From 3e4179bebb78f907b89aa750b76f3fa85cec0a20 Mon Sep 17 00:00:00 2001 From: sathvik udupa Date: Fri, 28 Apr 2023 13:30:50 +0530 Subject: [PATCH] add all mucs files --- egs/mucs/ASR/conformer_ctc/__init__.py | 0 egs/mucs/ASR/conformer_ctc/ali.py | 395 ++++++++ egs/mucs/ASR/conformer_ctc/asr_datamodule.py | 473 +++++++++ egs/mucs/ASR/conformer_ctc/conformer.py | 910 +++++++++++++++++ egs/mucs/ASR/conformer_ctc/decode.py | 813 +++++++++++++++ egs/mucs/ASR/conformer_ctc/export.py | 163 +++ egs/mucs/ASR/conformer_ctc/label_smoothing.py | 109 ++ egs/mucs/ASR/conformer_ctc/pretrained.py | 430 ++++++++ egs/mucs/ASR/conformer_ctc/subsampling.py | 153 +++ .../ASR/conformer_ctc/test_label_smoothing.py | 52 + .../ASR/conformer_ctc/test_subsampling.py | 48 + .../ASR/conformer_ctc/test_transformer.py | 104 ++ egs/mucs/ASR/conformer_ctc/train.py | 824 ++++++++++++++++ egs/mucs/ASR/conformer_ctc/transformer.py | 928 ++++++++++++++++++ egs/mucs/ASR/local/compute_fbank_mucs.py | 27 +- egs/mucs/ASR/local/prepare_manifest.py | 11 +- egs/mucs/ASR/local/validate_manifest.py | 9 +- egs/mucs/ASR/prepare.sh | 71 +- egs/mucs/ASR/run.sh | 17 +- 19 files changed, 5479 insertions(+), 58 deletions(-) create mode 100644 egs/mucs/ASR/conformer_ctc/__init__.py create mode 100755 egs/mucs/ASR/conformer_ctc/ali.py create mode 100644 egs/mucs/ASR/conformer_ctc/asr_datamodule.py create mode 100644 egs/mucs/ASR/conformer_ctc/conformer.py create mode 100755 egs/mucs/ASR/conformer_ctc/decode.py create mode 100755 egs/mucs/ASR/conformer_ctc/export.py create mode 100644 egs/mucs/ASR/conformer_ctc/label_smoothing.py create mode 100755 egs/mucs/ASR/conformer_ctc/pretrained.py create mode 100644 egs/mucs/ASR/conformer_ctc/subsampling.py create mode 100755 egs/mucs/ASR/conformer_ctc/test_label_smoothing.py create mode 100755 egs/mucs/ASR/conformer_ctc/test_subsampling.py create mode 100644 egs/mucs/ASR/conformer_ctc/test_transformer.py create mode 100755 egs/mucs/ASR/conformer_ctc/train.py create mode 100644 egs/mucs/ASR/conformer_ctc/transformer.py diff --git a/egs/mucs/ASR/conformer_ctc/__init__.py b/egs/mucs/ASR/conformer_ctc/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/mucs/ASR/conformer_ctc/ali.py b/egs/mucs/ASR/conformer_ctc/ali.py new file mode 100755 index 000000000..42e14abac --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/ali.py @@ -0,0 +1,395 @@ +#!/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. + +""" +Usage: + ./conformer_ctc/ali.py \ + --exp-dir ./conformer_ctc/exp \ + --lang-dir ./data/lang_bpe_500 \ + --epoch 20 \ + --avg 10 \ + --max-duration 300 \ + --dataset train-clean-100 \ + --out-dir data/ali +""" + +import argparse +import logging +from pathlib import Path + +import k2 +import numpy as np +import torch +from asr_datamodule import LibriSpeechAsrDataModule +from conformer import Conformer +from lhotse import CutSet +from lhotse.features.io import FeaturesWriter, NumpyHdf5Writer + +from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.decode import one_best_decoding +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + encode_supervisions, + get_alignments, + setup_logger, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=34, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + parser.add_argument( + "--avg", + type=int, + default=20, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_bpe_500", + help="The lang dir", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="conformer_ctc/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--out-dir", + type=str, + required=True, + help="""Output directory. + It contains 3 generated files: + + - labels_xxx.h5 + - aux_labels_xxx.h5 + - librispeech_cuts_xxx.jsonl.gz + + where xxx is the value of `--dataset`. For instance, if + `--dataset` is `train-clean-100`, it will contain 3 files: + + - `labels_train-clean-100.h5` + - `aux_labels_train-clean-100.h5` + - `librispeech_cuts_train-clean-100.jsonl.gz` + + Note: Both labels_xxx.h5 and aux_labels_xxx.h5 contain framewise + alignment. The difference is that labels_xxx.h5 contains repeats. + """, + ) + + parser.add_argument( + "--dataset", + type=str, + required=True, + help="""The name of the dataset to compute alignments for. + Possible values are: + - test-clean. + - test-other + - train-clean-100 + - train-clean-360 + - train-other-500 + - dev-clean + - dev-other + """, + ) + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "lm_dir": Path("data/lm"), + "feature_dim": 80, + "nhead": 8, + "attention_dim": 512, + "subsampling_factor": 4, + # Set it to 0 since attention decoder + # is not used for computing alignments + "num_decoder_layers": 0, + "vgg_frontend": False, + "use_feat_batchnorm": True, + "output_beam": 10, + "use_double_scores": True, + "env_info": get_env_info(), + } + ) + return params + + +def compute_alignments( + model: torch.nn.Module, + dl: torch.utils.data.DataLoader, + labels_writer: FeaturesWriter, + aux_labels_writer: FeaturesWriter, + params: AttributeDict, + graph_compiler: BpeCtcTrainingGraphCompiler, +) -> CutSet: + """Compute the framewise alignments of a dataset. + + Args: + model: + The neural network model. + dl: + Dataloader containing the dataset. + params: + Parameters for computing alignments. + graph_compiler: + It converts token IDs to decoding graphs. + Returns: + Return a CutSet. Each cut has two custom fields: labels_alignment + and aux_labels_alignment, containing framewise alignments information. + Both are of type `lhotse.array.TemporalArray`. The difference between + the two alignments is that `labels_alignment` contain repeats. + """ + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + num_cuts = 0 + + device = graph_compiler.device + cuts = [] + for batch_idx, batch in enumerate(dl): + feature = batch["inputs"] + + # at entry, feature is [N, T, C] + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + cut_list = supervisions["cut"] + + for cut in cut_list: + assert len(cut.supervisions) == 1, f"{len(cut.supervisions)}" + + nnet_output, encoder_memory, memory_mask = model(feature, supervisions) + # nnet_output is [N, T, C] + supervision_segments, texts = encode_supervisions( + supervisions, subsampling_factor=params.subsampling_factor + ) + # we need also to sort cut_ids as encode_supervisions() + # reorders "texts". + # In general, new2old is an identity map since lhotse sorts the returned + # cuts by duration in descending order + new2old = supervision_segments[:, 0].tolist() + + cut_list = [cut_list[i] for i in new2old] + + token_ids = graph_compiler.texts_to_ids(texts) + decoding_graph = graph_compiler.compile(token_ids) + + dense_fsa_vec = k2.DenseFsaVec( + nnet_output, + supervision_segments, + allow_truncate=params.subsampling_factor - 1, + ) + + lattice = k2.intersect_dense( + decoding_graph, + dense_fsa_vec, + params.output_beam, + ) + + best_path = one_best_decoding( + lattice=lattice, + use_double_scores=params.use_double_scores, + ) + + labels_ali = get_alignments(best_path, kind="labels") + aux_labels_ali = get_alignments(best_path, kind="aux_labels") + assert len(labels_ali) == len(aux_labels_ali) == len(cut_list) + for cut, labels, aux_labels in zip(cut_list, labels_ali, aux_labels_ali): + cut.labels_alignment = labels_writer.store_array( + key=cut.id, + value=np.asarray(labels, dtype=np.int32), + # frame shift is 0.01s, subsampling_factor is 4 + frame_shift=0.04, + temporal_dim=0, + start=0, + ) + cut.aux_labels_alignment = aux_labels_writer.store_array( + key=cut.id, + value=np.asarray(aux_labels, dtype=np.int32), + # frame shift is 0.01s, subsampling_factor is 4 + frame_shift=0.04, + temporal_dim=0, + start=0, + ) + + cuts += cut_list + + num_cuts += len(cut_list) + + if batch_idx % 100 == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + + return CutSet.from_cuts(cuts) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + + args.enable_spec_aug = False + args.enable_musan = False + args.return_cuts = True + args.concatenate_cuts = False + + params = get_params() + params.update(vars(args)) + + setup_logger(f"{params.exp_dir}/log-ali") + + logging.info(f"Computing alignments for {params.dataset} - started") + logging.info(params) + + out_dir = Path(params.out_dir) + out_dir.mkdir(exist_ok=True) + + out_labels_ali_filename = out_dir / f"labels_{params.dataset}.h5" + out_aux_labels_ali_filename = out_dir / f"aux_labels_{params.dataset}.h5" + out_manifest_filename = out_dir / f"librispeech_cuts_{params.dataset}.jsonl.gz" + + for f in ( + out_labels_ali_filename, + out_aux_labels_ali_filename, + out_manifest_filename, + ): + if f.exists(): + logging.info(f"{f} exists - skipping") + return + + lexicon = Lexicon(params.lang_dir) + max_token_id = max(lexicon.tokens) + num_classes = max_token_id + 1 # +1 for the blank + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + logging.info(f"device: {device}") + + graph_compiler = BpeCtcTrainingGraphCompiler( + params.lang_dir, + device=device, + sos_token="", + eos_token="", + ) + + logging.info("About to create model") + model = Conformer( + num_features=params.feature_dim, + nhead=params.nhead, + d_model=params.attention_dim, + num_classes=num_classes, + subsampling_factor=params.subsampling_factor, + num_decoder_layers=params.num_decoder_layers, + vgg_frontend=params.vgg_frontend, + use_feat_batchnorm=params.use_feat_batchnorm, + ) + model.to(device) + + if params.avg == 1: + load_checkpoint( + f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=False + ) + 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, device=device), strict=False + ) + + model.eval() + + librispeech = LibriSpeechAsrDataModule(args) + if params.dataset == "test-clean": + test_clean_cuts = librispeech.test_clean_cuts() + dl = librispeech.test_dataloaders(test_clean_cuts) + elif params.dataset == "test-other": + test_other_cuts = librispeech.test_other_cuts() + dl = librispeech.test_dataloaders(test_other_cuts) + elif params.dataset == "train-clean-100": + train_clean_100_cuts = librispeech.train_clean_100_cuts() + dl = librispeech.train_dataloaders(train_clean_100_cuts) + elif params.dataset == "train-clean-360": + train_clean_360_cuts = librispeech.train_clean_360_cuts() + dl = librispeech.train_dataloaders(train_clean_360_cuts) + elif params.dataset == "train-other-500": + train_other_500_cuts = librispeech.train_other_500_cuts() + dl = librispeech.train_dataloaders(train_other_500_cuts) + elif params.dataset == "dev-clean": + dev_clean_cuts = librispeech.dev_clean_cuts() + dl = librispeech.valid_dataloaders(dev_clean_cuts) + else: + assert params.dataset == "dev-other", f"{params.dataset}" + dev_other_cuts = librispeech.dev_other_cuts() + dl = librispeech.valid_dataloaders(dev_other_cuts) + + logging.info(f"Processing {params.dataset}") + with NumpyHdf5Writer(out_labels_ali_filename) as labels_writer: + with NumpyHdf5Writer(out_aux_labels_ali_filename) as aux_labels_writer: + cut_set = compute_alignments( + model=model, + dl=dl, + labels_writer=labels_writer, + aux_labels_writer=aux_labels_writer, + params=params, + graph_compiler=graph_compiler, + ) + + cut_set.to_file(out_manifest_filename) + + logging.info( + f"For dataset {params.dataset}, its alignments with repeats are " + f"saved to {out_labels_ali_filename}, the alignments without repeats " + f"are saved to {out_aux_labels_ali_filename}, and the cut manifest " + f"file is {out_manifest_filename}. Number of cuts: {len(cut_set)}" + ) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/mucs/ASR/conformer_ctc/asr_datamodule.py b/egs/mucs/ASR/conformer_ctc/asr_datamodule.py new file mode 100644 index 000000000..85c39b91b --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/asr_datamodule.py @@ -0,0 +1,473 @@ +# Copyright 2021 Piotr Żelasko +# Copyright 2022 Xiaomi Corporation (Author: 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 inspect +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, Optional + +import torch +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples + AudioSamples, + OnTheFlyFeatures, +) +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class LibriSpeechAsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + group.add_argument( + "--full-libri", + type=str2bool, + default=True, + help="When enabled, use 960h LibriSpeech. Otherwise, use 100h subset.", + ) + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/valid/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=200.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=30, + help="The number of buckets for the DynamicBucketingSampler" + "(you might want to increase it for larger datasets).", + ) + group.add_argument( + "--concatenate-cuts", + type=str2bool, + default=False, + help="When enabled, utterances (cuts) will be concatenated " + "to minimize the amount of padding.", + ) + group.add_argument( + "--duration-factor", + type=float, + default=1.0, + help="Determines the maximum duration of a concatenated cut " + "relative to the duration of the longest cut in a batch.", + ) + group.add_argument( + "--gap", + type=float, + default=1.0, + help="The amount of padding (in seconds) inserted between " + "concatenated cuts. This padding is filled with noise when " + "noise augmentation is used.", + ) + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + group.add_argument( + "--drop-last", + type=str2bool, + default=True, + help="Whether to drop last batch. Used by sampler.", + ) + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it" + "with training dataset. ", + ) + + group.add_argument( + "--input-strategy", + type=str, + default="PrecomputedFeatures", + help="AudioSamples or PrecomputedFeatures", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + logging.info("About to get Musan cuts") + cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz") + transforms.append( + CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True) + ) + else: + logging.info("Disable MUSAN") + + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}") + # Set the value of num_frame_masks according to Lhotse's version. + # In different Lhotse's versions, the default of num_frame_masks is + # different. + num_frame_masks = 10 + num_frame_masks_parameter = inspect.signature( + SpecAugment.__init__ + ).parameters["num_frame_masks"] + if num_frame_masks_parameter.default == 1: + num_frame_masks = 2 + logging.info(f"Num frame mask: {num_frame_masks}") + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=num_frame_masks, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + train = K2SpeechRecognitionDataset( + input_strategy=eval(self.args.input_strategy)(), + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + drop_last=self.args.drop_last, + ) + else: + logging.info("Using SingleCutSampler.") + train_sampler = SingleCutSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + ) + logging.info("About to create train dataloader") + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_sampler.load_state_dict(sampler_state_dict) + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + worker_init_fn=worker_init_fn, + ) + + return train_dl + + def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: + transforms = [] + if self.args.concatenate_cuts: + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))), + return_cuts=self.args.return_cuts, + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + ) + valid_sampler = DynamicBucketingSampler( + cuts_valid, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=2, + persistent_workers=False, + ) + + return valid_dl + + def test_dataloaders(self, cuts: CutSet) -> DataLoader: + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) + if self.args.on_the_fly_feats + else eval(self.args.input_strategy)(), + return_cuts=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.debug("About to create test dataloader") + test_dl = DataLoader( + test, + batch_size=None, + sampler=sampler, + num_workers=self.args.num_workers, + ) + return test_dl + + @lru_cache() + def train_clean_100_cuts(self) -> CutSet: + logging.info("About to get train-clean-100 cuts") + return load_manifest_lazy( + self.args.manifest_dir / "mucs_cuts_train.jsonl.gz" + ) + @lru_cache() + def dev_mucs_cuts(self) -> CutSet: + logging.info("About to get valid-mucs") + return load_manifest_lazy( + self.args.manifest_dir / "mucs_cuts_dev.jsonl.gz" + ) + + @lru_cache() + def test_mucs_cuts(self) -> CutSet: + logging.info("About to get test-clean cuts") + return load_manifest_lazy( + self.args.manifest_dir / "mucs_cuts_test.jsonl.gz" + ) + @lru_cache() + def train_clean_mucs_cuts(self) -> CutSet: + logging.info("About to get train-mucs") + return load_manifest_lazy( + self.args.manifest_dir / "mucs_cuts_train.jsonl.gz" + ) + + + @lru_cache() + def train_clean_360_cuts(self) -> CutSet: + logging.info("About to get train-clean-360 cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_train-clean-360.jsonl.gz" + ) + + @lru_cache() + def train_other_500_cuts(self) -> CutSet: + logging.info("About to get train-other-500 cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_train-other-500.jsonl.gz" + ) + + @lru_cache() + def train_all_shuf_cuts(self) -> CutSet: + logging.info( + "About to get the shuffled train-clean-100, \ + train-clean-360 and train-other-500 cuts" + ) + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_train-all-shuf.jsonl.gz" + ) + + @lru_cache() + def dev_clean_cuts(self) -> CutSet: + logging.info("About to get dev-clean cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_dev-clean.jsonl.gz" + ) + + @lru_cache() + def dev_other_cuts(self) -> CutSet: + logging.info("About to get dev-other cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_dev-other.jsonl.gz" + ) + + @lru_cache() + def test_clean_cuts(self) -> CutSet: + logging.info("About to get test-clean cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz" + ) + + @lru_cache() + def test_other_cuts(self) -> CutSet: + logging.info("About to get test-other cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz" + ) diff --git a/egs/mucs/ASR/conformer_ctc/conformer.py b/egs/mucs/ASR/conformer_ctc/conformer.py new file mode 100644 index 000000000..a1cfe6e75 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/conformer.py @@ -0,0 +1,910 @@ +#!/usr/bin/env python3 +# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu) +# +# 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 warnings +from typing import Optional, Tuple, Union + +import torch +from torch import Tensor, nn +from transformer import Supervisions, Transformer, encoder_padding_mask + + +class Conformer(Transformer): + """ + Args: + num_features (int): Number of input features + num_classes (int): Number of output classes + subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers) + d_model (int): attention dimension + nhead (int): number of head + dim_feedforward (int): feedforward dimention + num_encoder_layers (int): number of encoder layers + num_decoder_layers (int): number of decoder layers + dropout (float): dropout rate + cnn_module_kernel (int): Kernel size of convolution module + normalize_before (bool): whether to use layer_norm before the first block. + vgg_frontend (bool): whether to use vgg frontend. + """ + + def __init__( + self, + num_features: int, + num_classes: int, + subsampling_factor: int = 4, + d_model: int = 256, + nhead: int = 4, + dim_feedforward: int = 2048, + num_encoder_layers: int = 12, + num_decoder_layers: int = 6, + dropout: float = 0.1, + cnn_module_kernel: int = 31, + normalize_before: bool = True, + vgg_frontend: bool = False, + use_feat_batchnorm: Union[float, bool] = 0.1, + ) -> None: + super(Conformer, self).__init__( + num_features=num_features, + num_classes=num_classes, + subsampling_factor=subsampling_factor, + d_model=d_model, + nhead=nhead, + dim_feedforward=dim_feedforward, + num_encoder_layers=num_encoder_layers, + num_decoder_layers=num_decoder_layers, + dropout=dropout, + normalize_before=normalize_before, + vgg_frontend=vgg_frontend, + use_feat_batchnorm=use_feat_batchnorm, + ) + + self.encoder_pos = RelPositionalEncoding(d_model, dropout) + + use_conv_batchnorm = True + if isinstance(use_feat_batchnorm, float): + use_conv_batchnorm = False + encoder_layer = ConformerEncoderLayer( + d_model, + nhead, + dim_feedforward, + dropout, + cnn_module_kernel, + normalize_before, + use_conv_batchnorm, + ) + self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers) + self.normalize_before = normalize_before + if self.normalize_before: + self.after_norm = nn.LayerNorm(d_model) + else: + # Note: TorchScript detects that self.after_norm could be used inside forward() + # and throws an error without this change. + self.after_norm = identity + + def run_encoder( + self, x: Tensor, supervisions: Optional[Supervisions] = None + ) -> Tuple[Tensor, Optional[Tensor]]: + """ + Args: + x: + The model input. Its shape is (N, T, C). + supervisions: + Supervision in lhotse format. + See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa + CAUTION: It contains length information, i.e., start and number of + frames, before subsampling + It is read directly from the batch, without any sorting. It is used + to compute encoder padding mask, which is used as memory key padding + mask for the decoder. + + Returns: + Tensor: Predictor tensor of dimension (input_length, batch_size, d_model). + Tensor: Mask tensor of dimension (batch_size, input_length) + """ + x = self.encoder_embed(x) + x, pos_emb = self.encoder_pos(x) + x = x.permute(1, 0, 2) # (B, T, F) -> (T, B, F) + mask = encoder_padding_mask(x.size(0), supervisions) + if mask is not None: + mask = mask.to(x.device) + x = self.encoder(x, pos_emb, src_key_padding_mask=mask) # (T, B, F) + + if self.normalize_before: + x = self.after_norm(x) + + return x, mask + + +class ConformerEncoderLayer(nn.Module): + """ + ConformerEncoderLayer is made up of self-attn, feedforward and convolution networks. + See: "Conformer: Convolution-augmented Transformer for Speech Recognition" + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + cnn_module_kernel (int): Kernel size of convolution module. + normalize_before: whether to use layer_norm before the first block. + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = encoder_layer(src, pos_emb) + """ + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + cnn_module_kernel: int = 31, + normalize_before: bool = True, + use_conv_batchnorm: bool = False, + ) -> None: + super(ConformerEncoderLayer, self).__init__() + self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0) + + self.feed_forward = nn.Sequential( + nn.Linear(d_model, dim_feedforward), + Swish(), + nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model), + ) + + self.feed_forward_macaron = nn.Sequential( + nn.Linear(d_model, dim_feedforward), + Swish(), + nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model), + ) + + self.conv_module = ConvolutionModule( + d_model, cnn_module_kernel, use_batchnorm=use_conv_batchnorm + ) + + self.norm_ff_macaron = nn.LayerNorm(d_model) # for the macaron style FNN module + self.norm_ff = nn.LayerNorm(d_model) # for the FNN module + self.norm_mha = nn.LayerNorm(d_model) # for the MHA module + + self.ff_scale = 0.5 + + self.norm_conv = nn.LayerNorm(d_model) # for the CNN module + self.norm_final = nn.LayerNorm(d_model) # for the final output of the block + + self.dropout = nn.Dropout(dropout) + + self.normalize_before = normalize_before + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + ) -> Tensor: + """ + Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + pos_emb: Positional embedding tensor (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + src_mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, N is the batch size, E is the feature number + """ + + # macaron style feed forward module + residual = src + if self.normalize_before: + src = self.norm_ff_macaron(src) + src = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(src)) + if not self.normalize_before: + src = self.norm_ff_macaron(src) + + # multi-headed self-attention module + residual = src + if self.normalize_before: + src = self.norm_mha(src) + src_att = self.self_attn( + src, + src, + src, + pos_emb=pos_emb, + attn_mask=src_mask, + key_padding_mask=src_key_padding_mask, + )[0] + src = residual + self.dropout(src_att) + if not self.normalize_before: + src = self.norm_mha(src) + + # convolution module + residual = src + if self.normalize_before: + src = self.norm_conv(src) + src = residual + self.dropout( + self.conv_module(src, src_key_padding_mask=src_key_padding_mask) + ) + if not self.normalize_before: + src = self.norm_conv(src) + + # feed forward module + residual = src + if self.normalize_before: + src = self.norm_ff(src) + src = residual + self.ff_scale * self.dropout(self.feed_forward(src)) + if not self.normalize_before: + src = self.norm_ff(src) + + if self.normalize_before: + src = self.norm_final(src) + + return src + + +class ConformerEncoder(nn.TransformerEncoder): + r"""ConformerEncoder is a stack of N encoder layers + + Args: + encoder_layer: an instance of the ConformerEncoderLayer() class (required). + num_layers: the number of sub-encoder-layers in the encoder (required). + norm: the layer normalization component (optional). + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> conformer_encoder = ConformerEncoder(encoder_layer, num_layers=6) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = conformer_encoder(src, pos_emb) + """ + + def __init__( + self, encoder_layer: nn.Module, num_layers: int, norm: nn.Module = None + ) -> None: + super(ConformerEncoder, self).__init__( + encoder_layer=encoder_layer, num_layers=num_layers, norm=norm + ) + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + ) -> Tensor: + r"""Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + pos_emb: Positional embedding tensor (required). + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number + + """ + output = src + + for mod in self.layers: + output = mod( + output, + pos_emb, + src_mask=mask, + src_key_padding_mask=src_key_padding_mask, + ) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class RelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module. + + See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py + + Args: + d_model: Embedding dimension. + dropout_rate: Dropout rate. + max_len: Maximum input length. + + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000) -> None: + """Construct an PositionalEncoding object.""" + super(RelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: Tensor) -> None: + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + # Note: TorchScript doesn't implement operator== for torch.Device + if self.pe.dtype != x.dtype or str(self.pe.device) != str(x.device): + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vector and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[Tensor, Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 + - x.size(1) + + 1 : self.pe.size(1) // 2 # noqa E203 + + x.size(1), + ] + return self.dropout(x), self.dropout(pos_emb) + + +class RelPositionMultiheadAttention(nn.Module): + r"""Multi-Head Attention layer with relative position encoding + + See reference: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + + Args: + embed_dim: total dimension of the model. + num_heads: parallel attention heads. + dropout: a Dropout layer on attn_output_weights. Default: 0.0. + + Examples:: + + >>> rel_pos_multihead_attn = RelPositionMultiheadAttention(embed_dim, num_heads) + >>> attn_output, attn_output_weights = multihead_attn(query, key, value, pos_emb) + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + ) -> None: + super(RelPositionMultiheadAttention, self).__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + + self.in_proj = nn.Linear(embed_dim, 3 * embed_dim, bias=True) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True) + + # linear transformation for positional encoding. + self.linear_pos = nn.Linear(embed_dim, embed_dim, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + self.pos_bias_v = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + + self._reset_parameters() + + def _reset_parameters(self) -> None: + nn.init.xavier_uniform_(self.in_proj.weight) + nn.init.constant_(self.in_proj.bias, 0.0) + nn.init.constant_(self.out_proj.bias, 0.0) + + nn.init.xavier_uniform_(self.pos_bias_u) + nn.init.xavier_uniform_(self.pos_bias_v) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. When given a binary mask and a value is True, + the corresponding value on the attention layer will be ignored. When given + a byte mask and a value is non-zero, the corresponding value on the attention + layer will be ignored + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + - Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the position + with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + - Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + return self.multi_head_attention_forward( + query, + key, + value, + pos_emb, + self.embed_dim, + self.num_heads, + self.in_proj.weight, + self.in_proj.bias, + self.dropout, + self.out_proj.weight, + self.out_proj.bias, + training=self.training, + key_padding_mask=key_padding_mask, + need_weights=need_weights, + attn_mask=attn_mask, + ) + + def rel_shift(self, x: Tensor) -> Tensor: + """Compute relative positional encoding. + + Args: + x: Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + Tensor: tensor of shape (batch, head, time1, time2) + (note: time2 has the same value as time1, but it is for + the key, while time1 is for the query). + """ + (batch_size, num_heads, time1, n) = x.shape + assert n == 2 * time1 - 1 + # Note: TorchScript requires explicit arg for stride() + batch_stride = x.stride(0) + head_stride = x.stride(1) + time1_stride = x.stride(2) + n_stride = x.stride(3) + return x.as_strided( + (batch_size, num_heads, time1, time1), + (batch_stride, head_stride, time1_stride - n_stride, n_stride), + storage_offset=n_stride * (time1 - 1), + ) + + def multi_head_attention_forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + embed_dim_to_check: int, + num_heads: int, + in_proj_weight: Tensor, + in_proj_bias: Tensor, + dropout_p: float, + out_proj_weight: Tensor, + out_proj_bias: Tensor, + training: bool = True, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + embed_dim_to_check: total dimension of the model. + num_heads: parallel attention heads. + in_proj_weight, in_proj_bias: input projection weight and bias. + dropout_p: probability of an element to be zeroed. + out_proj_weight, out_proj_bias: the output projection weight and bias. + training: apply dropout if is ``True``. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. This is an binary mask. When the value is True, + the corresponding value on the attention layer will be filled with -inf. + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` or :math:`(1, 2*L-1, E)` where L is the target sequence + length, N is the batch size, E is the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions + will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == embed_dim_to_check + assert key.size(0) == value.size(0) and key.size(1) == value.size(1) + + head_dim = embed_dim // num_heads + assert ( + head_dim * num_heads == embed_dim + ), "embed_dim must be divisible by num_heads" + scaling = float(head_dim) ** -0.5 + + if torch.equal(query, key) and torch.equal(key, value): + # self-attention + q, k, v = nn.functional.linear(query, in_proj_weight, in_proj_bias).chunk( + 3, dim=-1 + ) + + elif torch.equal(key, value): + # encoder-decoder attention + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + k, v = nn.functional.linear(key, _w, _b).chunk(2, dim=-1) + + else: + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = embed_dim * 2 + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + k = nn.functional.linear(key, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim * 2 + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + v = nn.functional.linear(value, _w, _b) + + if attn_mask is not None: + assert ( + attn_mask.dtype == torch.float32 + or attn_mask.dtype == torch.float64 + or attn_mask.dtype == torch.float16 + or attn_mask.dtype == torch.uint8 + or attn_mask.dtype == torch.bool + ), "Only float, byte, and bool types are supported for attn_mask, not {}".format( + attn_mask.dtype + ) + if attn_mask.dtype == torch.uint8: + warnings.warn( + "Byte tensor for attn_mask is deprecated. Use bool tensor instead." + ) + attn_mask = attn_mask.to(torch.bool) + + if attn_mask.dim() == 2: + attn_mask = attn_mask.unsqueeze(0) + if list(attn_mask.size()) != [1, query.size(0), key.size(0)]: + raise RuntimeError("The size of the 2D attn_mask is not correct.") + elif attn_mask.dim() == 3: + if list(attn_mask.size()) != [ + bsz * num_heads, + query.size(0), + key.size(0), + ]: + raise RuntimeError("The size of the 3D attn_mask is not correct.") + else: + raise RuntimeError( + "attn_mask's dimension {} is not supported".format(attn_mask.dim()) + ) + # attn_mask's dim is 3 now. + + # convert ByteTensor key_padding_mask to bool + if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8: + warnings.warn( + "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead." + ) + key_padding_mask = key_padding_mask.to(torch.bool) + + q = q.contiguous().view(tgt_len, bsz, num_heads, head_dim) + k = k.contiguous().view(-1, bsz, num_heads, head_dim) + v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1) + + src_len = k.size(0) + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz, "{} == {}".format( + key_padding_mask.size(0), bsz + ) + assert key_padding_mask.size(1) == src_len, "{} == {}".format( + key_padding_mask.size(1), src_len + ) + + q = q.transpose(0, 1) # (batch, time1, head, d_k) + + pos_emb_bsz = pos_emb.size(0) + assert pos_emb_bsz in (1, bsz) # actually it is 1 + p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim) + p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + q_with_bias_u = (q + self.pos_bias_u).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + q_with_bias_v = (q + self.pos_bias_v).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + # compute attention score + # first compute matrix a and matrix c + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + k = k.permute(1, 2, 3, 0) # (batch, head, d_k, time2) + matrix_ac = torch.matmul(q_with_bias_u, k) # (batch, head, time1, time2) + + # compute matrix b and matrix d + matrix_bd = torch.matmul( + q_with_bias_v, p.transpose(-2, -1) + ) # (batch, head, time1, 2*time1-1) + matrix_bd = self.rel_shift(matrix_bd) + + attn_output_weights = ( + matrix_ac + matrix_bd + ) * scaling # (batch, head, time1, time2) + + attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, -1) + + assert list(attn_output_weights.size()) == [ + bsz * num_heads, + tgt_len, + src_len, + ] + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_output_weights.masked_fill_(attn_mask, float("-inf")) + else: + attn_output_weights += attn_mask + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + attn_output_weights = attn_output_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + float("-inf"), + ) + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, src_len + ) + + attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1) + attn_output_weights = nn.functional.dropout( + attn_output_weights, p=dropout_p, training=training + ) + + attn_output = torch.bmm(attn_output_weights, v) + assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim] + attn_output = ( + attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + ) + attn_output = nn.functional.linear(attn_output, out_proj_weight, out_proj_bias) + + if need_weights: + # average attention weights over heads + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + return attn_output, attn_output_weights.sum(dim=1) / num_heads + else: + return attn_output, None + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model. + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py + + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernerl size of conv layers. + bias (bool): Whether to use bias in conv layers (default=True). + + """ + + def __init__( + self, + channels: int, + kernel_size: int, + bias: bool = True, + use_batchnorm: bool = False, + ) -> None: + """Construct an ConvolutionModule object.""" + super(ConvolutionModule, self).__init__() + # kernerl_size should be a odd number for 'SAME' padding + assert (kernel_size - 1) % 2 == 0 + self.use_batchnorm = use_batchnorm + + self.pointwise_conv1 = nn.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + groups=channels, + bias=bias, + ) + if self.use_batchnorm: + self.norm = nn.BatchNorm1d(channels) + self.pointwise_conv2 = nn.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = Swish() + + def forward( + self, + x: Tensor, + src_key_padding_mask: Optional[Tensor] = None, + ) -> Tensor: + """Compute convolution module. + + Args: + x: Input tensor (#time, batch, channels). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Returns: + Tensor: Output tensor (#time, batch, channels). + + """ + # exchange the temporal dimension and the feature dimension + x = x.permute(1, 2, 0) # (#batch, channels, time). + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channels, time) + x = nn.functional.glu(x, dim=1) # (batch, channels, time) + + # 1D Depthwise Conv + if src_key_padding_mask is not None: + x.masked_fill_(src_key_padding_mask.unsqueeze(1).expand_as(x), 0.0) + x = self.depthwise_conv(x) + if self.use_batchnorm: + x = self.norm(x) + x = self.activation(x) + + x = self.pointwise_conv2(x) # (batch, channel, time) + + return x.permute(2, 0, 1) + + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: Tensor) -> Tensor: + """Return Swich activation function.""" + return x * torch.sigmoid(x) + + +def identity(x): + return x diff --git a/egs/mucs/ASR/conformer_ctc/decode.py b/egs/mucs/ASR/conformer_ctc/decode.py new file mode 100755 index 000000000..c5ac7912b --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/decode.py @@ -0,0 +1,813 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from conformer import Conformer + +from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint +from icefall.decode import ( + get_lattice, + nbest_decoding, + nbest_oracle, + one_best_decoding, + rescore_with_attention_decoder, + rescore_with_n_best_list, + rescore_with_rnn_lm, + rescore_with_whole_lattice, +) +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.rnn_lm.model import RnnLmModel +from icefall.utils import ( + AttributeDict, + get_texts, + load_averaged_model, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=77, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + parser.add_argument( + "--avg", + type=int, + default=55, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--method", + type=str, + default="attention-decoder", + help="""Decoding method. + Supported values are: + - (0) ctc-decoding. Use CTC decoding. It uses a sentence piece + model, i.e., lang_dir/bpe.model, to convert word pieces to words. + It needs neither a lexicon nor an n-gram LM. + - (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. + - (5) attention-decoder. Extract n paths from the LM rescored + lattice, the path with the highest score is the decoding result. + - (6) rnn-lm. Rescoring with attention-decoder and RNN LM. We assume + you have trained an RNN LM using ./rnn_lm/train.py + - (7) nbest-oracle. Its WER is the lower bound of any n-best + rescoring method can achieve. Useful for debugging n-best + rescoring method. + """, + ) + + 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, attention-decoder, rnn-lm, and nbest-oracle + """, + ) + + 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, attention-decoder, rnn-lm, and nbest-oracle + A smaller value results in more unique paths. + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="conformer_ctc/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_bpe_500", + help="The lang dir", + ) + + parser.add_argument( + "--lm-dir", + type=str, + default="data/lm", + help="""The n-gram LM dir. + It should contain either G_4_gram.pt or G_4_gram.fst.txt + """, + ) + + parser.add_argument( + "--rnn-lm-exp-dir", + type=str, + default="rnn_lm/exp", + help="""Used only when --method is rnn-lm. + It specifies the path to RNN LM exp dir. + """, + ) + + parser.add_argument( + "--rnn-lm-epoch", + type=int, + default=7, + help="""Used only when --method is rnn-lm. + It specifies the checkpoint to use. + """, + ) + + parser.add_argument( + "--rnn-lm-avg", + type=int, + default=2, + help="""Used only when --method is rnn-lm. + It specifies the number of checkpoints to average. + """, + ) + + parser.add_argument( + "--rnn-lm-embedding-dim", + type=int, + default=2048, + help="Embedding dim of the model", + ) + + parser.add_argument( + "--rnn-lm-hidden-dim", + type=int, + default=2048, + help="Hidden dim of the model", + ) + + parser.add_argument( + "--rnn-lm-num-layers", + type=int, + default=4, + help="Number of RNN layers the model", + ) + parser.add_argument( + "--rnn-lm-tie-weights", + type=str2bool, + default=False, + help="""True to share the weights between the input embedding layer and the + last output linear layer + """, + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + # parameters for conformer + "subsampling_factor": 4, + "vgg_frontend": False, + "use_feat_batchnorm": True, + "feature_dim": 80, + "nhead": 8, + "attention_dim": 512, + "num_decoder_layers": 6, + # parameters for decoding + "search_beam": 20, + "output_beam": 8, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + "env_info": get_env_info(), + } + ) + return params + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + rnn_lm_model: Optional[nn.Module], + HLG: Optional[k2.Fsa], + H: Optional[k2.Fsa], + bpe_model: Optional[spm.SentencePieceProcessor], + batch: dict, + word_table: k2.SymbolTable, + sos_id: int, + eos_id: int, + 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. + rnn_lm_model: + The neural model for RNN LM. + HLG: + The decoding graph. Used only when params.method is NOT ctc-decoding. + H: + The ctc topo. Used only when params.method is ctc-decoding. + bpe_model: + The BPE model. Used only when params.method is ctc-decoding. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + word_table: + The word symbol table. + sos_id: + The token ID of the SOS. + eos_id: + The token ID of the EOS. + G: + An LM. It is not None when params.method is "nbest-rescoring" + or "whole-lattice-rescoring". In general, the G in HLG + is a 3-gram LM, while this G is a 4-gram LM. + Returns: + Return the decoding result. See above description for the format of + the returned dict. Note: If it decodes to nothing, then return None. + """ + if HLG is not None: + device = HLG.device + else: + device = H.device + feature = batch["inputs"] + assert feature.ndim == 3 + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + + nnet_output, memory, memory_key_padding_mask = model(feature, supervisions) + # nnet_output is (N, T, C) + + supervision_segments = torch.stack( + ( + supervisions["sequence_idx"], + supervisions["start_frame"] // params.subsampling_factor, + supervisions["num_frames"] // params.subsampling_factor, + ), + 1, + ).to(torch.int32) + + if H is None: + assert HLG is not None + decoding_graph = HLG + else: + assert HLG is None + assert bpe_model is not None + decoding_graph = H + + lattice = get_lattice( + nnet_output=nnet_output, + decoding_graph=decoding_graph, + supervision_segments=supervision_segments, + search_beam=params.search_beam, + output_beam=params.output_beam, + min_active_states=params.min_active_states, + max_active_states=params.max_active_states, + subsampling_factor=params.subsampling_factor, + ) + + if params.method == "ctc-decoding": + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + # Note: `best_path.aux_labels` contains token IDs, not word IDs + # since we are using H, not HLG here. + # + # token_ids is a lit-of-list of IDs + token_ids = get_texts(best_path) + + # hyps is a list of str, e.g., ['xxx yyy zzz', ...] + hyps = bpe_model.decode(token_ids) + + # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ] + hyps = [s.split() for s in hyps] + key = "ctc-decoding" + return {key: hyps} + + if params.method == "nbest-oracle": + # Note: You can also pass rescored lattices to it. + # We choose the HLG decoded lattice for speed reasons + # as HLG decoding is faster and the oracle WER + # is only slightly worse than that of rescored lattices. + best_path = nbest_oracle( + lattice=lattice, + num_paths=params.num_paths, + ref_texts=supervisions["text"], + word_table=word_table, + nbest_scale=params.nbest_scale, + oov="", + ) + hyps = get_texts(best_path) + hyps = [[word_table[i] for i in ids] for ids in hyps] + key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}" # noqa + return {key: hyps} + + 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-nbest-scale-{params.nbest_scale}-{params.num_paths}" # noqa + + hyps = get_texts(best_path) + hyps = [[word_table[i] for i in ids] for ids in hyps] + return {key: hyps} + + assert params.method in [ + "nbest-rescoring", + "whole-lattice-rescoring", + "attention-decoder", + "rnn-lm", + ] + + 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, + ) + elif params.method == "whole-lattice-rescoring": + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=lm_scale_list, + ) + elif params.method == "attention-decoder": + # lattice uses a 3-gram Lm. We rescore it with a 4-gram LM. + rescored_lattice = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=None, + ) + + best_path_dict = rescore_with_attention_decoder( + lattice=rescored_lattice, + num_paths=params.num_paths, + model=model, + memory=memory, + memory_key_padding_mask=memory_key_padding_mask, + sos_id=sos_id, + eos_id=eos_id, + nbest_scale=params.nbest_scale, + ) + elif params.method == "rnn-lm": + # lattice uses a 3-gram Lm. We rescore it with a 4-gram LM. + rescored_lattice = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=None, + ) + + best_path_dict = rescore_with_rnn_lm( + lattice=rescored_lattice, + num_paths=params.num_paths, + rnn_lm_model=rnn_lm_model, + model=model, + memory=memory, + memory_key_padding_mask=memory_key_padding_mask, + sos_id=sos_id, + eos_id=eos_id, + blank_id=0, + nbest_scale=params.nbest_scale, + ) + else: + assert False, f"Unsupported decoding method: {params.method}" + + ans = dict() + if best_path_dict is not None: + for lm_scale_str, best_path in best_path_dict.items(): + hyps = get_texts(best_path) + hyps = [[word_table[i] for i in ids] for ids in hyps] + ans[lm_scale_str] = hyps + else: + ans = None + return ans + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + rnn_lm_model: Optional[nn.Module], + HLG: Optional[k2.Fsa], + H: Optional[k2.Fsa], + bpe_model: Optional[spm.SentencePieceProcessor], + word_table: k2.SymbolTable, + sos_id: int, + eos_id: int, + G: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + rnn_lm_model: + The neural model for RNN LM. + HLG: + The decoding graph. Used only when params.method is NOT ctc-decoding. + H: + The ctc topo. Used only when params.method is ctc-decoding. + bpe_model: + The BPE model. Used only when params.method is ctc-decoding. + word_table: + It is the word symbol table. + sos_id: + The token ID for SOS. + eos_id: + The token ID for EOS. + G: + An LM. It is not None when params.method is "nbest-rescoring" + or "whole-lattice-rescoring". In general, the G in HLG + is a 3-gram LM, while this G is a 4-gram LM. + Returns: + Return a dict, whose key may be "no-rescore" if no LM rescoring + is used, or it may be "lm_scale_0.7" if LM rescoring is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + rnn_lm_model=rnn_lm_model, + HLG=HLG, + H=H, + bpe_model=bpe_model, + batch=batch, + word_table=word_table, + G=G, + sos_id=sos_id, + eos_id=eos_id, + ) + + if hyps_dict is not None: + for lm_scale, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + ref_words = ref_text.split() + this_batch.append((cut_id, ref_words, hyp_words)) + + results[lm_scale].extend(this_batch) + else: + assert len(results) > 0, "It should not decode to empty in the first batch!" + this_batch = [] + hyp_words = [] + for ref_text in texts: + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + for lm_scale in results.keys(): + results[lm_scale].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % 100 == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[str, List[int], List[int]]]], +): + if params.method in ("attention-decoder", "rnn-lm"): + # Set it to False since there are too many logs. + enable_log = False + else: + enable_log = True + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt" + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + if enable_log: + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt" + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=enable_log + ) + test_set_wers[key] = wer + + if enable_log: + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = params.exp_dir / f"wer-summary-{test_set_name}.txt" + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + args.lang_dir = Path(args.lang_dir) + args.lm_dir = Path(args.lm_dir) + + params = get_params() + params.update(vars(args)) + + setup_logger(f"{params.exp_dir}/log-{params.method}/log-decode") + logging.info("Decoding started") + logging.info(params) + + lexicon = Lexicon(params.lang_dir) + max_token_id = max(lexicon.tokens) + num_classes = max_token_id + 1 # +1 for the blank + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + graph_compiler = BpeCtcTrainingGraphCompiler( + params.lang_dir, + device=device, + sos_token="", + eos_token="", + ) + sos_id = graph_compiler.sos_id + eos_id = graph_compiler.eos_id + + params.num_classes = num_classes + params.sos_id = sos_id + params.eos_id = eos_id + + if params.method == "ctc-decoding": + HLG = None + H = k2.ctc_topo( + max_token=max_token_id, + modified=False, + device=device, + ) + bpe_model = spm.SentencePieceProcessor() + bpe_model.load(str(params.lang_dir / "bpe.model")) + else: + H = None + bpe_model = None + HLG = k2.Fsa.from_dict( + torch.load(f"{params.lang_dir}/HLG.pt", map_location=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", + "attention-decoder", + "rnn-lm", + ): + 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 + # See https://github.com/k2-fsa/k2/issues/874 + # for why we need to set G.properties to None + G.__dict__["_properties"] = None + G = k2.Fsa.from_fsas([G]).to(device) + G = k2.arc_sort(G) + # Save a dummy value so that it can be loaded in C++. + # See https://github.com/pytorch/pytorch/issues/67902 + # for why we need to do this. + G.dummy = 1 + + 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=device) + G = k2.Fsa.from_dict(d) + + if params.method in [ + "whole-lattice-rescoring", + "attention-decoder", + "rnn-lm", + ]: + # 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 = Conformer( + num_features=params.feature_dim, + nhead=params.nhead, + d_model=params.attention_dim, + num_classes=num_classes, + subsampling_factor=params.subsampling_factor, + num_decoder_layers=params.num_decoder_layers, + vgg_frontend=params.vgg_frontend, + use_feat_batchnorm=params.use_feat_batchnorm, + ) + + if params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + model = load_averaged_model( + params.exp_dir, model, params.epoch, params.avg, device + ) + + model.to(device) + model.eval() + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + rnn_lm_model = None + if params.method == "rnn-lm": + rnn_lm_model = RnnLmModel( + vocab_size=params.num_classes, + embedding_dim=params.rnn_lm_embedding_dim, + hidden_dim=params.rnn_lm_hidden_dim, + num_layers=params.rnn_lm_num_layers, + tie_weights=params.rnn_lm_tie_weights, + ) + if params.rnn_lm_avg == 1: + load_checkpoint( + f"{params.rnn_lm_exp_dir}/epoch-{params.rnn_lm_epoch}.pt", + rnn_lm_model, + ) + rnn_lm_model.to(device) + else: + rnn_lm_model = load_averaged_model( + params.rnn_lm_exp_dir, + rnn_lm_model, + params.rnn_lm_epoch, + params.rnn_lm_avg, + device, + ) + rnn_lm_model.eval() + + # we need cut ids to display recognition results. + args.return_cuts = True + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_mucs_cuts() + + test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) + + test_sets = ["test"] + test_dl = [test_clean_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + rnn_lm_model=rnn_lm_model, + HLG=HLG, + H=H, + bpe_model=bpe_model, + word_table=lexicon.word_table, + G=G, + sos_id=sos_id, + eos_id=eos_id, + ) + + save_results(params=params, test_set_name=test_set, results_dict=results_dict) + + logging.info("Done!") + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/mucs/ASR/conformer_ctc/export.py b/egs/mucs/ASR/conformer_ctc/export.py new file mode 100755 index 000000000..fbcbd7b29 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/export.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: 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 converts several saved checkpoints +# to a single one using model averaging. + +import argparse +import logging +from pathlib import Path + +import torch +from conformer import Conformer + +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.lexicon import Lexicon +from icefall.utils import AttributeDict, str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=34, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--avg", + type=int, + default=20, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="conformer_ctc/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_bpe_500", + help="""It contains language related input files such as "lexicon.txt" + """, + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=True, + help="""True to save a model after applying torch.jit.script. + """, + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "feature_dim": 80, + "subsampling_factor": 4, + "use_feat_batchnorm": True, + "attention_dim": 512, + "nhead": 8, + "num_decoder_layers": 6, + } + ) + return params + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + args.lang_dir = Path(args.lang_dir) + + params = get_params() + params.update(vars(args)) + + logging.info(params) + + lexicon = Lexicon(params.lang_dir) + max_token_id = max(lexicon.tokens) + num_classes = max_token_id + 1 # +1 for the blank + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + model = Conformer( + num_features=params.feature_dim, + nhead=params.nhead, + d_model=params.attention_dim, + num_classes=num_classes, + subsampling_factor=params.subsampling_factor, + num_decoder_layers=params.num_decoder_layers, + vgg_frontend=False, + use_feat_batchnorm=params.use_feat_batchnorm, + ) + model.to(device) + + 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)) + + model.to("cpu") + model.eval() + + if params.jit: + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +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/mucs/ASR/conformer_ctc/label_smoothing.py b/egs/mucs/ASR/conformer_ctc/label_smoothing.py new file mode 100644 index 000000000..52d2eda3b --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/label_smoothing.py @@ -0,0 +1,109 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +class LabelSmoothingLoss(torch.nn.Module): + """ + Implement the LabelSmoothingLoss proposed in the following paper + https://arxiv.org/pdf/1512.00567.pdf + (Rethinking the Inception Architecture for Computer Vision) + + """ + + def __init__( + self, + ignore_index: int = -1, + label_smoothing: float = 0.1, + reduction: str = "sum", + ) -> None: + """ + Args: + ignore_index: + ignored class id + label_smoothing: + smoothing rate (0.0 means the conventional cross entropy loss) + reduction: + It has the same meaning as the reduction in + `torch.nn.CrossEntropyLoss`. It can be one of the following three + values: (1) "none": No reduction will be applied. (2) "mean": the + mean of the output is taken. (3) "sum": the output will be summed. + """ + super().__init__() + assert 0.0 <= label_smoothing < 1.0, f"{label_smoothing}" + assert reduction in ("none", "sum", "mean"), reduction + self.ignore_index = ignore_index + self.label_smoothing = label_smoothing + self.reduction = reduction + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """ + Compute loss between x and target. + + Args: + x: + prediction of dimension + (batch_size, input_length, number_of_classes). + target: + target masked with self.ignore_index of + dimension (batch_size, input_length). + + Returns: + A scalar tensor containing the loss without normalization. + """ + assert x.ndim == 3 + assert target.ndim == 2 + assert x.shape[:2] == target.shape + num_classes = x.size(-1) + x = x.reshape(-1, num_classes) + # Now x is of shape (N*T, C) + + # We don't want to change target in-place below, + # so we make a copy of it here + target = target.clone().reshape(-1) + + ignored = target == self.ignore_index + + # See https://github.com/k2-fsa/icefall/issues/240 + # and https://github.com/k2-fsa/icefall/issues/297 + # for why we don't use target[ignored] = 0 here + target = torch.where(ignored, torch.zeros_like(target), target) + + true_dist = torch.nn.functional.one_hot(target, num_classes=num_classes).to(x) + + true_dist = ( + true_dist * (1 - self.label_smoothing) + self.label_smoothing / num_classes + ) + + # Set the value of ignored indexes to 0 + # + # See https://github.com/k2-fsa/icefall/issues/240 + # and https://github.com/k2-fsa/icefall/issues/297 + # for why we don't use true_dist[ignored] = 0 here + true_dist = torch.where( + ignored.unsqueeze(1).repeat(1, true_dist.shape[1]), + torch.zeros_like(true_dist), + true_dist, + ) + + loss = -1 * (torch.log_softmax(x, dim=1) * true_dist) + if self.reduction == "sum": + return loss.sum() + elif self.reduction == "mean": + return loss.sum() / (~ignored).sum() + else: + return loss.sum(dim=-1) diff --git a/egs/mucs/ASR/conformer_ctc/pretrained.py b/egs/mucs/ASR/conformer_ctc/pretrained.py new file mode 100755 index 000000000..30def9c40 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/pretrained.py @@ -0,0 +1,430 @@ +#!/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 +import math +from typing import List + +import k2 +import kaldifeat +import sentencepiece as spm +import torch +import torchaudio +from conformer import Conformer +from torch.nn.utils.rnn import pad_sequence + +from icefall.decode import ( + get_lattice, + one_best_decoding, + rescore_with_attention_decoder, + 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, + help="""Path to words.txt. + Used only when method is not ctc-decoding. + """, + ) + + parser.add_argument( + "--HLG", + type=str, + help="""Path to HLG.pt. + Used only when method is not ctc-decoding. + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + help="""Path to bpe.model. + Used only when method is ctc-decoding. + """, + ) + + parser.add_argument( + "--method", + type=str, + default="1best", + help="""Decoding method. + Possible values are: + (0) ctc-decoding - Use CTC decoding. It uses a sentence + piece model, i.e., lang_dir/bpe.model, to convert + word pieces to words. It needs neither a lexicon + nor an n-gram LM. + (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. + (3) attention-decoder - Extract n paths from the rescored + lattice and use the transformer attention decoder for + rescoring. + We call it HLG decoding + n-gram LM rescoring + attention + decoder rescoring. + """, + ) + + parser.add_argument( + "--G", + type=str, + help="""An LM for rescoring. + Used only when method is + whole-lattice-rescoring or attention-decoder. + It's usually a 4-gram LM. + """, + ) + + parser.add_argument( + "--num-paths", + type=int, + default=100, + help=""" + Used only when method is attention-decoder. + It specifies the size of n-best list.""", + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=1.3, + help=""" + Used only when method is whole-lattice-rescoring and attention-decoder. + It specifies the scale for n-gram LM scores. + (Note: You need to tune it on a dataset.) + """, + ) + + parser.add_argument( + "--attention-decoder-scale", + type=float, + default=1.2, + help=""" + Used only when method is attention-decoder. + It specifies the scale for attention decoder scores. + (Note: You need to tune it on a dataset.) + """, + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help=""" + Used only when method is attention-decoder. + It specifies the scale for lattice.scores when + extracting n-best lists. A smaller value results in + more unique number of paths with the risk of missing + the best path. + """, + ) + + parser.add_argument( + "--sos-id", + type=int, + default=1, + help=""" + Used only when method is attention-decoder. + It specifies ID for the SOS token. + """, + ) + + parser.add_argument( + "--num-classes", + type=int, + default=500, + help=""" + Vocab size in the BPE model. + """, + ) + + parser.add_argument( + "--eos-id", + type=int, + default=1, + help=""" + Used only when method is attention-decoder. + It specifies ID for the EOS token. + """, + ) + + 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( + { + "sample_rate": 16000, + # parameters for conformer + "subsampling_factor": 4, + "vgg_frontend": False, + "use_feat_batchnorm": True, + "feature_dim": 80, + "nhead": 8, + "attention_dim": 512, + "num_decoder_layers": 6, + # parameters for decoding + "search_beam": 20, + "output_beam": 8, + "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) + assert ( + sample_rate == expected_sample_rate + ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}" + # We use only the first channel + ans.append(wave[0]) + return ans + + +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + if args.method != "attention-decoder": + # to save memory as the attention decoder + # will not be used + params.num_decoder_layers = 0 + + 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 = Conformer( + num_features=params.feature_dim, + nhead=params.nhead, + d_model=params.attention_dim, + num_classes=params.num_classes, + subsampling_factor=params.subsampling_factor, + num_decoder_layers=params.num_decoder_layers, + vgg_frontend=params.vgg_frontend, + use_feat_batchnorm=params.use_feat_batchnorm, + ) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"], strict=False) + model.to(device) + model.eval() + + 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) + + features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10)) + + # Note: We don't use key padding mask for attention during decoding + with torch.no_grad(): + nnet_output, memory, memory_key_padding_mask = model(features) + + 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, + ) + + if params.method == "ctc-decoding": + logging.info("Use CTC decoding") + bpe_model = spm.SentencePieceProcessor() + bpe_model.load(params.bpe_model) + max_token_id = params.num_classes - 1 + + H = k2.ctc_topo( + max_token=max_token_id, + modified=params.num_classes > 500, + device=device, + ) + + lattice = get_lattice( + nnet_output=nnet_output, + decoding_graph=H, + 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, + ) + + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + token_ids = get_texts(best_path) + hyps = bpe_model.decode(token_ids) + hyps = [s.split() for s in hyps] + elif params.method in [ + "1best", + "whole-lattice-rescoring", + "attention-decoder", + ]: + 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 in [ + "whole-lattice-rescoring", + "attention-decoder", + ]: + 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() + + 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())) + elif params.method == "attention-decoder": + logging.info("Use HLG + LM rescoring + attention decoder rescoring") + rescored_lattice = rescore_with_whole_lattice( + lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None + ) + best_path_dict = rescore_with_attention_decoder( + lattice=rescored_lattice, + num_paths=params.num_paths, + model=model, + memory=memory, + memory_key_padding_mask=memory_key_padding_mask, + sos_id=params.sos_id, + eos_id=params.eos_id, + nbest_scale=params.nbest_scale, + ngram_lm_scale=params.ngram_lm_scale, + attention_scale=params.attention_decoder_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] + else: + raise ValueError(f"Unsupported decoding method: {params.method}") + + 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/mucs/ASR/conformer_ctc/subsampling.py b/egs/mucs/ASR/conformer_ctc/subsampling.py new file mode 100644 index 000000000..8e0f73d05 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/subsampling.py @@ -0,0 +1,153 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn + + +class Conv2dSubsampling(nn.Module): + """Convolutional 2D subsampling (to 1/4 length). + + Convert an input of shape (N, T, idim) to an output + with shape (N, T', odim), where + T' = ((T-1)//2 - 1)//2, which approximates T' == T//4 + + It is based on + https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa + """ + + def __init__(self, idim: int, odim: int) -> None: + """ + Args: + idim: + Input dim. The input shape is (N, T, idim). + Caution: It requires: T >=7, idim >=7 + odim: + Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim) + """ + assert idim >= 7 + super().__init__() + self.conv = nn.Sequential( + nn.Conv2d(in_channels=1, out_channels=odim, kernel_size=3, stride=2), + nn.ReLU(), + nn.Conv2d(in_channels=odim, out_channels=odim, kernel_size=3, stride=2), + nn.ReLU(), + ) + self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Subsample x. + + Args: + x: + Its shape is (N, T, idim). + + Returns: + Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim) + """ + # On entry, x is (N, T, idim) + x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W) + x = self.conv(x) + # Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + # Now x is of shape (N, ((T-1)//2 - 1))//2, odim) + return x + + +class VggSubsampling(nn.Module): + """Trying to follow the setup described in the following paper: + https://arxiv.org/pdf/1910.09799.pdf + + This paper is not 100% explicit so I am guessing to some extent, + and trying to compare with other VGG implementations. + + Convert an input of shape (N, T, idim) to an output + with shape (N, T', odim), where + T' = ((T-1)//2 - 1)//2, which approximates T' = T//4 + """ + + def __init__(self, idim: int, odim: int) -> None: + """Construct a VggSubsampling object. + + This uses 2 VGG blocks with 2 Conv2d layers each, + subsampling its input by a factor of 4 in the time dimensions. + + Args: + idim: + Input dim. The input shape is (N, T, idim). + Caution: It requires: T >=7, idim >=7 + odim: + Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim) + """ + super().__init__() + + cur_channels = 1 + layers = [] + block_dims = [32, 64] + + # The decision to use padding=1 for the 1st convolution, then padding=0 + # for the 2nd and for the max-pooling, and ceil_mode=True, was driven by + # a back-compatibility concern so that the number of frames at the + # output would be equal to: + # (((T-1)//2)-1)//2. + # We can consider changing this by using padding=1 on the + # 2nd convolution, so the num-frames at the output would be T//4. + for block_dim in block_dims: + layers.append( + torch.nn.Conv2d( + in_channels=cur_channels, + out_channels=block_dim, + kernel_size=3, + padding=1, + stride=1, + ) + ) + layers.append(torch.nn.ReLU()) + layers.append( + torch.nn.Conv2d( + in_channels=block_dim, + out_channels=block_dim, + kernel_size=3, + padding=0, + stride=1, + ) + ) + layers.append( + torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=True) + ) + cur_channels = block_dim + + self.layers = nn.Sequential(*layers) + + self.out = nn.Linear(block_dims[-1] * (((idim - 1) // 2 - 1) // 2), odim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Subsample x. + + Args: + x: + Its shape is (N, T, idim). + + Returns: + Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim) + """ + x = x.unsqueeze(1) + x = self.layers(x) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + return x diff --git a/egs/mucs/ASR/conformer_ctc/test_label_smoothing.py b/egs/mucs/ASR/conformer_ctc/test_label_smoothing.py new file mode 100755 index 000000000..5d4438fd1 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/test_label_smoothing.py @@ -0,0 +1,52 @@ +#!/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. + +from distutils.version import LooseVersion + +import torch +from label_smoothing import LabelSmoothingLoss + +torch_ver = LooseVersion(torch.__version__) + + +def test_with_torch_label_smoothing_loss(): + if torch_ver < LooseVersion("1.10.0"): + print(f"Current torch version: {torch_ver}") + print("Please use torch >= 1.10 to run this test - skipping") + return + torch.manual_seed(20211105) + x = torch.rand(20, 30, 5000) + tgt = torch.randint(low=-1, high=x.size(-1), size=x.shape[:2]) + for reduction in ["none", "sum", "mean"]: + custom_loss_func = LabelSmoothingLoss( + ignore_index=-1, label_smoothing=0.1, reduction=reduction + ) + custom_loss = custom_loss_func(x, tgt) + + torch_loss_func = torch.nn.CrossEntropyLoss( + ignore_index=-1, reduction=reduction, label_smoothing=0.1 + ) + torch_loss = torch_loss_func(x.reshape(-1, x.size(-1)), tgt.reshape(-1)) + assert torch.allclose(custom_loss, torch_loss) + + +def main(): + test_with_torch_label_smoothing_loss() + + +if __name__ == "__main__": + main() diff --git a/egs/mucs/ASR/conformer_ctc/test_subsampling.py b/egs/mucs/ASR/conformer_ctc/test_subsampling.py new file mode 100755 index 000000000..81fa234dd --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/test_subsampling.py @@ -0,0 +1,48 @@ +#!/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. + + +import torch +from subsampling import Conv2dSubsampling, VggSubsampling + + +def test_conv2d_subsampling(): + N = 3 + odim = 2 + + for T in range(7, 19): + for idim in range(7, 20): + model = Conv2dSubsampling(idim=idim, odim=odim) + x = torch.empty(N, T, idim) + y = model(x) + assert y.shape[0] == N + assert y.shape[1] == ((T - 1) // 2 - 1) // 2 + assert y.shape[2] == odim + + +def test_vgg_subsampling(): + N = 3 + odim = 2 + + for T in range(7, 19): + for idim in range(7, 20): + model = VggSubsampling(idim=idim, odim=odim) + x = torch.empty(N, T, idim) + y = model(x) + assert y.shape[0] == N + assert y.shape[1] == ((T - 1) // 2 - 1) // 2 + assert y.shape[2] == odim diff --git a/egs/mucs/ASR/conformer_ctc/test_transformer.py b/egs/mucs/ASR/conformer_ctc/test_transformer.py new file mode 100644 index 000000000..667057c51 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/test_transformer.py @@ -0,0 +1,104 @@ +#!/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. + + +import torch +from torch.nn.utils.rnn import pad_sequence +from transformer import ( + Transformer, + add_eos, + add_sos, + decoder_padding_mask, + encoder_padding_mask, + generate_square_subsequent_mask, +) + + +def test_encoder_padding_mask(): + supervisions = { + "sequence_idx": torch.tensor([0, 1, 2]), + "start_frame": torch.tensor([0, 0, 0]), + "num_frames": torch.tensor([18, 7, 13]), + } + + max_len = ((18 - 1) // 2 - 1) // 2 + mask = encoder_padding_mask(max_len, supervisions) + expected_mask = torch.tensor( + [ + [False, False, False], # ((18 - 1)//2 - 1)//2 = 3, + [False, True, True], # ((7 - 1)//2 - 1)//2 = 1, + [False, False, True], # ((13 - 1)//2 - 1)//2 = 2, + ] + ) + assert torch.all(torch.eq(mask, expected_mask)) + + +def test_transformer(): + num_features = 40 + num_classes = 87 + model = Transformer(num_features=num_features, num_classes=num_classes) + + N = 31 + + for T in range(7, 30): + x = torch.rand(N, T, num_features) + y, _, _ = model(x) + assert y.shape == (N, (((T - 1) // 2) - 1) // 2, num_classes) + + +def test_generate_square_subsequent_mask(): + s = 5 + mask = generate_square_subsequent_mask(s) + inf = float("inf") + expected_mask = torch.tensor( + [ + [0.0, -inf, -inf, -inf, -inf], + [0.0, 0.0, -inf, -inf, -inf], + [0.0, 0.0, 0.0, -inf, -inf], + [0.0, 0.0, 0.0, 0.0, -inf], + [0.0, 0.0, 0.0, 0.0, 0.0], + ] + ) + assert torch.all(torch.eq(mask, expected_mask)) + + +def test_decoder_padding_mask(): + x = [torch.tensor([1, 2]), torch.tensor([3]), torch.tensor([2, 5, 8])] + y = pad_sequence(x, batch_first=True, padding_value=-1) + mask = decoder_padding_mask(y, ignore_id=-1) + expected_mask = torch.tensor( + [ + [False, False, True], + [False, True, True], + [False, False, False], + ] + ) + assert torch.all(torch.eq(mask, expected_mask)) + + +def test_add_sos(): + x = [[1, 2], [3], [2, 5, 8]] + y = add_sos(x, sos_id=0) + expected_y = [[0, 1, 2], [0, 3], [0, 2, 5, 8]] + assert y == expected_y + + +def test_add_eos(): + x = [[1, 2], [3], [2, 5, 8]] + y = add_eos(x, eos_id=0) + expected_y = [[1, 2, 0], [3, 0], [2, 5, 8, 0]] + assert y == expected_y diff --git a/egs/mucs/ASR/conformer_ctc/train.py b/egs/mucs/ASR/conformer_ctc/train.py new file mode 100755 index 000000000..760e749c7 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/train.py @@ -0,0 +1,824 @@ +#!/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. + +""" +Usage: + export CUDA_VISIBLE_DEVICES="0,1,2,3" + ./conformer_ctc/train.py \ + --exp-dir ./conformer_ctc/exp \ + --world-size 4 \ + --full-libri 1 \ + --max-duration 200 \ + --num-epochs 20 +""" + +import argparse +import logging +from pathlib import Path +from shutil import copyfile +from typing import Optional, Tuple + +import k2 +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from conformer import Conformer +from lhotse.cut import Cut +from lhotse.utils import fix_random_seed +from torch import Tensor +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.nn.utils import clip_grad_norm_ +from torch.utils.tensorboard import SummaryWriter +from transformer import Noam + +from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler +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.env import get_env_info +from icefall.graph_compiler import CtcTrainingGraphCompiler +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + MetricsTracker, + encode_supervisions, + 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=78, + 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 + conformer_ctc/exp/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="conformer_ctc/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_bpe_500", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--att-rate", + type=float, + default=0.8, + help="""The attention rate. + The total loss is (1 - att_rate) * ctc_loss + att_rate * att_loss + """, + ) + + parser.add_argument( + "--num-decoder-layers", + type=int, + default=6, + help="""Number of decoder layer of transformer decoder. + Setting this to 0 will not create the decoder at all (pure CTC model) + """, + ) + + parser.add_argument( + "--lr-factor", + type=float, + default=5.0, + help="The lr_factor for Noam optimizer", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - use_feat_batchnorm: Normalization for the input features, can be a + boolean indicating whether to do batch + normalization, or a float which means just scaling + the input features with this float value. + If given a float value, we will remove batchnorm + layer in `ConvolutionModule` as well. + + - attention_dim: Hidden dim for multi-head attention model. + + - head: Number of heads of multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - beam_size: It is used in k2.ctc_loss + + - reduction: It is used in k2.ctc_loss + + - use_double_scores: It is used in k2.ctc_loss + + - weight_decay: The weight_decay for the optimizer. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + "use_feat_batchnorm": True, + "attention_dim": 512, + "nhead": 8, + # parameters for loss + "beam_size": 10, + "reduction": "sum", + "use_double_scores": True, + # parameters for Noam + "weight_decay": 1e-6, + "warm_step": 80000, + "env_info": get_env_info(), + } + ) + + 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: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + """ + 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: BpeCtcTrainingGraphCompiler, + 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 Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + graph_compiler: + It is used to build a decoding graph from a ctc topo and training + transcript. The training transcript is contained in the given `batch`, + while the ctc topo is built when this compiler is instantiated. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + """ + device = graph_compiler.device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + with torch.set_grad_enabled(is_training): + nnet_output, encoder_memory, memory_mask = model(feature, supervisions) + # nnet_output 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( + supervisions, subsampling_factor=params.subsampling_factor + ) + + if isinstance(graph_compiler, BpeCtcTrainingGraphCompiler): + # Works with a BPE model + token_ids = graph_compiler.texts_to_ids(texts) + decoding_graph = graph_compiler.compile(token_ids) + elif isinstance(graph_compiler, CtcTrainingGraphCompiler): + # Works with a phone lexicon + decoding_graph = graph_compiler.compile(texts) + else: + raise ValueError(f"Unsupported type of graph compiler: {type(graph_compiler)}") + + dense_fsa_vec = k2.DenseFsaVec( + nnet_output, + supervision_segments, + allow_truncate=params.subsampling_factor - 1, + ) + + ctc_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, + ) + + if params.att_rate != 0.0: + with torch.set_grad_enabled(is_training): + mmodel = model.module if hasattr(model, "module") else model + # Note: We need to generate an unsorted version of token_ids + # `encode_supervisions()` called above sorts text, but + # encoder_memory and memory_mask are not sorted, so we + # use an unsorted version `supervisions["text"]` to regenerate + # the token_ids + # + # See https://github.com/k2-fsa/icefall/issues/97 + # for more details + unsorted_token_ids = graph_compiler.texts_to_ids(supervisions["text"]) + att_loss = mmodel.decoder_forward( + encoder_memory, + memory_mask, + token_ids=unsorted_token_ids, + sos_id=graph_compiler.sos_id, + eos_id=graph_compiler.eos_id, + ) + loss = (1.0 - params.att_rate) * ctc_loss + params.att_rate * att_loss + else: + loss = ctc_loss + att_loss = torch.tensor([0]) + + assert loss.requires_grad == is_training + + info = MetricsTracker() + info["frames"] = supervision_segments[:, 2].sum().item() + info["ctc_loss"] = ctc_loss.detach().cpu().item() + if params.att_rate != 0.0: + info["att_loss"] = att_loss.detach().cpu().item() + + info["loss"] = loss.detach().cpu().item() + + # `utt_duration` and `utt_pad_proportion` would be normalized by `utterances` # noqa + info["utterances"] = feature.size(0) + # averaged input duration in frames over utterances + info["utt_duration"] = supervisions["num_frames"].sum().item() + # averaged padding proportion over utterances + info["utt_pad_proportion"] = ( + ((feature.size(1) - supervisions["num_frames"]) / feature.size(1)).sum().item() + ) + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + graph_compiler: BpeCtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + graph_compiler: BpeCtcTrainingGraphCompiler, + 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["supervisions"]["text"]) + + 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 + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + + 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: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + # world_size = 2 + # params.master_port = 12355 + 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) + num_classes = max_token_id + 1 # +1 for the blank + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + + if "lang_bpe" in str(params.lang_dir): + graph_compiler = BpeCtcTrainingGraphCompiler( + params.lang_dir, + device=device, + sos_token="", + eos_token="", + ) + elif "lang_phone" in str(params.lang_dir): + assert params.att_rate == 0, ( + "Attention decoder training does not support phone lang dirs " + "at this time due to a missing symbol. Set --att-rate=0 " + "for pure CTC training when using a phone-based lang dir." + ) + assert params.num_decoder_layers == 0, ( + "Attention decoder training does not support phone lang dirs " + "at this time due to a missing symbol. " + "Set --num-decoder-layers=0 for pure CTC training when using " + "a phone-based lang dir." + ) + graph_compiler = CtcTrainingGraphCompiler( + lexicon, + device=device, + ) + # Manually add the sos/eos ID with their default values + # from the BPE recipe which we're adapting here. + graph_compiler.sos_id = 1 + graph_compiler.eos_id = 1 + else: + raise ValueError( + f"Unsupported type of lang dir (we expected it to have " + f"'lang_bpe' or 'lang_phone' in its name): {params.lang_dir}" + ) + + logging.info("About to create model") + model = Conformer( + num_features=params.feature_dim, + nhead=params.nhead, + d_model=params.attention_dim, + num_classes=num_classes, + subsampling_factor=params.subsampling_factor, + num_decoder_layers=params.num_decoder_layers, + vgg_frontend=False, + use_feat_batchnorm=params.use_feat_batchnorm, + ) + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + model = DDP(model, device_ids=[rank]) + + optimizer = Noam( + model.parameters(), + model_size=params.attention_dim, + factor=params.lr_factor, + warm_step=params.warm_step, + weight_decay=params.weight_decay, + ) + + if checkpoints: + optimizer.load_state_dict(checkpoints["optimizer"]) + + librispeech = LibriSpeechAsrDataModule(args) + # params.full_libri = False + # if params.full_libri: + # train_cuts = librispeech.train_all_shuf_cuts() + # else: + train_cuts = librispeech.train_clean_mucs_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + train_dl = librispeech.train_dataloaders(train_cuts) + + valid_cuts = librispeech.dev_mucs_cuts() + valid_dl = librispeech.valid_dataloaders(valid_cuts) + + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + graph_compiler=graph_compiler, + params=params, + ) + + for epoch in range(params.start_epoch, params.num_epochs): + fix_random_seed(params.seed + epoch) + train_dl.sampler.set_epoch(epoch) + + cur_lr = optimizer._rate + if tb_writer is not None: + tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train) + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + if rank == 0: + logging.info("epoch {}, learning rate {}".format(epoch, cur_lr)) + + 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, + ) + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: nn.Module, + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: BpeCtcTrainingGraphCompiler, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 0 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + optimizer.zero_grad() + loss, _ = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=True, + ) + loss.backward() + clip_grad_norm_(model.parameters(), 5.0, 2.0) + optimizer.step() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + args.lang_dir = Path(args.lang_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() + +#TypeError: trim_to_supervisions() got an unexpected keyword argument 'ignore_channel' + +#AssertionError: Trimmed cut has supervisions with different channels. Either set `ignore_channel=True` to keep original channels or `keep_overlapping=False` to retain only 1 supervision per trimmed cut. \ No newline at end of file diff --git a/egs/mucs/ASR/conformer_ctc/transformer.py b/egs/mucs/ASR/conformer_ctc/transformer.py new file mode 100644 index 000000000..0566cfc81 --- /dev/null +++ b/egs/mucs/ASR/conformer_ctc/transformer.py @@ -0,0 +1,928 @@ +# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu) +# +# 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 +from typing import Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +from label_smoothing import LabelSmoothingLoss +from subsampling import Conv2dSubsampling, VggSubsampling +from torch.nn.utils.rnn import pad_sequence + +# Note: TorchScript requires Dict/List/etc. to be fully typed. +Supervisions = Dict[str, torch.Tensor] + + +class Transformer(nn.Module): + def __init__( + self, + num_features: int, + num_classes: int, + subsampling_factor: int = 4, + d_model: int = 256, + nhead: int = 4, + dim_feedforward: int = 2048, + num_encoder_layers: int = 12, + num_decoder_layers: int = 6, + dropout: float = 0.1, + normalize_before: bool = True, + vgg_frontend: bool = False, + use_feat_batchnorm: Union[float, bool] = 0.1, + ) -> None: + """ + Args: + num_features: + The input dimension of the model. + num_classes: + The output dimension of the model. + subsampling_factor: + Number of output frames is num_in_frames // subsampling_factor. + Currently, subsampling_factor MUST be 4. + d_model: + Attention dimension. + nhead: + Number of heads in multi-head attention. + Must satisfy d_model // nhead == 0. + dim_feedforward: + The output dimension of the feedforward layers in encoder/decoder. + num_encoder_layers: + Number of encoder layers. + num_decoder_layers: + Number of decoder layers. + dropout: + Dropout in encoder/decoder. + normalize_before: + If True, use pre-layer norm; False to use post-layer norm. + vgg_frontend: + True to use vgg style frontend for subsampling. + use_feat_batchnorm: + True to use batchnorm for the input layer. + Float value to scale the input layer. + False to do nothing. + """ + super().__init__() + self.use_feat_batchnorm = use_feat_batchnorm + assert isinstance(use_feat_batchnorm, (float, bool)) + if isinstance(use_feat_batchnorm, bool) and use_feat_batchnorm: + self.feat_batchnorm = nn.BatchNorm1d(num_features) + + self.num_features = num_features + self.num_classes = num_classes + self.subsampling_factor = subsampling_factor + if subsampling_factor != 4: + raise NotImplementedError("Support only 'subsampling_factor=4'.") + + # self.encoder_embed converts the input of shape (N, T, num_classes) + # to the shape (N, T//subsampling_factor, d_model). + # That is, it does two things simultaneously: + # (1) subsampling: T -> T//subsampling_factor + # (2) embedding: num_classes -> d_model + if vgg_frontend: + self.encoder_embed = VggSubsampling(num_features, d_model) + else: + self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.encoder_pos = PositionalEncoding(d_model, dropout) + + encoder_layer = TransformerEncoderLayer( + d_model=d_model, + nhead=nhead, + dim_feedforward=dim_feedforward, + dropout=dropout, + normalize_before=normalize_before, + ) + + if normalize_before: + encoder_norm = nn.LayerNorm(d_model) + else: + encoder_norm = None + + self.encoder = nn.TransformerEncoder( + encoder_layer=encoder_layer, + num_layers=num_encoder_layers, + norm=encoder_norm, + ) + + # TODO(fangjun): remove dropout + self.encoder_output_layer = nn.Sequential( + nn.Dropout(p=dropout), nn.Linear(d_model, num_classes) + ) + + if num_decoder_layers > 0: + self.decoder_num_class = ( + self.num_classes + ) # bpe model already has sos/eos symbol + + self.decoder_embed = nn.Embedding( + num_embeddings=self.decoder_num_class, embedding_dim=d_model + ) + self.decoder_pos = PositionalEncoding(d_model, dropout) + + decoder_layer = TransformerDecoderLayer( + d_model=d_model, + nhead=nhead, + dim_feedforward=dim_feedforward, + dropout=dropout, + normalize_before=normalize_before, + ) + + if normalize_before: + decoder_norm = nn.LayerNorm(d_model) + else: + decoder_norm = None + + self.decoder = nn.TransformerDecoder( + decoder_layer=decoder_layer, + num_layers=num_decoder_layers, + norm=decoder_norm, + ) + + self.decoder_output_layer = torch.nn.Linear(d_model, self.decoder_num_class) + + self.decoder_criterion = LabelSmoothingLoss() + else: + self.decoder_criterion = None + + def forward( + self, x: torch.Tensor, supervision: Optional[Supervisions] = None + ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + """ + Args: + x: + The input tensor. Its shape is (N, T, C). + supervision: + Supervision in lhotse format. + See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa + (CAUTION: It contains length information, i.e., start and number of + frames, before subsampling) + + Returns: + Return a tuple containing 3 tensors: + - CTC output for ctc decoding. Its shape is (N, T, C) + - Encoder output with shape (T, N, C). It can be used as key and + value for the decoder. + - Encoder output padding mask. It can be used as + memory_key_padding_mask for the decoder. Its shape is (N, T). + It is None if `supervision` is None. + """ + if isinstance(self.use_feat_batchnorm, bool) and self.use_feat_batchnorm: + x = x.permute(0, 2, 1) # (N, T, C) -> (N, C, T) + x = self.feat_batchnorm(x) + x = x.permute(0, 2, 1) # (N, C, T) -> (N, T, C) + if isinstance(self.use_feat_batchnorm, float): + x *= self.use_feat_batchnorm + encoder_memory, memory_key_padding_mask = self.run_encoder(x, supervision) + x = self.ctc_output(encoder_memory) + return x, encoder_memory, memory_key_padding_mask + + def run_encoder( + self, x: torch.Tensor, supervisions: Optional[Supervisions] = None + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Run the transformer encoder. + + Args: + x: + The model input. Its shape is (N, T, C). + supervisions: + Supervision in lhotse format. + See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa + CAUTION: It contains length information, i.e., start and number of + frames, before subsampling + It is read directly from the batch, without any sorting. It is used + to compute the encoder padding mask, which is used as memory key + padding mask for the decoder. + Returns: + Return a tuple with two tensors: + - The encoder output, with shape (T, N, C) + - encoder padding mask, with shape (N, T). + The mask is None if `supervisions` is None. + It is used as memory key padding mask in the decoder. + """ + x = self.encoder_embed(x) + x = self.encoder_pos(x) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + mask = encoder_padding_mask(x.size(0), supervisions) + mask = mask.to(x.device) if mask is not None else None + x = self.encoder(x, src_key_padding_mask=mask) # (T, N, C) + + return x, mask + + def ctc_output(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: + The output tensor from the transformer encoder. + Its shape is (T, N, C) + + Returns: + Return a tensor that can be used for CTC decoding. + Its shape is (N, T, C) + """ + x = self.encoder_output_layer(x) + x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + x = nn.functional.log_softmax(x, dim=-1) # (N, T, C) + return x + + @torch.jit.export + def decoder_forward( + self, + memory: torch.Tensor, + memory_key_padding_mask: torch.Tensor, + token_ids: List[List[int]], + sos_id: int, + eos_id: int, + ) -> torch.Tensor: + """ + Args: + memory: + It's the output of the encoder with shape (T, N, C) + memory_key_padding_mask: + The padding mask from the encoder. + token_ids: + A list-of-list IDs. Each sublist contains IDs for an utterance. + The IDs can be either phone IDs or word piece IDs. + sos_id: + sos token id + eos_id: + eos token id + + Returns: + A scalar, the **sum** of label smoothing loss over utterances + in the batch without any normalization. + """ + ys_in = add_sos(token_ids, sos_id=sos_id) + ys_in = [torch.tensor(y) for y in ys_in] + ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id)) + + ys_out = add_eos(token_ids, eos_id=eos_id) + ys_out = [torch.tensor(y) for y in ys_out] + ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1)) + + device = memory.device + ys_in_pad = ys_in_pad.to(device) + ys_out_pad = ys_out_pad.to(device) + + tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device) + + tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id) + # TODO: Use length information to create the decoder padding mask + # We set the first column to False since the first column in ys_in_pad + # contains sos_id, which is the same as eos_id in our current setting. + tgt_key_padding_mask[:, 0] = False + + tgt = self.decoder_embed(ys_in_pad) # (N, T) -> (N, T, C) + tgt = self.decoder_pos(tgt) + tgt = tgt.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + pred_pad = self.decoder( + tgt=tgt, + memory=memory, + tgt_mask=tgt_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + ) # (T, N, C) + pred_pad = pred_pad.permute(1, 0, 2) # (T, N, C) -> (N, T, C) + pred_pad = self.decoder_output_layer(pred_pad) # (N, T, C) + + decoder_loss = self.decoder_criterion(pred_pad, ys_out_pad) + + return decoder_loss + + @torch.jit.export + def decoder_nll( + self, + memory: torch.Tensor, + memory_key_padding_mask: torch.Tensor, + token_ids: List[torch.Tensor], + sos_id: int, + eos_id: int, + ) -> torch.Tensor: + """ + Args: + memory: + It's the output of the encoder with shape (T, N, C) + memory_key_padding_mask: + The padding mask from the encoder. + token_ids: + A list-of-list IDs (e.g., word piece IDs). + Each sublist represents an utterance. + sos_id: + The token ID for SOS. + eos_id: + The token ID for EOS. + Returns: + A 2-D tensor of shape (len(token_ids), max_token_length) + representing the cross entropy loss (i.e., negative log-likelihood). + """ + # The common part between this function and decoder_forward could be + # extracted as a separate function. + if isinstance(token_ids[0], torch.Tensor): + # This branch is executed by torchscript in C++. + # See https://github.com/k2-fsa/k2/pull/870 + # https://github.com/k2-fsa/k2/blob/3c1c18400060415b141ccea0115fd4bf0ad6234e/k2/torch/bin/attention_rescore.cu#L286 + token_ids = [tolist(t) for t in token_ids] + + ys_in = add_sos(token_ids, sos_id=sos_id) + ys_in = [torch.tensor(y) for y in ys_in] + ys_in_pad = pad_sequence(ys_in, batch_first=True, padding_value=float(eos_id)) + + ys_out = add_eos(token_ids, eos_id=eos_id) + ys_out = [torch.tensor(y) for y in ys_out] + ys_out_pad = pad_sequence(ys_out, batch_first=True, padding_value=float(-1)) + + device = memory.device + ys_in_pad = ys_in_pad.to(device, dtype=torch.int64) + ys_out_pad = ys_out_pad.to(device, dtype=torch.int64) + + tgt_mask = generate_square_subsequent_mask(ys_in_pad.shape[-1]).to(device) + + tgt_key_padding_mask = decoder_padding_mask(ys_in_pad, ignore_id=eos_id) + # TODO: Use length information to create the decoder padding mask + # We set the first column to False since the first column in ys_in_pad + # contains sos_id, which is the same as eos_id in our current setting. + tgt_key_padding_mask[:, 0] = False + + tgt = self.decoder_embed(ys_in_pad) # (B, T) -> (B, T, F) + tgt = self.decoder_pos(tgt) + tgt = tgt.permute(1, 0, 2) # (B, T, F) -> (T, B, F) + pred_pad = self.decoder( + tgt=tgt, + memory=memory, + tgt_mask=tgt_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + ) # (T, B, F) + pred_pad = pred_pad.permute(1, 0, 2) # (T, B, F) -> (B, T, F) + pred_pad = self.decoder_output_layer(pred_pad) # (B, T, F) + # nll: negative log-likelihood + nll = torch.nn.functional.cross_entropy( + pred_pad.view(-1, self.decoder_num_class), + ys_out_pad.view(-1), + ignore_index=-1, + reduction="none", + ) + + nll = nll.view(pred_pad.shape[0], -1) + + return nll + + +class TransformerEncoderLayer(nn.Module): + """ + Modified from torch.nn.TransformerEncoderLayer. + Add support of normalize_before, + i.e., use layer_norm before the first block. + + Args: + d_model: + the number of expected features in the input (required). + nhead: + the number of heads in the multiheadattention models (required). + dim_feedforward: + the dimension of the feedforward network model (default=2048). + dropout: + the dropout value (default=0.1). + activation: + the activation function of intermediate layer, relu or + gelu (default=relu). + normalize_before: + whether to use layer_norm before the first block. + + Examples:: + >>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> out = encoder_layer(src) + """ + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + activation: str = "relu", + normalize_before: bool = True, + ) -> None: + super(TransformerEncoderLayer, self).__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + self.normalize_before = normalize_before + + def __setstate__(self, state): + if "activation" not in state: + state["activation"] = nn.functional.relu + super(TransformerEncoderLayer, self).__setstate__(state) + + def forward( + self, + src: torch.Tensor, + src_mask: Optional[torch.Tensor] = None, + src_key_padding_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """ + Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional) + + Shape: + src: (S, N, E). + src_mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, + N is the batch size, E is the feature number + """ + residual = src + if self.normalize_before: + src = self.norm1(src) + src2 = self.self_attn( + src, + src, + src, + attn_mask=src_mask, + key_padding_mask=src_key_padding_mask, + )[0] + src = residual + self.dropout1(src2) + if not self.normalize_before: + src = self.norm1(src) + + residual = src + if self.normalize_before: + src = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = residual + self.dropout2(src2) + if not self.normalize_before: + src = self.norm2(src) + return src + + +class TransformerDecoderLayer(nn.Module): + """ + Modified from torch.nn.TransformerDecoderLayer. + Add support of normalize_before, + i.e., use layer_norm before the first block. + + Args: + d_model: + the number of expected features in the input (required). + nhead: + the number of heads in the multiheadattention models (required). + dim_feedforward: + the dimension of the feedforward network model (default=2048). + dropout: + the dropout value (default=0.1). + activation: + the activation function of intermediate layer, relu or + gelu (default=relu). + + Examples:: + >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) + >>> memory = torch.rand(10, 32, 512) + >>> tgt = torch.rand(20, 32, 512) + >>> out = decoder_layer(tgt, memory) + """ + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + activation: str = "relu", + normalize_before: bool = True, + ) -> None: + super(TransformerDecoderLayer, self).__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0) + self.src_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + self.normalize_before = normalize_before + + def __setstate__(self, state): + if "activation" not in state: + state["activation"] = nn.functional.relu + super(TransformerDecoderLayer, self).__setstate__(state) + + def forward( + self, + tgt: torch.Tensor, + memory: torch.Tensor, + tgt_mask: Optional[torch.Tensor] = None, + memory_mask: Optional[torch.Tensor] = None, + tgt_key_padding_mask: Optional[torch.Tensor] = None, + memory_key_padding_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + """Pass the inputs (and mask) through the decoder layer. + + Args: + tgt: + the sequence to the decoder layer (required). + memory: + the sequence from the last layer of the encoder (required). + tgt_mask: + the mask for the tgt sequence (optional). + memory_mask: + the mask for the memory sequence (optional). + tgt_key_padding_mask: + the mask for the tgt keys per batch (optional). + memory_key_padding_mask: + the mask for the memory keys per batch (optional). + + Shape: + tgt: (T, N, E). + memory: (S, N, E). + tgt_mask: (T, T). + memory_mask: (T, S). + tgt_key_padding_mask: (N, T). + memory_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, + N is the batch size, E is the feature number + """ + residual = tgt + if self.normalize_before: + tgt = self.norm1(tgt) + tgt2 = self.self_attn( + tgt, + tgt, + tgt, + attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask, + )[0] + tgt = residual + self.dropout1(tgt2) + if not self.normalize_before: + tgt = self.norm1(tgt) + + residual = tgt + if self.normalize_before: + tgt = self.norm2(tgt) + tgt2 = self.src_attn( + tgt, + memory, + memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + )[0] + tgt = residual + self.dropout2(tgt2) + if not self.normalize_before: + tgt = self.norm2(tgt) + + residual = tgt + if self.normalize_before: + tgt = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = residual + self.dropout3(tgt2) + if not self.normalize_before: + tgt = self.norm3(tgt) + return tgt + + +def _get_activation_fn(activation: str): + if activation == "relu": + return nn.functional.relu + elif activation == "gelu": + return nn.functional.gelu + + raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) + + +class PositionalEncoding(nn.Module): + """This class implements the positional encoding + proposed in the following paper: + + - Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf + + PE(pos, 2i) = sin(pos / (10000^(2i/d_modle)) + PE(pos, 2i+1) = cos(pos / (10000^(2i/d_modle)) + + Note:: + + 1 / (10000^(2i/d_model)) = exp(-log(10000^(2i/d_model))) + = exp(-1* 2i / d_model * log(100000)) + = exp(2i * -(log(10000) / d_model)) + """ + + def __init__(self, d_model: int, dropout: float = 0.1) -> None: + """ + Args: + d_model: + Embedding dimension. + dropout: + Dropout probability to be applied to the output of this module. + """ + super().__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = nn.Dropout(p=dropout) + # not doing: self.pe = None because of errors thrown by torchscript + self.pe = torch.zeros(1, 0, self.d_model, dtype=torch.float32) + + def extend_pe(self, x: torch.Tensor) -> None: + """Extend the time t in the positional encoding if required. + + The shape of `self.pe` is (1, T1, d_model). The shape of the input x + is (N, T, d_model). If T > T1, then we change the shape of self.pe + to (N, T, d_model). Otherwise, nothing is done. + + Args: + x: + It is a tensor of shape (N, T, C). + Returns: + Return None. + """ + if self.pe is not None: + if self.pe.size(1) >= x.size(1): + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + pe = torch.zeros(x.size(1), self.d_model, dtype=torch.float32) + position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, self.d_model, 2, dtype=torch.float32) + * -(math.log(10000.0) / self.d_model) + ) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) + # Now pe is of shape (1, T, d_model), where T is x.size(1) + self.pe = pe.to(device=x.device, dtype=x.dtype) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Add positional encoding. + + Args: + x: + Its shape is (N, T, C) + + Returns: + Return a tensor of shape (N, T, C) + """ + self.extend_pe(x) + x = x * self.xscale + self.pe[:, : x.size(1), :] + return self.dropout(x) + + +class Noam(object): + """ + Implements Noam optimizer. + + Proposed in + "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf + + Modified from + https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa + + Args: + params: + iterable of parameters to optimize or dicts defining parameter groups + model_size: + attention dimension of the transformer model + factor: + learning rate factor + warm_step: + warmup steps + """ + + def __init__( + self, + params, + model_size: int = 256, + factor: float = 10.0, + warm_step: int = 25000, + weight_decay=0, + ) -> None: + """Construct an Noam object.""" + self.optimizer = torch.optim.Adam( + params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay + ) + self._step = 0 + self.warmup = warm_step + self.factor = factor + self.model_size = model_size + self._rate = 0 + + @property + def param_groups(self): + """Return param_groups.""" + return self.optimizer.param_groups + + def step(self): + """Update parameters and rate.""" + self._step += 1 + rate = self.rate() + for p in self.optimizer.param_groups: + p["lr"] = rate + self._rate = rate + self.optimizer.step() + + def rate(self, step=None): + """Implement `lrate` above.""" + if step is None: + step = self._step + return ( + self.factor + * self.model_size ** (-0.5) + * min(step ** (-0.5), step * self.warmup ** (-1.5)) + ) + + def zero_grad(self): + """Reset gradient.""" + self.optimizer.zero_grad() + + def state_dict(self): + """Return state_dict.""" + return { + "_step": self._step, + "warmup": self.warmup, + "factor": self.factor, + "model_size": self.model_size, + "_rate": self._rate, + "optimizer": self.optimizer.state_dict(), + } + + def load_state_dict(self, state_dict): + """Load state_dict.""" + for key, value in state_dict.items(): + if key == "optimizer": + self.optimizer.load_state_dict(state_dict["optimizer"]) + else: + setattr(self, key, value) + + +def encoder_padding_mask( + max_len: int, supervisions: Optional[Supervisions] = None +) -> Optional[torch.Tensor]: + """Make mask tensor containing indexes of padded part. + + TODO:: + This function **assumes** that the model uses + a subsampling factor of 4. We should remove that + assumption later. + + Args: + max_len: + Maximum length of input features. + CAUTION: It is the length after subsampling. + supervisions: + Supervision in lhotse format. + See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa + (CAUTION: It contains length information, i.e., start and number of + frames, before subsampling) + + Returns: + Tensor: Mask tensor of dimension (batch_size, input_length), + True denote the masked indices. + """ + if supervisions is None: + return None + + supervision_segments = torch.stack( + ( + supervisions["sequence_idx"], + supervisions["start_frame"], + supervisions["num_frames"], + ), + 1, + ).to(torch.int32) + + lengths = [0 for _ in range(int(supervision_segments[:, 0].max().item()) + 1)] + for idx in range(supervision_segments.size(0)): + # Note: TorchScript doesn't allow to unpack tensors as tuples + sequence_idx = supervision_segments[idx, 0].item() + start_frame = supervision_segments[idx, 1].item() + num_frames = supervision_segments[idx, 2].item() + lengths[sequence_idx] = start_frame + num_frames + + lengths = [((i - 1) // 2 - 1) // 2 for i in lengths] + bs = int(len(lengths)) + seq_range = torch.arange(0, max_len, dtype=torch.int64) + seq_range_expand = seq_range.unsqueeze(0).expand(bs, max_len) + # Note: TorchScript doesn't implement Tensor.new() + seq_length_expand = torch.tensor( + lengths, device=seq_range_expand.device, dtype=seq_range_expand.dtype + ).unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + + return mask + + +def decoder_padding_mask(ys_pad: torch.Tensor, ignore_id: int = -1) -> torch.Tensor: + """Generate a length mask for input. + + The masked position are filled with True, + Unmasked positions are filled with False. + + Args: + ys_pad: + padded tensor of dimension (batch_size, input_length). + ignore_id: + the ignored number (the padding number) in ys_pad + + Returns: + Tensor: + a bool tensor of the same shape as the input tensor. + """ + ys_mask = ys_pad == ignore_id + return ys_mask + + +def generate_square_subsequent_mask(sz: int) -> torch.Tensor: + """Generate a square mask for the sequence. The masked positions are + filled with float('-inf'). Unmasked positions are filled with float(0.0). + The mask can be used for masked self-attention. + + For instance, if sz is 3, it returns:: + + tensor([[0., -inf, -inf], + [0., 0., -inf], + [0., 0., 0]]) + + Args: + sz: mask size + + Returns: + A square mask of dimension (sz, sz) + """ + mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) + mask = ( + mask.float() + .masked_fill(mask == 0, float("-inf")) + .masked_fill(mask == 1, float(0.0)) + ) + return mask + + +def add_sos(token_ids: List[List[int]], sos_id: int) -> List[List[int]]: + """Prepend sos_id to each utterance. + + Args: + token_ids: + A list-of-list of token IDs. Each sublist contains + token IDs (e.g., word piece IDs) of an utterance. + sos_id: + The ID of the SOS token. + + Return: + Return a new list-of-list, where each sublist starts + with SOS ID. + """ + return [[sos_id] + utt for utt in token_ids] + + +def add_eos(token_ids: List[List[int]], eos_id: int) -> List[List[int]]: + """Append eos_id to each utterance. + + Args: + token_ids: + A list-of-list of token IDs. Each sublist contains + token IDs (e.g., word piece IDs) of an utterance. + eos_id: + The ID of the EOS token. + + Return: + Return a new list-of-list, where each sublist ends + with EOS ID. + """ + return [utt + [eos_id] for utt in token_ids] + + +def tolist(t: torch.Tensor) -> List[int]: + """Used by jit""" + return torch.jit.annotate(List[int], t.tolist()) diff --git a/egs/mucs/ASR/local/compute_fbank_mucs.py b/egs/mucs/ASR/local/compute_fbank_mucs.py index fa486cd74..b987248cd 100755 --- a/egs/mucs/ASR/local/compute_fbank_mucs.py +++ b/egs/mucs/ASR/local/compute_fbank_mucs.py @@ -81,8 +81,11 @@ def compute_fbank_mucs( dataset_parts = ( "train", "test", + "dev", ) - + # dataset_parts = ( + # "test", + # ) prefix = "mucs" suffix = "jsonl.gz" manifests = read_manifests_if_cached( @@ -115,13 +118,18 @@ def compute_fbank_mucs( recordings=m["recordings"], supervisions=m["supervisions"], ) - - if "train" in partition: - if bpe_model: - cut_set = filter_cuts(cut_set, sp) - cut_set = ( - cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1) - ) + # print(len(m["supervisions"])) + # for s in m["supervisions"]: + # # print(s) + # if s.channel != 0: + # print(s) + # exit() + # if "train" in partition: + # if bpe_model: + # cut_set = filter_cuts(cut_set, sp) + # cut_set = ( + # cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1) + # ) cut_set = cut_set.compute_and_store_features( extractor=extractor, storage_path=f"{output_dir}/{prefix}_feats_{partition}", @@ -131,7 +139,8 @@ def compute_fbank_mucs( storage_type=LilcomChunkyWriter, ) cut_set = cut_set.trim_to_supervisions( - keep_overlapping=False, min_duration=None + keep_overlapping=False, min_duration=None, keep_all_channels=False, + ) cut_set.to_file(output_dir / cuts_filename) diff --git a/egs/mucs/ASR/local/prepare_manifest.py b/egs/mucs/ASR/local/prepare_manifest.py index 83627f85c..9dee0d1a9 100755 --- a/egs/mucs/ASR/local/prepare_manifest.py +++ b/egs/mucs/ASR/local/prepare_manifest.py @@ -20,15 +20,6 @@ from lhotse import ( from lhotse.recipes.utils import manifests_exist, read_manifests_if_cached from lhotse.utils import Pathlike, safe_extract, urlretrieve_progress -LIBRITTS = ( - "dev-clean", - "dev-other", - "test-clean", - "test-other", - "train-clean-100", - "train-clean-360", - "train-other-500", -) def prepare_mucs( corpus_dir: Pathlike, @@ -51,7 +42,7 @@ def prepare_mucs( corpus_dir = Path(corpus_dir) assert corpus_dir.is_dir(), f"No such directory: {corpus_dir}" - dataset_parts = ["train", "test"] + dataset_parts = ["train", "test", "dev"] manifests = {} diff --git a/egs/mucs/ASR/local/validate_manifest.py b/egs/mucs/ASR/local/validate_manifest.py index 5b96629a2..2452dbc43 100755 --- a/egs/mucs/ASR/local/validate_manifest.py +++ b/egs/mucs/ASR/local/validate_manifest.py @@ -84,15 +84,16 @@ def main(): logging.info(f"Validating {manifest}") assert manifest.is_file(), f"{manifest} does not exist" - print(manifest) + # print(manifest) cut_set = load_manifest_lazy(manifest) - print(cut_set) + # print(cut_set) assert isinstance(cut_set, CutSet) for c in cut_set: # print(len(c.supervisions)) - # validate_one_supervision_per_cut(c) - # validate_supervision_and_cut_time_bounds(c) + # print(c.supervisions) + validate_one_supervision_per_cut(c) + validate_supervision_and_cut_time_bounds(c) # Validation from K2 training # - checks supervision start is 0 diff --git a/egs/mucs/ASR/prepare.sh b/egs/mucs/ASR/prepare.sh index 37ed117c6..0b8ca628d 100755 --- a/egs/mucs/ASR/prepare.sh +++ b/egs/mucs/ASR/prepare.sh @@ -6,8 +6,8 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python set -eou pipefail nj=60 -stage=3 -stop_stage=3 +stage=-1 +stop_stage=9 # We assume dl_dir (download dir) contains the following # directories and files. If not, they will be downloaded @@ -94,7 +94,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then mkdir -p data/fbank if [ ! -e data/fbank/.mucs.done ]; then ./local/compute_fbank_mucs.py - touch data/fbank/.mucs.done + # touch data/fbank/.mucs.done fi # exit @@ -102,14 +102,15 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then if [ ! -e data/fbank/.mucs-validated.done ]; then log "Validating data/fbank for mucs" parts=( - train, + train test + dev ) for part in ${parts[@]}; do python3 ./local/validate_manifest.py \ data/fbank/mucs_cuts_${part}.jsonl.gz done - touch data/fbank/.mucs-validated.done + # touch data/fbank/.mucs-validated.done fi fi @@ -201,28 +202,35 @@ if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then for vocab_size in ${vocab_sizes[@]}; do lang_dir=data/lang_bpe_${vocab_size} - if [ ! -f $lang_dir/transcript_tokens.txt ]; then - ./local/convert_transcript_words_to_tokens.py \ - --lexicon $lang_dir/lexicon.txt \ - --transcript $lang_dir/transcript_words.txt \ - --oov "" \ - > $lang_dir/transcript_tokens.txt - fi + # if [ ! -f $lang_dir/transcript_tokens.txt ]; then + # ./local/convert_transcript_words_to_tokens.py \ + # --lexicon $lang_dir/lexicon.txt \ + # --transcript $lang_dir/transcript_words.txt \ + # --oov "" \ + # > $lang_dir/transcript_tokens.txt + # fi - if [ ! -f $lang_dir/P.arpa ]; then + if [ ! -f $lang_dir/lm_3.arpa ]; then ./shared/make_kn_lm.py \ - -ngram-order 2 \ - -text $lang_dir/transcript_tokens.txt \ - -lm $lang_dir/P.arpa + -ngram-order 3 \ + -text $lang_dir/transcript_words.txt \ + -lm $lang_dir/lm_3.arpa fi - if [ ! -f $lang_dir/P.fst.txt ]; then - python3 -m kaldilm \ - --read-symbol-table="$lang_dir/tokens.txt" \ - --disambig-symbol='#0' \ - --max-order=2 \ - $lang_dir/P.arpa > $lang_dir/P.fst.txt + if [ ! -f $lang_dir/lm_4.arpa ]; then + ./shared/make_kn_lm.py \ + -ngram-order 4 \ + -text $lang_dir/transcript_words.txt \ + -lm $lang_dir/lm_4.arpa fi + + # if [ ! -f $lang_dir/P.fst.txt ]; then + # python3 -m kaldilm \ + # --read-symbol-table="$lang_dir/tokens.txt" \ + # --disambig-symbol='#0' \ + # --max-order=2 \ + # $lang_dir/P.arpa > $lang_dir/P.fst.txt + # fi done fi @@ -238,22 +246,31 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then --read-symbol-table="data/lang_phone/words.txt" \ --disambig-symbol='#0' \ --max-order=3 \ - $dl_dir/lm/3-gram.pruned.1e-7.arpa > data/lm/G_3_gram.fst.txt + data/lang_bpe_200/lm_3.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 + # It is used in building HLG python3 -m kaldilm \ --read-symbol-table="data/lang_phone/words.txt" \ --disambig-symbol='#0' \ - --max-order=4 \ - $dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt + --max-order=3 \ + data/lang_bpe_200/lm_4.arpa > data/lm/G_4_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_phone/words.txt" \ + # --disambig-symbol='#0' \ + # --max-order=4 \ + # $dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt + # fi fi if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then log "Stage 9: Compile HLG" - ./local/compile_hlg.py --lang-dir data/lang_phone + # ./local/compile_hlg.py --lang-dir data/lang_phone # Note If ./local/compile_hlg.py throws OOM, # please switch to the following command diff --git a/egs/mucs/ASR/run.sh b/egs/mucs/ASR/run.sh index bf9b030a1..d232d3f04 100755 --- a/egs/mucs/ASR/run.sh +++ b/egs/mucs/ASR/run.sh @@ -3,14 +3,15 @@ export CUDA_VISIBLE_DEVICES="0" ./conformer_ctc/train.py \ --num-epochs 60 \ - --max-duration 100 \ - --exp-dir ./conformer_ctc/exp \ + --max-duration 300 \ + --exp-dir ./conformer_ctc/exp_with_devset_split \ --lang-dir data/lang_bpe_200 \ --enable-musan False \ -# ./conformer_ctc/decode.py \ -# --epoch 59 \ -# --avg 10 \ -# --exp-dir ./conformer_ctc/exp \ -# --max-duration 100 \ -# --lang-dir ./data/lang_bpe_2000 + +./conformer_ctc/decode.py \ + --epoch 59 \ + --avg 10 \ + --exp-dir ./conformer_ctc/exp_with_devset_split \ + --max-duration 100 \ + --lang-dir ./data/lang_bpe_200