auto frontend

游雁

2024-06-07 526c810bd7ab9fdab38d5ce279a064b8e5da8bdd

auto frontend

 funasr/datasets/openai_datasets/datasets.py

@@ -155,6 +155,9 @@
                fbank_beg.append(fbank_beg_i)

            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

 funasr/models/llm_asr/model.py

@@ -485,13 +485,24 @@
        # _, l, _ = encoder_out.shape
        for batch_idx in range(batch_size):

            l = fbank_fake_lens[batch_idx].item()
            fbank_fake_len = fbank_fake_lens[batch_idx].item()
            fbank_beg_idx = fbank_beg[batch_idx, 0].item()
            min_len = min(l, inputs_embeds.shape[1] - fbank_beg_idx)
            min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
            try:
                inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
                    batch_idx, :min_len, :
                ]
            except Exception as e:
                logging.error(f"{str(e)}, {traceback.format_exc()}")
                logging.info(
                    f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}"
                )
                fbank_fake_len = encoder_out_lens[batch_idx].item()
                min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
                inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
                    batch_idx, :min_len, :
                ]

            inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
                batch_idx, :min_len, :
            ]
        labels_ids[labels_ids == -1] = -100
        model_outputs = self.llm(
            inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids