From 9a17f4ce410d23080c26c7e2257e9a14f312862d Mon Sep 17 00:00:00 2001 From: Dongji Gao Date: Thu, 25 Apr 2024 12:55:44 -0400 Subject: [PATCH] add OTC related scripts using phone as units instead of BPEs (#1602) * add otc related scripts using phone instead of bpe --- .../WSASR/conformer_ctc2/decode_phone.py | 592 +++++++++ .../WSASR/conformer_ctc2/train_phone.py | 1124 +++++++++++++++++ egs/librispeech/WSASR/local/download_lm.py | 146 +++ .../WSASR/local/prepare_otc_lang.py | 469 +++++++ egs/librispeech/WSASR/prepare.sh | 44 +- egs/librispeech/WSASR/shared | 1 + icefall/otc_phone_graph_compiler.py | 232 ++++ 7 files changed, 2599 insertions(+), 9 deletions(-) create mode 100755 egs/librispeech/WSASR/conformer_ctc2/decode_phone.py create mode 100755 egs/librispeech/WSASR/conformer_ctc2/train_phone.py create mode 100755 egs/librispeech/WSASR/local/download_lm.py create mode 100755 egs/librispeech/WSASR/local/prepare_otc_lang.py create mode 120000 egs/librispeech/WSASR/shared create mode 100644 icefall/otc_phone_graph_compiler.py diff --git a/egs/librispeech/WSASR/conformer_ctc2/decode_phone.py b/egs/librispeech/WSASR/conformer_ctc2/decode_phone.py new file mode 100755 index 0000000000..b6b1cb0203 --- /dev/null +++ b/egs/librispeech/WSASR/conformer_ctc2/decode_phone.py @@ -0,0 +1,592 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, +# Fangjun Kuang, +# Quandong Wang) +# 2023 Johns Hopkins University (Author: Dongji Gao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from conformer import Conformer + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.decode import get_lattice, one_best_decoding +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + get_texts, + load_averaged_model, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--otc-token", + type=str, + default="", + help="OTC token", + ) + + parser.add_argument( + "--blank-bias", + type=float, + default=0, + help="bias (log-prob) added to blank token during decoding", + ) + + parser.add_argument( + "--epoch", + type=int, + default=20, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=5, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--method", + type=str, + default="ctc-greedy-search", + help="""Decoding method. + Supported values are: + - (0) 1best. Extract the best path from the decoding lattice as the + decoding result. + """, + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--num-decoder-layers", + type=int, + default=0, + help="""Number of decoder layer of transformer decoder. + Setting this to 0 will not create the decoder at all (pure CTC model) + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="conformer_ctc2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_phone", + help="The lang dir", + ) + + parser.add_argument( + "--lm-dir", + type=str, + default="data/lm", + help="""The n-gram LM dir. + It should contain either G_4_gram.pt or G_4_gram.fst.txt + """, + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + # parameters for conformer + "subsampling_factor": 4, + "feature_dim": 80, + "nhead": 8, + "dim_feedforward": 2048, + "encoder_dim": 512, + "num_encoder_layers": 12, + # parameters for decoding + "search_beam": 20, + "output_beam": 8, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + "env_info": get_env_info(), + } + ) + return params + + +def remove_duplicates_and_blank(hyp: List[int]) -> List[int]: + # from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/common.py + new_hyp: List[int] = [] + cur = 0 + while cur < len(hyp): + if hyp[cur] != 0: + new_hyp.append(hyp[cur]) + prev = cur + while cur < len(hyp) and hyp[cur] == hyp[prev]: + cur += 1 + return new_hyp + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + batch: dict, + word_table: k2.SymbolTable, + G: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if no rescoring is used, the key is the string `no_rescore`. + If LM rescoring is used, the key is the string `lm_scale_xxx`, + where `xxx` is the value of `lm_scale`. An example key is + `lm_scale_0.7` + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + + - params.method is "1best", it uses 1best decoding without LM rescoring. + + model: + The neural model. + HLG: + The decoding graph. Used only when params.method is NOT ctc-decoding. + H: + The ctc topo. Used only when params.method is ctc-decoding. + bpe_model: + The BPE model. Used only when params.method is ctc-decoding. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + word_table: + The word symbol table. + G: + An LM. It is not None when params.method is "nbest-rescoring" + or "whole-lattice-rescoring". In general, the G in HLG + is a 3-gram LM, while this G is a 4-gram LM. + Returns: + Return the decoding result. See above description for the format of + the returned dict. Note: If it decodes to nothing, then return None. + """ + device = HLG.device + feature = batch["inputs"] + assert feature.ndim == 3 + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + + nnet_output, memory, memory_key_padding_mask = model(feature, supervisions) + # nnet_output is (N, T, C) + nnet_output[:, :, 0] += params.blank_bias + + supervision_segments = torch.stack( + ( + supervisions["sequence_idx"], + torch.div( + supervisions["start_frame"], + params.subsampling_factor, + rounding_mode="trunc", + ), + torch.div( + supervisions["num_frames"], + params.subsampling_factor, + rounding_mode="trunc", + ), + ), + 1, + ).to(torch.int32) + + decoding_graph = HLG + + lattice = get_lattice( + nnet_output=nnet_output, + decoding_graph=decoding_graph, + supervision_segments=supervision_segments, + search_beam=params.search_beam, + output_beam=params.output_beam, + min_active_states=params.min_active_states, + max_active_states=params.max_active_states, + subsampling_factor=params.subsampling_factor + 2, + ) + + if params.method in ["1best"]: + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + key = "no_rescore" + + hyps = get_texts(best_path) + hyps = [[word_table[i] for i in ids] for ids in hyps] + + return {key: hyps} + else: + assert False, f"Unsupported decoding method: {params.method}" + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + word_table: k2.SymbolTable, + G: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + HLG: + The decoding graph. Used only when params.method is NOT ctc-decoding. + H: + The ctc topo. Used only when params.method is ctc-decoding. + bpe_model: + The BPE model. Used only when params.method is ctc-decoding. + word_table: + It is the word symbol table. + sos_id: + The token ID for SOS. + eos_id: + The token ID for EOS. + G: + An LM. It is not None when params.method is "nbest-rescoring" + or "whole-lattice-rescoring". In general, the G in HLG + is a 3-gram LM, while this G is a 4-gram LM. + Returns: + Return a dict, whose key may be "no-rescore" if no LM rescoring + is used, or it may be "lm_scale_0.7" if LM rescoring is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + HLG=HLG, + batch=batch, + word_table=word_table, + G=G, + ) + + if hyps_dict is not None: + for lm_scale, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + ref_words = ref_text.split() + this_batch.append((cut_id, ref_words, hyp_words)) + + results[lm_scale].extend(this_batch) + else: + assert len(results) > 0, "It should not decode to empty in the first batch!" + this_batch = [] + hyp_words = [] + for ref_text in texts: + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + for lm_scale in results.keys(): + results[lm_scale].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % 100 == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]], +): + if params.method in ("attention-decoder", "rnn-lm"): + # Set it to False since there are too many logs. + enable_log = False + else: + enable_log = True + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt" + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + if enable_log: + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt" + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=enable_log + ) + test_set_wers[key] = wer + + if enable_log: + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = params.exp_dir / f"wer-summary-{test_set_name}.txt" + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + args.lang_dir = Path(args.lang_dir) + args.lm_dir = Path(args.lm_dir) + + params = get_params() + params.update(vars(args)) + + setup_logger(f"{params.exp_dir}/log-{params.method}/log-decode") + logging.info("Decoding started") + logging.info(params) + + lexicon = Lexicon(params.lang_dir) + # remove otc_token from decoding units + max_token_id = len(lexicon.tokens) - 1 + num_classes = max_token_id + 1 # +1 for the blank + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + params.num_classes = num_classes + + HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")) + HLG = HLG.to(device) + assert HLG.requires_grad is False + + if not hasattr(HLG, "lm_scores"): + HLG.lm_scores = HLG.scores.clone() + + G = None + + model = Conformer( + num_features=params.feature_dim, + nhead=params.nhead, + d_model=params.encoder_dim, + num_classes=num_classes, + subsampling_factor=params.subsampling_factor, + num_encoder_layers=params.num_encoder_layers, + num_decoder_layers=params.num_decoder_layers, + ) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + # we need cut ids to display recognition results. + args.return_cuts = True + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) + test_other_dl = librispeech.test_dataloaders(test_other_cuts) + + test_sets = ["test-clean", "test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + HLG=HLG, + word_table=lexicon.word_table, + ) + + save_results(params=params, test_set_name=test_set, results_dict=results_dict) + + logging.info("Done!") + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/WSASR/conformer_ctc2/train_phone.py b/egs/librispeech/WSASR/conformer_ctc2/train_phone.py new file mode 100755 index 0000000000..b276d05879 --- /dev/null +++ b/egs/librispeech/WSASR/conformer_ctc2/train_phone.py @@ -0,0 +1,1124 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo, +# Zengwei Yao, +# Quandong Wang) +# 2023 Johns Hopkins University (author: Dongji Gao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./conformer_ctc2/train.py \ + --world-size 4 \ + --manifest-dir data/ssl \ + --train-manifest librispeech_cuts_train-clean-100_0.17_0.17_0.17.jsonl.gz \ + --exp-dir conformer_ctc2/exp \ + --lang-dir data/lang_bpe_200 \ + --otc-token "" \ + --feature-dim 768 \ + --allow-bypass-arc true \ + --allow-self-loop-arc true \ + --initial-bypass-weight -19 \ + --initial-self-loop-weight 3.75 \ + --bypass-weight-decay 0.975 \ + --self-loop-weight-decay 0.999 \ + --show-alignment true +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from conformer import Conformer +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.decode import one_best_decoding +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.otc_phone_graph_compiler import OtcPhoneTrainingGraphCompiler +from icefall.utils import ( + AttributeDict, + MetricsTracker, + encode_supervisions_otc, + get_texts, + setup_logger, + str2bool, +) + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=20, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="conformer_ctc2/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_bpe_200", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--feature-dim", + type=int, + default=80, + help="""Number of features extracted in feature extraction stage.last dimension of feature vector. + 80 when using fbank features and 768 or 1024 whn using wave2vec""", + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="""The initial learning rate. This value should not need to be + changed.""", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate decreases. + We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--att-rate", + type=float, + default=0.0, + help="""The attention rate. + The total loss is (1 - att_rate) * ctc_loss + att_rate * att_loss + """, + ) + + parser.add_argument( + "--num-decoder-layers", + type=int, + default=0, + help="""Number of decoder layer of transformer decoder. + Setting this to 0 will not create the decoder at all (pure CTC model) + """, + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=10, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--otc-token", + type=str, + default="_", + help="OTC token", + ) + + parser.add_argument( + "--allow-bypass-arc", + type=str2bool, + default=True, + help="""Whether to add bypass arc to training graph for substitution + and insertion errors (wrong or extra words in the transcript).""", + ) + + parser.add_argument( + "--allow-self-loop-arc", + type=str2bool, + default=True, + help="""Whether to self-loop bypass arc to training graph for deletion errors + (missing words in the transcript).""", + ) + + parser.add_argument( + "--initial-bypass-weight", + type=float, + default=0.0, + help="Initial weight associated with bypass arc", + ) + + parser.add_argument( + "--initial-self-loop-weight", + type=float, + default=0.0, + help="Initial weight associated with self-loop arc", + ) + + parser.add_argument( + "--bypass-weight-decay", + type=float, + default=1.0, + help="""Weight decay factor of bypass arc weight: + bypass_arc_weight = intial_bypass_weight * bypass_weight_decay ^ ith-epoch""", + ) + + parser.add_argument( + "--self-loop-weight-decay", + type=float, + default=1.0, + help="""Weight decay factor of self-loop arc weight: + self_loop_arc_weight = intial_self_loop_weight * self_loop_weight_decay ^ ith-epoch""", + ) + + parser.add_argument( + "--show-alignment", + type=str2bool, + default=True, + help="Whether to print OTC alignment during training", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - beam_size: It is used in k2.ctc_loss + + - reduction: It is used in k2.ctc_loss + + - use_double_scores: It is used in k2.ctc_loss + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 1, + "reset_interval": 200, + "valid_interval": 800, # For the 100h subset, use 800 + "alignment_interval": 100, + # parameters for conformer + "subsampling_factor": 4, + "encoder_dim": 512, + "nhead": 8, + "dim_feedforward": 2048, + "num_encoder_layers": 12, + # parameters for ctc loss + "beam_size": 10, + "reduction": "sum", + "use_double_scores": True, + # parameters for Noam + "model_warm_step": 3000, # arg given to model, not for lrate + "env_info": get_env_info(), + } + ) + + return params + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + batch: dict, + graph_compiler: OtcPhoneTrainingGraphCompiler, + is_training: bool, + warmup: float = 2.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute OTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + graph_compiler: + It is used to build a decoding graph from a ctc topo and training + transcript. The training transcript is contained in the given `batch`, + while the ctc topo is built when this compiler is instantiated. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + with torch.set_grad_enabled(is_training): + nnet_output, encoder_memory, memory_mask = model( + feature, supervisions, warmup=warmup + ) + # Set the probability of OTC token as the average of non-blank tokens + # under the assumption that blank is the first and + # OTC token is the last token in tokens.txt + _, _, V = nnet_output.shape + + otc_token_log_prob = torch.logsumexp( + nnet_output[:, :, 1:], dim=-1, keepdim=True + ) - torch.log(torch.tensor([V - 1])).to(device) + + nnet_output = torch.cat([nnet_output, otc_token_log_prob], dim=-1) + + # NOTE: We need `encode_supervisions` to sort sequences with + # different duration in decreasing order, required by + # `k2.intersect_dense` called in `k2.ctc_loss` + supervision_segments, texts, utt_ids, verbatim_texts = encode_supervisions_otc( + supervisions, subsampling_factor=params.subsampling_factor + ) + + bypass_weight = graph_compiler.initial_bypass_weight * ( + graph_compiler.bypass_weight_decay ** (params.cur_epoch - 1) + ) + self_loop_weight = graph_compiler.initial_self_loop_weight * ( + graph_compiler.self_loop_weight_decay ** (params.cur_epoch - 1) + ) + + decoding_graph = graph_compiler.compile( + texts=texts, + allow_bypass_arc=params.allow_bypass_arc, + allow_self_loop_arc=params.allow_self_loop_arc, + bypass_weight=bypass_weight, + self_loop_weight=self_loop_weight, + ) + + dense_fsa_vec = k2.DenseFsaVec( + nnet_output, + supervision_segments, + allow_truncate=3, + ) + + otc_loss = k2.ctc_loss( + decoding_graph=decoding_graph, + dense_fsa_vec=dense_fsa_vec, + output_beam=params.beam_size, + reduction=params.reduction, + use_double_scores=params.use_double_scores, + ) + + assert params.att_rate == 0.0 + loss = otc_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + info["otc_loss"] = otc_loss.detach().cpu().item() + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + + # `utt_duration` and `utt_pad_proportion` would be normalized by `utterances` # noqa + info["utterances"] = feature.size(0) + # averaged input duration in frames over utterances + info["utt_duration"] = feature_lens.sum().item() + # averaged padding proportion over utterances + info["utt_pad_proportion"] = ( + ((feature.size(1) - feature_lens) / feature.size(1)).sum().item() + ) + + if params.show_alignment: + if params.batch_idx_train % params.alignment_interval == 0: + for index, utt_id in enumerate(utt_ids): + verbatim_text = verbatim_texts[index] + utt_id = utt_ids[index] + + lattice = k2.intersect_dense( + decoding_graph, + dense_fsa_vec, + params.beam_size, + ) + best_path = one_best_decoding( + lattice=lattice, + use_double_scores=params.use_double_scores, + ) + hyp_ids = get_texts(best_path)[index] + hyp_text_list = [graph_compiler.word_table[i] for i in hyp_ids] + hyp_text = " ".join(hyp_text_list) + + logging.info(f"[utterance id]: {utt_id}") + logging.info(f"[verbatim text]: {verbatim_text}") + logging.info(f"[best alignment]: {hyp_text}") + logging.info(bypass_weight) + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + graph_compiler: OtcPhoneTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + graph_compiler: OtcPhoneTrainingGraphCompiler, + scheduler: LRSchedulerType, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + graph_compiler: + It is used to convert transcripts to FSAs. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(train_dl): + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + # scaler.scale(loss).backward() + + try: + # loss.backward() + scaler.scale(loss).backward() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error(f"failing batch size:{batch_size} ") + raise + + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 30: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + if loss_info["otc_loss"] == float("inf"): + logging.error("Your loss contains inf, something goes wrong") + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + params.valid_interval = 1600 + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + logging.info(params) + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + + lexicon = Lexicon(params.lang_dir) + graph_compiler = OtcPhoneTrainingGraphCompiler( + lexicon, + otc_token=params.otc_token, + device=device, + initial_bypass_weight=params.initial_bypass_weight, + initial_self_loop_weight=params.initial_self_loop_weight, + bypass_weight_decay=params.bypass_weight_decay, + self_loop_weight_decay=params.self_loop_weight_decay, + ) + + # remove OTC token as it is the average of all non-blank tokens + max_token_id = graph_compiler.get_max_token_id() - 1 + # add blank + num_classes = max_token_id + 1 + + logging.info("About to create model") + model = Conformer( + num_features=params.feature_dim, + nhead=params.nhead, + d_model=params.encoder_dim, + num_classes=num_classes, + subsampling_factor=params.subsampling_factor, + num_encoder_layers=params.num_encoder_layers, + num_decoder_layers=params.num_decoder_layers, + ) + + print(model) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + diagnostic = diagnostics.attach_diagnostics(model) + + librispeech = LibriSpeechAsrDataModule(args) + + train_cuts = librispeech.train_clean_100_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = librispeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = librispeech.dev_clean_cuts() + valid_cuts += librispeech.dev_other_cuts() + valid_dl = librispeech.valid_dataloaders(valid_cuts) + + if params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + graph_compiler=graph_compiler, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + graph_compiler=graph_compiler, + scheduler=scheduler, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: OtcPhoneTrainingGraphCompiler, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=True, + warmup=0.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + args.otc_token = f"{args.otc_token}" + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/WSASR/local/download_lm.py b/egs/librispeech/WSASR/local/download_lm.py new file mode 100755 index 0000000000..5a36ff2a94 --- /dev/null +++ b/egs/librispeech/WSASR/local/download_lm.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file downloads the following LibriSpeech LM files: + + - 3-gram.pruned.1e-7.arpa.gz + - 4-gram.arpa.gz + - librispeech-vocab.txt + - librispeech-lexicon.txt + - librispeech-lm-norm.txt.gz + +from http://www.openslr.org/resources/11 +and save them in the user provided directory. + +Files are not re-downloaded if they already exist. + +Usage: + ./local/download_lm.py --out-dir ./download/lm +""" + +import argparse +import gzip +import logging +import os +import shutil +from pathlib import Path + +from tqdm.auto import tqdm + + +# This function is copied from lhotse +def tqdm_urlretrieve_hook(t): + """Wraps tqdm instance. + Don't forget to close() or __exit__() + the tqdm instance once you're done with it (easiest using `with` syntax). + Example + ------- + >>> from urllib.request import urlretrieve + >>> with tqdm(...) as t: + ... reporthook = tqdm_urlretrieve_hook(t) + ... urlretrieve(..., reporthook=reporthook) + + Source: https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py + """ + last_b = [0] + + def update_to(b=1, bsize=1, tsize=None): + """ + b : int, optional + Number of blocks transferred so far [default: 1]. + bsize : int, optional + Size of each block (in tqdm units) [default: 1]. + tsize : int, optional + Total size (in tqdm units). If [default: None] or -1, + remains unchanged. + """ + if tsize not in (None, -1): + t.total = tsize + displayed = t.update((b - last_b[0]) * bsize) + last_b[0] = b + return displayed + + return update_to + + +# This function is copied from lhotse +def urlretrieve_progress(url, filename=None, data=None, desc=None): + """ + Works exactly like urllib.request.urlretrieve, but attaches a tqdm hook to + display a progress bar of the download. + Use "desc" argument to display a user-readable string that informs what is + being downloaded. + """ + from urllib.request import urlretrieve + + with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=desc) as t: + reporthook = tqdm_urlretrieve_hook(t) + return urlretrieve(url=url, filename=filename, reporthook=reporthook, data=data) + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--out-dir", type=str, help="Output directory.") + + args = parser.parse_args() + return args + + +def main(out_dir: str): + url = "http://www.openslr.org/resources/11" + out_dir = Path(out_dir) + + files_to_download = ( + "3-gram.pruned.1e-7.arpa.gz", + "4-gram.arpa.gz", + "librispeech-vocab.txt", + "librispeech-lexicon.txt", + "librispeech-lm-norm.txt.gz", + ) + + for f in tqdm(files_to_download, desc="Downloading LibriSpeech LM files"): + filename = out_dir / f + if filename.is_file() is False: + urlretrieve_progress( + f"{url}/{f}", + filename=filename, + desc=f"Downloading {filename}", + ) + else: + logging.info(f"{filename} already exists - skipping") + + if ".gz" in str(filename): + unzipped = Path(os.path.splitext(filename)[0]) + if unzipped.is_file() is False: + with gzip.open(filename, "rb") as f_in: + with open(unzipped, "wb") as f_out: + shutil.copyfileobj(f_in, f_out) + else: + logging.info(f"{unzipped} already exist - skipping") + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + + args = get_args() + logging.info(f"out_dir: {args.out_dir}") + + main(out_dir=args.out_dir) diff --git a/egs/librispeech/WSASR/local/prepare_otc_lang.py b/egs/librispeech/WSASR/local/prepare_otc_lang.py new file mode 100755 index 0000000000..01865b8652 --- /dev/null +++ b/egs/librispeech/WSASR/local/prepare_otc_lang.py @@ -0,0 +1,469 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# 2024 Johns Hopkins University (author: Dongji Gao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input a lexicon file "data/lang_phone/lexicon.txt" +consisting of words and tokens (i.e., phones) and does the following: + +1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt + +2. Generate tokens.txt, the token table mapping a token to a unique integer. + +3. Generate words.txt, the word table mapping a word to a unique integer. + +4. Generate L.pt, in k2 format. It can be loaded by + + d = torch.load("L.pt") + lexicon = k2.Fsa.from_dict(d) + +5. Generate L_disambig.pt, in k2 format. +""" +import argparse +import logging +import math +import re +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import k2 +import torch + +from icefall.lexicon import write_lexicon +from icefall.utils import str2bool + +Lexicon = List[Tuple[str, List[str]]] + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", + type=str, + help="""Input and output directory. + It should contain a file lexicon.txt. + Generated files by this script are saved into this directory. + """, + ) + + parser.add_argument( + "--otc-token", + type=str, + default="", + help="The OTC token in lexicon", + ) + + parser.add_argument( + "--debug", + type=str2bool, + default=False, + help="""True for debugging, which will generate + a visualization of the lexicon FST. + + Caution: If your lexicon contains hundreds of thousands + of lines, please set it to False! + """, + ) + + return parser.parse_args() + + +def read_lexicon( + filename: str, +) -> List[Tuple[str, List[str]]]: + """Read a lexicon from `filename`. + + Each line in the lexicon contains "word p1 p2 p3 ...". + That is, the first field is a word and the remaining + fields are tokens. Fields are separated by space(s). + + Args: + filename: + Path to the lexicon.txt + + Returns: + A list of tuples., e.g., [('w', ['p1', 'p2']), ('w1', ['p3, 'p4'])] + """ + ans = [] + + with open(filename, "r", encoding="utf-8") as f: + whitespace = re.compile("[ \t]+") + for line in f: + a = whitespace.split(line.strip(" \t\r\n")) + if len(a) == 0: + continue + + if len(a) < 2: + logging.info(f"Found bad line {line} in lexicon file {filename}") + logging.info("Every line is expected to contain at least 2 fields") + continue + word = a[0] + if word == "": + logging.info(f"Found bad line {line} in lexicon file {filename}") + logging.info(" should not be a valid word") + continue + + tokens = a[1:] + ans.append((word, tokens)) + + return ans + + +def write_mapping(filename: str, sym2id: Dict[str, int]) -> None: + """Write a symbol to ID mapping to a file. + + Note: + No need to implement `read_mapping` as it can be done + through :func:`k2.SymbolTable.from_file`. + + Args: + filename: + Filename to save the mapping. + sym2id: + A dict mapping symbols to IDs. + Returns: + Return None. + """ + with open(filename, "w", encoding="utf-8") as f: + for sym, i in sym2id.items(): + f.write(f"{sym} {i}\n") + + +def get_tokens(lexicon: Lexicon) -> List[str]: + """Get tokens from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique tokens. + """ + ans = set() + for _, tokens in lexicon: + ans.update(tokens) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def get_words(lexicon: Lexicon) -> List[str]: + """Get words from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique words. + """ + ans = set() + for word, _ in lexicon: + ans.add(word) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]: + """It adds pseudo-token disambiguation symbols #1, #2 and so on + at the ends of tokens to ensure that all pronunciations are different, + and that none is a prefix of another. + + See also add_lex_disambig.pl from kaldi. + + Args: + lexicon: + It is returned by :func:`read_lexicon`. + Returns: + Return a tuple with two elements: + + - The output lexicon with disambiguation symbols + - The ID of the max disambiguation symbol that appears + in the lexicon + """ + + # (1) Work out the count of each token-sequence in the + # lexicon. + count = defaultdict(int) + for _, tokens in lexicon: + count[" ".join(tokens)] += 1 + + # (2) For each left sub-sequence of each token-sequence, note down + # that it exists (for identifying prefixes of longer strings). + issubseq = defaultdict(int) + for _, tokens in lexicon: + tokens = tokens.copy() + tokens.pop() + while tokens: + issubseq[" ".join(tokens)] = 1 + tokens.pop() + + # (3) For each entry in the lexicon: + # if the token sequence is unique and is not a + # prefix of another word, no disambig symbol. + # Else output #1, or #2, #3, ... if the same token-seq + # has already been assigned a disambig symbol. + ans = [] + + # We start with #1 since #0 has its own purpose + first_allowed_disambig = 1 + max_disambig = first_allowed_disambig - 1 + last_used_disambig_symbol_of = defaultdict(int) + + for word, tokens in lexicon: + tokenseq = " ".join(tokens) + assert tokenseq != "" + if issubseq[tokenseq] == 0 and count[tokenseq] == 1: + ans.append((word, tokens)) + continue + + cur_disambig = last_used_disambig_symbol_of[tokenseq] + if cur_disambig == 0: + cur_disambig = first_allowed_disambig + else: + cur_disambig += 1 + + if cur_disambig > max_disambig: + max_disambig = cur_disambig + last_used_disambig_symbol_of[tokenseq] = cur_disambig + tokenseq += f" #{cur_disambig}" + ans.append((word, tokenseq.split())) + return ans, max_disambig + + +def generate_id_map( + symbols: List[str], +) -> Dict[str, int]: + """Generate ID maps, i.e., map a symbol to a unique ID. + + Args: + symbols: + A list of unique symbols. + Returns: + A dict containing the mapping between symbols and IDs. + """ + return {sym: i for i, sym in enumerate(symbols)} + + +def add_self_loops( + arcs: List[List[Any]], disambig_token: int, disambig_word: int +) -> List[List[Any]]: + """Adds self-loops to states of an FST to propagate disambiguation symbols + through it. They are added on each state with non-epsilon output symbols + on at least one arc out of the state. + + See also fstaddselfloops.pl from Kaldi. One difference is that + Kaldi uses OpenFst style FSTs and it has multiple final states. + This function uses k2 style FSTs and it does not need to add self-loops + to the final state. + + The input label of a self-loop is `disambig_token`, while the output + label is `disambig_word`. + + Args: + arcs: + A list-of-list. The sublist contains + `[src_state, dest_state, label, aux_label, score]` + disambig_token: + It is the token ID of the symbol `#0`. + disambig_word: + It is the word ID of the symbol `#0`. + + Return: + Return new `arcs` containing self-loops. + """ + states_needs_self_loops = set() + for arc in arcs: + src, dst, ilabel, olabel, score = arc + if olabel != 0: + states_needs_self_loops.add(src) + + ans = [] + for s in states_needs_self_loops: + ans.append([s, s, disambig_token, disambig_word, 0]) + + return arcs + ans + + +def lexicon_to_fst( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + sil_token: str = "SIL", + sil_prob: float = 0.5, + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format) with optional silence at + the beginning and end of each word. + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + sil_token: + The silence token. + sil_prob: + The probability for adding a silence at the beginning and end + of the word. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + assert sil_prob > 0.0 and sil_prob < 1.0 + # CAUTION: we use score, i.e, negative cost. + sil_score = math.log(sil_prob) + no_sil_score = math.log(1.0 - sil_prob) + + start_state = 0 + loop_state = 1 # words enter and leave from here + sil_state = 2 # words terminate here when followed by silence; this state + # has a silence transition to loop_state. + next_state = 3 # the next un-allocated state, will be incremented as we go. + arcs = [] + + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + + sil_token = token2id[sil_token] + + arcs.append([start_state, loop_state, eps, eps, no_sil_score]) + arcs.append([start_state, sil_state, eps, eps, sil_score]) + arcs.append([sil_state, loop_state, sil_token, eps, 0]) + + for word, tokens in lexicon: + assert len(tokens) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + tokens = [token2id[i] for i in tokens] + + for i in range(len(tokens) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, tokens[i], w, 0]) + + cur_state = next_state + next_state += 1 + + # now for the last token of this word + # It has two out-going arcs, one to the loop state, + # the other one to the sil_state. + i = len(tokens) - 1 + w = word if i == 0 else eps + arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score]) + arcs.append([cur_state, sil_state, tokens[i], w, sil_score]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def main(): + args = get_args() + lang_dir = Path(args.lang_dir) + lexicon_filename = lang_dir / "lexicon.txt" + otc_token = args.otc_token + sil_token = "SIL" + sil_prob = 0.5 + + lexicon = read_lexicon(lexicon_filename) + tokens = get_tokens(lexicon) + words = get_words(lexicon) + + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + lexicon.append((otc_token, [otc_token])) + tokens.append(otc_token) + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in tokens + tokens.append(f"#{i}") + + assert "" not in tokens + tokens = [""] + tokens + + assert "" not in words + assert "#0" not in words + assert "" not in words + assert "" not in words + + words = [""] + words + [otc_token, "#0", "", ""] + + token2id = generate_id_map(tokens) + word2id = generate_id_map(words) + + write_mapping(lang_dir / "tokens.txt", token2id) + write_mapping(lang_dir / "words.txt", word2id) + write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst( + lexicon, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + ) + + L_disambig = lexicon_to_fst( + lexicon_disambig, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + need_self_loops=True, + ) + torch.save(L.as_dict(), lang_dir / "L.pt") + torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt") + + if args.debug: + labels_sym = k2.SymbolTable.from_file(lang_dir / "tokens.txt") + aux_labels_sym = k2.SymbolTable.from_file(lang_dir / "words.txt") + + L.labels_sym = labels_sym + L.aux_labels_sym = aux_labels_sym + L.draw(f"{lang_dir / 'L.svg'}", title="L.pt") + + L_disambig.labels_sym = labels_sym + L_disambig.aux_labels_sym = aux_labels_sym + L_disambig.draw(f"{lang_dir / 'L_disambig.svg'}", title="L_disambig.pt") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/WSASR/prepare.sh b/egs/librispeech/WSASR/prepare.sh index 0d2a672596..c242bcbb07 100755 --- a/egs/librispeech/WSASR/prepare.sh +++ b/egs/librispeech/WSASR/prepare.sh @@ -30,7 +30,8 @@ stop_stage=100 # - librispeech-lm-norm.txt.gz # otc_token="" -feature_type="ssl" +# ssl or fbank +feature_type="fbank" dl_dir=$PWD/download manifests_dir="data/manifests" @@ -40,9 +41,6 @@ lm_dir="data/lm" perturb_speed=false -# ssl or fbank - -. ./cmd.sh . shared/parse_options.sh || exit 1 # vocab size for sentence piece models. @@ -192,7 +190,23 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then fi if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then - log "Stage 5: Prepare G" + log "Stage 5: Prepare phone based lang" + lang_dir="data/lang_phone" + mkdir -p ${lang_dir} + + if [ ! -f $lang_dir/lexicon.txt ]; then + (echo '!SIL SIL'; echo ' SPN'; echo ' SPN'; ) | + cat - $dl_dir/lm/librispeech-lexicon.txt | + sort | uniq > $lang_dir/lexicon.txt + fi + + if [ ! -f $lang_dir/L_disambig.pt ]; then + ./local/prepare_otc_lang.py --lang-dir $lang_dir + fi +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + log "Stage 6: Prepare G" # We assume you have installed kaldilm, if not, please install # it using: pip install kaldilm @@ -216,18 +230,30 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then fi fi -if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then - log "Stage 6: Compile HLG" +if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then + log "Stage 7: Compile HLG" # Note If ./local/compile_hlg.py throws OOM, # please switch to the following command # # ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone - for vocab_size in ${vocab_sizes[@]}; do - bpe_lang_dir="data/lang_bpe_${vocab_size}" + lang_dir="data/lang_bpe_${vocab_size}" echo "LM DIR: ${lm_dir}" ./local/compile_hlg.py \ --lm-dir "${lm_dir}" \ --lang-dir "${bpe_lang_dir}" done fi + +if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then + log "Stage 7: Compile HLG" + # Note If ./local/compile_hlg.py throws OOM, + # please switch to the following command + # + # ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone + lang_dir="data/lang_phone" + echo "LM DIR: ${lm_dir}" + ./local/compile_hlg.py \ + --lm-dir "${lm_dir}" \ + --lang-dir "${lang_dir}" +fi diff --git a/egs/librispeech/WSASR/shared b/egs/librispeech/WSASR/shared new file mode 120000 index 0000000000..4c5e91438c --- /dev/null +++ b/egs/librispeech/WSASR/shared @@ -0,0 +1 @@ +../../../icefall/shared/ \ No newline at end of file diff --git a/icefall/otc_phone_graph_compiler.py b/icefall/otc_phone_graph_compiler.py new file mode 100644 index 0000000000..bebdffe0c6 --- /dev/null +++ b/icefall/otc_phone_graph_compiler.py @@ -0,0 +1,232 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# 2023 Johns Hopkins University (author: Dongji Gao) +# +# See ../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from pathlib import Path +from typing import List, Union + +import k2 +import torch + +from icefall.lexicon import Lexicon +from icefall.utils import str2bool + + +class OtcPhoneTrainingGraphCompiler(object): + def __init__( + self, + lexicon: Lexicon, + otc_token: str, + oov: str = "", + device: Union[str, torch.device] = "cpu", + initial_bypass_weight: float = 0.0, + initial_self_loop_weight: float = 0.0, + bypass_weight_decay: float = 0.0, + self_loop_weight_decay: float = 0.0, + ) -> None: + """ + Args: + lexicon: + It is built from `data/lang/lexicon.txt`. + otc_token: + The special token in OTC that represent all non-blank tokens + device: + It indicates CPU or CUDA. + """ + self.device = device + L_inv = lexicon.L_inv.to(self.device) + assert L_inv.requires_grad is False + assert oov in lexicon.word_table + + self.L_inv = k2.arc_sort(L_inv) + self.oov_id = lexicon.word_table[oov] + self.otc_id = lexicon.word_table[otc_token] + self.word_table = lexicon.word_table + + max_token_id = max(lexicon.tokens) + ctc_topo = k2.ctc_topo(max_token_id, modified=False) + self.ctc_topo = ctc_topo.to(self.device) + self.max_token_id = max_token_id + + self.initial_bypass_weight = initial_bypass_weight + self.initial_self_loop_weight = initial_self_loop_weight + self.bypass_weight_decay = bypass_weight_decay + self.self_loop_weight_decay = self_loop_weight_decay + + def get_max_token_id(self): + return self.max_token_id + + def make_arc( + self, + from_state: int, + to_state: int, + symbol: Union[str, int], + weight: float, + ): + return f"{from_state} {to_state} {symbol} {weight}" + + def texts_to_ids(self, texts: List[str]) -> List[List[int]]: + """Convert a list of texts to a list-of-list of word IDs. + + Args: + texts: + It is a list of strings. Each string consists of space(s) + separated words. An example containing two strings is given below: + + ['HELLO ICEFALL', 'HELLO k2'] + Returns: + Return a list-of-list of word IDs. + """ + word_ids_list = [] + for text in texts: + word_ids = [] + for word in text.split(): + if word in self.word_table: + word_ids.append(self.word_table[word]) + else: + word_ids.append(self.oov_id) + word_ids_list.append(word_ids) + return word_ids_list + + def compile( + self, + texts: List[str], + allow_bypass_arc: str2bool = True, + allow_self_loop_arc: str2bool = True, + bypass_weight: float = 0.0, + self_loop_weight: float = 0.0, + ) -> k2.Fsa: + """Build a OTC graph from a texts (list of words). + + Args: + texts: + A list of strings. Each string contains a sentence for an utterance. + A sentence consists of spaces separated words. An example `texts` + looks like: + ['hello icefall', 'CTC training with k2'] + allow_bypass_arc: + Whether to add bypass arc to training graph for substitution + and insertion errors (wrong or extra words in the transcript). + allow_self_loop_arc: + Whether to add self-loop arc to training graph for deletion + errors (missing words in the transcript). + bypass_weight: + Weight associated with bypass arc. + self_loop_weight: + Weight associated with self-loop arc. + + Return: + Return an FsaVec, which is the result of composing a + CTC topology with OTC FSAs constructed from the given texts. + """ + + transcript_fsa = self.convert_transcript_to_fsa( + texts, + allow_bypass_arc, + allow_self_loop_arc, + bypass_weight, + self_loop_weight, + ) + fsa_with_self_loop = k2.remove_epsilon_and_add_self_loops(transcript_fsa) + fsa_with_self_loop = k2.arc_sort(fsa_with_self_loop) + + graph = k2.compose( + self.ctc_topo, + fsa_with_self_loop, + treat_epsilons_specially=False, + ) + assert graph.requires_grad is False + + return graph + + def convert_transcript_to_fsa( + self, + texts: List[str], + allow_bypass_arc: str2bool = True, + allow_self_loop_arc: str2bool = True, + bypass_weight: float = 0.0, + self_loop_weight: float = 0.0, + ): + + word_fsa_list = [] + for text in texts: + word_ids = [] + + for word in text.split(): + if word in self.word_table: + word_ids.append(self.word_table[word]) + else: + word_ids.append(self.oov_id) + + arcs = [] + start_state = 0 + cur_state = start_state + next_state = 1 + + for word_id in word_ids: + if allow_self_loop_arc: + self_loop_arc = self.make_arc( + cur_state, + cur_state, + self.otc_id, + self_loop_weight, + ) + arcs.append(self_loop_arc) + + arc = self.make_arc(cur_state, next_state, word_id, 0.0) + arcs.append(arc) + + if allow_bypass_arc: + bypass_arc = self.make_arc( + cur_state, + next_state, + self.otc_id, + bypass_weight, + ) + arcs.append(bypass_arc) + + cur_state = next_state + next_state += 1 + + if allow_self_loop_arc: + self_loop_arc = self.make_arc( + cur_state, + cur_state, + self.otc_id, + self_loop_weight, + ) + arcs.append(self_loop_arc) + + # Deal with final state + final_state = next_state + final_arc = self.make_arc(cur_state, final_state, -1, 0.0) + arcs.append(final_arc) + arcs.append(f"{final_state}") + sorted_arcs = sorted(arcs, key=lambda a: int(a.split()[0])) + + word_fsa = k2.Fsa.from_str("\n".join(sorted_arcs)) + word_fsa = k2.arc_sort(word_fsa) + word_fsa_list.append(word_fsa) + + word_fsa_vec = k2.create_fsa_vec(word_fsa_list).to(self.device) + word_fsa_vec_with_self_loop = k2.add_epsilon_self_loops(word_fsa_vec) + + fsa = k2.intersect( + self.L_inv, word_fsa_vec_with_self_loop, treat_epsilons_specially=False + ) + ans_fsa = fsa.invert_() + return k2.arc_sort(ans_fsa)