decoder.py

# MIT License
# 
# Copyright (c) 2022 Tada Makepeace
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"""This module contains different functions used to decode the predictions from
the speech recognition network. It currently only contains a greedy decoder which
picks the most likely character at each time step, rather than considering longer
sequences."""

import torch

from torchnlp.encoders import LabelEncoder

closed_characters     = [x for x in " abcdefghijklmnopqrstuvwxyz0123456789-"]
open_characters       = [x for x in " abcdefghijklmnopqrstuvwxyz-"]

closed_vocab_encoder  = LabelEncoder(closed_characters)
open_vocab_encoder    = LabelEncoder(open_characters)

def GreedyDecoder(output, labels, label_lengths, encoder, blank_label=28,\
                  collapse_repeated=True):
    """Decodes the predicted text transcription by picking the character
    with the highest probability at each timestep. This decoding method
    has the fastest runtime but also the lowest accuracy, however it is
    also the simplest to implement."""

    arg_maxes = torch.argmax(output, dim=2)
    decodes = []
    targets = []
    for i, args in enumerate(arg_maxes):
        decode = []
        cur_target = labels[i][:label_lengths[i]]
        if len(cur_target) > 0:
            cur_target = \
                "".join(encoder.batch_decode(torch.tensor(cur_target)))
        else:
            cur_target = ""
        targets.append(cur_target)

        # Greedy decoding process
        for j, index in enumerate(args):
            if index != blank_label:
                if collapse_repeated and j != 0 and index == args[j -1]:
                    continue
                decode.append(index)
        cur_decode = decode
        
        if len(cur_decode) > 0:
            cur_decode = \
                "".join(encoder.batch_decode(torch.tensor(cur_decode)))
        else:
            cur_decode = ""
        decodes.append(cur_decode)

    return decodes, targets