Remove unused AudioDataset originally fetched from an upstream GigaAM/utils

src/gigaam_onnx/asr_abc.py

@@ -14,44 +14,6 @@ DTYPE = np.float32
 MAX_LETTERS_PER_FRAME = 3
 
 
-class AudioDataset(torch.utils.data.Dataset):
-    """
-    Helper class for creating batched inputs
-    """
-
-    def __init__(self, lst: list[str | np.ndarray | torch.Tensor]):
-        if len(lst) == 0:
-            raise ValueError("AudioDataset cannot be initialized with an empty list")
-        assert isinstance(
-            lst[0], (str, np.ndarray, torch.Tensor)
-        ), f"Unexpected dtype: {type(lst[0])}"
-        self.lst = lst
-
-    def __len__(self):
-        return len(self.lst)
-
-    def __getitem__(self, idx):
-        item = self.lst[idx]
-        if isinstance(item, str):
-            wav_tns = load_audio(item)
-        elif isinstance(item, np.ndarray):
-            wav_tns = torch.from_numpy(item)
-        elif isinstance(item, torch.Tensor):
-            wav_tns = item
-        else:
-            raise RuntimeError(f"Unexpected sample type: {type(item)} at idx={idx}")
-        return wav_tns
-
-    @staticmethod
-    def collate(wavs):
-        lengths = torch.tensor([len(wav) for wav in wavs])
-        max_len = lengths.max().item()
-        wav_tns = torch.zeros(len(wavs), max_len, dtype=wavs[0].dtype)
-        for idx, wav in enumerate(wavs):
-            wav_tns[idx, : wav.shape[-1]] = wav.squeeze()
-        return wav_tns, lengths
-
-
 class ASRABCModel(abc.ABC):
     encoder: ort.InferenceSession
     preprocessor: FeatureExtractor