update data2vec pretrain: dataset

jmwang66

2023-02-06 55b45487c75e683cafe63dba29e32be8243b6cc7

update data2vec pretrain: dataset

 funasr/datasets/collate_fn.py

@@ -80,4 +80,56 @@

    output = (uttids, output)
    assert check_return_type(output)
    return output

def crop_to_max_size(feature, target_size):
    size = len(feature)
    diff = size - target_size
    if diff <= 0:
        return feature

    start = np.random.randint(0, diff + 1)
    end = size - diff + start
    return feature[start:end]


def clipping_collate_fn(
        data: Collection[Tuple[str, Dict[str, np.ndarray]]],
        max_sample_size=None,
        not_sequence: Collection[str] = (),
) -> Tuple[List[str], Dict[str, torch.Tensor]]:
    # mainly for pre-training
    assert check_argument_types()
    uttids = [u for u, _ in data]
    data = [d for _, d in data]

    assert all(set(data[0]) == set(d) for d in data), "dict-keys mismatching"
    assert all(
        not k.endswith("_lengths") for k in data[0]
    ), f"*_lengths is reserved: {list(data[0])}"

    output = {}
    for key in data[0]:
        array_list = [d[key] for d in data]
        tensor_list = [torch.from_numpy(a) for a in array_list]
        sizes = [len(s) for s in tensor_list]
        if max_sample_size is None:
            target_size = min(sizes)
        else:
            target_size = min(min(sizes), max_sample_size)
        tensor = tensor_list[0].new_zeros(len(tensor_list), target_size, tensor_list[0].shape[1])
        for i, (source, size) in enumerate(zip(tensor_list, sizes)):
            diff = size - target_size
            if diff == 0:
                tensor[i] = source
            else:
                tensor[i] = crop_to_max_size(source, target_size)
        output[key] = tensor

        if key not in not_sequence:
            lens = torch.tensor([source.shape[0] for source in tensor], dtype=torch.long)
            output[key + "_lengths"] = lens

    output = (uttids, output)
    assert check_return_type(output)
    return output

 funasr/datasets/large_datasets/dataset.py

@@ -102,6 +102,8 @@
                elif data_type == "text" or data_type == "sound":
                    text_reader = open(data_file, "r")
                    reader_list.append(text_reader)
                elif data_type == "none":
                    continue
                else:
                    raise TypeError("Data type {} is not supported".format(data_type))


 funasr/datasets/large_datasets/utils/filter.py

@@ -6,13 +6,21 @@
           speech_length_max=15000,
           token_length_min=0,
           token_length_max=200):
    assert "speech" in data
    assert "text" in data
    assert "speech" in data or "text" in data

    if "sampling_rate" in data:
        speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
    if "speech" in data and "text" in data:
        if "sampling_rate" in data:
            speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
        else:
            speech_length = data["speech"].shape[0]
        num_tokens = len(data['text'])
        return speech_length_min < speech_length < speech_length_max and token_length_min < num_tokens < token_length_max
    elif "speech" in data:
        if "sampling_rate" in data:
            speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
        else:
            speech_length = data["speech"].shape[0]
        return speech_length_min < speech_length < speech_length_max
    else:
        speech_length = data["speech"].shape[0]
    num_tokens = len(data['text'])

    return speech_length_min < speech_length < speech_length_max and token_length_min < num_tokens < token_length_max
        num_tokens = len(data['text'])
        return token_length_min < num_tokens < token_length_max