readme

游雁

2023-03-13 fc08b62d05723cdc1ce021bb8ba044ca014fb1f7

 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
@@ -127,14 +133,17 @@
                            sample_dict["key"] = key
                    else:
                        text = item
                        sample_dict[data_name] = text.strip().split()[1:]
                        segs = text.strip().split()
                        sample_dict[data_name] = segs[1:]
                        if "key" not in sample_dict:
                            sample_dict["key"] = segs[0]
                yield sample_dict

            self.close_reader(reader_list)


def len_fn_example(data):
    return len(data)
    return 1


def len_fn_token(data):
@@ -148,21 +157,23 @@
def Dataset(data_list_file,
            dict,
            seg_dict,
            punc_dict,
            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}
        vocab = {'vocab': dict, 'seg_dict': seg_dict, 'punc_dict': punc_dict}
        tokenize_fn = partial(tokenize, **vocab)
        dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)

@@ -191,6 +202,10 @@
                                             sort_size=sort_size,
                                             batch_mode=batch_mode)

    dataset = MapperIterDataPipe(dataset, fn=padding if batch_mode == "padding" else clipping)
    int_pad_value = conf.get("int_pad_value", -1)
    float_pad_value = conf.get("float_pad_value", 0.0)
    padding_conf = {"int_pad_value": int_pad_value, "float_pad_value": float_pad_value}
    padding_fn = partial(padding, **padding_conf)
    dataset = MapperIterDataPipe(dataset, fn=padding_fn if batch_mode == "padding" else clipping)

    return dataset