decoding

游雁

2024-06-14 08114ae27d85949106aeab03b3fa5d764d100b33

 funasr/datasets/openai_datasets/datasets.py

@@ -334,6 +334,7 @@

                splits = self.pattern.split(source_input)
                source_ids = []
                fbank_i = []
                fbank_mask_i = []
                fbank_beg_i = []
                fbank_lens_i = []
@@ -381,9 +382,11 @@
                target_ids = self.tokenizer.encode(target_out)
                input_ids += source_ids + target_ids
                labels += source_mask + target_ids
                fbank.append(speech)
                fbank.append(speech[0, :, :])
                fbank_mask += fbank_mask_i
                fbank_beg.append(fbank_beg_i)
                if len(fbank_beg_i) < 1:
                    fbank_beg_i = [-1]
                fbank_beg += fbank_beg_i

            if len(input_ids) > self.max_token_length:
                logging.info(
@@ -396,7 +399,7 @@
            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 = speech[0, :, :]
            fbank_lens = speech_lengths
            fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
            fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
@@ -426,7 +429,10 @@
                for key in sample.keys():
                    if key not in outputs:
                        outputs[key] = []
                    outputs[key].append(sample[key])
                    if isinstance(sample[key], (list, tuple)):
                        outputs[key].extend(sample[key])
                    else:
                        outputs[key].append(sample[key])

            for key, data_list in outputs.items():
                if isinstance(data_list[0], torch.Tensor):