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| import torchaudio | |
| def speech_file_to_array_fn(path): | |
| speech_array, sampling_rate = torchaudio.load(path) | |
| resampler = torchaudio.transforms.Resample(sampling_rate, target_sampling_rate) | |
| speech = resampler(speech_array).squeeze().numpy() | |
| return speech | |
| def label_to_id(label, label_list): | |
| if len(label_list) > 0: | |
| return label_list.index(label) if label in label_list else -1 | |
| return label | |
| def preprocess_function(examples): | |
| speech_list = [speech_file_to_array_fn(path) for path in examples[input_column]] | |
| target_list = [label_to_id(label, label_list) for label in examples[output_column]] | |
| result = processor(speech_list, sampling_rate=target_sampling_rate) | |
| result["labels"] = list(target_list) | |
| return result | |
| class SpeechClassifierOutput(ModelOutput): | |
| loss: Optional[torch.FloatTensor] = None | |
| logits: torch.FloatTensor = None | |
| hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
| attentions: Optional[Tuple[torch.FloatTensor]] = None |