update repo

嘉渊

2023-05-11 81a5b29804800a4edd76c8dda2727d6fdf4b5643

 funasr/datasets/large_datasets/dataset.py

@@ -28,10 +28,11 @@


class AudioDataset(IterableDataset):
    def __init__(self, scp_lists, data_names, data_types, shuffle=True, mode="train"):
    def __init__(self, scp_lists, data_names, data_types, frontend_conf=None, shuffle=True, mode="train"):
        self.scp_lists = scp_lists
        self.data_names = data_names
        self.data_types = data_types
        self.frontend_conf = frontend_conf
        self.shuffle = shuffle
        self.mode = mode
        self.epoch = -1
@@ -119,6 +120,11 @@
                    elif data_type == "sound":
                        key, path = item.strip().split()
                        waveform, sampling_rate = torchaudio.load(path)
                        if self.frontend_conf is not None:
                            if sampling_rate != self.frontend_conf["fs"]:
                                waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
                                                                          new_freq=self.frontend_conf["fs"])(waveform)
                                sampling_rate = self.frontend_conf["fs"] 
                        waveform = waveform.numpy()
                        mat = waveform[0]
                        sample_dict[data_name] = mat
@@ -152,21 +158,23 @@
            dict,
            seg_dict,
            punc_dict,
            bpe_tokenizer,
            conf,
            frontend_conf,
            mode="train",
            batch_mode="padding"):
    scp_lists = read_lists(data_list_file)
    shuffle = conf.get('shuffle', True)
    data_names = conf.get("data_names", "speech,text")
    data_types = conf.get("data_types", "kaldi_ark,text")
    dataset = AudioDataset(scp_lists, data_names, data_types, shuffle=shuffle, mode=mode)
    dataset = AudioDataset(scp_lists, data_names, data_types, frontend_conf=frontend_conf, shuffle=shuffle, mode=mode)

    filter_conf = conf.get('filter_conf', {})
    filter_fn = partial(filter, **filter_conf)
    dataset = FilterIterDataPipe(dataset, fn=filter_fn)

    if "text" in data_names:
        vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict}
        vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict, 'bpe_tokenizer': bpe_tokenizer}
        tokenize_fn = partial(tokenize, **vocab)
        dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)