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"""Decoder for CTC-based ASR.""" ""
import torch
from swr2_asr.utils.tokenizer import CharTokenizer
from swr2_asr.utils.data import create_lexicon
import os
from torchaudio.datasets.utils import _extract_tar
from torchaudio.models.decoder import ctc_decoder
LEXICON = "lexicon.txt"
# TODO: refactor to use torch CTC decoder class
def greedy_decoder(output, labels, label_lengths, tokenizer: CharTokenizer, collapse_repeated=True):
"""Greedily decode a sequence."""
blank_label = tokenizer.get_blank_token()
arg_maxes = torch.argmax(output, dim=2) # pylint: disable=no-member
decodes = []
targets = []
for i, args in enumerate(arg_maxes):
decode = []
targets.append(tokenizer.decode(labels[i][: label_lengths[i]].tolist()))
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.item())
decodes.append(tokenizer.decode(decode))
return decodes, targets
# TODO: add beam search decoder
def beam_search_decoder(output, tokenizer:CharTokenizer, tokenizer_txt_path,lang_model_path):
if not os.path.isdir(lang_model_path):
url = f"https://dl.fbaipublicfiles.com/mls/mls_lm_german.tar.gz"
torch.hub.download_url_to_file(
url, "data/mls_lm_german.tar.gz" )
_extract_tar("data/mls_lm_german.tar.gz", overwrite=True)
if not os.path.isfile(tokenizer_txt_path):
tokenizer.create_txt(tokenizer_txt_path)
lexicon_path= os.join(lang_model_path, LEXICON)
if not os.path.isfile(lexicon_path):
occurences_path = os.join(lang_model_path,"vocab_counts.txt")
create_lexicon(occurences_path, lexicon_path)
lm_path = os.join(lang_model_path,"3-gram_lm.apa")
decoder = ctc_decoder(lexicon = lexicon_path,
tokenizer = tokenizer_txt_path,
lm =lm_path,
blank_token = '_',
nbest =1,
sil_token= '<SPACE>',
unk_word = '<UNK>')
return decoder
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