FunASR/funasr/datasets/llm_datasets/datasets.py

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", "AudioLLMDataset")
class AudioLLMDataset(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_pre_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_pre_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_pre_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_pre_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_pre_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_pre_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:
    
                    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_pre_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_pre_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_pre_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_pre_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_pre_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_pre_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:
                    
                    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