From c34ee676914aa58a4145c93a15c49d634d7f8960 Mon Sep 17 00:00:00 2001 From: Yifan Yang <64255737+yfyeung@users.noreply.github.com> Date: Mon, 13 Feb 2023 14:05:38 +0800 Subject: [PATCH] Update generate_model_from_checkpoint.py (#901) --- .../generate_averaged_model.py | 203 ------------- .../generate_model_from_checkpoint.py | 282 ++++++++++++++++++ 2 files changed, 282 insertions(+), 203 deletions(-) delete mode 100755 egs/librispeech/ASR/pruned_transducer_stateless7/generate_averaged_model.py create mode 100755 egs/librispeech/ASR/pruned_transducer_stateless7/generate_model_from_checkpoint.py diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/generate_averaged_model.py b/egs/librispeech/ASR/pruned_transducer_stateless7/generate_averaged_model.py deleted file mode 100755 index 381772ce7..000000000 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/generate_averaged_model.py +++ /dev/null @@ -1,203 +0,0 @@ -#!/usr/bin/env python3 -# -# Copyright 2021-2022 Xiaomi Corporation (Author: Yifan Yang) -# -# 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) use the checkpoint exp_dir/epoch-xxx.pt -./pruned_transducer_stateless7/generate_averaged_model.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless7/exp - -It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`. -You can later load it by `torch.load("epoch-28-avg-15.pt")`. - -(2) use the checkpoint exp_dir/checkpoint-iter.pt -./pruned_transducer_stateless7/generate_averaged_model.py \ - --iter 22000 \ - --avg 5 \ - --exp-dir ./pruned_transducer_stateless7/exp - -It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`. -You can later load it by `torch.load("iter-22000-avg-5.pt")`. -""" - - -import argparse -from pathlib import Path -from typing import Dict, List - -import sentencepiece as spm -import torch -from asr_datamodule import LibriSpeechAsrDataModule - -from train import add_model_arguments, get_params, get_transducer_model - -from icefall.checkpoint import ( - average_checkpoints_with_averaged_model, - find_checkpoints, -) - - -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=9, - help="Number of checkpoints to average. Automatically select " - "consecutive checkpoints before the checkpoint specified by " - "'--epoch' and '--iter'", - ) - - parser.add_argument( - "--exp-dir", - type=str, - default="pruned_transducer_stateless7/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( - "--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 - - -@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)) - - if params.iter > 0: - params.suffix = f"iter-{params.iter}-avg-{params.avg}" - else: - params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - - print("Script started") - - device = torch.device("cpu") - print(f"Device: {device}") - - sp = spm.SentencePieceProcessor() - sp.load(params.bpe_model) - - # is 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() - - print("About to create model") - model = get_transducer_model(params) - - 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 --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] - print( - "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, - ) - ) - filename = params.exp_dir / f"iter-{params.iter}-avg-{params.avg}.pt" - torch.save({"model": model.state_dict()}, filename) - 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" - print( - 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, - ) - ) - filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt" - torch.save({"model": model.state_dict()}, filename) - - num_param = sum([p.numel() for p in model.parameters()]) - print(f"Number of model parameters: {num_param}") - - print("Done!") - - -if __name__ == "__main__": - main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/generate_model_from_checkpoint.py b/egs/librispeech/ASR/pruned_transducer_stateless7/generate_model_from_checkpoint.py new file mode 100755 index 000000000..37edc0390 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/generate_model_from_checkpoint.py @@ -0,0 +1,282 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Yifan Yang) +# +# 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) use the averaged model with checkpoint exp_dir/epoch-xxx.pt +./pruned_transducer_stateless7/generate_model_from_checkpoint.py \ + --epoch 28 \ + --avg 15 \ + --use-averaged-model True \ + --exp-dir ./pruned_transducer_stateless7/exp + +It will generate a file `epoch-28-avg-15-use-averaged-model.pt` in the given `exp_dir`. +You can later load it by `torch.load("epoch-28-avg-15-use-averaged-model.pt")`. + +(2) use the averaged model with checkpoint exp_dir/checkpoint-iter.pt +./pruned_transducer_stateless7/generate_model_from_checkpoint.py \ + --iter 22000 \ + --avg 5 \ + --use-averaged-model True \ + --exp-dir ./pruned_transducer_stateless7/exp + +It will generate a file `iter-22000-avg-5-use-averaged-model.pt` in the given `exp_dir`. +You can later load it by `torch.load("iter-22000-avg-5-use-averaged-model.pt")`. + +(3) use the original model with checkpoint exp_dir/epoch-xxx.pt +./pruned_transducer_stateless7/generate_model_from_checkpoint.py \ + --epoch 28 \ + --avg 15 \ + --use-averaged-model False \ + --exp-dir ./pruned_transducer_stateless7/exp + +It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`. +You can later load it by `torch.load("epoch-28-avg-15.pt")`. + +(4) use the original model with checkpoint exp_dir/checkpoint-iter.pt +./pruned_transducer_stateless7/generate_model_from_checkpoint.py \ + --iter 22000 \ + --avg 5 \ + --use-averaged-model False \ + --exp-dir ./pruned_transducer_stateless7/exp + +It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`. +You can later load it by `torch.load("iter-22000-avg-5.pt")`. +""" + + +import argparse +from pathlib import Path +from typing import Dict, List + +import sentencepiece as spm +import torch + +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.utils import str2bool +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) + + +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=9, + 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=True, + help="Whether to load averaged model." + "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_transducer_stateless7/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( + "--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 + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + print("Script started") + + device = torch.device("cpu") + print(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is 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() + + print("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}" + ) + print(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + filename = params.exp_dir / f"iter-{params.iter}-avg-{params.avg}.pt" + torch.save({"model": model.state_dict()}, filename) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt" + torch.save({"model": model.state_dict()}, filename) + 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") + print(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt" + torch.save({"model": model.state_dict()}, filename) + 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 --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] + print( + "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, + ) + ) + filename = ( + params.exp_dir + / f"iter-{params.iter}-avg-{params.avg}-use-averaged-model.pt" + ) + torch.save({"model": model.state_dict()}, filename) + 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" + print( + 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, + ) + ) + filename = ( + params.exp_dir + / f"epoch-{params.epoch}-avg-{params.avg}-use-averaged-model.pt" + ) + torch.save({"model": model.state_dict()}, filename) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + + print("Done!") + + +if __name__ == "__main__": + main()