from tqdm import tqdm 
from pydub import AudioSegment

transcription_with_vad = []

for file_path in tqdm(audio_files, desc='Transcribing'):      
        
    audio_name = file_path.split('/')[-1]
    
    output = vad_pipeline(file_path)
    list_vad = []
    for speech in output.get_timeline().support():
        list_vad.append((speech.start, speech.end))
        
    audio = AudioSegment.from_file(file_path)
    combined = AudioSegment.empty()

    for start, end in list_vad:
        start_ms = int(start * 1000)
        end_ms = int(end * 1000)   
        segment = audio[start_ms:end_ms]    
        combined += segment    

    new_path = '/mnt/ai_disk/bitbucket/sc-personal-osipova/asr_kk/vad_kk_audio/' + audio_name

    combined.export(
        new_path,
        format='ipod',
        codec='aac',
        bitrate='192k'
    )  
    
    print(new_path)
    
    transcription_with_vad.append(transcribe_speech(new_path, language='kk'))

Мой код отдает ошибку, почему?
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_3700733/3828791925.py in <cell line: 6>()
     33     print(new_path)
     34 
---> 35     transcription_with_vad.append(transcribe_speech(new_path, language='kk'))

/tmp/ipykernel_3700733/3265429639.py in transcribe_speech(filepath, language)
      1 def transcribe_speech(filepath, language):
----> 2     output = pipe(
      3         filepath,
      4         max_new_tokens=256,
      5         generate_kwargs={

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/transformers/pipelines/automatic_speech_recognition.py in __call__(self, inputs, **kwargs)
    281                     `"".join(chunk["text"] for chunk in output["chunks"])`.
    282         """
--> 283         return super().__call__(inputs, **kwargs)
    284 
    285     def _sanitize_parameters(

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/transformers/pipelines/base.py in __call__(self, inputs, num_workers, batch_size, *args, **kwargs)
   1292             return self.iterate(inputs, preprocess_params, forward_params, postprocess_params)
   1293         elif self.framework == "pt" and isinstance(self, ChunkPipeline):
-> 1294             return next(
   1295                 iter(
   1296                     self.get_iterator(

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/transformers/pipelines/pt_utils.py in __next__(self)
    122 
    123         # We're out of items within a batch
--> 124         item = next(self.iterator)
    125         processed = self.infer(item, **self.params)
    126         # We now have a batch of "inferred things".

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/transformers/pipelines/pt_utils.py in __next__(self)
    267 
    268         while not is_last:
--> 269             processed = self.infer(next(self.iterator), **self.params)
    270             if self.loader_batch_size is not None:
    271                 if isinstance(processed, torch.Tensor):

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/torch/utils/data/dataloader.py in __next__(self)
    699                 # TODO(https://github.com/pytorch/pytorch/issues/76750)
    700                 self._reset()  # type: ignore[call-arg]
--> 701             data = self._next_data()
    702             self._num_yielded += 1
    703             if (

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/torch/utils/data/dataloader.py in _next_data(self)
    755     def _next_data(self):
    756         index = self._next_index()  # may raise StopIteration
--> 757         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    758         if self._pin_memory:
    759             data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
     31             for _ in possibly_batched_index:
     32                 try:
---> 33                     data.append(next(self.dataset_iter))
     34                 except StopIteration:
     35                     self.ended = True

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/transformers/pipelines/pt_utils.py in __next__(self)
    184         try:
    185             # Try to return next item
--> 186             processed = next(self.subiterator)
    187         except StopIteration:
    188             # When a preprocess iterator ends, we can start lookig at the next item

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/transformers/pipelines/automatic_speech_recognition.py in preprocess(self, inputs, chunk_length_s, stride_length_s)
    362 
    363         if isinstance(inputs, bytes):
--> 364             inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
    365 
    366         stride = None

/mnt/ai_disk/bitbucket/sc-personal-osipova/myenv/lib/python3.10/site-packages/transformers/pipelines/audio_utils.py in ffmpeg_read(bpayload, sampling_rate)
     39     audio = np.frombuffer(out_bytes, np.float32)
     40     if audio.shape[0] == 0:
---> 41         raise ValueError(
     42             "Soundfile is either not in the correct format or is malformed. Ensure that the soundfile has "
     43             "a valid audio file extension (e.g. wav, flac or mp3) and is not corrupted. If reading from a remote "

ValueError: Soundfile is either not in the correct format or is malformed. Ensure that the soundfile has a valid audio file extension (e.g. wav, flac or mp3) and is not corrupted. If reading from a remote URL, ensure that the URL is the full address to **download** the audio file.