因此，我想使用AudioSegment打开一个mp3，然后我想将AudioSegment对象转换为numpy数组，并使用此numpy数组作为whisper模型的输入，我遵循了这个问题How to create a numpy array from a pydub AudioSegment?，但结果没有帮助，因为我总是得到错误，如

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.

这个错误是奇怪的，因为如果我直接提供文件，像下面我得到没有问题

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

这是我写的代码

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)

我该怎么做？

• -------编辑------我编辑了代码，现在我使用下面的代码。我没有以前的错误，但模型似乎没有正确转录。我测试了我传递给模型导出wav文件的音频，我播放了它，有很多噪音，我不明白他们在说什么，所以这就是为什么模型不转录。我正在做的正常化的通道好吗？

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

language = "en"
model = "medium"
model_path = "../models"

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Loading whisper {model} model {language}...")
audio_model = whisper.load_model(model, download_root=model_path, device=device)

# load wav file with pydub
audio_path = "20230611-004146_audio_chunk.wav"
audio_segment = AudioSegment.from_wav(audio_path)
#audio_segment = audio_segment.low_pass_filter(1000)
# get sample rate
sample_rate = audio_segment.frame_rate
arr = np.array(audio_segment.get_array_of_samples())
arr_copy = arr.copy()
arr_copy = torch.from_numpy(arr_copy)
arr_copy = arr_copy.to(torch.float32)
# normalize
arr_copy = arr_copy / 32768.0
# to device
arr_copy = arr_copy.to(device)

print(f"Transcribing...")
result = audio_model.transcribe(arr_copy, language=language, fp16=torch.cuda.is_available())
text = result['text'].strip()
print(text)

waveform = arr_copy.cpu().numpy()
audio_segment = AudioSegment(
    waveform.tobytes(),
    frame_rate=sample_rate,
    sample_width=waveform.dtype.itemsize,
    channels=1
)
audio_segment.export("test.wav", format="wav")