_event_loop()
    # Faster whisper
    segments, info = await loop.run_in_executor(None, lambda: whisper_model.transcribe(audio_file, beam_size=5))
    segments = await loop.run_in_executor(None, lambda: list(segments))
    ic(segments)
    # Create directory
    #os.makedirs(f'/tmp/{org_id}/{session}', exist_ok=True)
    work_dir = f'{file_path.split(f"/{filename}")[0]}'
    ic(work_dir)
    os.makedirs(work_dir, exist_ok=True)

    # Create files to upload
    segments_x = [{"text": segment.text, "start": round(segment.start, 3), "end": round(segment.end, 3)} for segment in segments]

    json_path = f'{work_dir}/{filename}_{info.language}_segments.json'
     # Check if SRT file (subtitles options is being called)
    srt_path = f'{work_dir}/{filename}_transcribe.srt'
    srt_content = await loop.run_in_executor(None, create_srt, segments_x)
    txt_path = f'{work_dir}/{filename}_transcribe.txt'
    txt_content = "".join([x['text'] for x in segments_x]) #Devolver esta variable.
    # Save to S3 using bytes
    await loop.run_in_executor(None, save_string_to_s3, txt_path, txt_content)
    await loop.run_in_executor(None, save_string_to_s3, srt_path, srt_content)
    await async_write_json(json_path, segments_x)
    await async_upload_s3(json_path, settings.AWS_BUCKET_NAME, f'{work_dir}/{filename}_{info.language}_segments.json')
    ic(f'{filename}_{info.language}_segments.json') 
    return info, txt_path, srt_path, json_path

async def transcribe_option(
        file_path: str,
        is_diarization: bool,
        ):
    # Download from s3
    loop = asyncio.get_event_loop()
    file_contents = await loop.run_in_executor(None, download_s3_object_to_memory, file_path)
    filename, file_extension, clean_filename = sanitize_file_name_s3path(file_path)

    # Create transcription
    info, txt_path, srt_path, json_path = await async_transcribe_wfiles(file_contents, filename, file_path)

    return {
        "transcripts": [txt_path, srt_path],
        "type": "diarization" if is_diarization else "transcription"
    }

### Handler
# Define the handler function.
def handler(job):
    job_input = job["input"]  # Access the input from the request.
    endpoint = job_input["endpoint"]
    try:
        # Check endpoint and process
        if endpoint == "fast_transcribe":
            job_input.pop("endpoint")
            return transcribe(**job_input)
        if endpoint == "transcribe":
            job_input.pop("endpoint")
            return transcribe_wfiles(**job_input)
        if endpoint == "transcribe_option":
            job_input.pop("endpoint")
            return transcribe_option(**job_input)
        if endpoint == "transcribe_diarize":
            job_input.pop("endpoint")
            return transcribe_diarize(**job_input)
    except Exception as e:
        return {"error": f"Invalid input data: {str(e)}"}

runpod.serverless.start({"handler": handler})

async def async_transcribe_wfiles(
        audio_file, 
        filename: str, 
        file_path:str,
        ):
    # Create timestamp
    ic(type(audio_file))
    loop = asyncio.get_event_loop()
    # Faster whisper
    segments, info = await loop.run_in_executor(None, lambda: whisper_model.transcribe(audio_file, beam_size=5))
    segments = await loop.run_in_executor(None, lambda: list(segments))
    ic(segments)
    # Create directory
    #os.makedirs(f'/tmp/{org_id}/{session}', exist_ok=True)
    work_dir = f'{file_path.split(f"/{filename}")[0]}'
    ic(work_dir)
    os.makedirs(work_dir, exist_ok=True)

    # Create files to upload
    segments_x = [{"text": segment.text, "start": round(segment.start, 3), "end": round(segment.end, 3)} for segment in segments]

    json_path = f'{work_dir}/{filename}_{info.language}_segments.json'
     # Check if SRT file (subtitles options is being called)
    srt_path = f'{work_dir}/{filename}_transcribe.srt'
    srt_content = await loop.run_in_executor(None, create_srt, segments_x)
    txt_path = f'{work_dir}/{filename}_transcribe.txt'
    txt_content = "".join([x['text'] for x in segments_x]) #Devolver esta variable.
    # Save to S3 using bytes
    await loop.run_in_executor(None, save_string_to_s3, txt_path, txt_content)
    await loop.run_in_executor(None, save_string_to_s3, srt_path, srt_content)
    await async_write_json(json_path, segments_x)
    await async_upload_s3(json_path, settings.AWS_BUCKET_NAME, f'{work_dir}/{filename}_{info.language}_segments.json')
    ic(f'{filename}_{info.language}_segments.json') 
    return info, txt_path, srt_path, json_path

async def transcribe_option(
        file_path: str,
        is_diarization: bool,
        ):
    # Download from s3
    loop = asyncio.get_event_loop()
    file_contents = await loop.run_in_executor(None, download_s3_object_to_memory, file_path)
    filename, file_extension, clean_filename = sanitize_file_name_s3path(file_path)

    # Create transcription
    info, txt_path, srt_path, json_path = await async_transcribe_wfiles(file_contents, filename, file_path)

    return {
        "transcripts": [txt_path, srt_path],
        "type": "diarization" if is_diarization else "transcription"
    }

### Handler
# Define the handler function.
def handler(job):
    job_input = job["input"]  # Access the input from the request.
    endpoint = job_input["endpoint"]
    try:
        # Check endpoint and process
        if endpoint == "fast_transcribe":
            job_input.pop("endpoint")
            return transcribe(**job_input)
        if endpoint == "transcribe":
            job_input.pop("endpoint")
            return transcribe_wfiles(**job_input)
        if endpoint == "transcribe_option":
            job_input.pop("endpoint")
            return transcribe_option(**job_input)
        if endpoint == "transcribe_diarize":
            job_input.pop("endpoint")
            return transcribe_diarize(**job_input)
    except Exception as e:
        return {"error": f"Invalid input data: {str(e)}"}

runpod.serverless.start({"handler": handler})

--- Starting Serverless Worker |  Version 1.6.0 ---
{"requestId": null, "message": "test_input set, using test_input as job input.", "level": "INFO"}
{"requestId": "local_test", "message": "Started.", "level": "INFO"}
credentials.py      :1123 2024-02-04 22:58:15,683 Found credentials in environment variables.
ic| type(audio_file): <class '_io.BytesIO'>
transcribe.py       :263  2024-02-04 22:58:26,199 Processing audio with duration 02:36:32.192
transcribe.py       :317  2024-02-04 22:58:40,144 Detected language 'es' with probability 0.98

--- Starting Serverless Worker |  Version 1.6.0 ---
{"requestId": null, "message": "test_input set, using test_input as job input.", "level": "INFO"}
{"requestId": "local_test", "message": "Started.", "level": "INFO"}
credentials.py      :1123 2024-02-04 22:58:15,683 Found credentials in environment variables.
ic| type(audio_file): <class '_io.BytesIO'>
transcribe.py       :263  2024-02-04 22:58:26,199 Processing audio with duration 02:36:32.192
transcribe.py       :317  2024-02-04 22:58:40,144 Detected language 'es' with probability 0.98

transcribe.py       :263  2024-02-04 22:58:26,199 Processing audio with duration 02:36:32.192
transcribe.py       :317  2024-02-04 22:58:40,144 Detected language 'es' with probability 0.98

transcribe.py       :263  2024-02-04 22:58:26,199 Processing audio with duration 02:36:32.192
transcribe.py       :317  2024-02-04 22:58:40,144 Detected language 'es' with probability 0.98

async def async_transcribe_wfiles(
        audio_file, 
        filename: str, 
        file_path:str,
        ):
    # Create timestamp
    ic(type(audio_file))
    loop = asyncio.get_event_loop()

    segments, info = await loop.run_in_executor(None, lambda: whisper_model.transcribe(audio_file, beam_size=5))
    segments = await loop.run_in_executor(None, lambda: list(segments))
    ic(segments)
    # Create directory
    #os.makedirs(f'/tmp/{org_id}/{session}', exist_ok=True)
    work_dir = f'{file_path.split(f"/{filename}")[0]}'
    ic(work_dir)
    os.makedirs(work_dir, exist_ok=True)

async def async_transcribe_wfiles(
        audio_file, 
        filename: str, 
        file_path:str,
        ):
    # Create timestamp
    ic(type(audio_file))
    loop = asyncio.get_event_loop()

    segments, info = await loop.run_in_executor(None, lambda: whisper_model.transcribe(audio_file, beam_size=5))
    segments = await loop.run_in_executor(None, lambda: list(segments))
    ic(segments)
    # Create directory
    #os.makedirs(f'/tmp/{org_id}/{session}', exist_ok=True)
    work_dir = f'{file_path.split(f"/{filename}")[0]}'
    ic(work_dir)
    os.makedirs(work_dir, exist_ok=True)