auto frontend

游雁

2024-06-07 4a99b828a834d1d3870abbe3ee477518470f3dd9

 funasr/datasets/openai_datasets/datasets.py

@@ -51,7 +51,7 @@
        self.batch_size = kwargs.get("batch_size")
        self.batch_type = kwargs.get("batch_type")
        self.prompt_ids_len = 0
        self.retry = kwargs.get("retry", 5)
        self.retry = kwargs.get("retry", 10)

        self.permute = False
        from funasr.frontends.whisper_frontend import WhisperFrontend
@@ -60,6 +60,8 @@
            self.permute = True

        self.pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
        # self.kwargs = kwargs
        self.max_token_length = kwargs.get("max_token_length", 1024)

    def get_source_len(self, index):
        item = self.index_ds[index]
@@ -77,7 +79,9 @@
        # pdb.set_trace()

        output = None

        for idx in range(self.retry):
            badcase_flag = False
            if idx == 0:
                index_cur = index
            else:
@@ -112,9 +116,14 @@
                            "<|endofspeech|>", ""
                        )
                        if sub_str.startswith("!"):

                            data_src = load_audio_text_image_video(sub_str[1:], fs=self.fs)

                            try:
                                data_src = load_audio_text_image_video(sub_str[1:], fs=self.fs)
                            except Exception as e:
                                logging.error(
                                    f"Loading wav failed! {str(e)}, {traceback.format_exc()}"
                                )
                                badcase_flag = True
                                continue
                            speech, speech_lengths = extract_fbank(
                                data_src,
                                data_type=self.data_type,
@@ -134,6 +143,8 @@
                            source_ids += sub_token
                            fbank_mask_i += [1] * len(sub_token)

                if badcase_flag:
                    continue
                source_mask = [-100] * len(source_ids)
                target_out = f"{target_out}<|im_end|>"
                target_ids = self.tokenizer.encode(target_out)
@@ -142,6 +153,10 @@
                fbank_mask += fbank_mask_i
                fbank_beg.append(fbank_beg_i)

            if len(input_ids) > self.max_token_length:
                badcase_flag = True
            if badcase_flag:
                continue
            input_ids = torch.tensor(input_ids, dtype=torch.int64)
            attention_mask = torch.tensor([len(input_ids)], dtype=torch.int32)
            labels = torch.tensor(labels, dtype=torch.int64)
@@ -186,9 +201,9 @@
                    data_list, batch_first=True, padding_value=pad_value
                )

        if self.batch_type != "example":
            for i in range(10):
                outputs = self._filter_badcase(outputs, i=i)
        # if self.batch_type != "example":
        #     for i in range(10):
        #         outputs = self._filter_badcase(outputs, i=i)

        return outputs