import torch import copy from funasr.register import tables from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video @tables.register("dataset_classes", "AudioLLMQwenAudioDataset") class AudioLLMQwenAudioDataset(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 self.audio_adaptor_downsample_rate = kwargs.get("audio_adaptor_downsample_rate", 5) self.audio_encoder_downsample_rate = kwargs.get("audio_encoder_downsample_rate", 2) self.prompt_template = "{}" self.answer_template = "{}" 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] 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) audio_pseudo_length = ( (speech.shape[0] + 1) // self.audio_adaptor_downsample_rate // self.audio_encoder_downsample_rate ) audio_pseudo = torch.full((audio_pseudo_length,), -1) # placeholder target = item["target"] if self.preprocessor_text: target = self.preprocessor_text(target) self.prompt_pre = self.prompt_template.format(self.prompt) prompt_ids_pre = self.tokenizer.encode(self.prompt_pre) # [bos,prompt] prompt_pre_length = len(prompt_ids_pre) # input input = self.answer_template.format(target.lower()) prompt_input = "{}{}".format(self.prompt_pre, input) prompt_input_ids = self.tokenizer.encode(prompt_input) # [bos, prompt, input] # audio_length = len(prompt_input_ids) - prompt_pre_length input_ids = prompt_input_ids + [self.tokenizer.pad_token_id] # [bos, prompt, input, pad] input_ids_length = len(input_ids) input_ids = torch.tensor(input_ids, dtype=torch.int64) # [bos, prompt, input, pad] input_ids = torch.cat((audio_pseudo, input_ids)) # [audio, bos, prompt, input, pad] # input_ids[:audio_pseudo_length] = -1 # [-1, bos, prompt, input, pad] attention_mask = input_ids.ge(-1) # [true, true, true, true, true], length mask # input_ids[prompt_pre_length:] = -1 # [bos, prompt,-1,-1] # attention_mask = input_ids.ge(-1) # [true, true, true, true], length mask # label answer = self.answer_template.format(target.lower()) prompt_answer = "{}{}".format(self.prompt_pre, answer) prompt_answer_ids = self.tokenizer.encode(prompt_answer) # answer_length = len(prompt_answer_ids) - prompt_pre_length labels_ids = copy.deepcopy(prompt_answer_ids) + [self.tokenizer.eos_token_id] labels_ids = torch.tensor(labels_ids, dtype=torch.int64) # [bos, prompt, answer, eos] labels_ids = torch.cat((audio_pseudo, labels_ids)) # [audio, bos, prompt, answer, eos] labels_ids[: audio_pseudo_length + prompt_pre_length] = -1 # [-1, -1, -1, answer, eos] # labels_ids[:prompt_pre_length] = -1 # [-1, -1, input, eos] label_mask = labels_ids.ge(0) # [false, false, false, true, true] labels_ids[~label_mask] = self.IGNORE_INDEX # [-100, -100, -100, answer, eos] # audio_mask for input_ids audio_mask = [1] * audio_pseudo_length + [0] * input_ids_length 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