From 27bfcc4ea81914f94110bf02bb707e1c3432c9c9 Mon Sep 17 00:00:00 2001 From: Mingshuang Luo <37799481+luomingshuang@users.noreply.github.com> Date: Mon, 20 Dec 2021 15:13:41 +0800 Subject: [PATCH] Add grid ASR task results --- egs/grid/AVSR/audionet_ctc_asr/__init__.py | 0 egs/grid/AVSR/audionet_ctc_asr/decode.py | 504 +++++++++++++++++ egs/grid/AVSR/audionet_ctc_asr/model.py | 111 ++++ egs/grid/AVSR/audionet_ctc_asr/train.py | 612 +++++++++++++++++++++ egs/grid/AVSR/audionet_ctc_asr/utils.py | 51 ++ egs/grid/AVSR/local/dataset_audio.py | 83 +++ 6 files changed, 1361 insertions(+) create mode 100644 egs/grid/AVSR/audionet_ctc_asr/__init__.py create mode 100644 egs/grid/AVSR/audionet_ctc_asr/decode.py create mode 100644 egs/grid/AVSR/audionet_ctc_asr/model.py create mode 100644 egs/grid/AVSR/audionet_ctc_asr/train.py create mode 100644 egs/grid/AVSR/audionet_ctc_asr/utils.py create mode 100644 egs/grid/AVSR/local/dataset_audio.py diff --git a/egs/grid/AVSR/audionet_ctc_asr/__init__.py b/egs/grid/AVSR/audionet_ctc_asr/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/grid/AVSR/audionet_ctc_asr/decode.py b/egs/grid/AVSR/audionet_ctc_asr/decode.py new file mode 100644 index 000000000..3c0abf960 --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/decode.py @@ -0,0 +1,504 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# 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 + +from utils import encode_supervisions + +import k2 +import torch +import torch.nn as nn + +from torch.utils.data import DataLoader +from local.dataset_audio import MyDataset +from model import TdnnLstm + +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.decode import ( + get_lattice, + nbest_decoding, + one_best_decoding, + rescore_with_n_best_list, + rescore_with_whole_lattice, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=19, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + parser.add_argument( + "--avg", + type=int, + default=5, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + parser.add_argument( + "--method", + type=str, + default="whole-lattice-rescoring", + help="""Decoding method. + Supported values are: + - (1) 1best. Extract the best path from the decoding lattice as the + decoding result. + - (2) nbest. Extract n paths from the decoding lattice; the path + with the highest score is the decoding result. + - (3) nbest-rescoring. Extract n paths from the decoding lattice, + rescore them with an n-gram LM (e.g., a 4-gram LM), the path with + the highest score is the decoding result. + - (4) whole-lattice-rescoring. Rescore the decoding lattice with an + n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice + is the decoding result. + """, + ) + + parser.add_argument( + "--num-paths", + type=int, + default=100, + help="""Number of paths for n-best based decoding method. + Used only when "method" is one of the following values: + nbest, nbest-rescoring + """, + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""The scale to be applied to `lattice.scores`. + It's needed if you use any kinds of n-best based rescoring. + Used only when "method" is one of the following values: + nbest, nbest-rescoring + A smaller value results in more unique paths. + """, + ) + + parser.add_argument( + "--export", + type=str2bool, + default=False, + help="""When enabled, the averaged model is saved to + tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved. + pretrained.pt contains a dict {"model": model.state_dict()}, + which can be loaded by `icefall.checkpoint.load_checkpoint()`. + """, + ) + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "exp_dir": Path("audionet_ctc_asr/exp"), + "lang_dir": Path("data/lang_character"), + "lm_dir": Path("data/lm"), + "feature_dim": 80, + "subsampling_factor": 3, + "search_beam": 20, + "output_beam": 5, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + # parameters for dataset + "video_path": Path("download/GRID/lip/"), + "anno_path": Path("download/GRID/GRID_align_txt"), + "val_list": Path("download/GRID/unseen_val.txt"), + "aud_padding": 200, + "num_workers": 1, + "batch_size": 120, + } + ) + return params + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + batch: dict, + lexicon: Lexicon, + 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. + - params.method is "nbest", it uses nbest decoding without LM rescoring. + - params.method is "nbest-rescoring", it uses nbest LM rescoring. + - params.method is "whole-lattice-rescoring", it uses whole lattice LM + rescoring. + + model: + The neural model. + HLG: + The decoding graph. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + lexicon: + It contains 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. + """ + device = HLG.device + feature = batch["aud"] + assert feature.ndim == 3 + feature = feature.to(device) + nnet_output = model(feature.permute(0, 2, 1)) + + nnet_output_shape = nnet_output.size() + supervision_segments, text = encode_supervisions(nnet_output_shape, batch) + + lattice = get_lattice( + nnet_output=nnet_output, + decoding_graph=HLG, + 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, + ) + + if params.method in ["1best", "nbest"]: + if params.method == "1best": + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + key = "no_rescore" + else: + best_path = nbest_decoding( + lattice=lattice, + num_paths=params.num_paths, + use_double_scores=params.use_double_scores, + nbest_scale=params.nbest_scale, + ) + key = f"no_rescore-{params.num_paths}" + hyps = get_texts(best_path) + hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps] + return {key: hyps} + + assert params.method in ["nbest-rescoring", "whole-lattice-rescoring"] + + lm_scale_list = [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09] + lm_scale_list += [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] + lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3] + lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0] + + if params.method == "nbest-rescoring": + best_path_dict = rescore_with_n_best_list( + lattice=lattice, + G=G, + num_paths=params.num_paths, + lm_scale_list=lm_scale_list, + nbest_scale=params.nbest_scale, + ) + else: + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=lm_scale_list, + ) + + ans = dict() + for lm_scale_str, best_path in best_path_dict.items(): + hyps = get_texts(best_path) + hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps] + ans[lm_scale_str] = hyps + return ans + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + lexicon: Lexicon, + G: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[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. + lexicon: + It contains 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 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. + """ + results = [] + + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["txt"] + hyps_dict = decode_one_batch( + params=params, + model=model, + HLG=HLG, + batch=batch, + lexicon=lexicon, + G=G, + ) + + for lm_scale, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[lm_scale].extend(this_batch) + + num_cuts += len(batch["txt"]) + + if batch_idx % 10 == 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[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt" + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out PERs, per-phone 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) + test_set_wers[key] = wer + + 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"per-summary-{test_set_name}.txt" + with open(errs_info, "w") as f: + print("settings\tPER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, PER 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() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + + setup_logger(f"{params.exp_dir}/log/log-decode") + logging.info("Decoding started") + logging.info(params) + + lexicon = Lexicon(params.lang_dir) + max_token_id = max(lexicon.tokens) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + 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() + + if params.method in ["nbest-rescoring", "whole-lattice-rescoring"]: + if not (params.lm_dir / "G_4_gram.pt").is_file(): + logging.info("Loading G_4_gram.fst.txt") + logging.warning("It may take 8 minutes.") + with open(params.lm_dir / "G_4_gram.fst.txt") as f: + first_word_disambig_id = lexicon.word_table["#0"] + + G = k2.Fsa.from_openfst(f.read(), acceptor=False) + # G.aux_labels is not needed in later computations, so + # remove it here. + del G.aux_labels + # CAUTION: The following line is crucial. + # Arcs entering the back-off state have label equal to #0. + # We have to change it to 0 here. + G.labels[G.labels >= first_word_disambig_id] = 0 + G = k2.Fsa.from_fsas([G]).to(device) + G = k2.arc_sort(G) + torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt") + else: + logging.info("Loading pre-compiled G_4_gram.pt") + d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu") + G = k2.Fsa.from_dict(d).to(device) + + if params.method == "whole-lattice-rescoring": + # Add epsilon self-loops to G as we will compose + # it with the whole lattice later + G = k2.add_epsilon_self_loops(G) + G = k2.arc_sort(G) + G = G.to(device) + + # G.lm_scores is used to replace HLG.lm_scores during + # LM rescoring. + G.lm_scores = G.scores.clone() + else: + G = None + + model = TdnnLstm( + num_features=params.feature_dim, + num_classes=max_token_id + 1, # +1 for the blank symbol + subsampling_factor=params.subsampling_factor, + ) + if 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 start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.load_state_dict(average_checkpoints(filenames)) + + if params.export: + logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt") + torch.save( + {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt" + ) + return + + model.to(device) + model.eval() + + grid = MyDataset( + params.video_path, + params.anno_path, + params.val_list, + params.aud_padding, + "test", + 16000, + params.feature_dim, + ) + test_dl = DataLoader( + grid, + batch_size=params.batch_size, + shuffle=False, + num_workers=params.num_workers, + drop_last=False, + ) + test_set = "test" + + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + HLG=HLG, + lexicon=lexicon, + G=G, + ) + + save_results( + params=params, test_set_name=test_set, results_dict=results_dict + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/grid/AVSR/audionet_ctc_asr/model.py b/egs/grid/AVSR/audionet_ctc_asr/model.py new file mode 100644 index 000000000..8f9969ec0 --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/model.py @@ -0,0 +1,111 @@ +# 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. + + +import torch +import torch.nn as nn + + +class TdnnLstm(nn.Module): + def __init__( + self, num_features: int, num_classes: int, subsampling_factor: int = 3 + ) -> None: + """ + Args: + num_features: + The input dimension of the model. + num_classes: + The output dimension of the model. + subsampling_factor: + It reduces the number of output frames by this factor. + """ + super().__init__() + self.num_features = num_features + self.num_classes = num_classes + self.subsampling_factor = subsampling_factor + self.tdnn = nn.Sequential( + nn.Conv1d( + in_channels=num_features, + out_channels=512, + kernel_size=3, + stride=1, + padding=1, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=512, affine=False), + nn.Conv1d( + in_channels=512, + out_channels=512, + kernel_size=3, + stride=1, + padding=1, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=512, affine=False), + nn.Conv1d( + in_channels=512, + out_channels=512, + kernel_size=3, + # stride=self.subsampling_factor, # stride: subsampling_factor! + stride=1, + padding=1, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=512, affine=False), + nn.Conv1d( + in_channels=512, + out_channels=512, + kernel_size=3, + stride=self.subsampling_factor, # stride: subsampling_factor! + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=512, affine=False), + ) + self.lstms = nn.ModuleList( + [ + nn.LSTM(input_size=512, hidden_size=512, num_layers=1) + for _ in range(4) + ] + ) + self.lstm_bnorms = nn.ModuleList( + [nn.BatchNorm1d(num_features=512, affine=False) for _ in range(5)] + ) + self.dropout = nn.Dropout(0.2) + self.linear = nn.Linear(in_features=512, out_features=self.num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: + Its shape is [N, C, T] + Returns: + The output tensor has shape [N, T, C] + """ + x = self.tdnn(x) + x = x.permute(2, 0, 1) # (N, C, T) -> (T, N, C) -> how LSTM expects it + for lstm, bnorm in zip(self.lstms, self.lstm_bnorms): + x_new, _ = lstm(x) + x_new = bnorm(x_new.permute(1, 2, 0)).permute( + 2, 0, 1 + ) # (T, N, C) -> (N, C, T) -> (T, N, C) + x_new = self.dropout(x_new) + x = x_new + x # skip connections + x = x.transpose( + 1, 0 + ) # (T, N, C) -> (N, T, C) -> linear expects "features" in the last dim + x = self.linear(x) + x = nn.functional.log_softmax(x, dim=-1) + return x diff --git a/egs/grid/AVSR/audionet_ctc_asr/train.py b/egs/grid/AVSR/audionet_ctc_asr/train.py new file mode 100644 index 000000000..1ebc1aa11 --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/train.py @@ -0,0 +1,612 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# 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 pathlib import Path +from shutil import copyfile +from typing import Optional, Tuple + +from utils import encode_supervisions + +import k2 +import torch +import torch.multiprocessing as mp +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import DataLoader + +from local.dataset_audio import MyDataset +from lhotse.utils import fix_random_seed +from model import TdnnLstm +from torch import Tensor +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.nn.utils import clip_grad_norm_ +from torch.optim.lr_scheduler import StepLR +from torch.utils.tensorboard import SummaryWriter + +from icefall.checkpoint import load_checkpoint +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.dist import cleanup_dist, setup_dist +from icefall.graph_compiler import CtcTrainingGraphCompiler +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + MetricsTracker, + get_env_info, + setup_logger, + str2bool, +) + + +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=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + audionet_ctc_asr/exp/epoch-{start_epoch-1}.pt + """, + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + is 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`: + + - exp_dir: It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + + - lang_dir: It contains language related input files such as + "lexicon.txt" + + - lr: It specifies the initial learning rate + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - weight_decay: The weight_decay for the optimizer. + + - subsampling_factor: The subsampling factor for the model. + + - 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 + + - 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 + """ + params = AttributeDict( + { + "exp_dir": Path("audionet_ctc_asr/exp"), + "lang_dir": Path("data/lang_character"), + "lr": 4e-4, + "feature_dim": 80, + "weight_decay": 5e-4, + "subsampling_factor": 3, + "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": 1000, + "beam_size": 10, + "reduction": "sum", + "use_double_scores": True, + "env_info": get_env_info(), + # parameters for dataset + "video_path": Path("download/GRID/lip/"), + "anno_path": Path("download/GRID/GRID_align_txt"), + "train_list": Path("download/GRID/unseen_train.txt"), + "aud_padding": 200, + "num_workers": 1, + "batch_size": 120, + } + ) + + return params + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, +) -> None: + """Load checkpoint from file. + + If params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. Otherwise, this function does nothing. + + Apart from loading state dict for `model`, `optimizer` and `scheduler`, + 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. + optimizer: + The optimizer that we are using. + scheduler: + The learning rate scheduler we are using. + Returns: + Return None. + """ + if params.start_epoch <= 0: + return + + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + saved_params = load_checkpoint( + filename, + model=model, + 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] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler._LRScheduler, + 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. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + 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: nn.Module, + batch: dict, + graph_compiler: CtcTrainingGraphCompiler, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC 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 TdnnLstm 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. + """ + device = graph_compiler.device + feature = batch["aud"] + feature = feature.permute(0, 2, 1) # now feature size is (N, C, T) + assert feature.ndim == 3 + feature = feature.to(device) + with torch.set_grad_enabled(is_training): + nnet_output = model(feature) # nnet_output size is (N, T, C) + + # 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 = encode_supervisions(nnet_output.size(), batch) + + decoding_graph = graph_compiler.compile(texts) + dense_fsa_vec = k2.DenseFsaVec( + nnet_output, + supervision_segments, + allow_truncate=params.subsampling_factor - 1, + ) + 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 loss.requires_grad == is_training + + info = MetricsTracker() + info["frames"] = supervision_segments[:, 2].sum().item() + info["loss"] = loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + graph_compiler: CtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process. The validation loss + is saved in `params.valid_loss`. + """ + 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: nn.Module, + optimizer: torch.optim.Optimizer, + graph_compiler: CtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, +) -> 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. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + """ + model.train() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(train_dl): + params.batch_idx_train += 1 + batch_size = len(batch["txt"]) + + loss, loss_info = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=True, + ) + # summary stats. + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + optimizer.zero_grad() + loss.backward() + clip_grad_norm_(model.parameters(), 5.0, 2.0) + optimizer.step() + + if batch_idx % params.log_interval == 0: + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}" + ) + if batch_idx % params.log_interval == 0: + + if tb_writer is not None: + 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: + 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)) + + fix_random_seed(42) + 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 + + lexicon = Lexicon(params.lang_dir) + max_token_id = max(lexicon.tokens) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + + graph_compiler = CtcTrainingGraphCompiler(lexicon=lexicon, device=device) + + model = TdnnLstm( + num_features=params.feature_dim, + num_classes=max_token_id + 1, # +1 for the blank symbol + subsampling_factor=params.subsampling_factor, + ) + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + model = DDP(model, device_ids=[rank]) + + optimizer = optim.AdamW( + model.parameters(), + lr=params.lr, + weight_decay=params.weight_decay, + ) + scheduler = StepLR(optimizer, step_size=10, gamma=0.8) + + if checkpoints: + optimizer.load_state_dict(checkpoints["optimizer"]) + scheduler.load_state_dict(checkpoints["scheduler"]) + + grid = MyDataset( + params.video_path, + params.anno_path, + params.train_list, + params.vid_padding, + "train", + 16000, + params.feature_dim, + ) + + train_dl = DataLoader( + grid, + batch_size=params.batch_size, + shuffle=True, + num_workers=params.num_workers, + drop_last=False, + ) + valid_dl = train_dl + + for epoch in range(params.start_epoch, params.num_epochs): + # train_dl.sampler.set_epoch(epoch) + + if epoch > params.start_epoch: + logging.info(f"epoch {epoch}, lr: {scheduler.get_last_lr()[0]}") + + if tb_writer is not None: + tb_writer.add_scalar( + "train/lr", + scheduler.get_last_lr()[0], + params.batch_idx_train, + ) + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + ) + + scheduler.step() + + if epoch % 1 == 0: + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + scheduler=scheduler, + rank=rank, + ) + + logging.info("Done!") + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def main(): + parser = get_parser() + # TimitAsrDataModule.add_arguments(parser) + args = parser.parse_args() + + 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) + + +if __name__ == "__main__": + main() diff --git a/egs/grid/AVSR/audionet_ctc_asr/utils.py b/egs/grid/AVSR/audionet_ctc_asr/utils.py new file mode 100644 index 000000000..dbe01bb25 --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/utils.py @@ -0,0 +1,51 @@ +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# 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 torch + + +def encode_supervisions(nnet_output_shape, batch): + """ + Encodes Lhotse's ``batch["supervisions"]`` dict into + a pair of torch Tensor, and a list of transcription strings. + + The supervision tensor has shape ``(batch_size, 3)``. + Its second dimension contains information about sequence index [0], + start frames [1] and num frames [2]. + + The batch items might become re-ordered during this operation -- the + returned tensor and list of strings are guaranteed to be consistent with + each other. + """ + N, T, D = nnet_output_shape + + supervisions_idx = torch.arange(0, N).to(torch.int32) + start_frames = [0 for _ in range(N)] + supervisions_start_frame = torch.tensor(start_frames).to(torch.int32) + num_frames = [T for _ in range(N)] + supervisions_num_frames = torch.tensor(num_frames).to(torch.int32) + + supervision_segments = torch.stack( + ( + supervisions_idx, + supervisions_start_frame, + supervisions_num_frames, + ), + 1, + ).to(torch.int32) + texts = batch["txt"] + + return supervision_segments, texts diff --git a/egs/grid/AVSR/local/dataset_audio.py b/egs/grid/AVSR/local/dataset_audio.py new file mode 100644 index 000000000..eca6f1c55 --- /dev/null +++ b/egs/grid/AVSR/local/dataset_audio.py @@ -0,0 +1,83 @@ +# encoding: utf-8 +import os +import kaldifeat +import numpy as np + +import torch +import torchaudio +from torch.utils.data import Dataset + + +class MyDataset(Dataset): + def __init__( + self, + video_path, + anno_path, + file_list, + aud_padding, + phase, + sample_rate, + feature_dim, + ): + self.anno_path = anno_path + self.aud_padding = aud_padding + self.phase = phase + self.sample_rate = sample_rate + self.feature_dim = feature_dim + with open(file_list, "r") as f: + self.videos = [ + os.path.join(video_path, line.strip()) for line in f.readlines() + ] + + self.data = [] + for vid in self.videos: + items = vid.split(os.path.sep) + aud = ( + vid.replace("lip", "audio_25k").replace("/video/mpg_6000", "") + + ".wav" + ) + self.data.append((aud, items[-4], items[-1])) + + def __getitem__(self, idx): + (aud, spk, name) = self.data[idx] + aud = self._load_aud(aud) + aud = self._padding(aud, self.aud_padding) + anno = self._load_anno( + os.path.join(self.anno_path, spk, "align", name + ".align") + ) + + return { + "aud": torch.FloatTensor(aud), + "txt": anno.upper(), + } + + def __len__(self): + return len(self.data) + + def _load_aud(self, filename): + opts = kaldifeat.FbankOptions() + opts.device = "cpu" + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = self.sample_rate + opts.mel_opts.num_bins = self.feature_dim + fbank = kaldifeat.Fbank(opts) + wave, sr = torchaudio.load(filename) + wave = wave[0] + features = fbank(wave) + return features + + def _load_anno(self, name): + with open(name, "r") as f: + lines = [line.strip().split(" ") for line in f.readlines()] + txt = [line[2] for line in lines] + txt = list(filter(lambda s: not s.upper() in ["SIL", "SP"], txt)) + txt = " ".join(txt) + return txt + + def _padding(self, array, length): + array = [array[_] for _ in range(array.shape[0])] + size = array[0].shape + for i in range(length - len(array)): + array.append(np.zeros(size)) + return np.stack(array, axis=0)