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Fangjun Kuang 2022-02-21 13:08:56 +08:00
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152
.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml vendored Normal file
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# Copyright 2021 Fangjun Kuang (csukuangfj@gmail.com)
# 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.
name: run-pre-trained-trandsucer-stateless-multi-datasets-librispeech-100h
on:
push:
branches:
- master
pull_request:
types: [labeled]
jobs:
run_pre_trained_transducer_stateless_multi_datasets_librispeech_100h:
if: github.event.label.name == 'ready' || github.event_name == 'push'
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-18.04]
python-version: [3.7, 3.8, 3.9]
torch: ["1.10.0"]
torchaudio: ["0.10.0"]
k2-version: ["1.9.dev20211101"]
fail-fast: false
steps:
- uses: actions/checkout@v2
with:
fetch-depth: 0
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v1
with:
python-version: ${{ matrix.python-version }}
- name: Install Python dependencies
run: |
python3 -m pip install --upgrade pip pytest
# numpy 1.20.x does not support python 3.6
pip install numpy==1.19
pip install torch==${{ matrix.torch }}+cpu torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
python3 -m pip install git+https://github.com/lhotse-speech/lhotse
python3 -m pip install kaldifeat
# We are in ./icefall and there is a file: requirements.txt in it
pip install -r requirements.txt
- name: Install graphviz
shell: bash
run: |
python3 -m pip install -qq graphviz
sudo apt-get -qq install graphviz
- name: Download pre-trained model
shell: bash
run: |
sudo apt-get -qq install git-lfs tree sox
cd egs/librispeech/ASR
mkdir tmp
cd tmp
git lfs install
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21
cd ..
tree tmp
soxi tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/*.wav
ls -lh tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/*.wav
- name: Run greedy search decoding (max-sym-per-frame 1)
shell: bash
run: |
export PYTHONPATH=$PWD:PYTHONPATH
cd egs/librispeech/ASR
./transducer_stateless_multi_datasets/pretrained.py \
--method greedy_search \
--max-sym-per-frame 1 \
--checkpoint ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0002.wav
- name: Run greedy search decoding (max-sym-per-frame 2)
shell: bash
run: |
export PYTHONPATH=$PWD:PYTHONPATH
cd egs/librispeech/ASR
./transducer_stateless_multi_datasets/pretrained.py \
--method greedy_search \
--max-sym-per-frame 2 \
--checkpoint ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0002.wav
- name: Run greedy search decoding (max-sym-per-frame 3)
shell: bash
run: |
export PYTHONPATH=$PWD:PYTHONPATH
cd egs/librispeech/ASR
./transducer_stateless_multi_datasets/pretrained.py \
--method greedy_search \
--max-sym-per-frame 3 \
--checkpoint ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0002.wav
- name: Run beam search decoding
shell: bash
run: |
export PYTHONPATH=$PWD:$PYTHONPATH
cd egs/librispeech/ASR
./transducer_stateless_multi_datasets/pretrained.py \
--method beam_search \
--beam-size 4 \
--checkpoint ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0002.wav
- name: Run modified beam search decoding
shell: bash
run: |
export PYTHONPATH=$PWD:$PYTHONPATH
cd egs/librispeech/ASR
./transducer_stateless_multi_datasets/pretrained.py \
--method modified_beam_search \
--beam-size 4 \
--checkpoint ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/exp/pretrained.pt \
--bpe-model ./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/data/lang_bpe_500/bpe.model \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1089-134686-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0001.wav \
./tmp/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/test_wavs/1221-135766-0002.wav

11
egs/librispeech/ASR/README.md
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@ -9,11 +9,12 @@ for how to run models in this recipe.
There are various folders containing the name `transducer` in this folder.
The following table lists the differences among them.
| | Encoder | Decoder |
|------------------------|-----------|--------------------|
| `transducer` | Conformer | LSTM |
| `transducer_stateless` | Conformer | Embedding + Conv1d |
| `transducer_lstm ` | LSTM | LSTM |
| | Encoder | Decoder | Comment |
|---------------------------------------|-----------|--------------------|---------------------------------------------------|
| `transducer` | Conformer | LSTM | |
| `transducer_stateless` | Conformer | Embedding + Conv1d | |
| `transducer_lstm` | LSTM | LSTM | |
| `transducer_stateless_multi_datasets` | Conformer | Embedding + Conv1d | Using data from GigaSpeech as extra training data |
The decoder in `transducer_stateless` is modified from the paper
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).

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egs/librispeech/ASR/RESULTS-100hours.md Normal file
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@ -0,0 +1,75 @@
# Results for train-clean-100
This page shows the WERs for test-clean/test-other using only
train-clean-100 subset as training data.
## Conformer encoder + embedding decoder
### 2022-02-21
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
| greedy search (max sym per frame 2) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
| greedy search (max sym per frame 3) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
| modified beam search (beam size 4) | 6.31 | 16.3 | --epoch 57, --avg 17, --max-duration 100 |
The training command for reproducing is given below:
```bash
cd egs/librispeech/ASR/
./prepare.sh
./prepare_giga_speech.sh
export CUDA_VISIBLE_DEVICES="0,1"
./transducer_stateless_multi_datasets/train.py \
--world-size 2 \
--num-epochs 60 \
--start-epoch 0 \
--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
--full-libri 0 \
--max-duration 300 \
--lr-factor 1 \
--bpe-model data/lang_bpe_500/bpe.model \
--modified-transducer-prob 0.25
--giga-prob 0.2
```
The decoding command is given below:
```bash
for epoch in 57; do
for avg in 17; do
for sym in 1 2 3; do
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--max-sym-per-frame $sym
done
done
done
epoch=57
avg=17
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
```
The tensorboard log is available at
<https://tensorboard.dev/experiment/qUEKzMnrTZmOz1EXPda9RA/>
A pre-trained model and decoding logs can be found at
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21>

5
egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py
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@ -191,15 +191,10 @@ def get_transducer_model(params: AttributeDict):
decoder = get_decoder_model(params)
joiner = get_joiner_model(params)
decoder_giga = get_decoder_model(params)
joiner_giga = get_joiner_model(params)
model = Transducer(
encoder=encoder,
decoder=decoder,
joiner=joiner,
decoder_giga=decoder_giga,
joiner_giga=joiner_giga,
)
return model

17
egs/librispeech/ASR/transducer_stateless_multi_datasets/export.py
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@ -20,22 +20,23 @@
# to a single one using model averaging.
"""
Usage:
./transducer_stateless/export.py \
--exp-dir ./transducer_stateless/exp \
./transducer_stateless_multi_datasets/export.py \
--exp-dir ./transducer_stateless_multi_datasets/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 `transducer_stateless/decode.py`, you can do:
To use the generated file with `transducer_stateless_multi_datasets/decode.py`,
you can do::
cd /path/to/exp_dir
ln -s pretrained.pt epoch-9999.pt
cd /path/to/egs/librispeech/ASR
./transducer_stateless/decode.py \
--exp-dir ./transducer_stateless/exp \
./transducer_stateless_multi_datasets/decode.py \
--exp-dir ./transducer_stateless_multi_datasets/exp \
--epoch 9999 \
--avg 1 \
--max-duration 1 \
@ -84,7 +85,7 @@ def get_parser():
parser.add_argument(
"--exp-dir",
type=str,
default="transducer_stateless/exp",
default="transducer_stateless_multi_datasets/exp",
help="""It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",
@ -218,7 +219,9 @@ def main():
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))
model.load_state_dict(
average_checkpoints(filenames, device=device), strict=False
)
model.eval()

2
egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py
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@ -49,7 +49,7 @@ class LibriSpeech:
return load_manifest(f)
def train_other_500_cuts(self) -> CutSet:
f = self.args.manifest_dir / "cuts_train-other-500.json.gz"
f = self.manifest_dir / "cuts_train-other-500.json.gz"
logging.info(f"About to get train-other-500 cuts from {f}")
return load_manifest(f)

9
egs/librispeech/ASR/transducer_stateless_multi_datasets/model.py
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@ -15,6 +15,7 @@
# limitations under the License.
import random
from typing import Optional
import k2
import torch
@ -34,8 +35,8 @@ class Transducer(nn.Module):
encoder: EncoderInterface,
decoder: nn.Module,
joiner: nn.Module,
decoder_giga: nn.Module,
joiner_giga: nn.Module,
decoder_giga: Optional[nn.Module] = None,
joiner_giga: Optional[nn.Module] = None,
):
"""
Args:
@ -60,7 +61,9 @@ class Transducer(nn.Module):
super().__init__()
assert isinstance(encoder, EncoderInterface), type(encoder)
assert hasattr(decoder, "blank_id")
assert hasattr(decoder_giga, "blank_id")
if decoder_giga is not None:
assert hasattr(decoder_giga, "blank_id")
self.encoder = encoder

11
egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py
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@ -738,8 +738,13 @@ def run(rank, world_size, args):
# XS 10 hours
# DEV 12 hours
# Test 40 hours
# train_giga_cuts = gigaspeech.train_M_cuts()
train_giga_cuts = gigaspeech.train_S_cuts()
if params.full_libri:
logging.info("Using the L subset of GigaSpeech (2.5k hours)")
train_giga_cuts = gigaspeech.train_L_cuts()
else:
logging.info("Using the S subset of GigaSpeech (250 hours)")
train_giga_cuts = gigaspeech.train_S_cuts()
train_giga_cuts = filter_short_and_long_utterances(train_giga_cuts)
if args.enable_musan:
@ -868,7 +873,7 @@ def main():
args = parser.parse_args()
args.exp_dir = Path(args.exp_dir)
assert 0 < args.giga_prob < 1, args.giga_prob
assert 0 <= args.giga_prob < 1, args.giga_prob
world_size = args.world_size
assert world_size >= 1