From fbfc98f1d37b0f4c8b58307eff3b5f82808e88fb Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Wed, 1 Jun 2022 14:31:47 +0800 Subject: [PATCH] Add streaming Emformer stateless RNN-T. (#390) * Add streaming Emformer stateless RNN-T. * Update results for streaming Emformer. * Minor fixes. --- egs/librispeech/ASR/README.md | 1 + egs/librispeech/ASR/RESULTS.md | 66 ++ .../asr_datamodule.py | 1 + .../beam_search.py | 1 + .../pruned_stateless_emformer_rnnt2/decode.py | 645 ++++++++++ .../decoder.py | 1 + .../emformer.py | 313 +++++ .../encoder_interface.py | 1 + .../pruned_stateless_emformer_rnnt2/export.py | 281 +++++ .../pruned_stateless_emformer_rnnt2/joiner.py | 1 + .../pruned_stateless_emformer_rnnt2/model.py | 169 +++ .../pruned_stateless_emformer_rnnt2/noam.py | 104 ++ .../subsampling.py | 1 + .../test_emformer.py | 142 +++ .../test_model.py | 59 + .../pruned_stateless_emformer_rnnt2/train.py | 1034 +++++++++++++++++ .../ASR/pruned_transducer_stateless/joiner.py | 10 +- .../asr_datamodule.py | 12 +- .../ASR/pruned_transducer_stateless3/train.py | 2 +- .../ASR/tdnn_lstm_ctc/asr_datamodule.py | 17 +- 20 files changed, 2845 insertions(+), 16 deletions(-) create mode 120000 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/asr_datamodule.py create mode 120000 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/beam_search.py create mode 100755 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py create mode 120000 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decoder.py create mode 100644 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/emformer.py create mode 120000 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/encoder_interface.py create mode 100755 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py create mode 120000 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/joiner.py create mode 100644 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/model.py create mode 100644 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/noam.py create mode 120000 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/subsampling.py create mode 100755 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_emformer.py create mode 100755 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_model.py create mode 100755 egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py diff --git a/egs/librispeech/ASR/README.md b/egs/librispeech/ASR/README.md index a738b652f..e2aaa9d7e 100644 --- a/egs/librispeech/ASR/README.md +++ b/egs/librispeech/ASR/README.md @@ -22,6 +22,7 @@ The following table lists the differences among them. | `pruned_transducer_stateless4` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless2 + save averaged models periodically during training | | `pruned_transducer_stateless5` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner| | `pruned_transducer_stateless6` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + distillation with hubert| +| `pruned_stateless_emformer_rnnt2` | Emformer(from torchaudio) | Embedding + Conv1d | Using Emformer from torchaudio for streaming ASR| The decoder in `transducer_stateless` is modified from the paper diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md index 453751ba5..32352b221 100644 --- a/egs/librispeech/ASR/RESULTS.md +++ b/egs/librispeech/ASR/RESULTS.md @@ -1,5 +1,71 @@ ## Results +### LibriSpeech BPE training results (Pruned Stateless Emformer RNN-T) + +[pruned_stateless_emformer_rnnt2](./pruned_stateless_emformer_rnnt2) + +Use . + +Use [Emformer](https://arxiv.org/abs/2010.10759) from [torchaudio](https://github.com/pytorch/audio) +for streaming ASR. The Emformer model is imported from torchaudio without modifications. + +| | test-clean | test-other | comment | +|-------------------------------------|------------|------------|----------------------------------------| +| greedy search (max sym per frame 1) | 4.28 | 11.42 | --epoch 39 --avg 6 --max-duration 600 | +| modified beam search | 4.22 | 11.16 | --epoch 39 --avg 6 --max-duration 600 | +| fast beam search | 4.29 | 11.26 | --epoch 39 --avg 6 --max-duration 600 | + + +The training commands are: +```bash +export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" + +./pruned_stateless_emformer_rnnt2/train.py \ + --world-size 8 \ + --num-epochs 40 \ + --start-epoch 1 \ + --exp-dir pruned_stateless_emformer_rnnt2/exp-full \ + --full-libri 1 \ + --use-fp16 0 \ + --max-duration 200 \ + --prune-range 5 \ + --lm-scale 0.25 \ + --master-port 12358 \ + --num-encoder-layers 18 \ + --left-context-length 128 \ + --segment-length 8 \ + --right-context-length 4 +``` + +The tensorboard log can be found at + + +The decoding commands are: +```bash +for m in greedy_search fast_beam_search modified_beam_search; do + for epoch in 39; do + for avg in 6; do + ./pruned_stateless_emformer_rnnt2/decode.py \ + --epoch $epoch \ + --avg $avg \ + --use-averaged-model 1 \ + --exp-dir pruned_stateless_emformer_rnnt2/exp-full \ + --max-duration 50 \ + --decoding-method $m \ + --num-encoder-layers 18 \ + --left-context-length 128 \ + --segment-length 8 \ + --right-context-length 4 + done + done +done +``` + +You can find a pretrained model, training logs, decoding logs, and decoding +results at: + + + ### LibriSpeech BPE training results (Pruned Stateless Transducer 5) [pruned_transducer_stateless5](./pruned_transducer_stateless5) diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/asr_datamodule.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/asr_datamodule.py new file mode 120000 index 000000000..b4e5427e0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/beam_search.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/beam_search.py new file mode 120000 index 000000000..227d2247c --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/beam_search.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/beam_search.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py new file mode 100755 index 000000000..e9989579b --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py @@ -0,0 +1,645 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao) +# +# 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: +(1) greedy search +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +import math +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 beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_stateless_emformer_rnnt/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + decoding_graph: 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 greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_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`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + feature_lens += params.left_context_length + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.left_context_length), + value=LOG_EPS, + ) + + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i + 1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 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 = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 10 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + 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.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.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) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) + test_other_dl = librispeech.test_dataloaders(test_other_cuts) + + test_sets = ["test-clean", "test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decoder.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decoder.py new file mode 120000 index 000000000..0d5f10dc0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decoder.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/decoder.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/emformer.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/emformer.py new file mode 100644 index 000000000..2ed7dab53 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/emformer.py @@ -0,0 +1,313 @@ +# 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 math +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface +from subsampling import Conv2dSubsampling, VggSubsampling + +try: + from torchaudio.models import Emformer as _Emformer +except ImportError: + import torchaudio + + print( + "Please install torchaudio >= 0.11.0. " + f"Current version: {torchaudio.__version__}" + ) + raise + + +def unstack_states( + states: List[List[torch.Tensor]], +) -> List[List[List[torch.Tensor]]]: + """Unstack the emformer state corresponding to a batch of utterances + into a list of states, were the i-th entry is the state from the i-th + utterance in the batch. + + Args: + states: + A list-of-list of tensors. ``len(states)`` equals to number of + layers in the emformer. ``states[i]]`` contains the states for + the i-th layer. ``states[i][k]`` is either a 3-D tensor of shape + ``(T, N, C)`` or a 2-D tensor of shape ``(C, N)`` + """ + batch_size = states[0][0].size(1) + num_layers = len(states) + + ans = [None] * batch_size + for i in range(batch_size): + ans[i] = [[] for _ in range(num_layers)] + + for li, layer in enumerate(states): + for s in layer: + s_list = s.unbind(dim=1) + # We will use stack(dim=1) later in stack_states() + for bi, b in enumerate(ans): + b[li].append(s_list[bi]) + return ans + + +def stack_states( + state_list: List[List[List[torch.Tensor]]], +) -> List[List[torch.Tensor]]: + """Stack list of emformer states that correspond to separate utterances + into a single emformer state so that it can be used as an input for + emformer when those utterances are formed into a batch. + + Note: + It is the inverse of :func:`unstack_states`. + + Args: + state_list: + Each element in state_list corresponding to the internal state + of the emformer model for a single utterance. + Returns: + Return a new state corresponding to a batch of utterances. + See the input argument of :func:`unstack_states` for the meaning + of the returned tensor. + """ + batch_size = len(state_list) + ans = [] + for layer in state_list[0]: + # layer is a list of tensors + if batch_size > 1: + ans.append([[s] for s in layer]) + # Note: We will stack ans[layer][s][] later to get ans[layer][s] + else: + ans.append([s.unsqueeze(1) for s in layer]) + + for b, states in enumerate(state_list[1:], 1): + for li, layer in enumerate(states): + for si, s in enumerate(layer): + ans[li][si].append(s) + if b == batch_size - 1: + ans[li][si] = torch.stack(ans[li][si], dim=1) + # We will use unbind(dim=1) later in unstack_states() + return ans + + +class Emformer(EncoderInterface): + """This is just a simple wrapper around torchaudio.models.Emformer. + We may replace it with our own implementation some time later. + """ + + def __init__( + self, + num_features: int, + output_dim: int, + d_model: int, + nhead: int, + dim_feedforward: int, + num_encoder_layers: int, + segment_length: int, + left_context_length: int, + right_context_length: int, + max_memory_size: int = 0, + dropout: float = 0.1, + subsampling_factor: int = 4, + vgg_frontend: bool = False, + ) -> None: + """ + Args: + num_features: + The input dimension of the model. + output_dim: + The output dimension of the model. + d_model: + Attention dimension. + nhead: + Number of heads in multi-head attention. + dim_feedforward: + The output dimension of the feedforward layers in encoder. + num_encoder_layers: + Number of encoder layers. + segment_length: + Number of frames per segment before subsampling. + left_context_length: + Number of frames in the left context before subsampling. + right_context_length: + Number of frames in the right context before subsampling. + max_memory_size: + TODO. + dropout: + Dropout in encoder. + subsampling_factor: + Number of output frames is num_in_frames // subsampling_factor. + Currently, subsampling_factor MUST be 4. + vgg_frontend: + True to use vgg style frontend for subsampling. + """ + super().__init__() + + 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_features) + # 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_features -> d_model + if vgg_frontend: + self.encoder_embed = VggSubsampling(num_features, d_model) + else: + self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.segment_length = segment_length # before subsampling + self.right_context_length = right_context_length + + assert right_context_length % subsampling_factor == 0 + assert segment_length % subsampling_factor == 0 + assert left_context_length % subsampling_factor == 0 + + left_context_length = left_context_length // subsampling_factor + right_context_length = right_context_length // subsampling_factor + segment_length = segment_length // subsampling_factor + + self.model = _Emformer( + input_dim=d_model, + num_heads=nhead, + ffn_dim=dim_feedforward, + num_layers=num_encoder_layers, + segment_length=segment_length, + dropout=dropout, + activation="relu", + left_context_length=left_context_length, + right_context_length=right_context_length, + max_memory_size=max_memory_size, + weight_init_scale_strategy="depthwise", + tanh_on_mem=False, + negative_inf=-1e8, + ) + + self.encoder_output_layer = nn.Sequential( + nn.Dropout(p=dropout), nn.Linear(d_model, output_dim) + ) + self.log_eps = math.log(1e-10) + + self._init_state = torch.jit.Attribute([], List[List[torch.Tensor]]) + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + x: + Input features of shape (N, T, C). + x_lens: + A int32 tensor of shape (N,) containing valid frames in `x` before + padding. We have `x.size(1) == x_lens.max()` + Returns: + Return a tuple containing two tensors: + + - encoder_out, a tensor of shape (N, T', C) + - encoder_out_lens, a int32 tensor of shape (N,) containing the + valid frames in `encoder_out` before padding + """ + x = nn.functional.pad( + x, + # (left, right, top, bottom) + # left/right are for the channel dimension, i.e., axis 2 + # top/bottom are for the time dimension, i.e., axis 1 + (0, 0, 0, self.right_context_length), + value=self.log_eps, + ) # (N, T, C) -> (N, T+right_context_length, C) + + x = self.encoder_embed(x) + + # Caution: We assume the subsampling factor is 4! + x_lens = (((x_lens - 1) >> 1) - 1) >> 1 + + emformer_out, emformer_out_lens = self.model(x, x_lens) + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens + + @torch.jit.export + def streaming_forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + states: Optional[List[List[torch.Tensor]]] = None, + ): + """ + Args: + x: + A 3-D tensor of shape (N, T, C). Note: x also contains right + context frames. + x_lens: + A 2-D tensor of shap containing the number of valid frames for each + element in `x` before padding. Note: It also counts right context + frames. + states: + Internal states of the model. + Returns: + Return a tuple containing 3 tensors: + - encoder_out, a 3-D tensor of shape (N, T, C) + - encoder_out_lens: a 1-D tensor of shape (N,) + - next_state, internal model states for the next invocation + """ + x = self.encoder_embed(x) + + # Caution: We assume the subsampling factor is 4! + x_lens = (((x_lens - 1) >> 1) - 1) >> 1 + + emformer_out, emformer_out_lens, states = self.model.infer( + x, x_lens, states + ) + + if x.size(1) != ( + self.model.segment_length + self.model.right_context_length + ): + raise ValueError( + "Incorrect input shape." + f"{x.size(1)} vs {self.model.segment_length} + " + f"{self.model.right_context_length}" + ) + + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens, states + + @torch.jit.export + def get_init_state(self, device: torch.device) -> List[List[torch.Tensor]]: + """Return the initial state of each layer. + + Returns: + Return the initial state of each layer. NOTE: the returned + tensors are on the given device. `len(ans) == num_emformer_layers`. + """ + if self._init_state: + # Note(fangjun): It is OK to share the init state as it is + # not going to be modified by the model + return self._init_state + + batch_size = 1 + + ans: List[List[torch.Tensor]] = [] + for layer in self.model.emformer_layers: + s = layer._init_state(batch_size=batch_size, device=device) + ans.append(s) + + self._init_state = ans + + return ans diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/encoder_interface.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/encoder_interface.py new file mode 120000 index 000000000..a478f2351 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/encoder_interface.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/encoder_interface.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py new file mode 100755 index 000000000..2375f5001 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py @@ -0,0 +1,281 @@ +#!/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. +""" +Usage: +./prunted_stateless_emformer_rnnt/export.py \ + --exp-dir ./prunted_stateless_emformer_rnnt/exp \ + --bpe-model data/lang_bpe_500/bpe.model \ + --epoch 20 \ + --avg 10 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `prunted_stateless_emformer_rnnt/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/librispeech/ASR + ./prunted_stateless_emformer_rnnt/decode.py \ + --exp-dir ./prunted_stateless_emformer_rnnt/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 600 \ + --decoding-method greedy_search \ + --bpe-model data/lang_bpe_500/bpe.model +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="prunted_stateless_emformer_rnnt/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + add_model_arguments(parser) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.eval() + + model.to("cpu") + model.eval() + for p in model.parameters(): + p.requires_grad_(False) + + if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + 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/librispeech/ASR/pruned_stateless_emformer_rnnt2/joiner.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/joiner.py new file mode 120000 index 000000000..81ad47c55 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/joiner.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/joiner.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/model.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/model.py new file mode 100644 index 000000000..2f019bcdb --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/model.py @@ -0,0 +1,169 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang) +# +# 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 k2 +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface + +from icefall.utils import add_sos + + +class Transducer(nn.Module): + """It implements https://arxiv.org/pdf/1211.3711.pdf + "Sequence Transduction with Recurrent Neural Networks" + """ + + def __init__( + self, + encoder: EncoderInterface, + decoder: nn.Module, + joiner: nn.Module, + ): + """ + Args: + encoder: + It is the transcription network in the paper. Its accepts + two inputs: `x` of (N, T, C) and `x_lens` of shape (N,). + It returns two tensors: `logits` of shape (N, T, C) and + `logit_lens` of shape (N,). + decoder: + It is the prediction network in the paper. Its input shape + is (N, U) and its output shape is (N, U, C). It should contain + one attribute: `blank_id`. + joiner: + It has two inputs with shapes: (N, T, C) and (N, U, C). Its + output shape is (N, T, U, C). Note that its output contains + unnormalized probs, i.e., not processed by log-softmax. + """ + super().__init__() + assert isinstance(encoder, EncoderInterface), type(encoder) + assert hasattr(decoder, "blank_id") + + self.encoder = encoder + self.decoder = decoder + self.joiner = joiner + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: k2.RaggedTensor, + prune_range: int = 5, + am_scale: float = 0.0, + lm_scale: float = 0.0, + ) -> torch.Tensor: + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 1-D tensor of shape (N,). It contains the number of frames in `x` + before padding. + y: + A ragged tensor with 2 axes [utt][label]. It contains labels of each + utterance. + prune_range: + The prune range for rnnt loss, it means how many symbols(context) + we are considering for each frame to compute the loss. + am_scale: + The scale to smooth the loss with am (output of encoder network) + part + lm_scale: + The scale to smooth the loss with lm (output of predictor network) + part + Returns: + Return the transducer loss. + + Note: + Regarding am_scale & lm_scale, it will make the loss-function one of + the form: + lm_scale * lm_probs + am_scale * am_probs + + (1-lm_scale-am_scale) * combined_probs + """ + assert x.ndim == 3, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.num_axes == 2, y.num_axes + + assert x.size(0) == x_lens.size(0) == y.dim0 + + encoder_out, x_lens = self.encoder(x, x_lens) + assert torch.all(x_lens > 0) + + # Now for the decoder, i.e., the prediction network + row_splits = y.shape.row_splits(1) + y_lens = row_splits[1:] - row_splits[:-1] + + blank_id = self.decoder.blank_id + sos_y = add_sos(y, sos_id=blank_id) + + # sos_y_padded: [B, S + 1], start with SOS. + sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) + + # decoder_out: [B, S + 1, C] + decoder_out = self.decoder(sos_y_padded) + + # Note: y does not start with SOS + # y_padded : [B, S] + y_padded = y.pad(mode="constant", padding_value=0) + + y_padded = y_padded.to(torch.int64) + boundary = torch.zeros( + (x.size(0), 4), dtype=torch.int64, device=x.device + ) + boundary[:, 2] = y_lens + boundary[:, 3] = x_lens + + simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( + lm=decoder_out, + am=encoder_out, + symbols=y_padded, + termination_symbol=blank_id, + lm_only_scale=lm_scale, + am_only_scale=am_scale, + boundary=boundary, + reduction="sum", + return_grad=True, + ) + + # ranges : [B, T, prune_range] + ranges = k2.get_rnnt_prune_ranges( + px_grad=px_grad, + py_grad=py_grad, + boundary=boundary, + s_range=prune_range, + ) + + # am_pruned : [B, T, prune_range, C] + # lm_pruned : [B, T, prune_range, C] + am_pruned, lm_pruned = k2.do_rnnt_pruning( + am=encoder_out, lm=decoder_out, ranges=ranges + ) + + # logits : [B, T, prune_range, C] + logits = self.joiner(am_pruned, lm_pruned) + + pruned_loss = k2.rnnt_loss_pruned( + logits=logits, + symbols=y_padded, + ranges=ranges, + termination_symbol=blank_id, + boundary=boundary, + reduction="sum", + ) + + return (simple_loss, pruned_loss) diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/noam.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/noam.py new file mode 100644 index 000000000..e46bf35fb --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/noam.py @@ -0,0 +1,104 @@ +# 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 torch + + +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) diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/subsampling.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/subsampling.py new file mode 120000 index 000000000..6fee09e58 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/subsampling.py @@ -0,0 +1 @@ +../conformer_ctc/subsampling.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_emformer.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_emformer.py new file mode 100755 index 000000000..aef506e81 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_emformer.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +# Copyright 2022 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. + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./pruned_stateless_emformer_rnnt/test_emformer.py +""" + +import torch +from emformer import Emformer, stack_states, unstack_states + + +def test_emformer(): + N = 3 + T = 300 + C = 80 + + output_dim = 500 + + encoder = Emformer( + num_features=C, + output_dim=output_dim, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=20, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + + x = torch.rand(N, T, C) + x_lens = torch.randint(100, T, (N,)) + x_lens[0] = T + + y, y_lens = encoder(x, x_lens) + + y_lens = (((x_lens - 1) >> 1) - 1) >> 1 + assert x.size(0) == x.size(0) + assert y.size(1) == max(y_lens) + assert y.size(2) == output_dim + + num_param = sum([p.numel() for p in encoder.parameters()]) + print(f"Number of encoder parameters: {num_param}") + + +def test_emformer_streaming_forward(): + N = 3 + C = 80 + + output_dim = 500 + + encoder = Emformer( + num_features=C, + output_dim=output_dim, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=20, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + + x = torch.rand(N, 23, C) + x_lens = torch.full((N,), 23) + y, y_lens, states = encoder.streaming_forward(x=x, x_lens=x_lens) + + state_list = unstack_states(states) + states2 = stack_states(state_list) + + for ss, ss2 in zip(states, states2): + for s, s2 in zip(ss, ss2): + assert torch.allclose(s, s2), f"{s.sum()}, {s2.sum()}" + + +def test_emformer_init_state(): + num_encoder_layers = 20 + d_model = 512 + encoder = Emformer( + num_features=80, + output_dim=500, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=num_encoder_layers, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + init_state = encoder.get_init_state() + assert len(init_state) == num_encoder_layers + layer0_state = init_state[0] + assert len(layer0_state) == 4 + + assert layer0_state[0].shape == ( + 0, # max_memory_size + 1, # batch_size + d_model, # input_dim + ) + + assert layer0_state[1].shape == ( + encoder.model.left_context_length, + 1, # batch_size + d_model, # input_dim + ) + assert layer0_state[2].shape == layer0_state[1].shape + assert layer0_state[3].shape == ( + 1, # always 1 + 1, # batch_size + ) + + +@torch.no_grad() +def main(): + test_emformer() + test_emformer_streaming_forward() + test_emformer_init_state() + + +if __name__ == "__main__": + torch.manual_seed(20220329) + main() diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_model.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_model.py new file mode 100755 index 000000000..573817b85 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_model.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +# Copyright 2022 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. + + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./pruned_stateless_emformer_rnnt/test_model.py +""" + +import torch +from train import get_params, get_transducer_model + + +def test_model(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.unk_id = 2 + + params.attention_dim = 512 + params.nhead = 8 + params.dim_feedforward = 2048 + params.num_encoder_layers = 18 + params.left_context_length = 128 + params.segment_length = 8 + params.right_context_length = 4 + params.memory_size = 0 + + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + torch.jit.script(model) + + +def main(): + test_model() + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py new file mode 100755 index 000000000..cd62787fa --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py @@ -0,0 +1,1034 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo,) +# Zengwei Yao) +# +# 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" + +./pruned_stateless_emformer_rnnt/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_stateless_emformer_rnnt/exp \ + --full-libri 1 \ + --max-duration 300 +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import sentencepiece as spm +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from decoder import Decoder +from emformer import Emformer +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from noam import Noam +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--attention-dim", + type=int, + default=512, + help="Attention dim for the Emformer", + ) + + parser.add_argument( + "--nhead", + type=int, + default=8, + help="Number of attention heads for the Emformer", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=2048, + help="Feed-forward dimension for the Emformer", + ) + + parser.add_argument( + "--num-encoder-layers", + type=int, + default=18, + help="Number of encoder layers for the Emformer", + ) + + parser.add_argument( + "--left-context-length", + type=int, + default=128, + help="Number of frames for the left context in the Emformer", + ) + + parser.add_argument( + "--segment-length", + type=int, + default=8, + help="Number of frames for each segment in the Emformer", + ) + + parser.add_argument( + "--right-context-length", + type=int, + default=4, + help="Number of frames for right context in the Emformer", + ) + + parser.add_argument( + "--memory-size", + type=int, + default=0, + help="Number of entries in the memory for the Emformer", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_stateless_emformer_rnnt/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--lr-factor", + type=float, + default=5.0, + help="The lr_factor for Noam optimizer", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=20, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + add_model_arguments(parser) + + 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. + + - attention_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - 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, # For the 100h subset, use 800 + # parameters for Emformer + "feature_dim": 80, + "subsampling_factor": 4, + "vgg_frontend": False, + # parameters for decoder + "embedding_dim": 512, + # parameters for Noam + "warm_step": 80000, # For the 100h subset, use 20000 + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = Emformer( + num_features=params.feature_dim, + output_dim=params.vocab_size, + subsampling_factor=params.subsampling_factor, + d_model=params.attention_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + vgg_frontend=params.vgg_frontend, + left_context_length=params.left_context_length, + segment_length=params.segment_length, + right_context_length=params.right_context_length, + max_memory_size=params.memory_size, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + embedding_dim=params.embedding_dim, + blank_id=params.blank_id, + unk_id=params.unk_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + input_dim=params.vocab_size, + inner_dim=params.embedding_dim, + output_dim=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute pruned RNN-T 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 Emformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + 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 = ( + model.device + if isinstance(model, DDP) + else next(model.parameters()).device + ) + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + loss = params.simple_loss_scale * simple_loss + pruned_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + 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, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + 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. + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 30: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = optimizer.rate() + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + 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)) + if params.full_libri is False: + params.valid_interval = 800 + params.warm_step = 20000 + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + + optimizer = Noam( + model.parameters(), + model_size=params.attention_dim, + factor=params.lr_factor, + warm_step=params.warm_step, + ) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if params.print_diagnostics: + diagnostic = diagnostics.attach_diagnostics(model) + + librispeech = LibriSpeechAsrDataModule(args) + + train_cuts = librispeech.train_clean_100_cuts() + if params.full_libri: + train_cuts += librispeech.train_clean_360_cuts() + train_cuts += librispeech.train_other_500_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = librispeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = librispeech.dev_clean_cuts() + valid_cuts += librispeech.dev_other_cuts() + valid_dl = librispeech.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + sp=sp, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py b/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py index 7c5a93a86..c9522df8a 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py @@ -32,13 +32,15 @@ class Joiner(nn.Module): """ Args: encoder_out: - Output from the encoder. Its shape is (N, T, s_range, C). + Output from the encoder. Its shape is (N, T, s_range, C) during + training or (N, C) in case of streaming decoding. decoder_out: - Output from the decoder. Its shape is (N, T, s_range, C). - Returns: + Output from the decoder. Its shape is (N, T, s_range, C) during + training or (N, C) in case of streaming decoding. Return a tensor of shape (N, T, s_range, C). """ - assert encoder_out.ndim == decoder_out.ndim == 4 + assert encoder_out.ndim == decoder_out.ndim + assert encoder_out.ndim in (2, 4) assert encoder_out.shape == decoder_out.shape logit = encoder_out + decoder_out diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py b/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py index df1e52202..8828285aa 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py @@ -277,10 +277,12 @@ class AsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, + num_buckets=self.args.num_buckets, + drop_last=False, ) logging.info("About to create dev dataloader") valid_dl = DataLoader( @@ -301,8 +303,12 @@ class AsrDataModule: else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + num_buckets=self.args.num_buckets, + drop_last=True, ) logging.debug("About to create test dataloader") test_dl = DataLoader( diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py index f5a25a226..a2a5519f1 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py @@ -799,7 +799,7 @@ def train_one_epoch( f"tot_loss[{tot_loss}], " f"libri_tot_loss[{libri_tot_loss}], " f"giga_tot_loss[{giga_tot_loss}], " - f"batch size: {batch_size}" + f"batch size: {batch_size}, " f"lr: {cur_lr:.2e}" ) diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py index e83009d4a..7628c8274 100644 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py @@ -26,9 +26,9 @@ from typing import Any, Dict, Optional import torch from lhotse import CutSet, Fbank, FbankConfig, load_manifest from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures - BucketingSampler, CutConcatenate, CutMix, + DynamicBucketingSampler, K2SpeechRecognitionDataset, PrecomputedFeatures, SingleCutSampler, @@ -113,7 +113,7 @@ class LibriSpeechAsrDataModule: "--num-buckets", type=int, default=30, - help="The number of buckets for the BucketingSampler" + help="The number of buckets for the DynamicBucketingSampler" "(you might want to increase it for larger datasets).", ) group.add_argument( @@ -306,13 +306,12 @@ class LibriSpeechAsrDataModule: ) if self.args.bucketing_sampler: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( + 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, - bucket_method="equal_duration", drop_last=self.args.drop_last, ) else: @@ -367,7 +366,7 @@ class LibriSpeechAsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, @@ -391,8 +390,10 @@ class LibriSpeechAsrDataModule: else eval(self.args.input_strategy)(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, ) logging.debug("About to create test dataloader") test_dl = DataLoader(