提交

kongdeqiang

5 天以前 28ccfbfc51068a663a80764e14074df5edf2b5ba

 funasr/datasets/openai_datasets/datasets.py

@@ -283,10 +283,11 @@

        self.pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
        # self.kwargs = kwargs
        self.max_token_length = kwargs.get("max_token_length", 1024)
        self.max_token_length = kwargs.get("max_token_length", 1500)
        self.batch_size_scale_ratio_max = kwargs.get("batch_size_scale_ratio_max", 1.5)
        self.batch_size_token_max = kwargs.get("batch_size_token_max", 2500)
        self.multiturn_num_max = kwargs.get("multiturn_num_max", 5)
        self.max_source_length = kwargs.get("max_source_length", 3000)

    def get_source_len(self, index):
        item = self.index_ds[index]
@@ -300,9 +301,9 @@
        return len(self.index_ds)

    def __getitem__(self, index):
        import pdb

        pdb.set_trace()
        # import pdb
        #
        # pdb.set_trace()

        output = None

@@ -334,6 +335,12 @@
            ):
                if i >= self.multiturn_num_max:
                    break
                if len(input_ids) > self.max_token_length:
                    logging.info(
                        f"input_ids > max_token_length: {len(input_ids)}>{self.max_token_length}, {item}"
                    )
                    break

                if i == 0:
                    source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
                else:
@@ -372,6 +379,11 @@
                                frontend=self.frontend,
                                is_final=True,
                            )  # speech: [b, T, d]
                            if speech_lengths > self.max_source_length:
                                logging.info(
                                    f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}"
                                )
                                badcase_flag = True
                            if self.permute:
                                speech = speech.permute(0, 2, 1)
                            # if speech_lengths > self.batch_size:
@@ -397,20 +409,17 @@
                labels += source_mask + target_ids
                fbank.append(speech[0, :, :])
                fbank_mask += fbank_mask_i
                fbank_lens.append(speech_lengths)

            if len(input_ids) > self.max_token_length:
                logging.info(
                    f"input_ids > max_token_length: {len(input_ids)}>{self.max_token_length}, {item}"
                )
                badcase_flag = True
            if badcase_flag:
                continue

            input_ids = torch.tensor(input_ids, dtype=torch.int64)  # [: self.max_token_length]
            attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
            labels = torch.tensor(labels, dtype=torch.int64)  # [: self.max_token_length]

            # fbank = speech[0, :, :]
            fbank_lens = speech_lengths
            # fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32)
            fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
            fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
            fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32)