diff options
Diffstat (limited to 'swr2_asr/utils')
| -rw-r--r-- | swr2_asr/utils/data.py | 20 | ||||
| -rw-r--r-- | swr2_asr/utils/decoder.py | 169 | ||||
| -rw-r--r-- | swr2_asr/utils/tokenizer.py | 14 |
3 files changed, 182 insertions, 21 deletions
diff --git a/swr2_asr/utils/data.py b/swr2_asr/utils/data.py index d551c98..74cd572 100644 --- a/swr2_asr/utils/data.py +++ b/swr2_asr/utils/data.py @@ -6,7 +6,7 @@ import numpy as np import torch import torchaudio from torch import Tensor, nn -from torch.utils.data import DataLoader, Dataset +from torch.utils.data import Dataset from torchaudio.datasets.utils import _extract_tar from swr2_asr.utils.tokenizer import CharTokenizer @@ -343,3 +343,21 @@ class MLSDataset(Dataset): dataset_lookup_entry["chapterid"], idx, ) # type: ignore + + +def create_lexicon(vocab_counts_path, lexicon_path): + """Create a lexicon from the vocab_counts.txt file""" + words_list = [] + with open(vocab_counts_path, "r", encoding="utf-8") as file: + for line in file: + words = line.split() + if len(words) >= 1: + word = words[0] + words_list.append(word) + + with open(lexicon_path, "w", encoding="utf-8") as file: + for word in words_list: + file.write(f"{word} ") + for char in word: + file.write(char + " ") + file.write("<SPACE>") diff --git a/swr2_asr/utils/decoder.py b/swr2_asr/utils/decoder.py index fcddb79..1fd002a 100644 --- a/swr2_asr/utils/decoder.py +++ b/swr2_asr/utils/decoder.py @@ -1,26 +1,155 @@ """Decoder for CTC-based ASR.""" "" +import os +from dataclasses import dataclass + import torch +from torchaudio.datasets.utils import _extract_tar +from torchaudio.models.decoder import ctc_decoder +from swr2_asr.utils.data import create_lexicon from swr2_asr.utils.tokenizer import CharTokenizer -# 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 +@dataclass +class DecoderOutput: + """Decoder output.""" + + words: list[str] + + +def decoder_factory(decoder_type: str = "greedy") -> callable: + """Decoder factory.""" + if decoder_type == "greedy": + return get_greedy_decoder + if decoder_type == "lm": + return get_beam_search_decoder + raise NotImplementedError + + +def get_greedy_decoder( + tokenizer: CharTokenizer, # pylint: disable=redefined-outer-name + *_, +): + """Greedy decoder.""" + return GreedyDecoder(tokenizer) + + +def get_beam_search_decoder( + tokenizer: CharTokenizer, # pylint: disable=redefined-outer-name + hparams: dict, # pylint: disable=redefined-outer-name +): + """Beam search decoder.""" + hparams = hparams.get("lm", {}) + language, lang_model_path, n_gram, beam_size, beam_threshold, n_best, lm_weight, word_score = ( + hparams["language"], + hparams["language_model_path"], + hparams["n_gram"], + hparams["beam_size"], + hparams["beam_threshold"], + hparams["n_best"], + hparams["lm_weight"], + hparams["word_score"], + ) + + if not os.path.isdir(os.path.join(lang_model_path, f"mls_lm_{language}")): + url = f"https://dl.fbaipublicfiles.com/mls/mls_lm_{language}.tar.gz" + torch.hub.download_url_to_file(url, f"data/mls_lm_{language}.tar.gz") + _extract_tar("data/mls_lm_{language}.tar.gz", overwrite=True) + + tokens_path = os.path.join(lang_model_path, f"mls_lm_{language}", "tokens.txt") + if not os.path.isfile(tokens_path): + tokenizer.create_tokens_txt(tokens_path) + + lexicon_path = os.path.join(lang_model_path, f"mls_lm_{language}", "lexicon.txt") + if not os.path.isfile(lexicon_path): + occurences_path = os.path.join(lang_model_path, f"mls_lm_{language}", "vocab_counts.txt") + create_lexicon(occurences_path, lexicon_path) + + lm_path = os.path.join(lang_model_path, f"mls_lm_{language}", f"{n_gram}-gram_lm.arpa") + + decoder = ctc_decoder( + lexicon=lexicon_path, + tokens=tokens_path, + lm=lm_path, + blank_token="_", + sil_token="<SPACE>", + unk_word="<UNK>", + nbest=n_best, + beam_size=beam_size, + beam_threshold=beam_threshold, + lm_weight=lm_weight, + word_score=word_score, + ) + return decoder + + +class GreedyDecoder: + """Greedy decoder.""" + + def __init__(self, tokenizer: CharTokenizer): # pylint: disable=redefined-outer-name + self.tokenizer = tokenizer + + def __call__( + self, output, greedy_type: str = "inference", labels=None, label_lengths=None + ): # pylint: disable=redefined-outer-name + """Greedily decode a sequence.""" + if greedy_type == "train": + res = self.train(output, labels, label_lengths) + if greedy_type == "inference": + res = self.inference(output) + + res = [x.split(" ") for x in res] + res = [[DecoderOutput(x)] for x in res] + return res + + def train(self, output, labels, label_lengths): + """Greedily decode a sequence with known labels.""" + 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(self.tokenizer.decode(labels[i][: label_lengths[i]].tolist())) + for j, index in enumerate(args): + if index != blank_label: + if j != 0 and index == args[j - 1]: + continue + decode.append(index.item()) + decodes.append(self.tokenizer.decode(decode)) + return decodes, targets + + def inference(self, output): + """Greedily decode a sequence.""" + collapse_repeated = True + arg_maxes = torch.argmax(output, dim=2) # pylint: disable=no-member + blank_label = self.tokenizer.get_blank_token() + decodes = [] + for args in arg_maxes: + decode = [] + 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(self.tokenizer.decode(decode)) + + return decodes + + +if __name__ == "__main__": + tokenizer = CharTokenizer.from_file("data/tokenizers/char_tokenizer_german.json") + tokenizer.create_tokens_txt("data/tokenizers/tokens_german.txt") + + hparams = { + "language": "german", + "lang_model_path": "data", + "n_gram": 3, + "beam_size": 100, + "beam_threshold": 100, + "n_best": 1, + "lm_weight": 0.5, + "word_score": 1.0, + } + + get_beam_search_decoder(tokenizer, hparams) diff --git a/swr2_asr/utils/tokenizer.py b/swr2_asr/utils/tokenizer.py index 1cc7b84..4e3fddd 100644 --- a/swr2_asr/utils/tokenizer.py +++ b/swr2_asr/utils/tokenizer.py @@ -29,9 +29,17 @@ class CharTokenizer: """Use a character map and convert integer labels to an text sequence""" string = [] for i in labels: + i = int(i) string.append(self.index_map[i]) return "".join(string).replace("<SPACE>", " ") + def decode_batch(self, labels: list[list[int]]) -> list[str]: + """Use a character map and convert integer labels to an text sequence""" + string = [] + for label in labels: + string.append(self.decode(label)) + return string + def get_vocab_size(self) -> int: """Get the number of unique characters in the dataset""" return len(self.char_map) @@ -120,3 +128,9 @@ class CharTokenizer: load_tokenizer.char_map[char] = int(index) load_tokenizer.index_map[int(index)] = char return load_tokenizer + + def create_tokens_txt(self, path: str): + """Create a txt file with all the characters""" + with open(path, "w", encoding="utf-8") as file: + for char, _ in self.char_map.items(): + file.write(f"{char}\n") |