DeepSpeech_RHL_AVSR.py 文件源码

def collect_results(results_tuple, returns):
    r'''
    This routine will help collecting partial results for the WER reports.
    The ``results_tuple`` is composed of an array of the original labels,
    an array of the corresponding decodings, an array of the corrsponding
    distances and an array of the corresponding losses. ``returns`` is built up
    in a similar way, containing just the unprocessed results of one
    ``session.run`` call (effectively of one batch).
    Labels and decodings are converted to text before splicing them into their
    corresponding results_tuple lists. In the case of decodings,
    for now we just pick the first available path.
    '''
    # Each of the arrays within results_tuple will get extended by a batch of each available device
    for i in range(len(available_devices)):
        # Collect the labels
        results_tuple[0].extend(sparse_tensor_value_to_texts(returns[0][i]))

        # Collect the decodings - at the moment we default to the first one
        results_tuple[1].extend(sparse_tensor_value_to_texts(returns[1][i][0]))

        # Collect the distances
        results_tuple[2].extend(returns[2][i])

        # Collect the losses
        results_tuple[3].extend(returns[3][i])


# For reporting we also need a standard way to do time measurements.