How to feed a numpy array as audio for whisper model

So I want to open an mp3 using AudioSegment, then I want to convert the AudioSegment object to numpy array and use this numpy array as input for whisper model, I followed this question How to create a numpy array from a pydub AudioSegment? but non of the result was helpful since I get always error like

    Traceback (most recent call last):
  File "E:\Programmi\PythonProjects\whisper_real_time\test\converting_test.py", line 19, in <module>
    result = audio_model.transcribe(arr_copy, language="en", word_timestamps=True,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "E:\Programmi\PythonProjects\whisper_real_time\venv\Lib\site-packages\whisper\transcribe.py", line 121, in transcribe
    mel = log_mel_spectrogram(audio, padding=N_SAMPLES)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "E:\Programmi\PythonProjects\whisper_real_time\venv\Lib\site-packages\whisper\audio.py", line 146, in log_mel_spectrogram
    audio = F.pad(audio, (0, padding))
            ^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 86261939712 bytes.

This error is strange because if I provide directly the file like below I get no problems

result = audio_model.transcribe("../audio_test_files/1001_IEO_DIS_HI.mp3", language="en", word_timestamps=True,
                                        fp16=torch.cuda.is_available())

This is the code I wrote

from pydub import AudioSegment
import numpy as np
import whisper
import torch


audio = AudioSegment.from_mp3("../audio_test_files/1001_IEO_DIS_HI.mp3")

dtype = getattr(np, "int{:d}".format(
    audio.sample_width * 8))  # Or could create a mapping: {1: np.int8, 2: np.int16, 4: np.int32, 8: np.int64}
arr = np.ndarray((int(audio.frame_count()), audio.channels), buffer=audio.raw_data, dtype=dtype)
arr_copy = arr.copy()
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Loading whisper...")
audio_model = whisper.load_model("small", download_root="../models",
                                     device=device)
print(f"Transcribing...")
result = audio_model.transcribe(audio=arr_copy, language="en", word_timestamps=True,
                                        fp16=torch.cuda.is_available())  # , initial_prompt=result.get('text', ""))
text = result['text'].strip()
print(text)

how can I do it?

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