diff --git a/README.md b/README.md index 214e85ad0..a7c7e830b 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ We provide four recipes at present: - [LibriSpeech][librispeech] - [Aishell][aishell] - [TIMIT][timit] + - [GRID][grid] ### yesno @@ -153,6 +154,54 @@ The PER for this model is: We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/11IT-k4HQIgQngXz1uvWsEYktjqQt7Tmb?usp=sharing) +### GRID + +For the VSR (visual speech recognition) task, we provide two models: [Conv3d Map BiGRU CTC model][GRID_conv3d_map_bigru_ctc] +and [Conv3d ResNet18 BiGRU CTC model][GRID_conv3d_resnet18_bigru_ctc]. + +#### Conv3d Map BiGRU CTC Model + +The WER for this model is: + +||TEST| +|--|--| +|WER| 15.68% | + +We provide a Colab notebook to run a pre-trained Conv3d Map BiGRU CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1X1U2VsHD3AmRQ4UvdVEuj2y8HKJ0ZJgS?usp=sharing) + +#### Conv3d ResNet18 BiGRU CTC Model + +The WER for this model is: + +||TEST| +|--|--| +|WER| 13.63% | + +We provide a Colab notebook to run a pre-trained Conv3d ResNet18 BiGRU CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1PC9Fd7QcOOONFKUQqwLODwjztCuI-Oh1?usp=sharing) + +For the ASR (automatic speech recognition) task, we provide one model: [Tdnn Lstm CTC model][GRID_tdnn_lstm_ctc]. + +#### Tdnn Lstm CTC Model + +The WER for this model is: + +||TEST| +|--|--| +|WER| 2.35% | + +We provide a Colab notebook to run a pre-trained Tdnn Lstm CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bkDyVDVBhGJS5TuvjNsJ1yJ3vlCoFk9p?usp=sharing) + +For the AVSR (audio-visual speech recognition) task, we provide one model: [CombineNet CTC model][GRID_combinenet_ctc]. + +#### CombineNet CTC Model + +The WER for this model is: + +||TEST| +|--|--| +|WER| 1.71% | + +We provide a Colab notebook to run a pre-trained CombineNet CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1UmCYX7GwbQ3Ms6SnoAuB8Tov46OD82hb?usp=sharing) ## Deployment with C++ @@ -175,8 +224,13 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad [Aishell_conformer_ctc]: egs/aishell/ASR/conformer_ctc [TIMIT_tdnn_lstm_ctc]: egs/timit/ASR/tdnn_lstm_ctc [TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc +[GRID_conv3d_map_bigru_ctc]: egs/grid/AVSR/visualnet_ctc_vsr +[GRID_conv3d_resnet18_bigru_ctc]:egs/grid/AVSR/visualnet2_ctc_vsr +[GRID_tdnn_lstm_ctc]: egs/grid/AVSR/audionet_ctc_asr +[GRID_combinenet_ctc]: egs/grid/AVSR/combinenet_ctc_avsr [yesno]: egs/yesno/ASR [librispeech]: egs/librispeech/ASR [aishell]: egs/aishell/ASR [timit]: egs/timit/ASR +[grid]: egs/grid/AVSR [k2]: https://github.com/k2-fsa/k2 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..af02a143f --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/decode.py @@ -0,0 +1,505 @@ +#!/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 AudioDataSet +from model import AudioNet + +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": 480, + "sample_rate": 16000, + "num_workers": 16, + "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 = AudioNet( + 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 = AudioDataSet( + params.video_path, + params.anno_path, + params.val_list, + params.aud_padding, + params.sample_rate, + params.feature_dim, + "test", + ) + 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..a368cd85a --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/model.py @@ -0,0 +1,111 @@ +# 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 torch +import torch.nn as nn + + +class AudioNet(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=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/pretrained.py b/egs/grid/AVSR/audionet_ctc_asr/pretrained.py new file mode 100644 index 000000000..f88d4e0fb --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/pretrained.py @@ -0,0 +1,274 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# 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 typing import List + +import k2 +import kaldifeat +import torch +import torchaudio +from model import AudioNet + +from icefall.decode import ( + get_lattice, + one_best_decoding, + rescore_with_whole_lattice, +) +from icefall.utils import AttributeDict, get_texts + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--words-file", + type=str, + required=True, + help="Path to words.txt", + ) + + parser.add_argument( + "--HLG", type=str, required=True, help="Path to HLG.pt." + ) + + parser.add_argument( + "--method", + type=str, + default="1best", + help="""Decoding method. + Possible values are: + (1) 1best - Use the best path as decoding output. Only + the transformer encoder output is used for decoding. + We call it HLG decoding. + (2) whole-lattice-rescoring - Use an LM to rescore the + decoding lattice and then use 1best to decode the + rescored lattice. + We call it HLG decoding + n-gram LM rescoring. + """, + ) + + parser.add_argument( + "--G", + type=str, + help="""An LM for rescoring. + Used only when method is + whole-lattice-rescoring. + It's usually a 4-gram LM. + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.1, + help=""" + Used only when method is whole-lattice-rescoring. + It specifies the scale for n-gram LM scores. + (Note: You need to tune it on a dataset.) + """, + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to transcribe. " + "Supported formats are those supported by torchaudio.load(). " + "For example, wav and flac are supported. " + "The sample rate has to be 16kHz.", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "feature_dim": 80, + "subsampling_factor": 3, + "num_classes": 28, + "sample_rate": 16000, + "search_beam": 20, + "output_beam": 5, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +def read_sound_files( + filenames: List[str], expected_sample_rate: float +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + # We use only the first channel + ans.append(wave[0]) + return ans + + +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + logging.info("Creating model") + model = AudioNet( + num_features=params.feature_dim, + num_classes=params.num_classes, + subsampling_factor=params.subsampling_factor, + ) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"]) + model.to(device) + model.eval() + + logging.info(f"Loading HLG from {params.HLG}") + HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu")) + HLG = HLG.to(device) + if not hasattr(HLG, "lm_scores"): + # For whole-lattice-rescoring and attention-decoder + HLG.lm_scores = HLG.scores.clone() + + if params.method == "whole-lattice-rescoring": + logging.info(f"Loading G from {params.G}") + G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu")) + # Add epsilon self-loops to G as we will compose + # it with the whole lattice later + G = G.to(device) + G = k2.add_epsilon_self_loops(G) + G = k2.arc_sort(G) + G.lm_scores = G.scores.clone() + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = params.sample_rate + opts.mel_opts.num_bins = params.feature_dim + + fbank = kaldifeat.Fbank(opts) + + logging.info(f"Reading sound files: {params.sound_files}") + waves = read_sound_files( + filenames=params.sound_files, expected_sample_rate=params.sample_rate + ) + waves = [w.to(device) for w in waves] + + logging.info("Decoding started") + features = fbank(waves) + # Here , we set aud_padding as 480. + features_new = torch.zeros(len(features), 480, params.feature_dim).to( + device + ) + for i in range(len(features)): + length = features[i].shape[0] + features_new[i][:length] = features[i] + + with torch.no_grad(): + nnet_output = model(features_new.permute(0, 2, 1)) + # nnet_output is (N, T, C) + + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, + ) + + 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, + subsampling_factor=params.subsampling_factor, + ) + + if params.method == "1best": + logging.info("Use HLG decoding") + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + elif params.method == "whole-lattice-rescoring": + logging.info("Use HLG decoding + LM rescoring") + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=[params.ngram_lm_scale], + ) + best_path = next(iter(best_path_dict.values())) + + hyps = get_texts(best_path) + word_sym_table = k2.SymbolTable.from_file(params.words_file) + hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + + s = "\n" + for filename, hyp in zip(params.sound_files, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() 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..f67d4c515 --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/train.py @@ -0,0 +1,613 @@ +#!/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 AudioDataSet +from lhotse.utils import fix_random_seed +from model import AudioNet +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": 480, + "sample_rate": 16000, + "num_workers": 16, + "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 = AudioNet( + 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 = AudioDataSet( + params.video_path, + params.anno_path, + params.train_list, + params.aud_padding, + params.sample_rate, + params.feature_dim, + "train", + ) + + 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..17889a31c --- /dev/null +++ b/egs/grid/AVSR/audionet_ctc_asr/utils.py @@ -0,0 +1,54 @@ +# 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. + +""" +This script is to encodes the supervisions as Tuple list. +The supervision tensor has shape ``(batch_size, 3)``. +Its second dimension contains information about sequence index [0], +start frames [1] and num frames [2]. +In GRID, the start frame of each audio sample is 0. +""" +import torch + + +def encode_supervisions(nnet_output_shape: int, batch: dict): + """ + Args: + nnet_output_shape: + The shape of nnet_output, e.g: (N, T, D). + batch: + A batch of dataloader, it's a dict file + including text and aud/vid arrays. + Return: + The tuple list of supervisions and the text in batch. + """ + N, T, D = nnet_output_shape + + supervisions_idx = torch.arange(0, N, dtype=torch.int32) + supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0] + supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0] + + supervision_segments = torch.stack( + ( + supervisions_idx, + supervisions_start_frame, + supervisions_num_frames, + ), + 1, + ) + texts = batch["txt"] + + return supervision_segments, texts diff --git a/egs/grid/AVSR/combinenet_ctc_avsr/__init__.py b/egs/grid/AVSR/combinenet_ctc_avsr/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/grid/AVSR/combinenet_ctc_avsr/decode.py b/egs/grid/AVSR/combinenet_ctc_avsr/decode.py new file mode 100644 index 000000000..885e7e17d --- /dev/null +++ b/egs/grid/AVSR/combinenet_ctc_avsr/decode.py @@ -0,0 +1,514 @@ +#!/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_av import AudioVisualDataset +from model import CombineNet + +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("combinenet_ctc_avsr/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"), + "vid_padding": 75, + "aud_padding": 450, + "sample_rate": 16000, + "num_workers": 16, + "batch_size": 100, + } + ) + 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 + audio_feature = batch["aud"] + video_feature = batch["vid"] + + audio_feature = audio_feature.permute(0, 2, 1) + assert audio_feature.ndim == 3 + audio_feature = audio_feature.to(device) + + assert video_feature.ndim == 5 + video_feature = video_feature.to(device) + + nnet_output = model(video_feature, audio_feature) + + 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 = CombineNet( + 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 = AudioVisualDataset( + params.video_path, + params.anno_path, + params.val_list, + params.feature_dim, + params.vid_padding, + params.aud_padding, + params.sample_rate, + "test", + ) + 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/combinenet_ctc_avsr/model.py b/egs/grid/AVSR/combinenet_ctc_avsr/model.py new file mode 100644 index 000000000..158727192 --- /dev/null +++ b/egs/grid/AVSR/combinenet_ctc_avsr/model.py @@ -0,0 +1,175 @@ +# 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 +import torch.nn as nn + + +class CombineNet(nn.Module): + def __init__( + self, num_features: int, num_classes: int, subsampling_factor: int = 3 + ) -> None: + """ + Args: + num_features: + The input dimension of the audio encoder. + num_classes: + The output dimension of the combinenet 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 + + # the audio encoder + self.audio_encoder = 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=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), + ) + + # the video encoder + self.video_encoder = nn.Sequential( + nn.Conv3d( + in_channels=3, + out_channels=32, + kernel_size=(3, 5, 5), + stride=(1, 2, 2), + padding=(1, 2, 2), + ), + nn.ReLU(inplace=True), + nn.Dropout3d(p=0.1), + nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)), + nn.Conv3d( + in_channels=32, + out_channels=64, + kernel_size=(3, 5, 5), + stride=(1, 1, 1), + padding=(1, 2, 2), + ), + nn.ReLU(inplace=True), + nn.Dropout3d(p=0.1), + nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)), + nn.Conv3d( + in_channels=64, + out_channels=96, + kernel_size=(3, 3, 3), + stride=(1, 1, 1), + padding=(1, 1, 1), + ), + nn.ReLU(inplace=True), + nn.Dropout3d(p=0.1), + nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)), + ) + self.linear_visual = nn.Linear(96 * 4 * 8, 512) + + # the audio-visual combining encoder based on GRU + self.grus = nn.ModuleList( + [ + nn.GRU( + input_size=512 * 2, + hidden_size=512, + num_layers=1, + bidirectional=True, + ) + for _ in range(4) + ] + ) + self.gru_bnorms = nn.ModuleList( + [nn.BatchNorm1d(num_features=1024, affine=False) for _ in range(4)] + ) + self.dropout = nn.Dropout(0.2) + self.linear = nn.Linear( + in_features=512 * 2, out_features=self.num_classes + ) + + def forward(self, x_v, x_a): + """ + Args: + x_v: + Its shape is [N, 3, T, H, W] + x_a: + Its shape is [N, C, T] + Returns: + The output tensor has shape [N, T, C] + """ + x_v = self.video_encoder(x_v) + x_v = x_v.permute(2, 0, 1, 3, 4).contiguous() + x_v = x_v.view(x_v.size(0), x_v.size(1), -1) + x_v = self.linear_visual(x_v) + x_a = self.audio_encoder(x_a) + + x_v = x_v.permute(1, 0, 2) + x_a = x_a.permute(0, 2, 1) + + # Repeat the visual features + # to cat with the audio features in time axis. + x_v_copy = x_v + x_v_stack = torch.stack((x_v, x_v_copy), dim=2) + x_v = x_v_stack.view( + x_v_stack.size(0), 2 * x_v_stack.size(1), x_v_stack.size(3) + ) + + x = torch.cat((x_v, x_a), dim=2) + x = x.permute(1, 0, 2) # (N, C, T) -> (T, N, C) -> how GRU expects it + for gru, bnorm in zip(self.grus, self.gru_bnorms): + x_new, _ = gru(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/combinenet_ctc_avsr/pretrained.py b/egs/grid/AVSR/combinenet_ctc_avsr/pretrained.py new file mode 100644 index 000000000..65801471d --- /dev/null +++ b/egs/grid/AVSR/combinenet_ctc_avsr/pretrained.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# 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 cv2 +import logging +import numpy as np +import os + +import k2 +import kaldifeat +import torch +import torchaudio +from model import TdnnLstm + +from icefall.decode import ( + get_lattice, + one_best_decoding, + rescore_with_whole_lattice, +) +from icefall.utils import AttributeDict, get_texts + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--words-file", + type=str, + required=True, + help="Path to words.txt", + ) + + parser.add_argument( + "--HLG", type=str, required=True, help="Path to HLG.pt." + ) + + parser.add_argument( + "--method", + type=str, + default="1best", + help="""Decoding method. + Possible values are: + (1) 1best - Use the best path as decoding output. Only + the transformer encoder output is used for decoding. + We call it HLG decoding. + (2) whole-lattice-rescoring - Use an LM to rescore the + decoding lattice and then use 1best to decode the + rescored lattice. + We call it HLG decoding + n-gram LM rescoring. + """, + ) + + parser.add_argument( + "--G", + type=str, + help="""An LM for rescoring. + Used only when method is + whole-lattice-rescoring. + It's usually a 4-gram LM. + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.1, + help=""" + Used only when method is whole-lattice-rescoring. + It specifies the scale for n-gram LM scores. + (Note: You need to tune it on a dataset.) + """, + ) + + parser.add_argument( + "--lipframes-dirs", + type=str, + nargs="+", + help="The input visual file(s) to transcribe. " + "Supported formats are those supported by cv2.imread(). " + "The frames sample rate is 25fps.", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "num_classes": 28, + "search_beam": 20, + "output_beam": 5, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + logging.info("Creating model") + model = TdnnLstm(num_features=80, num_classes=28, subsampling_factor=3) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"]) + model.to(device) + model.eval() + + logging.info(f"Loading HLG from {params.HLG}") + HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu")) + HLG = HLG.to(device) + if not hasattr(HLG, "lm_scores"): + # For whole-lattice-rescoring and attention-decoder + HLG.lm_scores = HLG.scores.clone() + + if params.method == "whole-lattice-rescoring": + logging.info(f"Loading G from {params.G}") + G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu")) + # Add epsilon self-loops to G as we will compose + # it with the whole lattice later + G = G.to(device) + G = k2.add_epsilon_self_loops(G) + G = k2.arc_sort(G) + G.lm_scores = G.scores.clone() + + logging.info("Loading lip roi frames and audio wav files") + aud = [] + vid = [] + + opts = kaldifeat.FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + fbank = kaldifeat.Fbank(opts) + + for sample_dir in params.lipframes_dirs: + wave, sr = torchaudio.load( + sample_dir.replace("lip", "audio_25k").replace( + "video/mpg_6000/", "" + ) + + ".wav" + ) + wave = wave[0] + aud.append(fbank(wave)) + + files = os.listdir(sample_dir) + files = list(filter(lambda file: file.find(".jpg") != -1, files)) + files = sorted(files, key=lambda file: int(os.path.splitext(file)[0])) + array = [cv2.imread(os.path.join(sample_dir, file)) for file in files] + array = list(filter(lambda im: im is not None, array)) + array = [ + cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4) + for im in array + ] + array = np.stack(array, axis=0).astype(np.float32) + vid.append(array) + + L, H, W, C = vid[0].shape + # vid_padding = 75 + # aud_padding = 480 + features_v = torch.zeros(len(vid), 75, H, W, C).to(device) + for i in range(len(vid)): + length = vid[i].shape[0] + features_v[i][:length] = torch.FloatTensor(vid[i]).to(device) + + features_a = torch.zeros(len(aud), 450, 80).to(device) + for i in range(len(aud)): + length = aud[i].shape[0] + features_a[i][:length] = torch.FloatTensor(aud[i]).to(device) + + logging.info("Decoding started") + with torch.no_grad(): + nnet_output = model( + features_v.permute(0, 4, 1, 2, 3) / 255.0, + features_a.permute(0, 2, 1), + ) + # nnet_output is (N, T, C) + + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, + ) + + 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 == "1best": + logging.info("Use HLG decoding") + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + elif params.method == "whole-lattice-rescoring": + logging.info("Use HLG decoding + LM rescoring") + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=[params.ngram_lm_scale], + ) + best_path = next(iter(best_path_dict.values())) + + hyps = get_texts(best_path) + word_sym_table = k2.SymbolTable.from_file(params.words_file) + hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + + s = "\n" + for filename, hyp in zip(params.lipframes_dirs, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/grid/AVSR/combinenet_ctc_avsr/train.py b/egs/grid/AVSR/combinenet_ctc_avsr/train.py new file mode 100644 index 000000000..7eac415d5 --- /dev/null +++ b/egs/grid/AVSR/combinenet_ctc_avsr/train.py @@ -0,0 +1,625 @@ +#!/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_av import AudioVisualDataset +from lhotse.utils import fix_random_seed +from model import CombineNet +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("combinenet_ctc_avsr/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"), + "vid_padding": 80, + "aud_padding": 480, + "sample_rate": 16000, + "num_workers": 16, + "batch_size": 100, + } + ) + + 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 + audio_feature = batch["aud"] + video_feature = batch["vid"] + + audio_feature = audio_feature.permute( + 0, 2, 1 + ) # now feature size is (N, C, T) + assert audio_feature.ndim == 3 + audio_feature = audio_feature.to(device) + + assert video_feature.ndim == 5 + video_feature = video_feature.to(device) + + with torch.set_grad_enabled(is_training): + nnet_output = model( + video_feature, audio_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 = CombineNet( + 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 = AudioVisualDataset( + params.video_path, + params.anno_path, + params.train_list, + params.feature_dim, + params.vid_padding, + params.aud_padding, + params.sample_rate, + "train", + ) + + 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/combinenet_ctc_avsr/utils.py b/egs/grid/AVSR/combinenet_ctc_avsr/utils.py new file mode 100644 index 000000000..66823874a --- /dev/null +++ b/egs/grid/AVSR/combinenet_ctc_avsr/utils.py @@ -0,0 +1,47 @@ +# 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 the output of net and texts 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]. + + In GRID, the start frame of each audio sample is 0. + """ + N, T, D = nnet_output_shape + + supervisions_idx = torch.arange(0, N, dtype=torch.int32) + supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0] + supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0] + + supervision_segments = torch.stack( + ( + supervisions_idx, + supervisions_start_frame, + supervisions_num_frames, + ), + 1, + ) + texts = batch["txt"] + + return supervision_segments, texts diff --git a/egs/grid/AVSR/local/compile_hlg.py b/egs/grid/AVSR/local/compile_hlg.py new file mode 100644 index 000000000..a94a9ac8c --- /dev/null +++ b/egs/grid/AVSR/local/compile_hlg.py @@ -0,0 +1,166 @@ +#!/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 script takes as input lang_dir and generates HLG from + + - H, the ctc topology, built from tokens contained in lang_dir/lexicon.txt + - L, the lexicon, built from lang_dir/L_disambig.pt + + Caution: We use a lexicon that contains disambiguation symbols + + - G, the LM, built from data/lm/G_3_gram.fst.txt + +The generated HLG is saved in $lang_dir/HLG.pt +""" +import argparse +import logging +from pathlib import Path + +import k2 +import torch + +from icefall.lexicon import Lexicon + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", + type=str, + help="""Input and output directory. + """, + ) + + return parser.parse_args() + + +def compile_HLG(lang_dir: str) -> k2.Fsa: + """ + Args: + lang_dir: + The language directory, e.g., data/lang_phone or data/lang_bpe_5000. + + Return: + An FSA representing HLG. + """ + lexicon = Lexicon(lang_dir) + max_token_id = max(lexicon.tokens) + logging.info(f"Building ctc_topo. max_token_id: {max_token_id}") + H = k2.ctc_topo(max_token_id) + + if Path(lang_dir / "L_disambig.pt").is_file(): + logging.info("Loading L_disambig.pt") + d = torch.load(Path(lang_dir / "L_disambig.pt")) + L = k2.Fsa.from_dict(d) + else: + logging.info("Loading L_disambig.fst.txt") + with open(Path(lang_dir / "L_disambig.fst.txt")) as f: + L = k2.Fsa.from_openfst(f.read(), acceptor=False) + torch.save(L.as_dict(), Path(lang_dir / "L_disambig.pt")) + + # L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt")) + + if Path("data/lm/G.pt").is_file(): + logging.info("Loading pre-compiled G") + d = torch.load("data/lm/G.pt") + G = k2.Fsa.from_dict(d) + else: + logging.info("Loading G_3_gram.fst.txt") + with open("data/lm/G_3_gram.fst.txt") as f: + G = k2.Fsa.from_openfst(f.read(), acceptor=False) + torch.save(G.as_dict(), "data/lm/G.pt") + + first_token_disambig_id = lexicon.token_table["#0"] + first_word_disambig_id = lexicon.word_table["#0"] + + L = k2.arc_sort(L) + G = k2.arc_sort(G) + + logging.info("Intersecting L and G") + LG = k2.compose(L, G) + logging.info(f"LG shape: {LG.shape}") + + logging.info("Connecting LG") + LG = k2.connect(LG) + logging.info(f"LG shape after k2.connect: {LG.shape}") + + logging.info(type(LG.aux_labels)) + logging.info("Determinizing LG") + + LG = k2.determinize(LG) + logging.info(type(LG.aux_labels)) + + logging.info("Connecting LG after k2.determinize") + LG = k2.connect(LG) + + logging.info("Removing disambiguation symbols on LG") + + LG.labels[LG.labels >= first_token_disambig_id] = 0 + + LG.aux_labels.values[LG.aux_labels.values >= first_word_disambig_id] = 0 + + LG = k2.remove_epsilon(LG) + logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}") + + LG = k2.connect(LG) + LG.aux_labels = LG.aux_labels.remove_values_eq(0) + + logging.info("Arc sorting LG") + LG = k2.arc_sort(LG) + + logging.info("Composing H and LG") + # CAUTION: The name of the inner_labels is fixed + # to `tokens`. If you want to change it, please + # also change other places in icefall that are using + # it. + HLG = k2.compose(H, LG, inner_labels="tokens") + + logging.info("Connecting LG") + HLG = k2.connect(HLG) + + logging.info("Arc sorting LG") + HLG = k2.arc_sort(HLG) + logging.info(f"HLG.shape: {HLG.shape}") + + return HLG + + +def main(): + args = get_args() + lang_dir = Path(args.lang_dir) + + if (lang_dir / "HLG.pt").is_file(): + logging.info(f"{lang_dir}/HLG.pt already exists - skipping") + return + + logging.info(f"Processing {lang_dir}") + + HLG = compile_HLG(lang_dir) + logging.info(f"Saving HLG.pt to {lang_dir}") + torch.save(HLG.as_dict(), f"{lang_dir}/HLG.pt") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + main() diff --git a/egs/grid/AVSR/local/cvtransforms.py b/egs/grid/AVSR/local/cvtransforms.py new file mode 100644 index 000000000..0e64c632e --- /dev/null +++ b/egs/grid/AVSR/local/cvtransforms.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +# 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. + +""" +In this script, there are two functions: +the function HorizontalFlip is to flip the images, +the function ColorNormalize is to normalize the images. +The above two functions is to augment the images. + +The input for the above functions is a sequence of images. +""" +import random + + +def horizontal_flip(batch_img: float, p: float): + """ + Args: + batch_img: + The float array of a sequence of images, the shape of the + arrat is (T, H, W, C). + p: + The probability of implementing horizontal flip, the defaults + value is 0.5. + Return: + A new float array of the sequence of images after flipping. + """ + if random.random() > p: + batch_img = batch_img[:, :, ::-1, ...] + return batch_img + + +def color_normalize(batch_img: float): + """ + Args: + batch_img: + The float array of a sequence of images, the shape of the + arrat is (T, H, W, C). + Return: + A new float array of the sequence of images after normalizing. + """ + batch_img = batch_img / 255.0 + return batch_img diff --git a/egs/grid/AVSR/local/dataset_audio.py b/egs/grid/AVSR/local/dataset_audio.py new file mode 100644 index 000000000..1e99f7607 --- /dev/null +++ b/egs/grid/AVSR/local/dataset_audio.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +# 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. + +""" +This script is to load the audio data in GRID. +The class dataset_audio makes each audio batch data have the same shape. +""" +import numpy as np +import os + +import torch +import torchaudio +from torch.utils.data import Dataset + +import kaldifeat + + +class AudioDataSet(Dataset): + def __init__( + self, + video_path: str, + anno_path: str, + file_list: str, + aud_padding: int, + sample_rate: int, + feature_dim: int, + phase: str, + ) -> None: + """ + Args: + video_path: + The dir path of the visual data. + anno_path: + The dir path of the texts data. + file_list: + A txt file which listing all samples for training or testing. + aud_padding: + The padding for each audio sample. + sample_rate: + The sample rate for extracting fbank feature. + feature_dim: + The dim for fbank feature. + phase: + "train" or "test". + """ + self.anno_path = anno_path + self.aud_padding = aud_padding + self.sample_rate = sample_rate + self.feature_dim = feature_dim + self.phase = phase + + 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 + self.fbank = kaldifeat.Fbank(opts) + + 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): + """Load the audio data. + Args: + filename: + The full path of a wav file. + Return: + The fbank feature array. + """ + wave, _ = torchaudio.load(filename) + wave = wave[0] + features = self.fbank(wave) + return features + + def _load_anno(self, name): + """Load the text file. + Args: + name: + The file which records the text. + Return: + A sequence of words. + """ + 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): + """Pad zeros for the feature array. + Args: + array: + The feature arry. (Audio or Visual feature) + length: + The length for padding. + Return: + A new feature array after padding. + """ + 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) diff --git a/egs/grid/AVSR/local/dataset_av.py b/egs/grid/AVSR/local/dataset_av.py new file mode 100644 index 000000000..a0faefd68 --- /dev/null +++ b/egs/grid/AVSR/local/dataset_av.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 +# 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. + +""" +This script is to load the pair of audio-visual data in GRID. +The class dataset_av makes each audio-visual batch data have the same shape. +""" +import cv2 +import numpy as np +import os + +import torch +import torchaudio +from torch.utils.data import Dataset + +import kaldifeat +from .cvtransforms import horizontal_flip, color_normalize + + +class AudioVisualDataset(Dataset): + def __init__( + self, + video_path, + anno_path, + file_list, + feature_dim, + vid_pading, + aud_pading, + sample_rate, + phase, + ): + """ + Args: + video_path: + The dir path of the visual data. + anno_path: + The dir path of the texts data. + file_list: + The file which listing all samples for training or testing. + feature_dim: + The dimension for fbank feature. + vid_padding: + The padding for each visual sample. + aud_padding: + The padding for each audio sample. + sample_rate: + The sample rate for extracting fbank feature. + phase: + "train" or "test". + """ + self.anno_path = anno_path + self.vid_pading = vid_pading + self.aud_pading = aud_pading + self.feature_dim = feature_dim + self.sample_rate = sample_rate + self.phase = phase + 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((vid, aud, items[-4], items[-1])) + + def __getitem__(self, idx): + (vid, aud, spk, name) = self.data[idx] + + vid = self._load_vid(vid) + aud = self._load_aud(aud) + vid = self._padding(vid, self.vid_pading) + aud = self._padding(aud, self.aud_pading) + anno = self._load_anno( + os.path.join(self.anno_path, spk, "align", name + ".align") + ) + + if self.phase == "train": + vid = horizontal_flip(vid) + vid = color_normalize(vid) + + vid = self._padding(vid, self.vid_pading) + aud = self._padding(aud, self.aud_pading) + + return { + "vid": torch.FloatTensor(vid.transpose(3, 0, 1, 2)), + "aud": torch.FloatTensor(aud), + "txt": anno.upper(), + } + + def __len__(self): + return len(self.data) + + def _load_vid(self, p): + """Load the visual data. + Args: + p: + A directory which contains a sequence of frames + for a visual sample. + Return: + The array of a visual sample. + """ + files = os.listdir(p) + files = list(filter(lambda file: file.find(".jpg") != -1, files)) + files = sorted(files, key=lambda file: int(os.path.splitext(file)[0])) + array = [cv2.imread(os.path.join(p, file)) for file in files] + array = list(filter(lambda im: im is not None, array)) + array = [ + cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4) + for im in array + ] + array = np.stack(array, axis=0).astype(np.float32) + return array + + def _load_aud(self, filename): + """Load the audio data. + Args: + filename: + The full path of a wav file. + Return: + The fbank feature array. + """ + opts = kaldifeat.FbankOptions() + 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, sample_rate = torchaudio.load(filename) + features = fbank(wave[0]) + + return features + + def _load_anno(self, name): + """Load the text file. + Args: + name: + The file which records the text. + Return: + A sequence of words. + """ + 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): + """Pad zeros for the feature array. + Args: + array: + The feature arry. (Audio or Visual feature) + length: + The length for padding. + Return: + A new feature array after padding. + """ + 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) diff --git a/egs/grid/AVSR/local/dataset_visual.py b/egs/grid/AVSR/local/dataset_visual.py new file mode 100644 index 000000000..720f5d09a --- /dev/null +++ b/egs/grid/AVSR/local/dataset_visual.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +# 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. + +""" +This script is to load the visual data in GRID. +The class dataset_visual makes each visual batch data have the same shape. +""" +import cv2 +import os +import numpy as np +import torch +from torch.utils.data import Dataset +from .cvtransforms import ( + color_normalize, + horizontal_flip, +) + + +class VisualDataset(Dataset): + def __init__( + self, + video_path: str, + anno_path: str, + file_list: str, + vid_padding: int, + phase: str, + ): + """ + Args: + video_path: + The dir path of the visual data. + anno_path: + The dir path of the texts data. + file_list: + The file which listing all samples for training or testing. + vid_padding: + The padding for each visual sample. + phase: + "train" or "test". + """ + self.anno_path = anno_path + self.vid_padding = vid_padding + self.phase = phase + 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((vid, aud, items[-4], items[-1])) + + def __getitem__(self, idx): + (vid, aud, spk, name) = self.data[idx] + vid = self._load_vid(vid) + anno = self._load_anno( + os.path.join(self.anno_path, spk, "align", name + ".align") + ) + + if self.phase == "train": + vid = horizontal_flip(vid, p=0.5) + vid = color_normalize(vid) + + vid = self._padding(vid, self.vid_padding) + + return { + "vid": torch.FloatTensor(vid.transpose(3, 0, 1, 2)), + "txt": anno.upper(), + } + + def __len__(self): + return len(self.data) + + def _load_vid(self, p): + """Load the visual data. + Args: + p: + A directory which contains a sequence of frames + for a visual sample. + Return: + The array of a visual sample. + """ + files = os.listdir(p) + files = list(filter(lambda file: file.find(".jpg") != -1, files)) + files = sorted(files, key=lambda file: int(os.path.splitext(file)[0])) + array = [cv2.imread(os.path.join(p, file)) for file in files] + array = list(filter(lambda im: im is not None, array)) + array = [ + cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4) + for im in array + ] + array = np.stack(array, axis=0).astype(np.float32) + return array + + def _load_anno(self, name): + """Load the text file. + Args: + name: + The file which records the text. + Return: + A sequence of words. + """ + 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): + """Pad zeros for the feature array. + Args: + array: + The feature arry. (Audio or Visual feature) + length: + The length for padding. + Return: + A new feature array after padding. + """ + 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) diff --git a/egs/grid/AVSR/local/prepare_lang.py b/egs/grid/AVSR/local/prepare_lang.py new file mode 100644 index 000000000..0fc82513b --- /dev/null +++ b/egs/grid/AVSR/local/prepare_lang.py @@ -0,0 +1,370 @@ +#!/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 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 math +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import k2 +import torch + +from icefall.lexicon import read_lexicon, 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( + "--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 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 = 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], + 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. + 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. + """ + pronprob = 1.0 + score = -math.log(pronprob) + + loop_state = 0 # words enter and leave from here + next_state = 1 # the next un-allocated state, will be incremented as we go. + arcs = [] + + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + print("token2id: ", token2id) + print("word2id: ", word2id) + for word, tokens in lexicon: + print(word, tokens) + 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, score]) + + 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 + tokens[i] = tokens[i] if i >= 0 else eps + arcs.append([cur_state, loop_state, tokens[i], w, 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) + print(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" + + lexicon = read_lexicon(lexicon_filename) + print("lexicon: ", lexicon) + tokens = get_tokens(lexicon) + + words = get_words(lexicon) + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + 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 + ["#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, + ) + + L_disambig = lexicon_to_fst( + lexicon_disambig, + token2id=token2id, + word2id=word2id, + 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 False: + # Just for debugging, will remove it + L.labels_sym = k2.SymbolTable.from_file(lang_dir / "tokens.txt") + L.aux_labels_sym = k2.SymbolTable.from_file(lang_dir / "words.txt") + L_disambig.labels_sym = L.labels_sym + L_disambig.aux_labels_sym = L.aux_labels_sym + L.draw(lang_dir / "L.png", title="L") + L_disambig.draw(lang_dir / "L_disambig.png", title="L_disambig") + + +if __name__ == "__main__": + main() diff --git a/egs/grid/AVSR/local/prepare_lexicon.py b/egs/grid/AVSR/local/prepare_lexicon.py new file mode 100644 index 000000000..3b84863c2 --- /dev/null +++ b/egs/grid/AVSR/local/prepare_lexicon.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +# 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. + + +""" +This script takes as input dir "download/GRID/GRID_align_txt" +consisting of all samples' text files and does the following: + +1. Generate lexicon.txt. + +2. Generate train.text. +""" +import argparse +import logging +from pathlib import Path + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--samples-txt", + type=str, + help="""The file listing training samples. + """, + ) + parser.add_argument( + "--align-dir", + type=str, + help="""The directory including training samples' + text files. + """, + ) + parser.add_argument( + "--lang-dir", + type=str, + help="""Output directory. + """, + ) + + return parser.parse_args() + + +def prepare_lexicon( + train_samples_txt: str, train_align_dir: str, lang_dir: str +): + """ + Args: + train_samples_txt: + The file listing training samples, e.g., download/GRID/unseen_train.txt. + train_align_dir: + The directory including training samples' text files, + e.g., download/GRID/GRID_align_txt. + lang_dir: + Output directory, e.g., data/lang_character + Return: + The lexicon.txt file and the train.text in lang_dir. + """ + words = set() + + train_text = Path(lang_dir) / "train.text" + lexicon = Path(lang_dir) / "lexicon.txt" + + if train_text.exists() is False: + texts = [] + train_samples_txts = [] + with open(train_samples_txt, "r") as f: + train_samples_txts = [line.strip() for line in f.readlines()] + + for sample_txt in train_samples_txts: + anno = sample_txt.replace("video/mpg_6000", "align") + ".align" + anno = Path(train_align_dir) / anno + with open(anno, "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) + texts.append(txt.upper()) + + with open(train_text, "w") as f: + for txt in texts: + f.write(txt) + f.write("\n") + + with open(train_text, "r") as load_f: + lines = load_f.readlines() + for line in lines: + words_list = list(filter(None, line.rstrip("\n").split(" "))) + for word in words_list: + if word not in words: + words.add(word) + + with open(lexicon, "w") as f: + for word in words: + chars = list(word) + char_str = " ".join(chars) + f.write((word + " " + char_str).upper()) + f.write("\n") + f.write(" ") + f.write("\n") + + +def main(): + args = get_args() + train_samples_txt = Path(args.samples_txt) + train_align_dir = Path(args.align_dir) + lang_dir = Path(args.lang_dir) + + logging.info("Generating lexicon.txt and train.text") + + prepare_lexicon(train_samples_txt, train_align_dir, lang_dir) + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + main() diff --git a/egs/grid/AVSR/prepare.sh b/egs/grid/AVSR/prepare.sh new file mode 100644 index 000000000..ea8d2325d --- /dev/null +++ b/egs/grid/AVSR/prepare.sh @@ -0,0 +1,150 @@ +#!/usr/bin/env bash + +set -eou pipefail + +stage=-1 +stop_stage=100 + +# We assume dl_dir (download dir) contains the following +# directories and files. If not, they will be downloaded +# by this script automatically. +# +# - $dl_dir/GRID +# You can find lip, audio, align_text inside it. +# Here, for using easily and running our recipe quickly, +# we provide the processed lip data. +# If you want to know more entails about the original GRID +# dataset, you can have a look about this paper: +# An audio-visual corpus for speech perception and automatic +# speech recognition. +# You can also download the GRID dataset form this url: +# https://zenodo.org/record/3625687#.Ybn7HagzY2w. +# +# - audio_25k +# - lip +# - GRID_align_txt +# - unseen_train.txt +# - unseen_val.txt +# +# - $dl_dir/lm +# This directory contains the language model(LM) downloaded from +# https://huggingface.co/luomingshuang/grid_lm. +# About how to get these LM files, you can know it +# from https://github.com/luomingshuang/Train_LM_with_kaldilm. +# +# - lm_3_gram.arpa +# - lm_4_gram.arpa +# + +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then + log "Stage -1: Download LM" + # We assume that you have installed the git-lfs, if not, you could install it + # using: `sudo apt-get install git-lfs && git-lfs install` + #[ ! -e $dl_dir/lm ] && mkdir -p $dl_dir/lm + #git clone https://huggingface.co/luomingshuang/grid_lm $dl_dir/lm + #cd $dl_dir/lm && git lfs pull + + # You can also use the following commands to download the lm files. + # Because the texts among the samples in GRID are very similar, + # the lm_4_gram.arpa is nearly no use for decoding when use LM. + # In our experiments, the decoding results based on 1best is better + # than based on whole-lattice-rescoring. + wget -P $dl_dir/lm https://huggingface.co/luomingshuang/grid_lm/resolve/main/lm_3_gram.arpa + wget -P $dl_dir/lm https://huggingface.co/luomingshuang/grid_lm/resolve/main/lm_4_gram.arpa +fi + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Download data" + # The process of extracting lip region takes much time. + # Here, we provide the processed data (lip region) for using. + # So you can run this recipe quickly and easily. + # + # If you want to know more details about getting lip region, + # You can have a look at https://github.com/Fengdalu/LipNet-PyTorch/tree/master/scripts + + [ ! -e $dl_dir/GRID ] && mkdir -p $dl_dir/GRID + + # Download the GRID lip region data and text + # You can use the following commands to download the processed lip region data and text + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.00 + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.01 + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.02 + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.03 + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/grid_lip_160_80/resolve/main/GRID_LIP_160x80_TXT.zip.04 + + cat $dl_dir/GRID/GRID_LIP_160x80_TXT.zip.* > $dl_dir/GRID/GRID_LIP_160x80_TXT.zip + unzip $dl_dir/GRID/GRID_LIP_160x80_TXT.zip -d $dl_dir/GRID/ + rm -rf $dl_dir/GRID/GRID_LIP_160x80_TXT.zip + + # Download the GRID audio data + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/GRID_audio/resolve/main/audio_25k.zip + unzip $dl_dir/GRID/audio_25k.zip -d $dl_dir/GRID/ + rm -rf $dl_dir/GRID/audio_25k.zip + + # Download the spliting files for train and val + # Here, we just consider the unseen case, which means + # that there is no common speakers among train and val. + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/GRID_text/resolve/main/unseen_train.txt + wget -P $dl_dir/GRID https://huggingface.co/datasets/luomingshuang/GRID_text/resolve/main/unseen_val.txt +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Prepare character-based lang" + lang_dir=data/lang_character + mkdir -p $lang_dir + + ./local/prepare_lexicon.py \ + --samples-txt $dl_dir/GRID/unseen_train.txt \ + --align-dir $dl_dir/GRID/GRID_align_txt \ + --lang-dir $lang_dir + + if [ ! -f $lang_dir/L_disambig.pt ]; then + ./local/prepare_lang.py --lang-dir $lang_dir + fi +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Prepare G" + # We assume you have installed kaldilm, if not, please install + # it using: pip install kaldilm + + mkdir -p data/lm + if [ ! -f data/lm/G_3_gram.fst.txt ]; then + # It is used in building HLG + python3 -m kaldilm \ + --read-symbol-table="data/lang_character/words.txt" \ + --disambig-symbol='#0' \ + --max-order=3 \ + $dl_dir/lm/lm_3_gram.arpa > data/lm/G_3_gram.fst.txt + fi + + if [ ! -f data/lm/G_4_gram.fst.txt ]; then + # It is used for LM rescoring + python3 -m kaldilm \ + --read-symbol-table="data/lang_character/words.txt" \ + --disambig-symbol='#0' \ + --max-order=4 \ + $dl_dir/lm/lm_4_gram.arpa > data/lm/G_4_gram.fst.txt + fi +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Compile HLG" + ./local/compile_hlg.py --lang-dir data/lang_character +fi diff --git a/egs/grid/AVSR/shared b/egs/grid/AVSR/shared new file mode 100644 index 000000000..4c5e91438 --- /dev/null +++ b/egs/grid/AVSR/shared @@ -0,0 +1 @@ +../../../icefall/shared/ \ No newline at end of file diff --git a/egs/grid/AVSR/visualnet2_ctc_vsr/__init__.py b/egs/grid/AVSR/visualnet2_ctc_vsr/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/grid/AVSR/visualnet2_ctc_vsr/decode.py b/egs/grid/AVSR/visualnet2_ctc_vsr/decode.py new file mode 100644 index 000000000..a47c2b126 --- /dev/null +++ b/egs/grid/AVSR/visualnet2_ctc_vsr/decode.py @@ -0,0 +1,499 @@ +#!/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_visual import VisualDataset + +from model import VisualNet2 + +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("visualnet2_ctc_vsr/exp"), + "lang_dir": Path("data/lang_character"), + "lm_dir": Path("data/lm"), + "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"), + "vid_padding": 75, + "num_workers": 16, + "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["vid"] + assert feature.ndim == 5 + feature = feature.to(device) + + nnet_output = model(feature) + 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 = VisualNet2(num_classes=max_token_id + 1) + 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 = VisualDataset( + params.video_path, + params.anno_path, + params.val_list, + params.vid_padding, + "test", + ) + 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/visualnet2_ctc_vsr/model.py b/egs/grid/AVSR/visualnet2_ctc_vsr/model.py new file mode 100644 index 000000000..b5dd5879c --- /dev/null +++ b/egs/grid/AVSR/visualnet2_ctc_vsr/model.py @@ -0,0 +1,215 @@ +#!/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 math +import torch.nn as nn + + +def conv3x3(in_planes, out_planes, stride=1): + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + ) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__(self, block, layers): + self.inplanes = 64 + super(ResNet, self).__init__() + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AdaptiveAvgPool2d(1) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm1d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + return x + + +class VisualNet2(nn.Module): + def __init__(self, num_classes): + """ + Args: + num_classes: + The output dimension of the visualnet2 model. + """ + super().__init__() + self.num_classes = num_classes + self.inputDim = 512 + self.conv3d = nn.Conv3d( + 3, + 64, + kernel_size=(5, 7, 7), + stride=(1, 2, 2), + padding=(2, 3, 3), + bias=False, + ) + self.bn = nn.BatchNorm3d(64, track_running_stats=True) + self.relu = nn.ReLU(True) + self.maxpool = nn.MaxPool3d( + kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1) + ) + + # resnet + self.resnet18 = ResNet(BasicBlock, [2, 2, 2, 2]) + + # grus + self.gru1 = nn.GRU(512, 512, 1, bidirectional=True) + self.gru2 = nn.GRU(1024, 512, 1, bidirectional=True) + + # dropout + self.dropout = nn.Dropout(p=0.5) + + # fc + self.linear = nn.Linear(1024, self.num_classes) + + # initialize + self._initialize_weights() + + def forward(self, x): + frameLen = x.size(2) + x = self.conv3d(x) + x = self.bn(x) + x = self.relu(x) + x = self.maxpool(x) + + x = x.transpose(1, 2) + x = x.contiguous() + x = x.view(-1, 64, x.size(3), x.size(4)) + x = self.resnet18(x) + + x = self.dropout(x) + x = x.view(-1, frameLen, self.inputDim) + + x = x.permute(1, 0, 2) + x, h = self.gru1(x) + x, h = self.gru2(self.dropout(x)) + x = self.linear(x) + x = x.permute(1, 0, 2) + x = nn.functional.log_softmax(x, dim=-1) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + n = ( + m.kernel_size[0] + * m.kernel_size[1] + * m.kernel_size[2] + * m.out_channels + ) + m.weight.data.normal_(0, math.sqrt(2.0 / n)) + if m.bias is not None: + m.bias.data.zero_() + + elif isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / n)) + if m.bias is not None: + m.bias.data.zero_() + + elif isinstance(m, nn.Conv1d): + n = m.kernel_size[0] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / n)) + if m.bias is not None: + m.bias.data.zero_() + + elif isinstance(m, nn.BatchNorm3d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + elif isinstance(m, nn.BatchNorm1d): + m.weight.data.fill_(1) + m.bias.data.zero_() diff --git a/egs/grid/AVSR/visualnet2_ctc_vsr/pretrained.py b/egs/grid/AVSR/visualnet2_ctc_vsr/pretrained.py new file mode 100644 index 000000000..94eef79f7 --- /dev/null +++ b/egs/grid/AVSR/visualnet2_ctc_vsr/pretrained.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# 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 cv2 +import logging +import numpy as np +import os + +import k2 +import torch +from model import VisualNet2 + +from icefall.decode import ( + get_lattice, + one_best_decoding, + rescore_with_whole_lattice, +) +from icefall.utils import AttributeDict, get_texts + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--words-file", + type=str, + required=True, + help="Path to words.txt", + ) + + parser.add_argument( + "--HLG", type=str, required=True, help="Path to HLG.pt." + ) + + parser.add_argument( + "--method", + type=str, + default="1best", + help="""Decoding method. + Possible values are: + (1) 1best - Use the best path as decoding output. Only + the transformer encoder output is used for decoding. + We call it HLG decoding. + (2) whole-lattice-rescoring - Use an LM to rescore the + decoding lattice and then use 1best to decode the + rescored lattice. + We call it HLG decoding + n-gram LM rescoring. + """, + ) + + parser.add_argument( + "--G", + type=str, + help="""An LM for rescoring. + Used only when method is + whole-lattice-rescoring. + It's usually a 4-gram LM. + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.1, + help=""" + Used only when method is whole-lattice-rescoring. + It specifies the scale for n-gram LM scores. + (Note: You need to tune it on a dataset.) + """, + ) + + parser.add_argument( + "--lipframes-dirs", + type=str, + nargs="+", + help="The input visual file(s) to transcribe. " + "Supported formats are those supported by cv2.imread(). " + "The frames sample rate is 25fps.", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "num_classes": 28, + "search_beam": 20, + "output_beam": 5, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + logging.info("Creating model") + model = VisualNet2(num_classes=params.num_classes) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"]) + model.to(device) + model.eval() + + logging.info(f"Loading HLG from {params.HLG}") + HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu")) + HLG = HLG.to(device) + if not hasattr(HLG, "lm_scores"): + # For whole-lattice-rescoring and attention-decoder + HLG.lm_scores = HLG.scores.clone() + + if params.method == "whole-lattice-rescoring": + logging.info(f"Loading G from {params.G}") + G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu")) + # Add epsilon self-loops to G as we will compose + # it with the whole lattice later + G = G.to(device) + G = k2.add_epsilon_self_loops(G) + G = k2.arc_sort(G) + G.lm_scores = G.scores.clone() + + logging.info("Loading lip roi frames") + + vid = [] + for sample_dir in params.lipframes_dirs: + files = os.listdir(sample_dir) + files = list(filter(lambda file: file.find(".jpg") != -1, files)) + files = sorted(files, key=lambda file: int(os.path.splitext(file)[0])) + array = [cv2.imread(os.path.join(sample_dir, file)) for file in files] + array = list(filter(lambda im: im is not None, array)) + array = [ + cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4) + for im in array + ] + array = np.stack(array, axis=0).astype(np.float32) + vid.append(array) + + _, H, W, C = vid[0].shape + # vid_padding = 75 + features = torch.zeros(len(vid), 75, H, W, C).to(device) + for i in range(len(vid)): + length = vid[i].shape[0] + features[i][:length] = torch.FloatTensor(vid[i]).to(device) + + logging.info("Decoding started") + features = features / 255.0 + with torch.no_grad(): + nnet_output = model(features.permute(0, 4, 1, 2, 3)) + # nnet_output is (N, T, C) + + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, + ) + + 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 == "1best": + logging.info("Use HLG decoding") + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + elif params.method == "whole-lattice-rescoring": + logging.info("Use HLG decoding + LM rescoring") + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=[params.ngram_lm_scale], + ) + best_path = next(iter(best_path_dict.values())) + + hyps = get_texts(best_path) + word_sym_table = k2.SymbolTable.from_file(params.words_file) + hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + + s = "\n" + for filename, hyp in zip(params.lipframes_dirs, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/grid/AVSR/visualnet2_ctc_vsr/train.py b/egs/grid/AVSR/visualnet2_ctc_vsr/train.py new file mode 100644 index 000000000..35b691a5a --- /dev/null +++ b/egs/grid/AVSR/visualnet2_ctc_vsr/train.py @@ -0,0 +1,606 @@ +#!/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_visual import VisualDataset +from lhotse.utils import fix_random_seed + +from model import VisualNet2 +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 + tdnn_lstm_ctc/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("visualnet2_ctc_vsr/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"), + "vid_padding": 75, + "num_workers": 16, + "batch_size": 80, + } + ) + + 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["vid"] + assert feature.ndim == 5 + feature = feature.to(device) + + with torch.set_grad_enabled(is_training): + nnet_output = model(feature) + + # 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, + ) + + 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 = VisualNet2(num_classes=max_token_id + 1) + + 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 = VisualDataset( + params.video_path, + params.anno_path, + params.train_list, + params.vid_padding, + "train", + ) + + train_dl = DataLoader( + grid, + batch_size=params.batch_size, + shuffle=True, + num_workers=params.num_workers, + drop_last=False, + ) + # Here, we use train_dl as valid_dl because we don't have extra valid data. + valid_dl = train_dl + + for epoch in range(params.start_epoch, params.num_epochs): + + 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() + 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/visualnet2_ctc_vsr/utils.py b/egs/grid/AVSR/visualnet2_ctc_vsr/utils.py new file mode 100644 index 000000000..17889a31c --- /dev/null +++ b/egs/grid/AVSR/visualnet2_ctc_vsr/utils.py @@ -0,0 +1,54 @@ +# 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. + +""" +This script is to encodes the supervisions as Tuple list. +The supervision tensor has shape ``(batch_size, 3)``. +Its second dimension contains information about sequence index [0], +start frames [1] and num frames [2]. +In GRID, the start frame of each audio sample is 0. +""" +import torch + + +def encode_supervisions(nnet_output_shape: int, batch: dict): + """ + Args: + nnet_output_shape: + The shape of nnet_output, e.g: (N, T, D). + batch: + A batch of dataloader, it's a dict file + including text and aud/vid arrays. + Return: + The tuple list of supervisions and the text in batch. + """ + N, T, D = nnet_output_shape + + supervisions_idx = torch.arange(0, N, dtype=torch.int32) + supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0] + supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0] + + supervision_segments = torch.stack( + ( + supervisions_idx, + supervisions_start_frame, + supervisions_num_frames, + ), + 1, + ) + texts = batch["txt"] + + return supervision_segments, texts diff --git a/egs/grid/AVSR/visualnet_ctc_vsr/__init__.py b/egs/grid/AVSR/visualnet_ctc_vsr/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/grid/AVSR/visualnet_ctc_vsr/decode.py b/egs/grid/AVSR/visualnet_ctc_vsr/decode.py new file mode 100644 index 000000000..2373ada2c --- /dev/null +++ b/egs/grid/AVSR/visualnet_ctc_vsr/decode.py @@ -0,0 +1,498 @@ +#!/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_visual import VisualDataset +from model import VisualNet + +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("visualnet_ctc_vsr/exp"), + "lang_dir": Path("data/lang_character"), + "lm_dir": Path("data/lm"), + "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"), + "vid_padding": 75, + "num_workers": 16, + "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["vid"] + assert feature.ndim == 5 + feature = feature.to(device) + + nnet_output = model(feature) + 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 = VisualNet(num_classes=max_token_id + 1) + 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 = VisualDataset( + params.video_path, + params.anno_path, + params.val_list, + params.vid_padding, + "test", + ) + 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/visualnet_ctc_vsr/model.py b/egs/grid/AVSR/visualnet_ctc_vsr/model.py new file mode 100644 index 000000000..6ff30ff16 --- /dev/null +++ b/egs/grid/AVSR/visualnet_ctc_vsr/model.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +# 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 +import torch.nn as nn + + +class VisualNet(torch.nn.Module): + def __init__(self, num_classes: int) -> None: + """ + Args: + num_classes: + The output dimension of the visualnet model. + """ + super().__init__() + self.num_classes = num_classes + self.conv1 = nn.Conv3d(3, 32, (3, 5, 5), (1, 2, 2), (1, 2, 2)) + self.pool1 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) + + self.conv2 = nn.Conv3d(32, 64, (3, 5, 5), (1, 1, 1), (1, 2, 2)) + self.pool2 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) + + self.conv3 = nn.Conv3d(64, 96, (3, 3, 3), (1, 1, 1), (1, 1, 1)) + self.pool3 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) + + self.gru1 = nn.GRU(96 * 4 * 8, 256, 1, bidirectional=True) + self.gru2 = nn.GRU(512, 256, 1, bidirectional=True) + + self.FC = nn.Linear(512, self.num_classes) + self.dropout_p = 0.5 + + self.relu = nn.ReLU(inplace=True) + self.dropout = nn.Dropout(self.dropout_p) + self.dropout3d = nn.Dropout3d(self.dropout_p) + + def forward(self, x): + + x = self.conv1(x) + x = self.relu(x) + x = self.dropout3d(x) + x = self.pool1(x) + + x = self.conv2(x) + x = self.relu(x) + x = self.dropout3d(x) + x = self.pool2(x) + + x = self.conv3(x) + x = self.relu(x) + x = self.dropout3d(x) + x = self.pool3(x) + + # (B, C, T, H, W)->(T, B, C, H, W) + x = x.permute(2, 0, 1, 3, 4).contiguous() + # (B, C, T, H, W)->(T, B, C*H*W) + x = x.view(x.size(0), x.size(1), -1) + + self.gru1.flatten_parameters() + self.gru2.flatten_parameters() + + x, h = self.gru1(x) + x = self.dropout(x) + x, h = self.gru2(x) + x = self.dropout(x) + + x = x.permute(1, 0, 2).contiguous() + x = self.FC(x) + x = nn.functional.log_softmax(x, dim=-1) + + return x diff --git a/egs/grid/AVSR/visualnet_ctc_vsr/pretrained.py b/egs/grid/AVSR/visualnet_ctc_vsr/pretrained.py new file mode 100644 index 000000000..e479b46f2 --- /dev/null +++ b/egs/grid/AVSR/visualnet_ctc_vsr/pretrained.py @@ -0,0 +1,243 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# 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 cv2 +import logging +import numpy as np +import os + +import k2 +import torch +from model import VisualNet + +from icefall.decode import ( + get_lattice, + one_best_decoding, + rescore_with_whole_lattice, +) +from icefall.utils import AttributeDict, get_texts + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--words-file", + type=str, + required=True, + help="Path to words.txt", + ) + + parser.add_argument( + "--HLG", type=str, required=True, help="Path to HLG.pt." + ) + + parser.add_argument( + "--method", + type=str, + default="1best", + help="""Decoding method. + Possible values are: + (1) 1best - Use the best path as decoding output. Only + the transformer encoder output is used for decoding. + We call it HLG decoding. + (2) whole-lattice-rescoring - Use an LM to rescore the + decoding lattice and then use 1best to decode the + rescored lattice. + We call it HLG decoding + n-gram LM rescoring. + """, + ) + + parser.add_argument( + "--G", + type=str, + help="""An LM for rescoring. + Used only when method is + whole-lattice-rescoring. + It's usually a 4-gram LM. + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.1, + help=""" + Used only when method is whole-lattice-rescoring. + It specifies the scale for n-gram LM scores. + (Note: You need to tune it on a dataset.) + """, + ) + + parser.add_argument( + "--lipframes-dirs", + type=str, + nargs="+", + help="The input visual file(s) to transcribe. " + "Supported formats are those supported by cv2.imread(). " + "The frames sample rate is 25fps.", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "num_classes": 28, + "search_beam": 20, + "output_beam": 5, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + logging.info("Creating model") + model = VisualNet(num_classes=params.num_classes) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"]) + model.to(device) + model.eval() + + logging.info(f"Loading HLG from {params.HLG}") + HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu")) + HLG = HLG.to(device) + if not hasattr(HLG, "lm_scores"): + # For whole-lattice-rescoring and attention-decoder + HLG.lm_scores = HLG.scores.clone() + + if params.method == "whole-lattice-rescoring": + logging.info(f"Loading G from {params.G}") + G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu")) + # Add epsilon self-loops to G as we will compose + # it with the whole lattice later + G = G.to(device) + G = k2.add_epsilon_self_loops(G) + G = k2.arc_sort(G) + G.lm_scores = G.scores.clone() + + logging.info("Loading lip roi frames") + + vid = [] + for sample_dir in params.lipframes_dirs: + files = os.listdir(sample_dir) + files = list(filter(lambda file: file.find(".jpg") != -1, files)) + files = sorted(files, key=lambda file: int(os.path.splitext(file)[0])) + array = [cv2.imread(os.path.join(sample_dir, file)) for file in files] + array = list(filter(lambda im: im is not None, array)) + array = [ + cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4) + for im in array + ] + array = np.stack(array, axis=0).astype(np.float32) + vid.append(array) + + _, H, W, C = vid[0].shape + features = torch.zeros(len(vid), 75, H, W, C).to(device) + for i in range(len(vid)): + length = vid[i].shape[0] + features[i][:length] = torch.FloatTensor(vid[i]).to(device) + + logging.info("Decoding started") + features = features / 255.0 + with torch.no_grad(): + nnet_output = model(features.permute(0, 4, 1, 2, 3)) + # nnet_output is (N, T, C) + + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, + ) + + 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 == "1best": + logging.info("Use HLG decoding") + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + elif params.method == "whole-lattice-rescoring": + logging.info("Use HLG decoding + LM rescoring") + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=[params.ngram_lm_scale], + ) + best_path = next(iter(best_path_dict.values())) + + hyps = get_texts(best_path) + word_sym_table = k2.SymbolTable.from_file(params.words_file) + hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + + s = "\n" + for filename, hyp in zip(params.lipframes_dirs, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/grid/AVSR/visualnet_ctc_vsr/train.py b/egs/grid/AVSR/visualnet_ctc_vsr/train.py new file mode 100644 index 000000000..df66b5cc9 --- /dev/null +++ b/egs/grid/AVSR/visualnet_ctc_vsr/train.py @@ -0,0 +1,606 @@ +#!/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_visual import VisualDataset +from lhotse.utils import fix_random_seed +from model import VisualNet +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 + tdnn_lstm_ctc/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("visualnet_ctc_vsr/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"), + "vid_padding": 75, + "num_workers": 16, + "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["vid"] + assert feature.ndim == 5 + feature = feature.to(device) + + with torch.set_grad_enabled(is_training): + nnet_output = model(feature) + + # 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, + ) + + 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 = VisualNet(num_classes=max_token_id + 1) + + 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 = VisualDataset( + params.video_path, + params.anno_path, + params.train_list, + params.vid_padding, + params.txt_padding, + "train", + ) + + train_dl = DataLoader( + grid, + batch_size=params.batch_size, + shuffle=True, + num_workers=params.num_workers, + drop_last=False, + ) + # Here, we use train_dl as valid_dl because we don't have extra valid data. + valid_dl = train_dl + + for epoch in range(params.start_epoch, params.num_epochs): + + 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() + 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/visualnet_ctc_vsr/utils.py b/egs/grid/AVSR/visualnet_ctc_vsr/utils.py new file mode 100644 index 000000000..17889a31c --- /dev/null +++ b/egs/grid/AVSR/visualnet_ctc_vsr/utils.py @@ -0,0 +1,54 @@ +# 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. + +""" +This script is to encodes the supervisions as Tuple list. +The supervision tensor has shape ``(batch_size, 3)``. +Its second dimension contains information about sequence index [0], +start frames [1] and num frames [2]. +In GRID, the start frame of each audio sample is 0. +""" +import torch + + +def encode_supervisions(nnet_output_shape: int, batch: dict): + """ + Args: + nnet_output_shape: + The shape of nnet_output, e.g: (N, T, D). + batch: + A batch of dataloader, it's a dict file + including text and aud/vid arrays. + Return: + The tuple list of supervisions and the text in batch. + """ + N, T, D = nnet_output_shape + + supervisions_idx = torch.arange(0, N, dtype=torch.int32) + supervisions_start_frame = torch.full((1, N), 0, dtype=torch.int32)[0] + supervisions_num_frames = torch.full((1, N), T, dtype=torch.int32)[0] + + supervision_segments = torch.stack( + ( + supervisions_idx, + supervisions_start_frame, + supervisions_num_frames, + ), + 1, + ) + texts = batch["txt"] + + return supervision_segments, texts