| | |
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| | | from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video |
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| | | |
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| | | |
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| | | @tables.register("dataset_classes", "AudioLLMDataset") |
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| | | class AudioLLMDataset(torch.utils.data.Dataset): |
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| | | @tables.register("dataset_classes", "AudioLLMNARDataset") |
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| | | class AudioLLMNARDataset(torch.utils.data.Dataset): |
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| | | """ |
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| | | AudioLLMDataset |
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| | | """ |
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| | | def __init__(self, |
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| | | path, |
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| | | index_ds: str = None, |
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| | | frontend=None, |
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| | | tokenizer=None, |
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| | | int_pad_value: int = -1, |
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| | | float_pad_value: float = 0.0, |
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| | | **kwargs): |
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| | | |
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| | | def __init__( |
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| | | self, |
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| | | path, |
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| | | index_ds: str = None, |
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| | | frontend=None, |
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| | | tokenizer=None, |
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| | | int_pad_value: int = -1, |
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| | | float_pad_value: float = 0.0, |
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| | | **kwargs |
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| | | ): |
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| | | super().__init__() |
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| | | index_ds_class = tables.index_ds_classes.get(index_ds) |
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| | | self.index_ds = index_ds_class(path, **kwargs) |
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| | | preprocessor_speech = kwargs.get("preprocessor_speech", None) |
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| | | if preprocessor_speech: |
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| | | preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech) |
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| | | preprocessor_speech = preprocessor_speech_class(**kwargs.get("preprocessor_speech_conf")) |
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| | | preprocessor_speech = preprocessor_speech_class( |
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| | | **kwargs.get("preprocessor_speech_conf", {}) |
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| | | ) |
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| | | self.preprocessor_speech = preprocessor_speech |
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| | | preprocessor_text = kwargs.get("preprocessor_text", None) |
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| | | if preprocessor_text: |
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| | | preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text) |
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| | | preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf")) |
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| | | preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {})) |
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| | | self.preprocessor_text = preprocessor_text |
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| | | |
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| | | |
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| | | self.frontend = frontend |
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| | | self.fs = 16000 if frontend is None else frontend.fs |
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| | | self.data_type = "sound" |
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| | | self.tokenizer = tokenizer |
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| | | |
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| | | self.int_pad_value = int_pad_value |
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| | | self.float_pad_value = float_pad_value |
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| | | self.prompt = kwargs.get("prompt", "Transcribe speech to text.") |
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| | | self.prompt = kwargs.get("prompt", "Please copy the following text.") |
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| | | self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format( |
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| | | self.prompt) # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: " |
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| | | self.prompt |
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| | | ) # "USER: \nINSTRUCTION: {}\nINPUT: {}\nASSISTANT: " |
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| | | self.prompt_af = "" |
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| | | |
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| | | self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100) |
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| | | self.int_pad_value = self.IGNORE_INDEX |
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| | | |
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| | | def get_source_len(self, index): |
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| | | item = self.index_ds[index] |
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| | | return self.index_ds.get_source_len(item) |
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| | | |
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| | | |
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| | | def get_target_len(self, index): |
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| | | item = self.index_ds[index] |
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| | | return self.index_ds.get_target_len(item) |
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| | | |
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| | | |
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| | | def __len__(self): |
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| | | return len(self.index_ds) |
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| | | |
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| | | |
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| | | def __getitem__(self, index): |
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| | | item = self.index_ds[index] |
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| | | # import pdb; |
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| | | # pdb.set_trace() |
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| | | source = item["source"] |
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| | | data_src = load_audio_text_image_video(source, fs=self.fs) |
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| | | if self.preprocessor_speech: |
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| | | data_src = self.preprocessor_speech(data_src, fs=self.fs) |
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| | | speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend, is_final=True) # speech: [b, T, d] |
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| | | speech = speech.sequeeze(0) |
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| | | speech, speech_lengths = extract_fbank( |
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| | | data_src, data_type=self.data_type, frontend=self.frontend, is_final=True |
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| | | ) # speech: [b, T, d] |
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| | | speech = speech.squeeze(0) |
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| | | |
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| | | target = item["target"] |
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| | | if self.preprocessor_text: |
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| | | target = self.preprocessor_text(target) |
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| | | |
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| | | |
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| | | prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt] |
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| | | prompt_pre_length = len(prompt_ids_pre) |
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| | | |
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| | | prompt_input = "{}{}".format(self.prompt_pre, target) |
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| | | prompt_input_ids = self.tokenizer.encode(prompt_input) |
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| | | audio_length = len(prompt_input_ids) - prompt_pre_length |
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| | | input_ids = prompt_input_ids + [self.tokenizer.pad_token_id] |
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| | | input_ids = torch.tensor(input_ids, dtype=torch.int64) #[bos, prompt, input, pad] |
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| | | input_ids[prompt_pre_length:] = -1 # [bos, prompt,-1,-1] |
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| | | attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask |
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| | | |
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| | | prompt_answer = "{}{}".format(self.prompt_pre, target) |
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| | | prompt_answer_ids = self.tokenizer.encode(prompt_answer) |
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| | | answer_length = len(prompt_answer_ids) - prompt_pre_length |
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| | | labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id] |
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| | | labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, input, eos] |
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| | | labels_ids[:prompt_pre_length] = -1 # [-1, -1, input, eos] |
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| | | label_mask = labels_ids.ge(0) # [False,False,True,True] |
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| | | labels_ids[~label_mask] = self.IGNORE_INDEX # [-100,-100,input,eos] |
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| | | |
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| | | audio_mask = [0] * prompt_pre_length + [1] * audio_length |
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| | | torch.tensor(audio_mask, dtype=torch.float32) |
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| | | |
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| | | ids = self.tokenizer.encode(target) |
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| | | prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt] |
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| | | prompt_ids_length = len(prompt_ids_pre) |
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| | | |
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| | | # bos prompt audio bos target |
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| | | # prompt_input = "{}{}".format(self.prompt_pre, target) |
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| | | # prompt_input_ids = self.tokenizer.encode(prompt_input) #[bos, prompt, input] |
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| | | # audio_length = len(prompt_input_ids) - prompt_ids_length |
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| | | target_ids = self.tokenizer.encode(target) |
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| | | if target_ids[0] == self.tokenizer.bos_token_id: |
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| | | target_ids = target_ids[1:] |
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| | | target_ids_length = len(target_ids) |
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| | | audio_length = target_ids_length |
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| | | input_ids = ( |
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| | | prompt_ids_pre + target_ids + [self.tokenizer.pad_token_id] + target_ids |
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| | | ) # [bos, prompt, input, pad, target] |
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| | | input_ids = torch.tensor( |
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| | | copy.deepcopy(input_ids), dtype=torch.int64 |
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| | | ) # [bos, prompt, input, pad, target] |
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| | | input_ids[prompt_ids_length : prompt_ids_length + audio_length] = ( |
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| | | -1 |
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| | | ) # [bos, prompt,-1, pad, target] # it is no need, only for check |
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| | | attention_mask = input_ids.ge(-1) # [true, true, true, true, true], length mask |
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| | | |
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| | | # bos prompt audio target eos |
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| | | # prompt_answer = "{}{}".format(self.prompt_pre, target) |
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| | | # prompt_answer_ids = self.tokenizer.encode(prompt_answer) #[bos, prompt, input] |
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| | | # answer_length = len(prompt_answer_ids) - prompt_ids_length |
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| | | target_ids = self.tokenizer.encode(target) |
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| | | if target_ids[0] == self.tokenizer.bos_token_id: |
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| | | target_ids = target_ids[1:] |
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| | | # target_ids_length = len(target_ids) |
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| | | labels_ids = ( |
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| | | prompt_ids_pre + target_ids + target_ids + [self.tokenizer.eos_token_id] |
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| | | ) # [bos, prompt, input, target, eos] |
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| | | labels_ids = torch.tensor( |
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| | | copy.deepcopy(labels_ids), dtype=torch.int64 |
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| | | ) # [bos, prompt, input, target, eos] |
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| | | labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, target, eos] |
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| | | label_mask = labels_ids.ge(0) # [false, false, true, true, true], length mask |
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| | | labels_ids[~label_mask] = self.IGNORE_INDEX # [-1, -1, input, target, eos] |
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| | | |
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| | | audio_mask = ( |
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| | | [0] * prompt_ids_length + [1] * audio_length + [0] * target_ids_length + [0] |
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| | | ) # [0, 0, 1, 0, 0] |
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| | | audio_mask = torch.tensor(audio_mask, dtype=torch.float32) |
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| | | |
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| | | ids = target_ids # self.tokenizer.encode(target) # token ids is different from labels_ids |
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| | | text = torch.tensor(ids, dtype=torch.int64) |
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| | | text_lengths = torch.tensor([len(ids)], dtype=torch.int32) |
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| | | |
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| | | return {"speech": speech, |
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| | | "speech_lengths": speech_lengths, |
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| | | "text": text, |
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| | | "text_lengths": text_lengths, |
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| | | "input_ids": input_ids, |
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| | | "attention_mask": attention_mask, |
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| | | "labels_ids": labels_ids, |
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| | | "label_mask": label_mask, |
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| | | "audio_mask": audio_mask, |
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| | | } |
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| | | |
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| | | |
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| | | def collator(self, samples: list=None): |
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| | | |
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| | | prompt_bos_length = torch.tensor([len(prompt_ids_pre)], dtype=torch.int32) |
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| | | |
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| | | return { |
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| | | "speech": speech, |
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| | | "speech_lengths": speech_lengths, |
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| | | "text": text, |
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| | | "text_lengths": text_lengths, |
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| | | "input_ids": input_ids, |
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| | | "attention_mask": attention_mask, |
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| | | "labels_ids": labels_ids, |
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| | | "label_mask": label_mask, |
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| | | "audio_mask": audio_mask, |
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| | | "prompt_bos_length": prompt_bos_length, |
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| | | } |
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| | | |
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| | | def collator(self, samples: list = None): |
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| | | outputs = {} |
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| | | for sample in samples: |
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| | | for key in sample.keys(): |
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| | |
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| | | |
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| | | for key, data_list in outputs.items(): |
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| | | if isinstance(data_list[0], torch.Tensor): |
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| | | if data_list[0].dtype == torch.int64: |
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| | | |
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| | | if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32: |
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| | | |
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| | | pad_value = self.int_pad_value |
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| | | else: |
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| | | pad_value = self.float_pad_value |
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| | | |
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| | | outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value) |
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| | | |
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| | | outputs[key] = torch.nn.utils.rnn.pad_sequence( |
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| | | data_list, batch_first=True, padding_value=pad_value |
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| | | ) |
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| | | return outputs |
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| | | |
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| | | |
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| | | @tables.register("dataset_classes", "AudioLLMDataset") |
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| | | class AudioLLMDataset(torch.utils.data.Dataset): |
|---|
| | | """ |
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| | | AudioLLMDataset |
|---|
| | | """ |
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| | | |
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| | | def __init__( |
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| | | self, |
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| | | path, |
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| | | index_ds: str = None, |
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| | | frontend=None, |
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| | | tokenizer=None, |
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| | | int_pad_value: int = -1, |
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| | | float_pad_value: float = 0.0, |
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| | | **kwargs |
|---|
| | | ): |
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| | | super().__init__() |
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| | | index_ds_class = tables.index_ds_classes.get(index_ds) |
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| | | self.index_ds = index_ds_class(path, **kwargs) |
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| | | preprocessor_speech = kwargs.get("preprocessor_speech", None) |
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| | | if preprocessor_speech: |
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| | | preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech) |
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| | | preprocessor_speech = preprocessor_speech_class( |
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| | | **kwargs.get("preprocessor_speech_conf", {}) |
|---|
| | | ) |
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| | | self.preprocessor_speech = preprocessor_speech |
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| | | preprocessor_text = kwargs.get("preprocessor_text", None) |
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| | | if preprocessor_text: |
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| | | preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text) |
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| | | preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {})) |
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| | | self.preprocessor_text = preprocessor_text |
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| | | |
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| | | self.frontend = frontend |
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| | | self.fs = 16000 if frontend is None else frontend.fs |
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| | | self.data_type = "sound" |
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| | | self.tokenizer = tokenizer |
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| | | |
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| | | self.float_pad_value = float_pad_value |
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| | | self.prompt = kwargs.get("prompt", "Transcribe speech to text.") |
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| | | self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format( |
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| | | self.prompt |
|---|
| | | ) # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: " |
|---|
| | | self.prompt_af = "" |
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| | | self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100) |
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| | | self.int_pad_value = self.IGNORE_INDEX |
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| | | |
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| | | def get_source_len(self, index): |
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| | | item = self.index_ds[index] |
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| | | return self.index_ds.get_source_len(item) |
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| | | |
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| | | def get_target_len(self, index): |
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| | | item = self.index_ds[index] |
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| | | return self.index_ds.get_target_len(item) |
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| | | |
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| | | def __len__(self): |
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| | | return len(self.index_ds) |
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| | | |
|---|
| | | def __getitem__(self, index): |
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| | | item = self.index_ds[index] |
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| | | # import pdb; |
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| | | # pdb.set_trace() |
|---|
| | | source = item["source"] |
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| | | data_src = load_audio_text_image_video(source, fs=self.fs) |
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| | | if self.preprocessor_speech: |
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| | | data_src = self.preprocessor_speech(data_src, fs=self.fs) |
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| | | speech, speech_lengths = extract_fbank( |
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| | | data_src, data_type=self.data_type, frontend=self.frontend, is_final=True |
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| | | ) # speech: [b, T, d] |
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| | | speech = speech.squeeze(0) |
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| | | |
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| | | target = item["target"] |
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| | | if self.preprocessor_text: |
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| | | target = self.preprocessor_text(target) |
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| | | |
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| | | prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt] |
|---|
| | | prompt_ids_length = len(prompt_ids_pre) |
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| | | |
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| | | prompt_input = "{}{}".format(self.prompt_pre, target) |
|---|
| | | prompt_input_ids = self.tokenizer.encode(prompt_input) |
|---|
| | | audio_length = len(prompt_input_ids) - prompt_ids_length |
|---|
| | | input_ids = prompt_input_ids + [self.tokenizer.pad_token_id] |
|---|
| | | input_ids = torch.tensor(input_ids, dtype=torch.int64) # [bos, prompt, input, pad] |
|---|
| | | input_ids[prompt_ids_length:] = -1 # [bos, prompt,-1,-1] |
|---|
| | | attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask |
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| | | |
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| | | prompt_answer = "{}{}".format(self.prompt_pre, target) |
|---|
| | | prompt_answer_ids = self.tokenizer.encode(prompt_answer) |
|---|
| | | answer_length = len(prompt_answer_ids) - prompt_ids_length |
|---|
| | | labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id] |
|---|
| | | labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, input, eos] |
|---|
| | | labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, eos] |
|---|
| | | label_mask = labels_ids.ge(0) # [False,False,True,True] |
|---|
| | | labels_ids[~label_mask] = self.IGNORE_INDEX # [-100,-100,input,eos] |
|---|
| | | |
|---|
| | | audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0] |
|---|
| | | audio_mask = torch.tensor(audio_mask, dtype=torch.float32) |
|---|
| | | |
|---|
| | | ids = self.tokenizer.encode(target) # token ids is different from labels_ids |
|---|
| | | text = torch.tensor(ids, dtype=torch.int64) |
|---|
| | | text_lengths = torch.tensor([len(ids)], dtype=torch.int32) |
|---|
| | | |
|---|
| | | return { |
|---|
| | | "speech": speech, |
|---|
| | | "speech_lengths": speech_lengths, |
|---|
| | | "text": text, |
|---|
| | | "text_lengths": text_lengths, |
|---|
| | | "input_ids": input_ids, |
|---|
| | | "attention_mask": attention_mask, |
|---|
| | | "labels_ids": labels_ids, |
|---|
| | | "label_mask": label_mask, |
|---|
| | | "audio_mask": audio_mask, |
|---|
| | | } |
|---|
| | | |
|---|
| | | def collator(self, samples: list = None): |
|---|
| | | outputs = {} |
|---|
| | | for sample in samples: |
|---|
| | | for key in sample.keys(): |
|---|
| | | if key not in outputs: |
|---|
| | | outputs[key] = [] |
|---|
| | | outputs[key].append(sample[key]) |
|---|
| | | |
|---|
| | | for key, data_list in outputs.items(): |
|---|
| | | if isinstance(data_list[0], torch.Tensor): |
|---|
| | | if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32: |
|---|
| | | |
|---|
| | | pad_value = self.int_pad_value |
|---|
| | | else: |
|---|
| | | pad_value = self.float_pad_value |
|---|
| | | |
|---|
| | | outputs[key] = torch.nn.utils.rnn.pad_sequence( |
|---|
| | | data_list, batch_first=True, padding_value=pad_value |
|---|
| | | ) |
|---|
| | | return outputs |
|---|
| | | |
|---|
| | | |
|---|
| | | @tables.register("dataset_classes", "AudioLLMARDataset") |
|---|
| | | class AudioLLMARDataset(torch.utils.data.Dataset): |
|---|
| | | """ |
|---|
| | | AudioLLMDataset |
|---|
| | | """ |
|---|
| | | |
|---|
| | | def __init__( |
|---|
| | | self, |
|---|
| | | path, |
|---|
| | | index_ds: str = None, |
|---|
| | | frontend=None, |
|---|
| | | tokenizer=None, |
|---|
| | | int_pad_value: int = -1, |
|---|
| | | float_pad_value: float = 0.0, |
|---|
| | | **kwargs |
|---|
| | | ): |
|---|
| | | super().__init__() |
|---|
| | | index_ds_class = tables.index_ds_classes.get(index_ds) |
|---|
| | | self.index_ds = index_ds_class(path, **kwargs) |
|---|
| | | preprocessor_speech = kwargs.get("preprocessor_speech", None) |
|---|
| | | if preprocessor_speech: |
|---|
| | | preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech) |
|---|
| | | preprocessor_speech = preprocessor_speech_class( |
|---|
| | | **kwargs.get("preprocessor_speech_conf", {}) |
|---|
| | | ) |
|---|
| | | self.preprocessor_speech = preprocessor_speech |
|---|
| | | preprocessor_text = kwargs.get("preprocessor_text", None) |
|---|
| | | if preprocessor_text: |
|---|
| | | preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text) |
|---|
| | | preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {})) |
|---|
| | | self.preprocessor_text = preprocessor_text |
|---|
| | | |
|---|
| | | self.frontend = frontend |
|---|
| | | self.fs = 16000 if frontend is None else frontend.fs |
|---|
| | | self.data_type = "sound" |
|---|
| | | self.tokenizer = tokenizer |
|---|
| | | |
|---|
| | | self.float_pad_value = float_pad_value |
|---|
| | | self.prompt = kwargs.get("prompt", "Transcribe speech to text.") |
|---|
| | | self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format( |
|---|
| | | self.prompt |
|---|
| | | ) # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: " |
|---|
| | | self.prompt_af = "" |
|---|
| | | self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100) |
|---|
| | | self.int_pad_value = self.IGNORE_INDEX |
|---|
| | | |
|---|
| | | def get_source_len(self, index): |
|---|
| | | item = self.index_ds[index] |
|---|
| | | return self.index_ds.get_source_len(item) |
|---|
| | | |
|---|
| | | def get_target_len(self, index): |
|---|
| | | item = self.index_ds[index] |
|---|
| | | return self.index_ds.get_target_len(item) |
|---|
| | | |
|---|
| | | def __len__(self): |
|---|
| | | return len(self.index_ds) |
|---|
| | | |
|---|
| | | def __getitem__(self, index): |
|---|
| | | item = self.index_ds[index] |
|---|
| | | # import pdb; |
|---|
| | | # pdb.set_trace() |
|---|
| | | source = item["source"] |
|---|
| | | data_src = load_audio_text_image_video(source, fs=self.fs) |
|---|
| | | if self.preprocessor_speech: |
|---|
| | | data_src = self.preprocessor_speech(data_src, fs=self.fs) |
|---|
| | | speech, speech_lengths = extract_fbank( |
|---|
| | | data_src, data_type=self.data_type, frontend=self.frontend, is_final=True |
|---|
| | | ) # speech: [b, T, d] |
|---|
| | | speech = speech.squeeze(0) |
|---|
| | | |
|---|
| | | target = item["target"] |
|---|
| | | if self.preprocessor_text: |
|---|
| | | target = self.preprocessor_text(target) |
|---|
| | | |
|---|
| | | prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt] |
|---|
| | | prompt_ids_length = len(prompt_ids_pre) |
|---|
| | | |
|---|
| | | prompt_input = "{}{}".format(self.prompt_pre, target) |
|---|
| | | prompt_input_ids = self.tokenizer.encode(prompt_input) |
|---|
| | | audio_length = len(prompt_input_ids) - prompt_ids_length |
|---|
| | | input_ids = prompt_input_ids + [self.tokenizer.pad_token_id] |
|---|
| | | input_ids = torch.tensor(input_ids, dtype=torch.int64) # [bos, prompt, input, pad] |
|---|
| | | input_ids[prompt_ids_length:] = -1 # [bos, prompt,-1,-1] |
|---|
| | | attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask |
|---|
| | | |
|---|
| | | prompt_answer = "{}{}".format(self.prompt_pre, target) |
|---|
| | | prompt_answer_ids = self.tokenizer.encode(prompt_answer) |
|---|
| | | answer_length = len(prompt_answer_ids) - prompt_ids_length |
|---|
| | | labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id] |
|---|
| | | labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, input, eos] |
|---|
| | | labels_ids[:prompt_ids_length] = -1 # [-1, -1, input, eos] |
|---|
| | | label_mask = labels_ids.ge(0) # [False,False,True,True] |
|---|
| | | labels_ids[~label_mask] = self.IGNORE_INDEX # [-100,-100,input,eos] |
|---|
| | | |
|---|
| | | audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0] |
|---|
| | | audio_mask = torch.tensor(audio_mask, dtype=torch.float32) |
|---|
| | | |
|---|
| | | ids = self.tokenizer.encode(target) # token ids is different from labels_ids |
|---|
| | | text = torch.tensor(ids, dtype=torch.int64) |
|---|
| | | text_lengths = torch.tensor([len(ids)], dtype=torch.int32) |
|---|
| | | |
|---|
| | | return { |
|---|
| | | "speech": speech, |
|---|
| | | "speech_lengths": speech_lengths, |
|---|
| | | "text": text, |
|---|
| | | "text_lengths": text_lengths, |
|---|
| | | "input_ids": input_ids, |
|---|
| | | "attention_mask": attention_mask, |
|---|
| | | "labels_ids": labels_ids, |
|---|
| | | "label_mask": label_mask, |
|---|
| | | "audio_mask": audio_mask, |
|---|
| | | } |
|---|
| | | |
|---|
| | | def collator(self, samples: list = None): |
|---|
| | | outputs = {} |
|---|
| | | for sample in samples: |
|---|
| | | for key in sample.keys(): |
|---|
| | | if key not in outputs: |
|---|
| | | outputs[key] = [] |
|---|
| | | outputs[key].append(sample[key]) |
|---|
| | | |
|---|
| | | for key, data_list in outputs.items(): |
|---|
| | | if isinstance(data_list[0], torch.Tensor): |
|---|
| | | if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32: |
|---|
| | | |
|---|
| | | pad_value = self.int_pad_value |
|---|
| | | else: |
|---|
| | | pad_value = self.float_pad_value |
|---|
| | | |
|---|
| | | outputs[key] = torch.nn.utils.rnn.pad_sequence( |
|---|
| | | data_list, batch_first=True, padding_value=pad_value |
|---|
| | | ) |
|---|
| | | return outputs |
|---|
