我试图使一个程序，除其他事项外，是能够转录的音频文件选择的用户以及自愿选择的语言，其中特定的音频是在。
但我遇到了以下问题：

ERROR:root:Error at division
Traceback (most recent call last):
  File "C:\Users\...\AppData\Local\Temp\ipykernel_27364\1513539390.py", line 136, in transcribir
    result = model.transcribe(audio, language=f"{actual_language}", fp16=False, verbose=True)
  File "C:\Users\...\anaconda3\envs\python39\lib\site-packages\whisper\transcribe.py", line 181, in transcribe
    ):
  File "C:\Users\...\anaconda3\envs\python39\lib\site-packages\whisper\transcribe.py", line 117, in decode_with_fallback
  File "C:\Users\...\anaconda3\envs\python39\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "C:\Users\...\anaconda3\envs\python39\lib\site-packages\whisper\decoding.py", line 705, in decode
    if completed or tokens.shape[-1] > self.n_ctx:
  File "C:\Users\...\anaconda3\envs\python39\lib\site-packages\whisper\decoding.py", line 468, in __init__
    if last_was_timestamp:
ValueError: tuple.index(x): x not in tuple

字符串
我在应用程序的这一部分的代码是这样的：

actual_language=combo.get()
    
    archivo=filedialog.askopenfilename(initialdir = "./",
                 title = "Seleccione archivo",filetypes = (("all files","*.*"),("mp3 files","*.mp3"),
                 ("wav files","*.wav")))
    
    torch.cuda.is_available()
    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
    
    model = whisper.load_model("medium")
   
    if archivo!="":
        try:
                    
            audio = whisper.load_audio(archivo)
            audio_trim = whisper.pad_or_trim(audio)
            
            mel = whisper.log_mel_spectrogram(audio_trim).to(model.device)
            
            result = model.transcribe(audio, language=f"{actual_language}", fp16=False, verbose=True)
            
            print(result["text"])

        except Exception as e:
            actual_status.set("¡Error!")
            logging.error('Error at %s', 'division', exc_info=e)

型
在添加语言选择之前，我尝试了自动语言识别，但错误是一样的。

result = model.transcribe(audio)

型
或

audio = whisper.load_audio(file)
audio_trim = whisper.pad_or_trim(audio)

mel = whisper.log_mel_spectrogram(audio_trim).to(model.device)

_, probs = model.detect_language(mel)

options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)

型
随着选择，我尝试使用更少的选项

result = model.transcribe(archivo,language=f"{actual_language}")

型
我用的是python 3.9，我也升级到了wishper的最新版本。