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Update RESULTS.md (#388)

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* update RESULT.md about pruned_transducer_stateless4

* Update RESULT.md

This PR is only to update RESULT.md about pruned_transducer_stateless4.

* set default value of --use-averaged-model to True

* update RESULTS.md and add decode command

* minor fix

* update export.py

* add uploaded files links

* update link

* fix typos
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Zengwei Yao 2022-06-04 15:52:35 +08:00 committed by GitHub
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@ -259,6 +259,126 @@ You can find a pretrained model, training logs, decoding logs, and decoding
results at:
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-narrower-2022-05-13>
### LibriSpeech BPE training results (Pruned Stateless Transducer 4)
[pruned_transducer_stateless4](./pruned_transducer_stateless4)
This version saves averaged model during training, and decodes with averaged model.
See <https://github.com/k2-fsa/icefall/issues/337> for details about the idea of model averaging.
#### Training on full librispeech
See <https://github.com/k2-fsa/icefall/pull/344>
Using commit `ec0b0e92297cc03fdb09f48cd235e84d2c04156b`.
The WERs are:
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-------------------------------------------------------------------------------|
| greedy search (max sym per frame 1) | 2.75 | 6.74 | --epoch 30 --avg 6 --use_averaged_model False |
| greedy search (max sym per frame 1) | 2.69 | 6.64 | --epoch 30 --avg 6 --use_averaged_model True |
| fast beam search | 2.72 | 6.67 | --epoch 30 --avg 6 --use_averaged_model False |
| fast beam search | 2.66 | 6.6 | --epoch 30 --avg 6 --use_averaged_model True |
| modified beam search | 2.67 | 6.68 | --epoch 30 --avg 6 --use_averaged_model False |
| modified beam search | 2.62 | 6.57 | --epoch 30 --avg 6 --use_averaged_model True |
The training command is:
```bash
./pruned_transducer_stateless4/train.py \
--world-size 6 \
--num-epochs 30 \
--start-epoch 1 \
--exp-dir pruned_transducer_stateless4/exp \
--full-libri 1 \
--max-duration 300 \
--save-every-n 8000 \
--keep-last-k 20 \
--average-period 100
```
The tensorboard log can be found at
<https://tensorboard.dev/experiment/QOGSPBgsR8KzcRMmie9JGw/>
The decoding command using greedy search is:
```bash
./pruned_transducer_stateless4/decode.py \
--epoch 30 \
--avg 6 \
--exp-dir pruned_transducer_stateless4/exp \
--max-duration 300 \
--decoding-method greedy_search \
--use-averaged-model True
```
The decoding command using fast beam search is:
```bash
./pruned_transducer_stateless4/decode.py \
--epoch 30 \
--avg 6 \
--exp-dir pruned_transducer_stateless4/exp \
--max-duration 300 \
--decoding-method fast_beam_search \
--use-averaged-model True \
--beam 4 \
--max-contexts 4 \
--max-states 8
```
The decoding command using modified beam search is:
```bash
./pruned_transducer_stateless4/decode.py \
--epoch 30 \
--avg 6 \
--exp-dir pruned_transducer_stateless4/exp \
--max-duration 300 \
--decoding-method modified_beam_search \
--use-averaged-model True \
--beam-size 4
```
Pretrained models, training logs, decoding logs, and decoding results
are available at
<https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless4-2022-06-03>
#### Training on train-clean-100
See <https://github.com/k2-fsa/icefall/pull/344>
Using commit `ec0b0e92297cc03fdb09f48cd235e84d2c04156b`.
The WERs are:
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|-------------------------------------------------------------------------------|
| greedy search (max sym per frame 1) | 7.0 | 18.95 | --epoch 30 --avg 10 --use_averaged_model False |
| greedy search (max sym per frame 1) | 6.92 | 18.65 | --epoch 30 --avg 10 --use_averaged_model True |
| fast beam search | 6.82 | 18.47 | --epoch 30 --avg 10 --use_averaged_model False |
| fast beam search | 6.74 | 18.2 | --epoch 30 --avg 10 --use_averaged_model True |
| modified beam search | 6.74 | 18.39 | --epoch 30 --avg 10 --use_averaged_model False |
| modified beam search | 6.74 | 18.12 | --epoch 30 --avg 10 --use_averaged_model True |
The training command is:
```bash
./pruned_transducer_stateless4/train.py \
--world-size 3 \
--num-epochs 30 \
--start-epoch 1 \
--exp-dir pruned_transducer_stateless4/exp \
--full-libri 0 \
--max-duration 300 \
--save-every-n 8000 \
--keep-last-k 20 \
--average-period 100
```
The tensorboard log can be found at
<https://tensorboard.dev/experiment/YVYHq1irQS69s9bW1vQ06Q/>
### LibriSpeech BPE training results (Pruned Stateless Transducer 3, 2022-04-29)
[pruned_transducer_stateless3](./pruned_transducer_stateless3)

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@ -128,7 +128,7 @@ def get_parser():
parser.add_argument(
"--use-averaged-model",
type=str2bool,
default=False,
default=True,
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`."

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egs/librispeech/ASR/pruned_transducer_stateless4/export.py
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@ -1 +0,0 @@
../pruned_transducer_stateless2/export.py

273
egs/librispeech/ASR/pruned_transducer_stateless4/export.py Executable file
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@ -0,0 +1,273 @@
#!/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:
./pruned_transducer_stateless4/export.py \
--exp-dir ./pruned_transducer_stateless4/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 `pruned_transducer_stateless4/decode.py`,
you can do:
cd /path/to/exp_dir
ln -s pretrained.pt epoch-9999.pt
cd /path/to/egs/librispeech/ASR
./pruned_transducer_stateless4/decode.py \
--exp-dir ./pruned_transducer_stateless4/exp \
--epoch 9999 \
--avg 1 \
--max-duration 100 \
--bpe-model data/lang_bpe_500/bpe.model \
--use-averaged-model False
"""
import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import torch
from train import 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 0.
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(
"--exp-dir",
type=str,
default="pruned_transducer_stateless2/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",
)
parser.add_argument(
"--use-averaged-model",
type=str2bool,
default=True,
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. ",
)
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")
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params)
model.to(device)
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()
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()