fix bug

游雁

2024-06-09 3eee773814c392e497557bbad501e0add4c8eca9

 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 +60,9 @@
            self.permute = True

        self.pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
        # self.kwargs = kwargs
        self.max_token_length = kwargs.get("max_token_length", 1024)
        self.batch_size_scale_ratio_max = kwargs.get("batch_size_scale_ratio_max", 1.5)

    def get_source_len(self, index):
        item = self.index_ds[index]
@@ -77,7 +80,9 @@
        # pdb.set_trace()

        output = None

        for idx in range(self.retry):
            badcase_flag = False
            if idx == 0:
                index_cur = index
            else:
@@ -112,9 +117,14 @@
                            "<|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 +144,8 @@
                            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)
@@ -142,9 +154,16 @@
                fbank_mask += fbank_mask_i
                fbank_beg.append(fbank_beg_i)

            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)
            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
@@ -165,51 +184,43 @@
        return output

    def collator(self, samples: list = None):
        outputs = {}
        for sample in samples:
            if sample is None:
                continue
            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:
        for idx in range(self.retry):
            badcase_flag = False

                    pad_value = self.int_pad_value
                else:
                    pad_value = self.float_pad_value
            outputs = {}
            for sample in samples:
                if sample is None:
                    continue
                for key in sample.keys():
                    if key not in outputs:
                        outputs[key] = []
                    outputs[key].append(sample[key])

                outputs[key] = torch.nn.utils.rnn.pad_sequence(
                    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)

        return outputs

    def _filter_badcase(self, outputs, i=0):
        b, t, _ = outputs["speech"].shape

        if b * t > self.batch_size * 1.25:
            beg = torch.randint(0, 2, ()).item()
            if b < 2:
                beg = 0
            logging.info(
                f"Warning, b * t: {b * t} > {self.batch_size}, drop half data {i}th, beg:{beg}"
            )
            for key, data_list in outputs.items():
                outputs[key] = outputs[key][beg : beg + b : 2]
                if isinstance(data_list[0], torch.Tensor):
                    if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:

            speech_lengths_max = outputs["speech_lengths"].max().item()
            outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
            text_lengths_max = outputs["text_lengths"].max().item()
            outputs["text"] = outputs["text"][:, :text_lengths_max]
            target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
            outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
                        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
                    )

            if self.batch_type != "example":
                b, t = outputs["input_ids"].shape
                if b * t > self.batch_size * self.batch_size_scale_ratio_max:
                    beg = torch.randint(0, 2, ()).item()
                    if b < 2:
                        beg = 0
                    logging.info(
                        f"Warning, b * t: {b * t} > {self.batch_size_scale_ratio_max} * {self.batch_size}, b: {b}, t: {t}, drop half data {idx}th, beg:{beg}"
                    )
                    samples = samples[beg : beg + b : 2]
                    continue

            break

        return outputs