mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-27 10:44:19 +00:00
Merge remote-tracking branch 'upstream/master' into reazonspeech-recipe
This commit is contained in:
commit
3505a8ec45
1
.github/scripts/.gitignore
vendored
Normal file
1
.github/scripts/.gitignore
vendored
Normal file
@ -0,0 +1 @@
|
||||
piper_phonemize.html
|
94
.github/scripts/audioset/AT/run.sh
vendored
Executable file
94
.github/scripts/audioset/AT/run.sh
vendored
Executable file
@ -0,0 +1,94 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python3 -m pip install onnxoptimizer onnxsim
|
||||
|
||||
log() {
|
||||
# This function is from espnet
|
||||
local fname=${BASH_SOURCE[1]##*/}
|
||||
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
|
||||
}
|
||||
|
||||
cd egs/audioset/AT
|
||||
|
||||
function test_pretrained() {
|
||||
repo_url=https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12
|
||||
repo=$(basename $repo_url)
|
||||
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
|
||||
pushd $repo/exp
|
||||
git lfs pull --include pretrained.pt
|
||||
ln -s pretrained.pt epoch-99.pt
|
||||
ls -lh
|
||||
popd
|
||||
|
||||
log "test pretrained.pt"
|
||||
|
||||
python3 zipformer/pretrained.py \
|
||||
--checkpoint $repo/exp/pretrained.pt \
|
||||
--label-dict $repo/data/class_labels_indices.csv \
|
||||
$repo/test_wavs/1.wav \
|
||||
$repo/test_wavs/2.wav \
|
||||
$repo/test_wavs/3.wav \
|
||||
$repo/test_wavs/4.wav
|
||||
|
||||
log "test jit export"
|
||||
ls -lh $repo/exp/
|
||||
python3 zipformer/export.py \
|
||||
--exp-dir $repo/exp \
|
||||
--epoch 99 \
|
||||
--avg 1 \
|
||||
--use-averaged-model 0 \
|
||||
--jit 1
|
||||
ls -lh $repo/exp/
|
||||
|
||||
log "test jit models"
|
||||
python3 zipformer/jit_pretrained.py \
|
||||
--nn-model-filename $repo/exp/jit_script.pt \
|
||||
--label-dict $repo/data/class_labels_indices.csv \
|
||||
$repo/test_wavs/1.wav \
|
||||
$repo/test_wavs/2.wav \
|
||||
$repo/test_wavs/3.wav \
|
||||
$repo/test_wavs/4.wav
|
||||
|
||||
log "test onnx export"
|
||||
ls -lh $repo/exp/
|
||||
python3 zipformer/export-onnx.py \
|
||||
--exp-dir $repo/exp \
|
||||
--epoch 99 \
|
||||
--avg 1 \
|
||||
--use-averaged-model 0
|
||||
|
||||
ls -lh $repo/exp/
|
||||
|
||||
pushd $repo/exp/
|
||||
mv model-epoch-99-avg-1.onnx model.onnx
|
||||
mv model-epoch-99-avg-1.int8.onnx model.int8.onnx
|
||||
popd
|
||||
|
||||
ls -lh $repo/exp/
|
||||
|
||||
log "test onnx models"
|
||||
for m in model.onnx model.int8.onnx; do
|
||||
log "$m"
|
||||
python3 zipformer/onnx_pretrained.py \
|
||||
--model-filename $repo/exp/model.onnx \
|
||||
--label-dict $repo/data/class_labels_indices.csv \
|
||||
$repo/test_wavs/1.wav \
|
||||
$repo/test_wavs/2.wav \
|
||||
$repo/test_wavs/3.wav \
|
||||
$repo/test_wavs/4.wav
|
||||
done
|
||||
|
||||
log "prepare data for uploading to huggingface"
|
||||
dst=/icefall/model-onnx
|
||||
mkdir -p $dst
|
||||
cp -v $repo/exp/*.onnx $dst/
|
||||
cp -v $repo/data/* $dst/
|
||||
cp -av $repo/test_wavs $dst
|
||||
|
||||
ls -lh $dst
|
||||
ls -lh $dst/test_wavs
|
||||
}
|
||||
|
||||
test_pretrained
|
8
.github/scripts/docker/Dockerfile
vendored
8
.github/scripts/docker/Dockerfile
vendored
@ -11,6 +11,7 @@ ARG _KALDIFEAT_VERSION="${KALDIFEAT_VERSION}+cpu.torch${TORCH_VERSION}"
|
||||
|
||||
RUN apt-get update -y && \
|
||||
apt-get install -qq -y \
|
||||
cmake \
|
||||
ffmpeg \
|
||||
git \
|
||||
git-lfs \
|
||||
@ -35,7 +36,9 @@ RUN pip install --no-cache-dir \
|
||||
\
|
||||
git+https://github.com/lhotse-speech/lhotse \
|
||||
kaldifeat==${_KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cpu.html \
|
||||
cython \
|
||||
dill \
|
||||
espnet_tts_frontend \
|
||||
graphviz \
|
||||
kaldi-decoder \
|
||||
kaldi_native_io \
|
||||
@ -44,10 +47,15 @@ RUN pip install --no-cache-dir \
|
||||
kaldilm \
|
||||
matplotlib \
|
||||
multi_quantization \
|
||||
numba \
|
||||
numpy \
|
||||
onnxoptimizer \
|
||||
onnxsim \
|
||||
onnx \
|
||||
onnxmltools \
|
||||
onnxruntime \
|
||||
piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html \
|
||||
pypinyin==0.50.0 \
|
||||
pytest \
|
||||
sentencepiece>=0.1.96 \
|
||||
six \
|
||||
|
37
.github/scripts/docker/generate_build_matrix.py
vendored
37
.github/scripts/docker/generate_build_matrix.py
vendored
@ -6,8 +6,8 @@ import json
|
||||
|
||||
|
||||
def version_gt(a, b):
|
||||
a_major, a_minor = a.split(".")[:2]
|
||||
b_major, b_minor = b.split(".")[:2]
|
||||
a_major, a_minor = list(map(int, a.split(".")))[:2]
|
||||
b_major, b_minor = list(map(int, b.split(".")))[:2]
|
||||
if a_major > b_major:
|
||||
return True
|
||||
|
||||
@ -18,8 +18,8 @@ def version_gt(a, b):
|
||||
|
||||
|
||||
def version_ge(a, b):
|
||||
a_major, a_minor = a.split(".")[:2]
|
||||
b_major, b_minor = b.split(".")[:2]
|
||||
a_major, a_minor = list(map(int, a.split(".")))[:2]
|
||||
b_major, b_minor = list(map(int, b.split(".")))[:2]
|
||||
if a_major > b_major:
|
||||
return True
|
||||
|
||||
@ -43,11 +43,15 @@ def get_torchaudio_version(torch_version):
|
||||
|
||||
|
||||
def get_matrix():
|
||||
k2_version = "1.24.4.dev20231220"
|
||||
kaldifeat_version = "1.25.3.dev20231221"
|
||||
version = "1.2"
|
||||
python_version = ["3.8", "3.9", "3.10", "3.11"]
|
||||
torch_version = ["1.13.0", "1.13.1", "2.0.0", "2.0.1", "2.1.0", "2.1.1", "2.1.2"]
|
||||
k2_version = "1.24.4.dev20240223"
|
||||
kaldifeat_version = "1.25.4.dev20240223"
|
||||
version = "20240401"
|
||||
python_version = ["3.8", "3.9", "3.10", "3.11", "3.12"]
|
||||
torch_version = []
|
||||
torch_version += ["1.13.0", "1.13.1"]
|
||||
torch_version += ["2.0.0", "2.0.1"]
|
||||
torch_version += ["2.1.0", "2.1.1", "2.1.2"]
|
||||
torch_version += ["2.2.0", "2.2.1", "2.2.2"]
|
||||
|
||||
matrix = []
|
||||
for p in python_version:
|
||||
@ -57,10 +61,21 @@ def get_matrix():
|
||||
if version_gt(p, "3.10") and not version_gt(t, "2.0"):
|
||||
continue
|
||||
|
||||
# only torch>=2.2.0 supports python 3.12
|
||||
if version_gt(p, "3.11") and not version_gt(t, "2.1"):
|
||||
continue
|
||||
|
||||
k2_version_2 = k2_version
|
||||
kaldifeat_version_2 = kaldifeat_version
|
||||
|
||||
if t == "2.2.2":
|
||||
k2_version_2 = "1.24.4.dev20240328"
|
||||
kaldifeat_version_2 = "1.25.4.dev20240329"
|
||||
|
||||
matrix.append(
|
||||
{
|
||||
"k2-version": k2_version,
|
||||
"kaldifeat-version": kaldifeat_version,
|
||||
"k2-version": k2_version_2,
|
||||
"kaldifeat-version": kaldifeat_version_2,
|
||||
"version": version,
|
||||
"python-version": p,
|
||||
"torch-version": t,
|
||||
|
29
.github/scripts/generate-piper-phonemize-page.py
vendored
Executable file
29
.github/scripts/generate-piper-phonemize-page.py
vendored
Executable file
@ -0,0 +1,29 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
|
||||
def main():
|
||||
prefix = (
|
||||
"https://github.com/csukuangfj/piper-phonemize/releases/download/2023.12.5/"
|
||||
)
|
||||
files = [
|
||||
"piper_phonemize-1.2.0-cp310-cp310-macosx_10_14_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp311-cp311-macosx_10_14_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp312-cp312-macosx_10_14_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp37-cp37m-macosx_10_14_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp38-cp38-macosx_10_14_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp39-cp39-macosx_10_14_x86_64.whl",
|
||||
"piper_phonemize-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
|
||||
]
|
||||
with open("piper_phonemize.html", "w") as f:
|
||||
for file in files:
|
||||
url = prefix + file
|
||||
f.write(f'<a href="{url}">{file}</a><br/>\n')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
47
.github/scripts/librispeech/ASR/run.sh
vendored
47
.github/scripts/librispeech/ASR/run.sh
vendored
@ -15,9 +15,9 @@ function prepare_data() {
|
||||
# cause OOM error for CI later.
|
||||
mkdir -p download/lm
|
||||
pushd download/lm
|
||||
wget -q http://www.openslr.org/resources/11/librispeech-vocab.txt
|
||||
wget -q http://www.openslr.org/resources/11/librispeech-lexicon.txt
|
||||
wget -q http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz
|
||||
wget -q https://huggingface.co/csukuangfj/librispeech-for-ci/resolve/main/librispeech-lm-norm.txt.gz
|
||||
wget -q https://huggingface.co/csukuangfj/librispeech-for-ci/resolve/main/librispeech-lexicon.txt
|
||||
wget -q https://huggingface.co/csukuangfj/librispeech-for-ci/resolve/main/librispeech-vocab.txt
|
||||
ls -lh
|
||||
gunzip librispeech-lm-norm.txt.gz
|
||||
|
||||
@ -64,6 +64,46 @@ function run_diagnostics() {
|
||||
--print-diagnostics 1
|
||||
}
|
||||
|
||||
function test_streaming_zipformer_ctc_hlg() {
|
||||
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-streaming-zipformer-small-2024-03-18
|
||||
|
||||
log "Downloading pre-trained model from $repo_url"
|
||||
git lfs install
|
||||
git clone $repo_url
|
||||
repo=$(basename $repo_url)
|
||||
|
||||
rm $repo/exp-ctc-rnnt-small/*.onnx
|
||||
ls -lh $repo/exp-ctc-rnnt-small
|
||||
|
||||
# export models to onnx
|
||||
./zipformer/export-onnx-streaming-ctc.py \
|
||||
--tokens $repo/data/lang_bpe_500/tokens.txt \
|
||||
--epoch 30 \
|
||||
--avg 3 \
|
||||
--exp-dir $repo/exp-ctc-rnnt-small \
|
||||
--causal 1 \
|
||||
--use-ctc 1 \
|
||||
--chunk-size 16 \
|
||||
--left-context-frames 128 \
|
||||
\
|
||||
--num-encoder-layers 2,2,2,2,2,2 \
|
||||
--feedforward-dim 512,768,768,768,768,768 \
|
||||
--encoder-dim 192,256,256,256,256,256 \
|
||||
--encoder-unmasked-dim 192,192,192,192,192,192
|
||||
|
||||
ls -lh $repo/exp-ctc-rnnt-small
|
||||
|
||||
for wav in 0.wav 1.wav 8k.wav; do
|
||||
python3 ./zipformer/onnx_pretrained_ctc_HLG_streaming.py \
|
||||
--nn-model $repo/exp-ctc-rnnt-small/ctc-epoch-30-avg-3-chunk-16-left-128.int8.onnx \
|
||||
--words $repo/data/lang_bpe_500/words.txt \
|
||||
--HLG $repo/data/lang_bpe_500/HLG.fst \
|
||||
$repo/test_wavs/$wav
|
||||
done
|
||||
|
||||
rm -rf $repo
|
||||
}
|
||||
|
||||
function test_pruned_transducer_stateless_2022_03_12() {
|
||||
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12
|
||||
|
||||
@ -1577,6 +1617,7 @@ function test_transducer_bpe_500_2021_12_23() {
|
||||
|
||||
prepare_data
|
||||
run_diagnostics
|
||||
test_streaming_zipformer_ctc_hlg
|
||||
test_pruned_transducer_stateless_2022_03_12
|
||||
test_pruned_transducer_stateless2_2022_04_29
|
||||
test_pruned_transducer_stateless3_2022_04_29
|
||||
|
157
.github/scripts/ljspeech/TTS/run.sh
vendored
Executable file
157
.github/scripts/ljspeech/TTS/run.sh
vendored
Executable file
@ -0,0 +1,157 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
set -ex
|
||||
|
||||
python3 -m pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html
|
||||
python3 -m pip install espnet_tts_frontend
|
||||
python3 -m pip install numba
|
||||
|
||||
log() {
|
||||
# This function is from espnet
|
||||
local fname=${BASH_SOURCE[1]##*/}
|
||||
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
|
||||
}
|
||||
|
||||
cd egs/ljspeech/TTS
|
||||
|
||||
sed -i.bak s/600/8/g ./prepare.sh
|
||||
sed -i.bak s/"first 100"/"first 3"/g ./prepare.sh
|
||||
sed -i.bak s/500/5/g ./prepare.sh
|
||||
git diff
|
||||
|
||||
function prepare_data() {
|
||||
# We have created a subset of the data for testing
|
||||
#
|
||||
mkdir download
|
||||
pushd download
|
||||
wget -q https://huggingface.co/csukuangfj/ljspeech-subset-for-ci-test/resolve/main/LJSpeech-1.1.tar.bz2
|
||||
tar xvf LJSpeech-1.1.tar.bz2
|
||||
popd
|
||||
|
||||
./prepare.sh
|
||||
tree .
|
||||
}
|
||||
|
||||
function train() {
|
||||
pushd ./vits
|
||||
sed -i.bak s/200/3/g ./train.py
|
||||
git diff .
|
||||
popd
|
||||
|
||||
for t in low medium high; do
|
||||
./vits/train.py \
|
||||
--exp-dir vits/exp-$t \
|
||||
--model-type $t \
|
||||
--num-epochs 1 \
|
||||
--save-every-n 1 \
|
||||
--num-buckets 2 \
|
||||
--tokens data/tokens.txt \
|
||||
--max-duration 20
|
||||
|
||||
ls -lh vits/exp-$t
|
||||
done
|
||||
}
|
||||
|
||||
function infer() {
|
||||
for t in low medium high; do
|
||||
./vits/infer.py \
|
||||
--num-buckets 2 \
|
||||
--model-type $t \
|
||||
--epoch 1 \
|
||||
--exp-dir ./vits/exp-$t \
|
||||
--tokens data/tokens.txt \
|
||||
--max-duration 20
|
||||
done
|
||||
}
|
||||
|
||||
function export_onnx() {
|
||||
for t in low medium high; do
|
||||
./vits/export-onnx.py \
|
||||
--model-type $t \
|
||||
--epoch 1 \
|
||||
--exp-dir ./vits/exp-$t \
|
||||
--tokens data/tokens.txt
|
||||
|
||||
ls -lh vits/exp-$t/
|
||||
done
|
||||
}
|
||||
|
||||
function test_medium() {
|
||||
git clone https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-medium-2024-03-12
|
||||
|
||||
./vits/export-onnx.py \
|
||||
--model-type medium \
|
||||
--epoch 820 \
|
||||
--exp-dir ./icefall-tts-ljspeech-vits-medium-2024-03-12/exp \
|
||||
--tokens ./icefall-tts-ljspeech-vits-medium-2024-03-12/data/tokens.txt
|
||||
|
||||
ls -lh ./icefall-tts-ljspeech-vits-medium-2024-03-12/exp
|
||||
|
||||
./vits/test_onnx.py \
|
||||
--model-filename ./icefall-tts-ljspeech-vits-medium-2024-03-12/exp/vits-epoch-820.onnx \
|
||||
--tokens ./icefall-tts-ljspeech-vits-medium-2024-03-12/data/tokens.txt \
|
||||
--output-filename /icefall/test-medium.wav
|
||||
|
||||
ls -lh /icefall/test-medium.wav
|
||||
|
||||
d=/icefall/vits-icefall-en_US-ljspeech-medium
|
||||
mkdir $d
|
||||
cp -v ./icefall-tts-ljspeech-vits-medium-2024-03-12/data/tokens.txt $d/
|
||||
cp -v ./icefall-tts-ljspeech-vits-medium-2024-03-12/exp/vits-epoch-820.onnx $d/model.onnx
|
||||
|
||||
rm -rf icefall-tts-ljspeech-vits-medium-2024-03-12
|
||||
|
||||
pushd $d
|
||||
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
|
||||
tar xf espeak-ng-data.tar.bz2
|
||||
rm espeak-ng-data.tar.bz2
|
||||
cd ..
|
||||
tar cjf vits-icefall-en_US-ljspeech-medium.tar.bz2 vits-icefall-en_US-ljspeech-medium
|
||||
rm -rf vits-icefall-en_US-ljspeech-medium
|
||||
ls -lh *.tar.bz2
|
||||
popd
|
||||
}
|
||||
|
||||
function test_low() {
|
||||
git clone https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-low-2024-03-12
|
||||
|
||||
./vits/export-onnx.py \
|
||||
--model-type low \
|
||||
--epoch 1600 \
|
||||
--exp-dir ./icefall-tts-ljspeech-vits-low-2024-03-12/exp \
|
||||
--tokens ./icefall-tts-ljspeech-vits-low-2024-03-12/data/tokens.txt
|
||||
|
||||
ls -lh ./icefall-tts-ljspeech-vits-low-2024-03-12/exp
|
||||
|
||||
./vits/test_onnx.py \
|
||||
--model-filename ./icefall-tts-ljspeech-vits-low-2024-03-12/exp/vits-epoch-1600.onnx \
|
||||
--tokens ./icefall-tts-ljspeech-vits-low-2024-03-12/data/tokens.txt \
|
||||
--output-filename /icefall/test-low.wav
|
||||
|
||||
ls -lh /icefall/test-low.wav
|
||||
|
||||
d=/icefall/vits-icefall-en_US-ljspeech-low
|
||||
mkdir $d
|
||||
cp -v ./icefall-tts-ljspeech-vits-low-2024-03-12/data/tokens.txt $d/
|
||||
cp -v ./icefall-tts-ljspeech-vits-low-2024-03-12/exp/vits-epoch-1600.onnx $d/model.onnx
|
||||
|
||||
rm -rf icefall-tts-ljspeech-vits-low-2024-03-12
|
||||
|
||||
pushd $d
|
||||
wget -q https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
|
||||
tar xf espeak-ng-data.tar.bz2
|
||||
rm espeak-ng-data.tar.bz2
|
||||
cd ..
|
||||
tar cjf vits-icefall-en_US-ljspeech-low.tar.bz2 vits-icefall-en_US-ljspeech-low
|
||||
rm -rf vits-icefall-en_US-ljspeech-low
|
||||
ls -lh *.tar.bz2
|
||||
popd
|
||||
}
|
||||
|
||||
prepare_data
|
||||
train
|
||||
infer
|
||||
export_onnx
|
||||
rm -rf vits/exp-{low,medium,high}
|
||||
test_medium
|
||||
test_low
|
@ -30,7 +30,7 @@ log "Test exporting to ONNX format"
|
||||
|
||||
./pruned_transducer_stateless2/export-onnx.py \
|
||||
--exp-dir $repo/exp \
|
||||
--lang-dir $repo/data/lang_char \
|
||||
--tokens $repo/data/lang_char/tokens.txt \
|
||||
--epoch 99 \
|
||||
--avg 1
|
||||
|
||||
@ -38,14 +38,14 @@ log "Export to torchscript model"
|
||||
|
||||
./pruned_transducer_stateless2/export.py \
|
||||
--exp-dir $repo/exp \
|
||||
--lang-dir $repo/data/lang_char \
|
||||
--tokens $repo/data/lang_char/tokens.txt \
|
||||
--epoch 99 \
|
||||
--avg 1 \
|
||||
--jit 1
|
||||
|
||||
./pruned_transducer_stateless2/export.py \
|
||||
--exp-dir $repo/exp \
|
||||
--lang-dir $repo/data/lang_char \
|
||||
--tokens $repo/data/lang_char/tokens.txt \
|
||||
--epoch 99 \
|
||||
--avg 1 \
|
||||
--jit-trace 1
|
||||
|
137
.github/workflows/audioset.yml
vendored
Normal file
137
.github/workflows/audioset.yml
vendored
Normal file
@ -0,0 +1,137 @@
|
||||
name: audioset
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
|
||||
pull_request:
|
||||
branches:
|
||||
- master
|
||||
|
||||
workflow_dispatch:
|
||||
|
||||
concurrency:
|
||||
group: audioset-${{ github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
generate_build_matrix:
|
||||
if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
|
||||
# see https://github.com/pytorch/pytorch/pull/50633
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
- name: Generating build matrix
|
||||
id: set-matrix
|
||||
run: |
|
||||
# outputting for debugging purposes
|
||||
python ./.github/scripts/docker/generate_build_matrix.py
|
||||
MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
|
||||
echo "::set-output name=matrix::${MATRIX}"
|
||||
|
||||
audioset:
|
||||
needs: generate_build_matrix
|
||||
name: py${{ matrix.python-version }} torch${{ matrix.torch-version }} v${{ matrix.version }}
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Free space
|
||||
shell: bash
|
||||
run: |
|
||||
ls -lh
|
||||
df -h
|
||||
rm -rf /opt/hostedtoolcache
|
||||
df -h
|
||||
echo "pwd: $PWD"
|
||||
echo "github.workspace ${{ github.workspace }}"
|
||||
|
||||
- name: Run tests
|
||||
uses: addnab/docker-run-action@v3
|
||||
with:
|
||||
image: ghcr.io/${{ github.repository_owner }}/icefall:cpu-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}-v${{ matrix.version }}
|
||||
options: |
|
||||
--volume ${{ github.workspace }}/:/icefall
|
||||
shell: bash
|
||||
run: |
|
||||
export PYTHONPATH=/icefall:$PYTHONPATH
|
||||
cd /icefall
|
||||
git config --global --add safe.directory /icefall
|
||||
|
||||
.github/scripts/audioset/AT/run.sh
|
||||
|
||||
- name: Show model files
|
||||
shell: bash
|
||||
run: |
|
||||
sudo chown -R runner ./model-onnx
|
||||
ls -lh ./model-onnx
|
||||
chmod -x ./model-onnx/class_labels_indices.csv
|
||||
|
||||
echo "----------"
|
||||
ls -lh ./model-onnx/*
|
||||
|
||||
- name: Upload model to huggingface
|
||||
if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
|
||||
env:
|
||||
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
||||
uses: nick-fields/retry@v3
|
||||
with:
|
||||
max_attempts: 20
|
||||
timeout_seconds: 200
|
||||
shell: bash
|
||||
command: |
|
||||
git config --global user.email "csukuangfj@gmail.com"
|
||||
git config --global user.name "Fangjun Kuang"
|
||||
|
||||
rm -rf huggingface
|
||||
export GIT_LFS_SKIP_SMUDGE=1
|
||||
|
||||
git clone https://huggingface.co/k2-fsa/sherpa-onnx-zipformer-audio-tagging-2024-04-09 huggingface
|
||||
cd huggingface
|
||||
git fetch
|
||||
git pull
|
||||
git merge -m "merge remote" --ff origin main
|
||||
cp ../model-onnx/*.onnx ./
|
||||
cp ../model-onnx/*.csv ./
|
||||
cp -a ../model-onnx/test_wavs ./
|
||||
ls -lh
|
||||
git add .
|
||||
git status
|
||||
git commit -m "update models"
|
||||
git status
|
||||
|
||||
git push https://csukuangfj:$HF_TOKEN@huggingface.co/k2-fsa/sherpa-onnx-zipformer-audio-tagging-2024-04-09 main || true
|
||||
rm -rf huggingface
|
||||
|
||||
- name: Prepare for release
|
||||
if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
|
||||
shell: bash
|
||||
run: |
|
||||
d=sherpa-onnx-zipformer-audio-tagging-2024-04-09
|
||||
mv ./model-onnx $d
|
||||
tar cjvf ${d}.tar.bz2 $d
|
||||
ls -lh
|
||||
|
||||
- name: Release exported onnx models
|
||||
if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
|
||||
uses: svenstaro/upload-release-action@v2
|
||||
with:
|
||||
file_glob: true
|
||||
overwrite: true
|
||||
file: sherpa-onnx-*.tar.bz2
|
||||
repo_name: k2-fsa/sherpa-onnx
|
||||
repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
|
||||
tag: audio-tagging-models
|
||||
|
3
.github/workflows/build-doc.yml
vendored
3
.github/workflows/build-doc.yml
vendored
@ -56,11 +56,14 @@ jobs:
|
||||
- name: Build doc
|
||||
shell: bash
|
||||
run: |
|
||||
.github/scripts/generate-piper-phonemize-page.py
|
||||
cd docs
|
||||
python3 -m pip install -r ./requirements.txt
|
||||
make html
|
||||
touch build/html/.nojekyll
|
||||
|
||||
cp -v ../piper_phonemize.html ./build/html/
|
||||
|
||||
- name: Deploy
|
||||
uses: peaceiris/actions-gh-pages@v3
|
||||
with:
|
||||
|
2
.github/workflows/build-docker-image.yml
vendored
2
.github/workflows/build-docker-image.yml
vendored
@ -16,7 +16,7 @@ jobs:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
os: [ubuntu-latest]
|
||||
image: ["torch2.1.0-cuda12.1", "torch2.1.0-cuda11.8", "torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
|
||||
image: ["torch2.2.2-cuda12.1", "torch2.2.2-cuda11.8", "torch2.2.1-cuda12.1", "torch2.2.1-cuda11.8", "torch2.2.0-cuda12.1", "torch2.2.0-cuda11.8", "torch2.1.0-cuda12.1", "torch2.1.0-cuda11.8", "torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
|
||||
|
||||
steps:
|
||||
# refer to https://github.com/actions/checkout
|
||||
|
102
.github/workflows/ljspeech.yml
vendored
Normal file
102
.github/workflows/ljspeech.yml
vendored
Normal file
@ -0,0 +1,102 @@
|
||||
name: ljspeech
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
|
||||
pull_request:
|
||||
branches:
|
||||
- master
|
||||
|
||||
workflow_dispatch:
|
||||
|
||||
concurrency:
|
||||
group: ljspeech-${{ github.ref }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
generate_build_matrix:
|
||||
if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
|
||||
# see https://github.com/pytorch/pytorch/pull/50633
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
- name: Generating build matrix
|
||||
id: set-matrix
|
||||
run: |
|
||||
# outputting for debugging purposes
|
||||
python ./.github/scripts/docker/generate_build_matrix.py
|
||||
MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
|
||||
echo "::set-output name=matrix::${MATRIX}"
|
||||
|
||||
ljspeech:
|
||||
needs: generate_build_matrix
|
||||
name: py${{ matrix.python-version }} torch${{ matrix.torch-version }} v${{ matrix.version }}
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Free space
|
||||
shell: bash
|
||||
run: |
|
||||
ls -lh
|
||||
df -h
|
||||
rm -rf /opt/hostedtoolcache
|
||||
df -h
|
||||
echo "pwd: $PWD"
|
||||
echo "github.workspace ${{ github.workspace }}"
|
||||
|
||||
- name: Run tests
|
||||
uses: addnab/docker-run-action@v3
|
||||
with:
|
||||
image: ghcr.io/${{ github.repository_owner }}/icefall:cpu-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}-v${{ matrix.version }}
|
||||
options: |
|
||||
--volume ${{ github.workspace }}/:/icefall
|
||||
shell: bash
|
||||
run: |
|
||||
export PYTHONPATH=/icefall:$PYTHONPATH
|
||||
cd /icefall
|
||||
git config --global --add safe.directory /icefall
|
||||
|
||||
.github/scripts/ljspeech/TTS/run.sh
|
||||
|
||||
- name: display files
|
||||
shell: bash
|
||||
run: |
|
||||
ls -lh
|
||||
|
||||
- uses: actions/upload-artifact@v4
|
||||
if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0'
|
||||
with:
|
||||
name: generated-test-files-${{ matrix.python-version }}-${{ matrix.torch-version }}
|
||||
path: ./*.wav
|
||||
|
||||
- uses: actions/upload-artifact@v4
|
||||
if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0'
|
||||
with:
|
||||
name: generated-models-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}
|
||||
path: ./*.wav
|
||||
|
||||
- name: Release exported onnx models
|
||||
if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
|
||||
uses: svenstaro/upload-release-action@v2
|
||||
with:
|
||||
file_glob: true
|
||||
overwrite: true
|
||||
file: vits-icefall-*.tar.bz2
|
||||
repo_name: k2-fsa/sherpa-onnx
|
||||
repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
|
||||
tag: tts-models
|
||||
|
9
.github/workflows/run-docker-image.yml
vendored
9
.github/workflows/run-docker-image.yml
vendored
@ -14,13 +14,20 @@ jobs:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
os: [ubuntu-latest]
|
||||
image: ["torch2.1.0-cuda12.1", "torch2.1.0-cuda11.8", "torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
|
||||
image: ["torch2.2.2-cuda12.1", "torch2.2.2-cuda11.8", "torch2.2.1-cuda12.1", "torch2.2.1-cuda11.8", "torch2.2.0-cuda12.1", "torch2.2.0-cuda11.8", "torch2.1.0-cuda12.1", "torch2.1.0-cuda11.8", "torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
|
||||
steps:
|
||||
# refer to https://github.com/actions/checkout
|
||||
- uses: actions/checkout@v2
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Free space
|
||||
shell: bash
|
||||
run: |
|
||||
df -h
|
||||
rm -rf /opt/hostedtoolcache
|
||||
df -h
|
||||
|
||||
- name: Run the build process with Docker
|
||||
uses: addnab/docker-run-action@v3
|
||||
with:
|
||||
|
8
.github/workflows/style_check.yml
vendored
8
.github/workflows/style_check.yml
vendored
@ -49,7 +49,7 @@ jobs:
|
||||
|
||||
- name: Install Python dependencies
|
||||
run: |
|
||||
python3 -m pip install --upgrade pip black==22.3.0 flake8==5.0.4 click==8.1.0
|
||||
python3 -m pip install --upgrade pip black==22.3.0 flake8==5.0.4 click==8.1.0 isort==5.10.1
|
||||
# Click issue fixed in https://github.com/psf/black/pull/2966
|
||||
|
||||
- name: Run flake8
|
||||
@ -67,3 +67,9 @@ jobs:
|
||||
working-directory: ${{github.workspace}}
|
||||
run: |
|
||||
black --check --diff .
|
||||
|
||||
- name: Run isort
|
||||
shell: bash
|
||||
working-directory: ${{github.workspace}}
|
||||
run: |
|
||||
isort --check --diff .
|
||||
|
3
.github/workflows/yesno.yml
vendored
3
.github/workflows/yesno.yml
vendored
@ -59,4 +59,7 @@ jobs:
|
||||
cd /icefall
|
||||
git config --global --add safe.directory /icefall
|
||||
|
||||
python3 -m torch.utils.collect_env
|
||||
python3 -m k2.version
|
||||
|
||||
.github/scripts/yesno/ASR/run.sh
|
||||
|
@ -26,7 +26,7 @@ repos:
|
||||
# E121,E123,E126,E226,E24,E704,W503,W504
|
||||
|
||||
- repo: https://github.com/pycqa/isort
|
||||
rev: 5.11.5
|
||||
rev: 5.12.0
|
||||
hooks:
|
||||
- id: isort
|
||||
args: ["--profile=black"]
|
||||
|
442
README.md
442
README.md
@ -2,46 +2,86 @@
|
||||
<img src="https://raw.githubusercontent.com/k2-fsa/icefall/master/docs/source/_static/logo.png" width=168>
|
||||
</div>
|
||||
|
||||
## Introduction
|
||||
# Introduction
|
||||
|
||||
icefall contains ASR recipes for various datasets
|
||||
using <https://github.com/k2-fsa/k2>.
|
||||
The icefall project contains speech-related recipes for various datasets
|
||||
using [k2-fsa](https://github.com/k2-fsa/k2) and [lhotse](https://github.com/lhotse-speech/lhotse).
|
||||
|
||||
You can use <https://github.com/k2-fsa/sherpa> to deploy models
|
||||
trained with icefall.
|
||||
You can use [sherpa](https://github.com/k2-fsa/sherpa), [sherpa-ncnn](https://github.com/k2-fsa/sherpa-ncnn) or [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) for deployment with models
|
||||
in icefall; these frameworks also support models not included in icefall; please refer to respective documents for more details.
|
||||
|
||||
You can try pre-trained models from within your browser without the need
|
||||
to download or install anything by visiting <https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>
|
||||
See <https://k2-fsa.github.io/icefall/huggingface/spaces.html> for more details.
|
||||
to download or install anything by visiting this [huggingface space](https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition).
|
||||
Please refer to [document](https://k2-fsa.github.io/icefall/huggingface/spaces.html) for more details.
|
||||
|
||||
## Installation
|
||||
# Installation
|
||||
|
||||
Please refer to <https://icefall.readthedocs.io/en/latest/installation/index.html>
|
||||
Please refer to [document](https://icefall.readthedocs.io/en/latest/installation/index.html)
|
||||
for installation.
|
||||
|
||||
## Recipes
|
||||
# Recipes
|
||||
|
||||
Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
|
||||
for more information.
|
||||
Please refer to [document](https://icefall.readthedocs.io/en/latest/recipes/index.html)
|
||||
for more details.
|
||||
|
||||
We provide the following recipes:
|
||||
## ASR: Automatic Speech Recognition
|
||||
|
||||
### Supported Datasets
|
||||
- [yesno][yesno]
|
||||
- [LibriSpeech][librispeech]
|
||||
- [GigaSpeech][gigaspeech]
|
||||
- [AMI][ami]
|
||||
|
||||
- [Aidatatang_200zh][aidatatang_200zh]
|
||||
- [Aishell][aishell]
|
||||
- [Aishell2][aishell2]
|
||||
- [Aishell4][aishell4]
|
||||
- [Alimeeting][alimeeting]
|
||||
- [AMI][ami]
|
||||
- [CommonVoice][commonvoice]
|
||||
- [Corpus of Spontaneous Japanese][csj]
|
||||
- [GigaSpeech][gigaspeech]
|
||||
- [LibriCSS][libricss]
|
||||
- [LibriSpeech][librispeech]
|
||||
- [Libriheavy][libriheavy]
|
||||
- [Multi-Dialect Broadcast News Arabic Speech Recognition][mgb2]
|
||||
- [PeopleSpeech][peoplespeech]
|
||||
- [SPGISpeech][spgispeech]
|
||||
- [Switchboard][swbd]
|
||||
- [TIMIT][timit]
|
||||
- [TED-LIUM3][tedlium3]
|
||||
- [Aidatatang_200zh][aidatatang_200zh]
|
||||
- [WenetSpeech][wenetspeech]
|
||||
- [Alimeeting][alimeeting]
|
||||
- [Switchboard][swbd]
|
||||
- [TAL_CSASR][tal_csasr]
|
||||
- [Voxpopuli][voxpopuli]
|
||||
- [XBMU-AMDO31][xbmu-amdo31]
|
||||
- [WenetSpeech][wenetspeech]
|
||||
|
||||
### yesno
|
||||
More datasets will be added in the future.
|
||||
|
||||
### Supported Models
|
||||
|
||||
The [LibriSpeech][librispeech] recipe supports the most comprehensive set of models, you are welcome to try them out.
|
||||
|
||||
#### CTC
|
||||
- TDNN LSTM CTC
|
||||
- Conformer CTC
|
||||
- Zipformer CTC
|
||||
|
||||
#### MMI
|
||||
- Conformer MMI
|
||||
- Zipformer MMI
|
||||
|
||||
#### Transducer
|
||||
- Conformer-based Encoder
|
||||
- LSTM-based Encoder
|
||||
- Zipformer-based Encoder
|
||||
- LSTM-based Predictor
|
||||
- [Stateless Predictor](https://research.google/pubs/rnn-transducer-with-stateless-prediction-network/)
|
||||
|
||||
#### Whisper
|
||||
- [OpenAi Whisper](https://arxiv.org/abs/2212.04356) (We support fine-tuning on AiShell-1.)
|
||||
|
||||
If you are willing to contribute to icefall, please refer to [contributing](https://icefall.readthedocs.io/en/latest/contributing/index.html) for more details.
|
||||
|
||||
We would like to highlight the performance of some of the recipes here.
|
||||
|
||||
### [yesno][yesno]
|
||||
|
||||
This is the simplest ASR recipe in `icefall` and can be run on CPU.
|
||||
Training takes less than 30 seconds and gives you the following WER:
|
||||
@ -52,350 +92,264 @@ Training takes less than 30 seconds and gives you the following WER:
|
||||
We provide a Colab notebook for this recipe: [](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
|
||||
|
||||
|
||||
### LibriSpeech
|
||||
### [LibriSpeech][librispeech]
|
||||
|
||||
Please see <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>
|
||||
Please see [RESULTS.md](https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md)
|
||||
for the **latest** results.
|
||||
|
||||
We provide 5 models for this recipe:
|
||||
|
||||
- [conformer CTC model][LibriSpeech_conformer_ctc]
|
||||
- [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc]
|
||||
- [Transducer: Conformer encoder + LSTM decoder][LibriSpeech_transducer]
|
||||
- [Transducer: Conformer encoder + Embedding decoder][LibriSpeech_transducer_stateless]
|
||||
- [Transducer: Zipformer encoder + Embedding decoder][LibriSpeech_zipformer]
|
||||
|
||||
#### Conformer CTC Model
|
||||
|
||||
The best WER we currently have is:
|
||||
#### [Conformer CTC](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conformer_ctc)
|
||||
|
||||
| | test-clean | test-other |
|
||||
|-----|------------|------------|
|
||||
| WER | 2.42 | 5.73 |
|
||||
|
||||
|
||||
We provide a Colab notebook to run a pre-trained conformer CTC model: [](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
|
||||
|
||||
#### TDNN LSTM CTC Model
|
||||
|
||||
The WER for this model is:
|
||||
#### [TDNN LSTM CTC](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/tdnn_lstm_ctc)
|
||||
|
||||
| | test-clean | test-other |
|
||||
|-----|------------|------------|
|
||||
| WER | 6.59 | 17.69 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1-iSfQMp2So-We_Uu49N4AAcMInB72u9z?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1-iSfQMp2So-We_Uu49N4AAcMInB72u9z?usp=sharing)
|
||||
|
||||
|
||||
#### Transducer: Conformer encoder + LSTM decoder
|
||||
#### [Transducer (Conformer Encoder + LSTM Predictor)](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/transducer)
|
||||
|
||||
Using Conformer as encoder and LSTM as decoder.
|
||||
| | test-clean | test-other |
|
||||
|---------------|------------|------------|
|
||||
| greedy_search | 3.07 | 7.51 |
|
||||
|
||||
The best WER with greedy search is:
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1_u6yK9jDkPwG_NLrZMN2XK7Aeq4suMO2?usp=sharing)
|
||||
|
||||
| | test-clean | test-other |
|
||||
|-----|------------|------------|
|
||||
| WER | 3.07 | 7.51 |
|
||||
#### [Transducer (Conformer Encoder + Stateless Predictor)](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/transducer)
|
||||
|
||||
We provide a Colab notebook to run a pre-trained RNN-T conformer model: [](https://colab.research.google.com/drive/1_u6yK9jDkPwG_NLrZMN2XK7Aeq4suMO2?usp=sharing)
|
||||
|
||||
#### Transducer: Conformer encoder + Embedding decoder
|
||||
|
||||
Using Conformer as encoder. The decoder consists of 1 embedding layer
|
||||
and 1 convolutional layer.
|
||||
|
||||
The best WER using modified beam search with beam size 4 is:
|
||||
|
||||
| | test-clean | test-other |
|
||||
|-----|------------|------------|
|
||||
| WER | 2.56 | 6.27 |
|
||||
|
||||
Note: No auxiliary losses are used in the training and no LMs are used
|
||||
in the decoding.
|
||||
|
||||
We provide a Colab notebook to run a pre-trained transducer conformer + stateless decoder model: [](https://colab.research.google.com/drive/1CO1bXJ-2khDckZIW8zjOPHGSKLHpTDlp?usp=sharing)
|
||||
| | test-clean | test-other |
|
||||
|---------------------------------------|------------|------------|
|
||||
| modified_beam_search (`beam_size=4`) | 2.56 | 6.27 |
|
||||
|
||||
|
||||
#### k2 pruned RNN-T
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1CO1bXJ-2khDckZIW8zjOPHGSKLHpTDlp?usp=sharing)
|
||||
|
||||
|
||||
#### [Transducer (Zipformer Encoder + Stateless Predictor)](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/zipformer)
|
||||
|
||||
WER (modified_beam_search `beam_size=4` unless further stated)
|
||||
|
||||
1. LibriSpeech-960hr
|
||||
|
||||
| Encoder | Params | test-clean | test-other | epochs | devices |
|
||||
|-----------------|--------|------------|------------|---------|------------|
|
||||
| zipformer | 65.5M | 2.21 | 4.79 | 50 | 4 32G-V100 |
|
||||
| zipformer-small | 23.2M | 2.42 | 5.73 | 50 | 2 32G-V100 |
|
||||
| zipformer-large | 148.4M | 2.06 | 4.63 | 50 | 4 32G-V100 |
|
||||
| zipformer-large | 148.4M | 2.00 | 4.38 | 174 | 8 80G-A100 |
|
||||
| Zipformer | 65.5M | 2.21 | 4.79 | 50 | 4 32G-V100 |
|
||||
| Zipformer-small | 23.2M | 2.42 | 5.73 | 50 | 2 32G-V100 |
|
||||
| Zipformer-large | 148.4M | 2.06 | 4.63 | 50 | 4 32G-V100 |
|
||||
| Zipformer-large | 148.4M | 2.00 | 4.38 | 174 | 8 80G-A100 |
|
||||
|
||||
Note: No auxiliary losses are used in the training and no LMs are used
|
||||
in the decoding.
|
||||
2. LibriSpeech-960hr + GigaSpeech
|
||||
|
||||
#### k2 pruned RNN-T + GigaSpeech
|
||||
|
||||
| | test-clean | test-other |
|
||||
|-----|------------|------------|
|
||||
| WER | 1.78 | 4.08 |
|
||||
|
||||
Note: No auxiliary losses are used in the training and no LMs are used
|
||||
in the decoding.
|
||||
|
||||
#### k2 pruned RNN-T + GigaSpeech + CommonVoice
|
||||
|
||||
| | test-clean | test-other |
|
||||
|-----|------------|------------|
|
||||
| WER | 1.90 | 3.98 |
|
||||
|
||||
Note: No auxiliary losses are used in the training and no LMs are used
|
||||
in the decoding.
|
||||
| Encoder | Params | test-clean | test-other |
|
||||
|-----------------|--------|------------|------------|
|
||||
| Zipformer | 65.5M | 1.78 | 4.08 |
|
||||
|
||||
|
||||
### GigaSpeech
|
||||
3. LibriSpeech-960hr + GigaSpeech + CommonVoice
|
||||
|
||||
We provide three models for this recipe:
|
||||
| Encoder | Params | test-clean | test-other |
|
||||
|-----------------|--------|------------|------------|
|
||||
| Zipformer | 65.5M | 1.90 | 3.98 |
|
||||
|
||||
- [Conformer CTC model][GigaSpeech_conformer_ctc]
|
||||
- [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][GigaSpeech_pruned_transducer_stateless2].
|
||||
- [Transducer: Zipformer encoder + Embedding decoder][GigaSpeech_zipformer]
|
||||
|
||||
#### Conformer CTC
|
||||
### [GigaSpeech][gigaspeech]
|
||||
|
||||
#### [Conformer CTC](https://github.com/k2-fsa/icefall/tree/master/egs/gigaspeech/ASR/conformer_ctc)
|
||||
|
||||
| | Dev | Test |
|
||||
|-----|-------|-------|
|
||||
| WER | 10.47 | 10.58 |
|
||||
|
||||
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
|
||||
#### [Transducer (pruned_transducer_stateless2)](https://github.com/k2-fsa/icefall/tree/master/egs/gigaspeech/ASR/pruned_transducer_stateless2)
|
||||
|
||||
Conformer Encoder + Stateless Predictor + k2 Pruned RNN-T Loss
|
||||
|
||||
| | Dev | Test |
|
||||
|----------------------|-------|-------|
|
||||
| greedy search | 10.51 | 10.73 |
|
||||
| fast beam search | 10.50 | 10.69 |
|
||||
| modified beam search | 10.40 | 10.51 |
|
||||
| greedy_search | 10.51 | 10.73 |
|
||||
| fast_beam_search | 10.50 | 10.69 |
|
||||
| modified_beam_search | 10.40 | 10.51 |
|
||||
|
||||
#### Transducer: Zipformer encoder + Embedding decoder
|
||||
#### [Transducer (Zipformer Encoder + Stateless Predictor)](https://github.com/k2-fsa/icefall/tree/master/egs/gigaspeech/ASR/zipformer)
|
||||
|
||||
| | Dev | Test |
|
||||
|----------------------|-------|-------|
|
||||
| greedy search | 10.31 | 10.50 |
|
||||
| fast beam search | 10.26 | 10.48 |
|
||||
| modified beam search | 10.25 | 10.38 |
|
||||
| greedy_search | 10.31 | 10.50 |
|
||||
| fast_beam_search | 10.26 | 10.48 |
|
||||
| modified_beam_search | 10.25 | 10.38 |
|
||||
|
||||
|
||||
### Aishell
|
||||
### [Aishell][aishell]
|
||||
|
||||
We provide three models for this recipe: [conformer CTC model][Aishell_conformer_ctc],
|
||||
[TDNN LSTM CTC model][Aishell_tdnn_lstm_ctc], and [Transducer Stateless Model][Aishell_pruned_transducer_stateless7],
|
||||
|
||||
#### Conformer CTC Model
|
||||
|
||||
The best CER we currently have is:
|
||||
|
||||
| | test |
|
||||
|-----|------|
|
||||
| CER | 4.26 |
|
||||
|
||||
#### TDNN LSTM CTC Model
|
||||
|
||||
The CER for this model is:
|
||||
#### [TDNN LSTM CTC](https://github.com/k2-fsa/icefall/tree/master/egs/aishell/ASR/tdnn_lstm_ctc)
|
||||
|
||||
| | test |
|
||||
|-----|-------|
|
||||
| CER | 10.16 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1jbyzYq3ytm6j2nlEt-diQm-6QVWyDDEa?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1jbyzYq3ytm6j2nlEt-diQm-6QVWyDDEa?usp=sharing)
|
||||
|
||||
#### Transducer Stateless Model
|
||||
|
||||
The best CER we currently have is:
|
||||
#### [Transducer (Conformer Encoder + Stateless Predictor)](https://github.com/k2-fsa/icefall/tree/master/egs/aishell/ASR/transducer_stateless)
|
||||
|
||||
| | test |
|
||||
|-----|------|
|
||||
| CER | 4.38 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained TransducerStateless model: [](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC?usp=sharing)
|
||||
|
||||
#### [Transducer (Zipformer Encoder + Stateless Predictor)](https://github.com/k2-fsa/icefall/tree/master/egs/aishell/ASR/zipformer)
|
||||
|
||||
WER (modified_beam_search `beam_size=4`)
|
||||
|
||||
| Encoder | Params | dev | test | epochs |
|
||||
|-----------------|--------|-----|------|---------|
|
||||
| Zipformer | 73.4M | 4.13| 4.40 | 55 |
|
||||
| Zipformer-small | 30.2M | 4.40| 4.67 | 55 |
|
||||
| Zipformer-large | 157.3M | 4.03| 4.28 | 56 |
|
||||
|
||||
|
||||
### Aishell2
|
||||
### [Aishell4][aishell4]
|
||||
|
||||
We provide one model for this recipe: [Transducer Stateless Model][Aishell2_pruned_transducer_stateless5].
|
||||
|
||||
#### Transducer Stateless Model
|
||||
|
||||
The best WER we currently have is:
|
||||
|
||||
| | dev-ios | test-ios |
|
||||
|-----|------------|------------|
|
||||
| WER | 5.32 | 5.56 |
|
||||
|
||||
|
||||
### Aishell4
|
||||
|
||||
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aishell4_pruned_transducer_stateless5].
|
||||
|
||||
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with all subsets)
|
||||
|
||||
The best CER we currently have is:
|
||||
#### [Transducer (pruned_transducer_stateless5)](https://github.com/k2-fsa/icefall/tree/master/egs/aishell4/ASR/pruned_transducer_stateless5)
|
||||
|
||||
1 Trained with all subsets:
|
||||
| | test |
|
||||
|-----|------------|
|
||||
| CER | 29.08 |
|
||||
|
||||
|
||||
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
|
||||
|
||||
|
||||
### TIMIT
|
||||
### [TIMIT][timit]
|
||||
|
||||
We provide two models for this recipe: [TDNN LSTM CTC model][TIMIT_tdnn_lstm_ctc]
|
||||
and [TDNN LiGRU CTC model][TIMIT_tdnn_ligru_ctc].
|
||||
#### [TDNN LSTM CTC](https://github.com/k2-fsa/icefall/tree/master/egs/timit/ASR/tdnn_lstm_ctc)
|
||||
|
||||
#### TDNN LSTM CTC Model
|
||||
|
||||
The best PER we currently have is:
|
||||
|
||||
||TEST|
|
||||
|--|--|
|
||||
| |TEST|
|
||||
|---|----|
|
||||
|PER| 19.71% |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1Hs9DA4V96uapw_30uNp32OMJgkuR5VVd?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1Hs9DA4V96uapw_30uNp32OMJgkuR5VVd?usp=sharing)
|
||||
|
||||
#### TDNN LiGRU CTC Model
|
||||
#### [TDNN LiGRU CTC](https://github.com/k2-fsa/icefall/tree/master/egs/timit/ASR/tdnn_ligru_ctc)
|
||||
|
||||
The PER for this model is:
|
||||
|
||||
||TEST|
|
||||
|--|--|
|
||||
| |TEST|
|
||||
|---|----|
|
||||
|PER| 17.66% |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model: [](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
|
||||
|
||||
|
||||
### TED-LIUM3
|
||||
### [TED-LIUM3][tedlium3]
|
||||
|
||||
We provide two models for this recipe: [Transducer Stateless: Conformer encoder + Embedding decoder][TED-LIUM3_transducer_stateless] and [Pruned Transducer Stateless: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][TED-LIUM3_pruned_transducer_stateless].
|
||||
#### [Transducer (Conformer Encoder + Stateless Predictor)](https://github.com/k2-fsa/icefall/tree/master/egs/tedlium3/ASR/transducer_stateless)
|
||||
|
||||
#### Transducer Stateless: Conformer encoder + Embedding decoder
|
||||
|
||||
The best WER using modified beam search with beam size 4 is:
|
||||
|
||||
| | dev | test |
|
||||
|-----|-------|--------|
|
||||
| WER | 6.91 | 6.33 |
|
||||
|
||||
Note: No auxiliary losses are used in the training and no LMs are used in the decoding.
|
||||
|
||||
We provide a Colab notebook to run a pre-trained Transducer Stateless model: [](https://colab.research.google.com/drive/1MmY5bBxwvKLNT4A2DJnwiqRXhdchUqPN?usp=sharing)
|
||||
|
||||
#### Pruned Transducer Stateless: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
|
||||
|
||||
The best WER using modified beam search with beam size 4 is:
|
||||
|
||||
| | dev | test |
|
||||
|-----|-------|--------|
|
||||
| WER | 6.77 | 6.14 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1je_1zGrOkGVVd4WLzgkXRHxl-I27yWtz?usp=sharing)
|
||||
| | dev | test |
|
||||
|--------------------------------------|-------|--------|
|
||||
| modified_beam_search (`beam_size=4`) | 6.91 | 6.33 |
|
||||
|
||||
|
||||
### Aidatatang_200zh
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1MmY5bBxwvKLNT4A2DJnwiqRXhdchUqPN?usp=sharing)
|
||||
|
||||
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aidatatang_200zh_pruned_transducer_stateless2].
|
||||
#### [Transducer (pruned_transducer_stateless)](https://github.com/k2-fsa/icefall/tree/master/egs/tedlium3/ASR/pruned_transducer_stateless)
|
||||
|
||||
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
|
||||
| | dev | test |
|
||||
|--------------------------------------|-------|--------|
|
||||
| modified_beam_search (`beam_size=4`) | 6.77 | 6.14 |
|
||||
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1je_1zGrOkGVVd4WLzgkXRHxl-I27yWtz?usp=sharing)
|
||||
|
||||
|
||||
### [Aidatatang_200zh][aidatatang_200zh]
|
||||
|
||||
#### [Transducer (pruned_transducer_stateless2)](https://github.com/k2-fsa/icefall/tree/master/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2)
|
||||
|
||||
| | Dev | Test |
|
||||
|----------------------|-------|-------|
|
||||
| greedy search | 5.53 | 6.59 |
|
||||
| fast beam search | 5.30 | 6.34 |
|
||||
| modified beam search | 5.27 | 6.33 |
|
||||
| greedy_search | 5.53 | 6.59 |
|
||||
| fast_beam_search | 5.30 | 6.34 |
|
||||
| modified_beam_search | 5.27 | 6.33 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1wNSnSj3T5oOctbh5IGCa393gKOoQw2GH?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1wNSnSj3T5oOctbh5IGCa393gKOoQw2GH?usp=sharing)
|
||||
|
||||
|
||||
### WenetSpeech
|
||||
### [WenetSpeech][wenetspeech]
|
||||
|
||||
We provide some models for this recipe: [Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless2] and [Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless5].
|
||||
|
||||
#### Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset, offline ASR)
|
||||
#### [Transducer (pruned_transducer_stateless2)](https://github.com/k2-fsa/icefall/tree/master/egs/wenetspeech/ASR/pruned_transducer_stateless2)
|
||||
|
||||
| | Dev | Test-Net | Test-Meeting |
|
||||
|----------------------|-------|----------|--------------|
|
||||
| greedy search | 7.80 | 8.75 | 13.49 |
|
||||
| modified beam search| 7.76 | 8.71 | 13.41 |
|
||||
| fast beam search | 7.94 | 8.74 | 13.80 |
|
||||
| greedy_search | 7.80 | 8.75 | 13.49 |
|
||||
| fast_beam_search | 7.94 | 8.74 | 13.80 |
|
||||
| modified_beam_search | 7.76 | 8.71 | 13.41 |
|
||||
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing)
|
||||
|
||||
#### [Transducer **Streaming** (pruned_transducer_stateless5) ](https://github.com/k2-fsa/icefall/tree/master/egs/wenetspeech/ASR/pruned_transducer_stateless5)
|
||||
|
||||
#### Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset)
|
||||
**Streaming**:
|
||||
| | Dev | Test-Net | Test-Meeting |
|
||||
|----------------------|-------|----------|--------------|
|
||||
| greedy_search | 8.78 | 10.12 | 16.16 |
|
||||
| modified_beam_search | 8.53| 9.95 | 15.81 |
|
||||
| fast_beam_search| 9.01 | 10.47 | 16.28 |
|
||||
| modified_beam_search | 8.53| 9.95 | 15.81 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless2 model: [](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing)
|
||||
|
||||
### Alimeeting
|
||||
### [Alimeeting][alimeeting]
|
||||
|
||||
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Alimeeting_pruned_transducer_stateless2].
|
||||
|
||||
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with far subset)
|
||||
#### [Transducer (pruned_transducer_stateless2)](https://github.com/k2-fsa/icefall/tree/master/egs/alimeeting/ASR/pruned_transducer_stateless2)
|
||||
|
||||
| | Eval | Test-Net |
|
||||
|----------------------|--------|----------|
|
||||
| greedy search | 31.77 | 34.66 |
|
||||
| fast beam search | 31.39 | 33.02 |
|
||||
| modified beam search | 30.38 | 34.25 |
|
||||
| greedy_search | 31.77 | 34.66 |
|
||||
| fast_beam_search | 31.39 | 33.02 |
|
||||
| modified_beam_search | 30.38 | 34.25 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1tKr3f0mL17uO_ljdHGKtR7HOmthYHwJG?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1tKr3f0mL17uO_ljdHGKtR7HOmthYHwJG?usp=sharing)
|
||||
|
||||
|
||||
### TAL_CSASR
|
||||
### [TAL_CSASR][tal_csasr]
|
||||
|
||||
We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][TAL_CSASR_pruned_transducer_stateless5].
|
||||
|
||||
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
|
||||
#### [Transducer (pruned_transducer_stateless5)](https://github.com/k2-fsa/icefall/tree/master/egs/tal_csasr/ASR/pruned_transducer_stateless5)
|
||||
|
||||
The best results for Chinese CER(%) and English WER(%) respectively (zh: Chinese, en: English):
|
||||
|decoding-method | dev | dev_zh | dev_en | test | test_zh | test_en |
|
||||
|--|--|--|--|--|--|--|
|
||||
|greedy_search| 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13|
|
||||
|modified_beam_search| 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 |
|
||||
|fast_beam_search| 7.18 | 6.39| 18.90 | 7.27| 6.55 | 18.77|
|
||||
|modified_beam_search| 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 |
|
||||
|
||||
We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [](https://colab.research.google.com/drive/1DmIx-NloI1CMU5GdZrlse7TRu4y3Dpf8?usp=sharing)
|
||||
We provide a Colab notebook to test the pre-trained model: [](https://colab.research.google.com/drive/1DmIx-NloI1CMU5GdZrlse7TRu4y3Dpf8?usp=sharing)
|
||||
|
||||
## Deployment with C++
|
||||
## TTS: Text-to-Speech
|
||||
|
||||
Once you have trained a model in icefall, you may want to deploy it with C++,
|
||||
without Python dependencies.
|
||||
### Supported Datasets
|
||||
|
||||
Please refer to the documentation
|
||||
<https://icefall.readthedocs.io/en/latest/recipes/Non-streaming-ASR/librispeech/conformer_ctc.html#deployment-with-c>
|
||||
- [LJSpeech][ljspeech]
|
||||
- [VCTK][vctk]
|
||||
|
||||
### Supported Models
|
||||
|
||||
- [VITS](https://arxiv.org/abs/2106.06103)
|
||||
|
||||
# Deployment with C++
|
||||
|
||||
Once you have trained a model in icefall, you may want to deploy it with C++ without Python dependencies.
|
||||
|
||||
Please refer to the [document](https://icefall.readthedocs.io/en/latest/recipes/Non-streaming-ASR/librispeech/conformer_ctc.html#deployment-with-c)
|
||||
for how to do this.
|
||||
|
||||
We also provide a Colab notebook, showing you how to run a torch scripted model in [k2][k2] with C++.
|
||||
Please see: [](https://colab.research.google.com/drive/1BIGLWzS36isskMXHKcqC9ysN6pspYXs_?usp=sharing)
|
||||
|
||||
|
||||
[LibriSpeech_tdnn_lstm_ctc]: egs/librispeech/ASR/tdnn_lstm_ctc
|
||||
[LibriSpeech_conformer_ctc]: egs/librispeech/ASR/conformer_ctc
|
||||
[LibriSpeech_transducer]: egs/librispeech/ASR/transducer
|
||||
[LibriSpeech_transducer_stateless]: egs/librispeech/ASR/transducer_stateless
|
||||
[LibriSpeech_zipformer]: egs/librispeech/ASR/zipformer
|
||||
[Aishell_tdnn_lstm_ctc]: egs/aishell/ASR/tdnn_lstm_ctc
|
||||
[Aishell_conformer_ctc]: egs/aishell/ASR/conformer_ctc
|
||||
[Aishell_pruned_transducer_stateless7]: egs/aishell/ASR/pruned_transducer_stateless7_bbpe
|
||||
[Aishell2_pruned_transducer_stateless5]: egs/aishell2/ASR/pruned_transducer_stateless5
|
||||
[Aishell4_pruned_transducer_stateless5]: egs/aishell4/ASR/pruned_transducer_stateless5
|
||||
[TIMIT_tdnn_lstm_ctc]: egs/timit/ASR/tdnn_lstm_ctc
|
||||
[TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc
|
||||
[TED-LIUM3_transducer_stateless]: egs/tedlium3/ASR/transducer_stateless
|
||||
[TED-LIUM3_pruned_transducer_stateless]: egs/tedlium3/ASR/pruned_transducer_stateless
|
||||
[GigaSpeech_conformer_ctc]: egs/gigaspeech/ASR/conformer_ctc
|
||||
[GigaSpeech_pruned_transducer_stateless2]: egs/gigaspeech/ASR/pruned_transducer_stateless2
|
||||
[GigaSpeech_zipformer]: egs/gigaspeech/ASR/zipformer
|
||||
[Aidatatang_200zh_pruned_transducer_stateless2]: egs/aidatatang_200zh/ASR/pruned_transducer_stateless2
|
||||
[WenetSpeech_pruned_transducer_stateless2]: egs/wenetspeech/ASR/pruned_transducer_stateless2
|
||||
[WenetSpeech_pruned_transducer_stateless5]: egs/wenetspeech/ASR/pruned_transducer_stateless5
|
||||
[Alimeeting_pruned_transducer_stateless2]: egs/alimeeting/ASR/pruned_transducer_stateless2
|
||||
[TAL_CSASR_pruned_transducer_stateless5]: egs/tal_csasr/ASR/pruned_transducer_stateless5
|
||||
[yesno]: egs/yesno/ASR
|
||||
[librispeech]: egs/librispeech/ASR
|
||||
[aishell]: egs/aishell/ASR
|
||||
@ -411,3 +365,15 @@ Please see: [ is first proposed `here <https://arxiv.org/abs/2002.11268>`_
|
||||
to address the language information mismatch between the training
|
||||
corpus (source domain) and the testing corpus (target domain). Assuming that the source domain and the test domain
|
||||
are acoustically similar, DR derives the following formular for decoding with Bayes' theorem:
|
||||
are acoustically similar, DR derives the following formula for decoding with Bayes' theorem:
|
||||
|
||||
.. math::
|
||||
|
||||
@ -41,7 +41,7 @@ are acoustically similar, DR derives the following formular for decoding with Ba
|
||||
|
||||
|
||||
where :math:`\lambda_1` and :math:`\lambda_2` are the weights of LM scores for target domain and source domain respectively.
|
||||
Here, the source domain LM is trained on the training corpus. The only difference in the above formular compared to
|
||||
Here, the source domain LM is trained on the training corpus. The only difference in the above formula compared to
|
||||
shallow fusion is the subtraction of the source domain LM.
|
||||
|
||||
Some works treat the predictor and the joiner of the neural transducer as its internal LM. However, the LM is
|
||||
@ -58,7 +58,7 @@ during decoding for transducer model:
|
||||
|
||||
In LODR, an additional bi-gram LM estimated on the source domain (e.g training corpus) is required. Compared to DR,
|
||||
the only difference lies in the choice of source domain LM. According to the original `paper <https://arxiv.org/abs/2203.16776>`_,
|
||||
LODR achieves similar performance compared DR in both intra-domain and cross-domain settings.
|
||||
LODR achieves similar performance compared to DR in both intra-domain and cross-domain settings.
|
||||
As a bi-gram is much faster to evaluate, LODR is usually much faster.
|
||||
|
||||
Now, we will show you how to use LODR in ``icefall``.
|
||||
|
@ -139,7 +139,7 @@ A few parameters can be tuned to further boost the performance of shallow fusion
|
||||
- ``--lm-scale``
|
||||
|
||||
Controls the scale of the LM. If too small, the external language model may not be fully utilized; if too large,
|
||||
the LM score may dominant during decoding, leading to bad WER. A typical value of this is around 0.3.
|
||||
the LM score might be dominant during decoding, leading to bad WER. A typical value of this is around 0.3.
|
||||
|
||||
- ``--beam-size``
|
||||
|
||||
|
@ -34,6 +34,12 @@ which will give you something like below:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
"torch2.2.2-cuda12.1"
|
||||
"torch2.2.2-cuda11.8"
|
||||
"torch2.2.1-cuda12.1"
|
||||
"torch2.2.1-cuda11.8"
|
||||
"torch2.2.0-cuda12.1"
|
||||
"torch2.2.0-cuda11.8"
|
||||
"torch2.1.0-cuda12.1"
|
||||
"torch2.1.0-cuda11.8"
|
||||
"torch2.0.0-cuda11.7"
|
||||
|
@ -74,6 +74,10 @@ to install dependencies of `icefall`_:
|
||||
|
||||
pip install k2==1.24.4.dev20231220+cpu.torch2.0.0 -f https://k2-fsa.github.io/k2/cpu.html
|
||||
|
||||
# For users from China
|
||||
# 中国国内用户,如果访问不了 huggingface, 请使用
|
||||
# pip install k2==1.24.4.dev20231220+cpu.torch2.0.0 -f https://k2-fsa.github.io/k2/cpu-cn.html
|
||||
|
||||
# Install the latest version of lhotse
|
||||
|
||||
pip install git+https://github.com/lhotse-speech/lhotse
|
||||
|
@ -206,6 +206,9 @@ We will install `k2`_ from pre-compiled wheels by following
|
||||
.. code-block:: bash
|
||||
|
||||
(test-icefall) kuangfangjun:~$ pip install k2==1.24.3.dev20230725+cuda11.6.torch1.13.0 -f https://k2-fsa.github.io/k2/cuda.html
|
||||
# For users from China
|
||||
# 中国国内用户,如果访问不了 huggingface, 请使用
|
||||
# pip install k2==1.24.3.dev20230725+cuda11.6.torch1.13.0 -f https://k2-fsa.github.io/k2/cuda-cn.html
|
||||
|
||||
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
Looking in links: https://k2-fsa.github.io/k2/cuda.html
|
||||
|
225
docs/source/recipes/Finetune/adapter/finetune_adapter.rst
Normal file
225
docs/source/recipes/Finetune/adapter/finetune_adapter.rst
Normal file
@ -0,0 +1,225 @@
|
||||
Finetune from a pre-trained Zipformer model with adapters
|
||||
=========================================================
|
||||
|
||||
This tutorial shows you how to fine-tune a pre-trained **Zipformer**
|
||||
transducer model on a new dataset with adapters.
|
||||
Adapters are compact and efficient module that can be integrated into a pre-trained model
|
||||
to improve the model's performance on a new domain. Adapters are injected
|
||||
between different modules in the well-trained neural network. During training, only the parameters
|
||||
in the adapters will be updated. It achieves competitive performance
|
||||
while requiring much less GPU memory than full fine-tuning. For more details about adapters,
|
||||
please refer to the original `paper <https://arxiv.org/pdf/1902.00751.pdf#/>`_ for more details.
|
||||
|
||||
.. HINT::
|
||||
|
||||
We assume you have read the page :ref:`install icefall` and have setup
|
||||
the environment for ``icefall``.
|
||||
|
||||
.. HINT::
|
||||
|
||||
We recommend you to use a GPU or several GPUs to run this recipe
|
||||
|
||||
For illustration purpose, we fine-tune the Zipformer transducer model
|
||||
pre-trained on `LibriSpeech`_ on the small subset of `GigaSpeech`_. You could use your
|
||||
own data for fine-tuning if you create a manifest for your new dataset.
|
||||
|
||||
Data preparation
|
||||
----------------
|
||||
|
||||
Please follow the instructions in the `GigaSpeech recipe <https://github.com/k2-fsa/icefall/tree/master/egs/gigaspeech/ASR>`_
|
||||
to prepare the fine-tune data used in this tutorial. We only require the small subset in GigaSpeech for this tutorial.
|
||||
|
||||
|
||||
Model preparation
|
||||
-----------------
|
||||
|
||||
We are using the Zipformer model trained on full LibriSpeech (960 hours) as the intialization. The
|
||||
checkpoint of the model can be downloaded via the following command:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
|
||||
$ cd icefall-asr-librispeech-zipformer-2023-05-15/exp
|
||||
$ git lfs pull --include "pretrained.pt"
|
||||
$ ln -s pretrained.pt epoch-99.pt
|
||||
$ cd ../data/lang_bpe_500
|
||||
$ git lfs pull --include bpe.model
|
||||
$ cd ../../..
|
||||
|
||||
Before fine-tuning, let's test the model's WER on the new domain. The following command performs
|
||||
decoding on the GigaSpeech test sets:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./zipformer/decode_gigaspeech.py \
|
||||
--epoch 99 \
|
||||
--avg 1 \
|
||||
--exp-dir icefall-asr-librispeech-zipformer-2023-05-15/exp \
|
||||
--use-averaged-model 0 \
|
||||
--max-duration 1000 \
|
||||
--decoding-method greedy_search
|
||||
|
||||
You should see the following numbers:
|
||||
|
||||
.. code-block::
|
||||
|
||||
For dev, WER of different settings are:
|
||||
greedy_search 20.06 best for dev
|
||||
|
||||
For test, WER of different settings are:
|
||||
greedy_search 19.27 best for test
|
||||
|
||||
|
||||
Fine-tune with adapter
|
||||
----------------------
|
||||
|
||||
We insert 4 adapters with residual connection in each ``Zipformer2EncoderLayer``.
|
||||
The original model parameters remain untouched during training and only the parameters of
|
||||
the adapters are updated. The following command starts a fine-tuning experiment with adapters:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ do_finetune=1
|
||||
$ use_adapters=1
|
||||
$ adapter_dim=8
|
||||
|
||||
$ ./zipformer_adapter/train.py \
|
||||
--world-size 2 \
|
||||
--num-epochs 20 \
|
||||
--start-epoch 1 \
|
||||
--exp-dir zipformer_adapter/exp_giga_finetune_adapters${use_adapters}_adapter_dim${adapter_dim} \
|
||||
--use-fp16 1 \
|
||||
--base-lr 0.045 \
|
||||
--use-adapters $use_adapters --adapter-dim $adapter_dim \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--do-finetune $do_finetune \
|
||||
--master-port 13022 \
|
||||
--finetune-ckpt icefall-asr-librispeech-zipformer-2023-05-15/exp/pretrained.pt \
|
||||
--max-duration 1000
|
||||
|
||||
The following arguments are related to fine-tuning:
|
||||
|
||||
- ``--do-finetune``
|
||||
If True, do fine-tuning by initializing the model from a pre-trained checkpoint.
|
||||
**Note that if you want to resume your fine-tuning experiment from certain epochs, you
|
||||
need to set this to False.**
|
||||
|
||||
- ``use-adapters``
|
||||
If adapters are used during fine-tuning.
|
||||
|
||||
- ``--adapter-dim``
|
||||
The bottleneck dimension of the adapter module. Typically a small number.
|
||||
|
||||
You should notice that in the training log, the total number of trainale parameters is shown:
|
||||
|
||||
.. code-block::
|
||||
|
||||
2024-02-22 21:22:03,808 INFO [train.py:1277] A total of 761344 trainable parameters (1.148% of the whole model)
|
||||
|
||||
The trainable parameters only makes up 1.15% of the entire model parameters, so the training will be much faster
|
||||
and requires less memory than full fine-tuning.
|
||||
|
||||
|
||||
Decoding
|
||||
--------
|
||||
|
||||
After training, let's test the WERs. To test the WERs on the GigaSpeech set,
|
||||
you can execute the following command:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ epoch=20
|
||||
$ avg=10
|
||||
$ use_adapters=1
|
||||
$ adapter_dim=8
|
||||
|
||||
% ./zipformer/decode.py \
|
||||
--epoch $epoch \
|
||||
--avg $avg \
|
||||
--use-averaged-model 1 \
|
||||
--exp-dir zipformer_adapter/exp_giga_finetune_adapters${use_adapters}_adapter_dim${adapter_dim} \
|
||||
--max-duration 600 \
|
||||
--use-adapters $use_adapters \
|
||||
--adapter-dim $adapter_dim \
|
||||
--decoding-method greedy_search
|
||||
|
||||
You should see the following numbers:
|
||||
|
||||
.. code-block::
|
||||
|
||||
For dev, WER of different settings are:
|
||||
greedy_search 15.44 best for dev
|
||||
|
||||
For test, WER of different settings are:
|
||||
greedy_search 15.42 best for test
|
||||
|
||||
|
||||
The WER on test set is improved from 19.27 to 15.42, demonstrating the effectiveness of adapters.
|
||||
|
||||
The same model can be used to perform decoding on LibriSpeech test sets. You can deactivate the adapters
|
||||
to keep the same performance of the original model:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ epoch=20
|
||||
$ avg=1
|
||||
$ use_adapters=0
|
||||
$ adapter_dim=8
|
||||
|
||||
% ./zipformer/decode.py \
|
||||
--epoch $epoch \
|
||||
--avg $avg \
|
||||
--use-averaged-model 1 \
|
||||
--exp-dir zipformer_adapter/exp_giga_finetune_adapters${use_adapters}_adapter_dim${adapter_dim} \
|
||||
--max-duration 600 \
|
||||
--use-adapters $use_adapters \
|
||||
--adapter-dim $adapter_dim \
|
||||
--decoding-method greedy_search
|
||||
|
||||
|
||||
.. code-block::
|
||||
|
||||
For dev, WER of different settings are:
|
||||
greedy_search 2.23 best for test-clean
|
||||
|
||||
For test, WER of different settings are:
|
||||
greedy_search 4.96 best for test-other
|
||||
|
||||
The numbers are the same as reported in `icefall <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md#normal-scaled-model-number-of-model-parameters-65549011-ie-6555-m>`_. So adapter-based
|
||||
fine-tuning is also very flexible as the same model can be used for decoding on the original and target domain.
|
||||
|
||||
|
||||
Export the model
|
||||
----------------
|
||||
|
||||
After training, the model can be exported to ``onnx`` format easily using the following command:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ use_adapters=1
|
||||
$ adapter_dim=16
|
||||
|
||||
$ ./zipformer_adapter/export-onnx.py \
|
||||
--tokens icefall-asr-librispeech-zipformer-2023-05-15/data/lang_bpe_500/tokens.txt \
|
||||
--use-averaged-model 1 \
|
||||
--epoch 20 \
|
||||
--avg 10 \
|
||||
--exp-dir zipformer_adapter/exp_giga_finetune_adapters${use_adapters}_adapter_dim${adapter_dim} \
|
||||
--use-adapters $use_adapters \
|
||||
--adapter-dim $adapter_dim \
|
||||
--num-encoder-layers "2,2,3,4,3,2" \
|
||||
--downsampling-factor "1,2,4,8,4,2" \
|
||||
--feedforward-dim "512,768,1024,1536,1024,768" \
|
||||
--num-heads "4,4,4,8,4,4" \
|
||||
--encoder-dim "192,256,384,512,384,256" \
|
||||
--query-head-dim 32 \
|
||||
--value-head-dim 12 \
|
||||
--pos-head-dim 4 \
|
||||
--pos-dim 48 \
|
||||
--encoder-unmasked-dim "192,192,256,256,256,192" \
|
||||
--cnn-module-kernel "31,31,15,15,15,31" \
|
||||
--decoder-dim 512 \
|
||||
--joiner-dim 512 \
|
||||
--causal False \
|
||||
--chunk-size "16,32,64,-1" \
|
||||
--left-context-frames "64,128,256,-1"
|
@ -0,0 +1,140 @@
|
||||
Finetune from a supervised pre-trained Zipformer model
|
||||
======================================================
|
||||
|
||||
This tutorial shows you how to fine-tune a supervised pre-trained **Zipformer**
|
||||
transducer model on a new dataset.
|
||||
|
||||
.. HINT::
|
||||
|
||||
We assume you have read the page :ref:`install icefall` and have setup
|
||||
the environment for ``icefall``.
|
||||
|
||||
.. HINT::
|
||||
|
||||
We recommend you to use a GPU or several GPUs to run this recipe
|
||||
|
||||
|
||||
For illustration purpose, we fine-tune the Zipformer transducer model
|
||||
pre-trained on `LibriSpeech`_ on the small subset of `GigaSpeech`_. You could use your
|
||||
own data for fine-tuning if you create a manifest for your new dataset.
|
||||
|
||||
Data preparation
|
||||
----------------
|
||||
|
||||
Please follow the instructions in the `GigaSpeech recipe <https://github.com/k2-fsa/icefall/tree/master/egs/gigaspeech/ASR>`_
|
||||
to prepare the fine-tune data used in this tutorial. We only require the small subset in GigaSpeech for this tutorial.
|
||||
|
||||
|
||||
Model preparation
|
||||
-----------------
|
||||
|
||||
We are using the Zipformer model trained on full LibriSpeech (960 hours) as the intialization. The
|
||||
checkpoint of the model can be downloaded via the following command:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
|
||||
$ cd icefall-asr-librispeech-zipformer-2023-05-15/exp
|
||||
$ git lfs pull --include "pretrained.pt"
|
||||
$ ln -s pretrained.pt epoch-99.pt
|
||||
$ cd ../data/lang_bpe_500
|
||||
$ git lfs pull --include bpe.model
|
||||
$ cd ../../..
|
||||
|
||||
Before fine-tuning, let's test the model's WER on the new domain. The following command performs
|
||||
decoding on the GigaSpeech test sets:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
./zipformer/decode_gigaspeech.py \
|
||||
--epoch 99 \
|
||||
--avg 1 \
|
||||
--exp-dir icefall-asr-librispeech-zipformer-2023-05-15/exp \
|
||||
--use-averaged-model 0 \
|
||||
--max-duration 1000 \
|
||||
--decoding-method greedy_search
|
||||
|
||||
You should see the following numbers:
|
||||
|
||||
.. code-block::
|
||||
|
||||
For dev, WER of different settings are:
|
||||
greedy_search 20.06 best for dev
|
||||
|
||||
For test, WER of different settings are:
|
||||
greedy_search 19.27 best for test
|
||||
|
||||
|
||||
Fine-tune
|
||||
---------
|
||||
|
||||
Since LibriSpeech and GigaSpeech are both English dataset, we can initialize the whole
|
||||
Zipformer model with the checkpoint downloaded in the previous step (otherwise we should consider
|
||||
initializing the stateless decoder and joiner from scratch due to the mismatch of the output
|
||||
vocabulary). The following command starts a fine-tuning experiment:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ use_mux=0
|
||||
$ do_finetune=1
|
||||
|
||||
$ ./zipformer/finetune.py \
|
||||
--world-size 2 \
|
||||
--num-epochs 20 \
|
||||
--start-epoch 1 \
|
||||
--exp-dir zipformer/exp_giga_finetune${do_finetune}_mux${use_mux} \
|
||||
--use-fp16 1 \
|
||||
--base-lr 0.0045 \
|
||||
--bpe-model data/lang_bpe_500/bpe.model \
|
||||
--do-finetune $do_finetune \
|
||||
--use-mux $use_mux \
|
||||
--master-port 13024 \
|
||||
--finetune-ckpt icefall-asr-librispeech-zipformer-2023-05-15/exp/pretrained.pt \
|
||||
--max-duration 1000
|
||||
|
||||
The following arguments are related to fine-tuning:
|
||||
|
||||
- ``--base-lr``
|
||||
The learning rate used for fine-tuning. We suggest to set a **small** learning rate for fine-tuning,
|
||||
otherwise the model may forget the initialization very quickly. A reasonable value should be around
|
||||
1/10 of the original lr, i.e 0.0045.
|
||||
|
||||
- ``--do-finetune``
|
||||
If True, do fine-tuning by initializing the model from a pre-trained checkpoint.
|
||||
**Note that if you want to resume your fine-tuning experiment from certain epochs, you
|
||||
need to set this to False.**
|
||||
|
||||
- ``--finetune-ckpt``
|
||||
The path to the pre-trained checkpoint (used for initialization).
|
||||
|
||||
- ``--use-mux``
|
||||
If True, mix the fine-tune data with the original training data by using `CutSet.mux <https://lhotse.readthedocs.io/en/latest/api.html#lhotse.supervision.SupervisionSet.mux>`_
|
||||
This helps maintain the model's performance on the original domain if the original training
|
||||
is available. **If you don't have the original training data, please set it to False.**
|
||||
|
||||
After fine-tuning, let's test the WERs. You can do this via the following command:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ use_mux=0
|
||||
$ do_finetune=1
|
||||
$ ./zipformer/decode_gigaspeech.py \
|
||||
--epoch 20 \
|
||||
--avg 10 \
|
||||
--exp-dir zipformer/exp_giga_finetune${do_finetune}_mux${use_mux} \
|
||||
--use-averaged-model 1 \
|
||||
--max-duration 1000 \
|
||||
--decoding-method greedy_search
|
||||
|
||||
You should see numbers similar to the ones below:
|
||||
|
||||
.. code-block:: text
|
||||
|
||||
For dev, WER of different settings are:
|
||||
greedy_search 13.47 best for dev
|
||||
|
||||
For test, WER of different settings are:
|
||||
greedy_search 13.66 best for test
|
||||
|
||||
Compared to the original checkpoint, the fine-tuned model achieves much lower WERs
|
||||
on the GigaSpeech test sets.
|
16
docs/source/recipes/Finetune/index.rst
Normal file
16
docs/source/recipes/Finetune/index.rst
Normal file
@ -0,0 +1,16 @@
|
||||
Fine-tune a pre-trained model
|
||||
=============================
|
||||
|
||||
After pre-training on public available datasets, the ASR model is already capable of
|
||||
performing general speech recognition with relatively high accuracy. However, the accuracy
|
||||
could be still low on certain domains that are quite different from the original training
|
||||
set. In this case, we can fine-tune the model with a small amount of additional labelled
|
||||
data to improve the performance on new domains.
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Table of Contents
|
||||
|
||||
from_supervised/finetune_zipformer
|
||||
adapter/finetune_adapter
|
@ -1,4 +1,4 @@
|
||||
VITS
|
||||
VITS-LJSpeech
|
||||
===============
|
||||
|
||||
This tutorial shows you how to train an VITS model
|
||||
@ -13,6 +13,14 @@ with the `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`_ dataset.
|
||||
The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
|
||||
|
||||
|
||||
Install extra dependencies
|
||||
--------------------------
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html
|
||||
pip install numba espnet_tts_frontend
|
||||
|
||||
Data preparation
|
||||
----------------
|
||||
|
||||
@ -56,7 +64,8 @@ Training
|
||||
--start-epoch 1 \
|
||||
--use-fp16 1 \
|
||||
--exp-dir vits/exp \
|
||||
--tokens data/tokens.txt
|
||||
--tokens data/tokens.txt \
|
||||
--model-type high \
|
||||
--max-duration 500
|
||||
|
||||
.. note::
|
||||
@ -64,6 +73,11 @@ Training
|
||||
You can adjust the hyper-parameters to control the size of the VITS model and
|
||||
the training configurations. For more details, please run ``./vits/train.py --help``.
|
||||
|
||||
.. warning::
|
||||
|
||||
If you want a model that runs faster on CPU, please use ``--model-type low``
|
||||
or ``--model-type medium``.
|
||||
|
||||
.. note::
|
||||
|
||||
The training can take a long time (usually a couple of days).
|
||||
@ -95,8 +109,8 @@ training part first. It will save the ground-truth and generated wavs to the dir
|
||||
Export models
|
||||
-------------
|
||||
|
||||
Currently we only support ONNX model exporting. It will generate two files in the given ``exp-dir``:
|
||||
``vits-epoch-*.onnx`` and ``vits-epoch-*.int8.onnx``.
|
||||
Currently we only support ONNX model exporting. It will generate one file in the given ``exp-dir``:
|
||||
``vits-epoch-*.onnx``.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
@ -120,4 +134,68 @@ Download pretrained models
|
||||
If you don't want to train from scratch, you can download the pretrained models
|
||||
by visiting the following link:
|
||||
|
||||
- `<https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2023-11-29>`_
|
||||
- ``--model-type=high``: `<https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2024-02-28>`_
|
||||
- ``--model-type=medium``: `<https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-medium-2024-03-12>`_
|
||||
- ``--model-type=low``: `<https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-low-2024-03-12>`_
|
||||
|
||||
Usage in sherpa-onnx
|
||||
--------------------
|
||||
|
||||
The following describes how to test the exported ONNX model in `sherpa-onnx`_.
|
||||
|
||||
.. hint::
|
||||
|
||||
`sherpa-onnx`_ supports different programming languages, e.g., C++, C, Python,
|
||||
Kotlin, Java, Swift, Go, C#, etc. It also supports Android and iOS.
|
||||
|
||||
We only describe how to use pre-built binaries from `sherpa-onnx`_ below.
|
||||
Please refer to `<https://k2-fsa.github.io/sherpa/onnx/>`_
|
||||
for more documentation.
|
||||
|
||||
Install sherpa-onnx
|
||||
^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install sherpa-onnx
|
||||
|
||||
To check that you have installed `sherpa-onnx`_ successfully, please run:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
which sherpa-onnx-offline-tts
|
||||
sherpa-onnx-offline-tts --help
|
||||
|
||||
Download lexicon files
|
||||
^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd /tmp
|
||||
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
|
||||
tar xf espeak-ng-data.tar.bz2
|
||||
|
||||
Run sherpa-onnx
|
||||
^^^^^^^^^^^^^^^
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
cd egs/ljspeech/TTS
|
||||
|
||||
sherpa-onnx-offline-tts \
|
||||
--vits-model=vits/exp/vits-epoch-1000.onnx \
|
||||
--vits-tokens=data/tokens.txt \
|
||||
--vits-data-dir=/tmp/espeak-ng-data \
|
||||
--num-threads=1 \
|
||||
--output-filename=./high.wav \
|
||||
"Ask not what your country can do for you; ask what you can do for your country."
|
||||
|
||||
.. hint::
|
||||
|
||||
You can also use ``sherpa-onnx-offline-tts-play`` to play the audio
|
||||
as it is generating.
|
||||
|
||||
You should get a file ``high.wav`` after running the above command.
|
||||
|
||||
Congratulations! You have successfully trained and exported a text-to-speech
|
||||
model and run it with `sherpa-onnx`_.
|
||||
|
@ -1,4 +1,4 @@
|
||||
VITS
|
||||
VITS-VCTK
|
||||
===============
|
||||
|
||||
This tutorial shows you how to train an VITS model
|
||||
|
@ -17,3 +17,4 @@ We may add recipes for other tasks as well in the future.
|
||||
Streaming-ASR/index
|
||||
RNN-LM/index
|
||||
TTS/index
|
||||
Finetune/index
|
||||
|
@ -16,8 +16,8 @@ perturb_speed=true
|
||||
#
|
||||
# - $dl_dir/aidatatang_200zh
|
||||
# You can find "corpus" and "transcript" inside it.
|
||||
# You can download it at
|
||||
# https://openslr.org/62/
|
||||
# You can download it at https://openslr.org/62/
|
||||
# If you download the data by yourself, DON'T FORGET to extract the *.tar.gz files under corpus.
|
||||
|
||||
dl_dir=$PWD/download
|
||||
|
||||
|
@ -288,8 +288,9 @@ class Aidatatang_200zhAsrDataModule:
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
buffer_size=self.args.num_buckets * 2000,
|
||||
shuffle_buffer_size=self.args.num_buckets * 5000,
|
||||
drop_last=True,
|
||||
buffer_size=50000,
|
||||
)
|
||||
else:
|
||||
logging.info("Using SimpleCutSampler.")
|
||||
|
25
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py
Normal file → Executable file
25
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py
Normal file → Executable file
@ -1,3 +1,4 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
@ -20,7 +21,7 @@
|
||||
Usage:
|
||||
./pruned_transducer_stateless2/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless2/exp \
|
||||
--lang-dir data/lang_char \
|
||||
--tokens data/lang_char/tokens.txt \
|
||||
--epoch 29 \
|
||||
--avg 19
|
||||
|
||||
@ -45,12 +46,13 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from scaling_converter import convert_scaled_to_non_scaled
|
||||
from train import get_params, get_transducer_model
|
||||
|
||||
from icefall.checkpoint import average_checkpoints, load_checkpoint
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import str2bool
|
||||
from icefall.utils import num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -85,10 +87,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char",
|
||||
help="The lang dir",
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -122,10 +124,14 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
# Load tokens.txt here
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
# Load id of the <blk> token and the vocab size
|
||||
# <blk> is defined in local/train_bpe_model.py
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.unk_id = token_table["<unk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
|
||||
|
||||
logging.info(params)
|
||||
|
||||
@ -152,6 +158,7 @@ def main():
|
||||
model.eval()
|
||||
|
||||
if params.jit:
|
||||
convert_scaled_to_non_scaled(model, inplace=True)
|
||||
# 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
|
||||
|
1
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/lstmp.py
Symbolic link
1
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/lstmp.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/lstm_transducer_stateless2/lstmp.py
|
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/pruned_transducer_stateless3/scaling_converter.py
|
@ -19,8 +19,17 @@ The following table lists the differences among them.
|
||||
| `transducer_stateless_modified` | Conformer | Embedding + Conv1d | with modified transducer from `optimized_transducer` |
|
||||
| `transducer_stateless_modified-2` | Conformer | Embedding + Conv1d | with modified transducer from `optimized_transducer` + extra data |
|
||||
| `pruned_transducer_stateless3` | Conformer (reworked) | Embedding + Conv1d | pruned RNN-T + reworked model with random combiner + using aidatatang_20zh as extra data|
|
||||
| `pruned_transducer_stateless7` | Zipformer | Embedding | pruned RNN-T + zipformer encoder + stateless decoder with context-size 1 |
|
||||
| `pruned_transducer_stateless7` | Zipformer | Embedding | pruned RNN-T + zipformer encoder + stateless decoder with context-size set to 1 |
|
||||
| `zipformer` | Upgraded Zipformer | Embedding + Conv1d | The latest recipe with context-size set to 1 |
|
||||
|
||||
|
||||
The decoder in `transducer_stateless` is modified from the paper
|
||||
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
|
||||
We place an additional Conv1d layer right after the input embedding layer.
|
||||
|
||||
# Whisper
|
||||
|
||||
Recipe to finetune large pretrained models
|
||||
| | Encoder | Decoder | Comment |
|
||||
|------------------------------------|-----------|--------------------|-----------------------------------------------------------------------------------|
|
||||
| `whisper` | Transformer | Transformer | support fine-tuning using deepspeed
|
||||
|
@ -1,10 +1,120 @@
|
||||
## Results
|
||||
|
||||
### Aishell training results (Fine-tuning Pretrained Models)
|
||||
#### Whisper
|
||||
[./whisper](./whisper)
|
||||
##### fine-tuning results on Aishell test set on whisper medium, large-v2, large-v3
|
||||
|
||||
| | test (before fine-tuning) | test (after fine-tuning) | comment |
|
||||
|------------------------|------|------|-----------------------------------------|
|
||||
| medium | 7.23 | 3.27 | --epoch 10 --avg 4, ddp |
|
||||
| large-v2 | 6.56 | 2.47 | --epoch 10 --avg 6, deepspeed zero stage1 |
|
||||
| large-v3 | 6.06 | 2.84 | --epoch 5 --avg 3, deepspeed zero stage1 |
|
||||
|
||||
Command for training is:
|
||||
```bash
|
||||
pip install -r whisper/requirements.txt
|
||||
|
||||
./prepare.sh --stage 30 --stop_stage 30
|
||||
|
||||
#fine-tuning with deepspeed zero stage 1
|
||||
torchrun --nproc-per-node 8 ./whisper/train.py \
|
||||
--max-duration 200 \
|
||||
--exp-dir whisper/exp_large_v2 \
|
||||
--model-name large-v2 \
|
||||
--deepspeed \
|
||||
--deepspeed_config ./whisper/ds_config_zero1.json
|
||||
|
||||
# fine-tuning with ddp
|
||||
torchrun --nproc-per-node 8 ./whisper/train.py \
|
||||
--max-duration 200 \
|
||||
--exp-dir whisper/exp_medium \
|
||||
--base-lr 1e-5 \
|
||||
--model-name medium
|
||||
```
|
||||
|
||||
Command for decoding using fine-tuned models:
|
||||
```bash
|
||||
git lfs install
|
||||
git clone https://huggingface.co/yuekai/icefall_asr_aishell_whisper
|
||||
ln -s icefall_asr_aishell_whisper/exp_large_v2/epoch-10-avg6.pt whisper/exp_large_v2/epoch-999.pt
|
||||
|
||||
python3 ./whisper/decode.py \
|
||||
--exp-dir whisper/exp_large_v2 \
|
||||
--model-name large-v2 \
|
||||
--epoch 999 --avg 1 \
|
||||
--beam-size 10 --max-duration 50
|
||||
```
|
||||
Command for decoding using pretrained models (before fine-tuning):
|
||||
```bash
|
||||
python3 ./whisper/decode.py \
|
||||
--exp-dir whisper/exp_large_v2 \
|
||||
--model-name large-v2 \
|
||||
--epoch -1 --avg 1 \
|
||||
--remove-whisper-encoder-input-length-restriction False \
|
||||
--beam-size 10 --max-duration 50
|
||||
```
|
||||
Fine-tuned models, training logs, decoding logs, tensorboard and decoding results
|
||||
are available at
|
||||
<https://huggingface.co/yuekai/icefall_asr_aishell_whisper>
|
||||
|
||||
### Aishell training result (Stateless Transducer)
|
||||
|
||||
#### Zipformer (Byte-level BPE)
|
||||
|
||||
[./zipformer](./zipformer/)
|
||||
|
||||
It's reworked Zipformer with Pruned RNNT loss, trained with Byte-level BPE, `vocab_size` set to 500.
|
||||
|
||||
##### normal-scaled model, number of model parameters: 65549011, i.e., 65.55 M
|
||||
|
||||
| | test | dev | comment |
|
||||
|------------------------|------|------|-----------------------------------------|
|
||||
| greedy search | 4.54 | 4.31 | --epoch 40 --avg 10 |
|
||||
| modified beam search | 4.37 | 4.11 | --epoch 40 --avg 10 |
|
||||
| fast beam search | 4.43 | 4.17 | --epoch 40 --avg 10 |
|
||||
|
||||
```bash
|
||||
./prepare.sh
|
||||
|
||||
export CUDA_VISIBLE_DEVICES="0,1"
|
||||
|
||||
./zipformer/train_bbpe.py \
|
||||
--world-size 2 \
|
||||
--num-epochs 40 \
|
||||
--start-epoch 1 \
|
||||
--use-fp16 1 \
|
||||
--context-size 2 \
|
||||
--enable-musan 0 \
|
||||
--exp-dir zipformer/exp_bbpe \
|
||||
--max-duration 1000 \
|
||||
--enable-musan 0 \
|
||||
--base-lr 0.045 \
|
||||
--lr-batches 7500 \
|
||||
--lr-epochs 10 \
|
||||
--spec-aug-time-warp-factor 20
|
||||
```
|
||||
|
||||
Command for decoding is:
|
||||
```bash
|
||||
for m in greedy_search modified_beam_search fast_beam_search ; do
|
||||
./zipformer/decode_bbpe.py \
|
||||
--epoch 40 \
|
||||
--avg 10 \
|
||||
--exp-dir ./zipformer_bbpe/exp \
|
||||
--bpe-model data/lang_bbpe_500/bbpe.model \
|
||||
--context-size 2 \
|
||||
--decoding-method $m
|
||||
done
|
||||
```
|
||||
Pretrained models, training logs, decoding logs, tensorboard and decoding results
|
||||
are available at
|
||||
<https://huggingface.co/zrjin/icefall-asr-aishell-zipformer-bbpe-2024-01-16>
|
||||
|
||||
|
||||
#### Zipformer (Non-streaming)
|
||||
|
||||
[./zipformer](./zipformer)
|
||||
[./zipformer](./zipformer/)
|
||||
|
||||
It's reworked Zipformer with Pruned RNNT loss.
|
||||
**Caution**: It uses `--context-size=1`.
|
||||
@ -260,7 +370,7 @@ done
|
||||
Pretrained models, training logs, decoding logs, and decoding results
|
||||
are available at
|
||||
<https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21>
|
||||
#### Pruned transducer stateless 7 (zipformer)
|
||||
#### Pruned transducer stateless 7 (Byte-level BPE)
|
||||
|
||||
See <https://github.com/k2-fsa/icefall/pull/986>
|
||||
|
||||
@ -703,7 +813,6 @@ python3 ./transducer_stateless/decode.py \
|
||||
--max-sym-per-frame 3
|
||||
```
|
||||
|
||||
### Aishell training results (Transducer-stateless)
|
||||
#### 2022-02-18
|
||||
(Pingfeng Luo) : The tensorboard log for training is available at <https://tensorboard.dev/experiment/k3QL6QMhRbCwCKYKM9po9w/>
|
||||
And pretrained model is available at <https://huggingface.co/pfluo/icefall-aishell-transducer-stateless-char-2021-12-29>
|
||||
|
@ -1,4 +1,4 @@
|
||||
|
||||
Please visit
|
||||
<https://icefall.readthedocs.io/en/latest/recipes/aishell/conformer_ctc.html>
|
||||
<https://k2-fsa.github.io/icefall/recipes/Non-streaming-ASR/aishell/conformer_ctc.html>
|
||||
for how to run this recipe.
|
||||
|
@ -419,7 +419,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
if enable_log:
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
@ -432,7 +432,11 @@ def save_results(
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=enable_log,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -431,7 +431,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
if enable_log:
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
@ -444,7 +444,11 @@ def save_results(
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=enable_log,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -29,7 +29,14 @@ import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
|
||||
from lhotse import (
|
||||
CutSet,
|
||||
Fbank,
|
||||
FbankConfig,
|
||||
LilcomChunkyWriter,
|
||||
WhisperFbank,
|
||||
WhisperFbankConfig,
|
||||
)
|
||||
from lhotse.recipes.utils import read_manifests_if_cached
|
||||
|
||||
from icefall.utils import get_executor, str2bool
|
||||
@ -42,9 +49,14 @@ torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
|
||||
|
||||
def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
def compute_fbank_aishell(
|
||||
num_mel_bins: int = 80,
|
||||
perturb_speed: bool = False,
|
||||
whisper_fbank: bool = False,
|
||||
output_dir: str = "data/fbank",
|
||||
):
|
||||
src_dir = Path("data/manifests")
|
||||
output_dir = Path("data/fbank")
|
||||
output_dir = Path(output_dir)
|
||||
num_jobs = min(15, os.cpu_count())
|
||||
|
||||
dataset_parts = (
|
||||
@ -68,8 +80,12 @@ def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
list(manifests.keys()),
|
||||
dataset_parts,
|
||||
)
|
||||
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
if whisper_fbank:
|
||||
extractor = WhisperFbank(
|
||||
WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
|
||||
)
|
||||
else:
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
for partition, m in manifests.items():
|
||||
@ -82,7 +98,7 @@ def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
supervisions=m["supervisions"],
|
||||
)
|
||||
if "train" in partition and perturb_speed:
|
||||
logging.info(f"Doing speed perturb")
|
||||
logging.info("Doing speed perturb")
|
||||
cut_set = (
|
||||
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
|
||||
)
|
||||
@ -111,6 +127,18 @@ def get_args():
|
||||
default=False,
|
||||
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--whisper-fbank",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Use WhisperFbank instead of Fbank. Default: False.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output-dir",
|
||||
type=str,
|
||||
default="data/fbank",
|
||||
help="Output directory. Default: data/fbank.",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
@ -121,5 +149,8 @@ if __name__ == "__main__":
|
||||
|
||||
args = get_args()
|
||||
compute_fbank_aishell(
|
||||
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
|
||||
num_mel_bins=args.num_mel_bins,
|
||||
perturb_speed=args.perturb_speed,
|
||||
whisper_fbank=args.whisper_fbank,
|
||||
output_dir=args.output_dir,
|
||||
)
|
||||
|
@ -360,7 +360,7 @@ if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
|
||||
fi
|
||||
|
||||
if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
|
||||
log "Stage 11: Train RNN LM model"
|
||||
log "Stage 12: Train RNN LM model"
|
||||
python ../../../icefall/rnn_lm/train.py \
|
||||
--start-epoch 0 \
|
||||
--world-size 1 \
|
||||
@ -376,3 +376,16 @@ if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
|
||||
--vocab-size 4336 \
|
||||
--master-port 12345
|
||||
fi
|
||||
|
||||
# whisper large-v3 using 128 mel bins, others using 80 mel bins
|
||||
whisper_mel_bins=80
|
||||
output_dir=data/fbank_whisper
|
||||
if [ $stage -le 30 ] && [ $stop_stage -ge 30 ]; then
|
||||
log "Stage 30: Compute ${whisper_mel_bins} dim fbank for whisper model fine-tuning"
|
||||
if [ ! -f $output_dir/.aishell.whisper.done ]; then
|
||||
mkdir -p $output_dir
|
||||
./local/compute_fbank_aishell.py --perturb-speed ${perturb_speed} --num-mel-bins ${whisper_mel_bins} --whisper-fbank true --output-dir $output_dir
|
||||
./local/compute_fbank_musan.py --num-mel-bins ${whisper_mel_bins} --whisper-fbank true --output-dir $output_dir
|
||||
touch $output_dir/.aishell.whisper.done
|
||||
fi
|
||||
fi
|
||||
|
@ -390,7 +390,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -402,7 +402,11 @@ def save_results(
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -47,12 +47,12 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import str2bool
|
||||
from icefall.utils import num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -106,10 +106,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default=Path("data/lang_char"),
|
||||
help="The lang dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -136,10 +136,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
@ -526,7 +526,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -538,7 +538,11 @@ def save_results(
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -47,6 +47,7 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from scaling_converter import convert_scaled_to_non_scaled
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
@ -57,8 +58,7 @@ from icefall.checkpoint import (
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import str2bool
|
||||
from icefall.utils import num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -123,10 +123,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default=Path("data/lang_char"),
|
||||
help="The lang dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -153,10 +153,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
params.datatang_prob = 0
|
||||
|
||||
logging.info(params)
|
||||
|
@ -444,7 +444,7 @@ def save_results(
|
||||
for res in results:
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
|
||||
store_transcripts(filename=recog_path, texts=results_char)
|
||||
store_transcripts(filename=recog_path, texts=results_char, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -452,7 +452,11 @@ def save_results(
|
||||
errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -89,6 +89,7 @@ from icefall.checkpoint import (
|
||||
)
|
||||
from icefall.dist import cleanup_dist, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.err import raise_grad_scale_is_too_small_error
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
@ -881,9 +882,7 @@ def train_one_epoch(
|
||||
if cur_grad_scale < 0.01:
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
raise_grad_scale_is_too_small_error()
|
||||
if batch_idx % params.log_interval == 0:
|
||||
cur_lr = scheduler.get_last_lr()[0]
|
||||
cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
|
||||
|
@ -49,14 +49,14 @@ import logging
|
||||
from pathlib import Path
|
||||
from typing import Dict, Tuple
|
||||
|
||||
import k2
|
||||
import onnx
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from decoder2 import Decoder
|
||||
from do_not_use_it_directly import add_model_arguments, get_params, get_transducer_model
|
||||
from onnxruntime.quantization import QuantType, quantize_dynamic
|
||||
from scaling_converter import convert_scaled_to_non_scaled
|
||||
from do_not_use_it_directly import add_model_arguments, get_params, get_transducer_model
|
||||
from zipformer import Zipformer
|
||||
|
||||
from icefall.checkpoint import (
|
||||
@ -65,8 +65,7 @@ from icefall.checkpoint import (
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import setup_logger, str2bool
|
||||
from icefall.utils import num_tokens, setup_logger, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -123,12 +122,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
help="""The lang dir
|
||||
It contains language related input files such as
|
||||
"lexicon.txt"
|
||||
""",
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -404,9 +401,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
@ -85,6 +85,7 @@ from icefall.checkpoint import (
|
||||
)
|
||||
from icefall.dist import cleanup_dist, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.err import raise_grad_scale_is_too_small_error
|
||||
from icefall.hooks import register_inf_check_hooks
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
@ -878,9 +879,7 @@ def train_one_epoch(
|
||||
if cur_grad_scale < 0.01:
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
raise_grad_scale_is_too_small_error(cur_grad_scale)
|
||||
if batch_idx % params.log_interval == 0:
|
||||
cur_lr = scheduler.get_last_lr()[0]
|
||||
cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
|
||||
|
@ -581,7 +581,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -594,7 +594,11 @@ def save_results(
|
||||
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -78,6 +78,7 @@ from icefall.checkpoint import (
|
||||
)
|
||||
from icefall.dist import cleanup_dist, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.err import raise_grad_scale_is_too_small_error
|
||||
from icefall.hooks import register_inf_check_hooks
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
@ -871,9 +872,7 @@ def train_one_epoch(
|
||||
if cur_grad_scale < 0.01:
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
raise_grad_scale_is_too_small_error(cur_grad_scale)
|
||||
|
||||
if batch_idx % params.log_interval == 0:
|
||||
cur_lr = scheduler.get_last_lr()[0]
|
||||
|
@ -250,7 +250,7 @@ def get_parser():
|
||||
parser.add_argument(
|
||||
"--context-size",
|
||||
type=int,
|
||||
default=1,
|
||||
default=2,
|
||||
help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
|
||||
)
|
||||
parser.add_argument(
|
||||
@ -492,7 +492,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -500,7 +500,11 @@ def save_results(
|
||||
errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -78,6 +78,7 @@ from icefall.checkpoint import (
|
||||
)
|
||||
from icefall.dist import cleanup_dist, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.err import raise_grad_scale_is_too_small_error
|
||||
from icefall.hooks import register_inf_check_hooks
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
|
||||
@ -882,9 +883,7 @@ def train_one_epoch(
|
||||
if cur_grad_scale < 0.01:
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
raise_grad_scale_is_too_small_error(cur_grad_scale)
|
||||
|
||||
if batch_idx % params.log_interval == 0:
|
||||
cur_lr = scheduler.get_last_lr()[0]
|
||||
|
@ -78,6 +78,7 @@ from icefall.checkpoint import (
|
||||
)
|
||||
from icefall.dist import cleanup_dist, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.err import raise_grad_scale_is_too_small_error
|
||||
from icefall.hooks import register_inf_check_hooks
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
|
||||
@ -881,9 +882,7 @@ def train_one_epoch(
|
||||
if cur_grad_scale < 0.01:
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
raise_grad_scale_is_too_small_error(cur_grad_scale)
|
||||
|
||||
if batch_idx % params.log_interval == 0:
|
||||
cur_lr = scheduler.get_last_lr()[0]
|
||||
|
@ -275,6 +275,8 @@ class AishellAsrDataModule:
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
buffer_size=self.args.num_buckets * 2000,
|
||||
shuffle_buffer_size=self.args.num_buckets * 5000,
|
||||
drop_last=self.args.drop_last,
|
||||
)
|
||||
else:
|
||||
|
@ -278,7 +278,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -289,7 +289,13 @@ def save_results(
|
||||
for res in results:
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(f, f"{test_set_name}-{key}", results_char)
|
||||
wer = write_error_stats(
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
@ -327,7 +327,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
@ -338,7 +338,11 @@ def save_results(
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -23,7 +23,7 @@
|
||||
Usage:
|
||||
./transducer_stateless/export.py \
|
||||
--exp-dir ./transducer_stateless/exp \
|
||||
--lang-dir data/lang_char \
|
||||
--tokens data/lang_char/tokens.txt \
|
||||
--epoch 20 \
|
||||
--avg 10
|
||||
|
||||
@ -47,6 +47,7 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from conformer import Conformer
|
||||
@ -56,8 +57,7 @@ from model import Transducer
|
||||
|
||||
from icefall.checkpoint import average_checkpoints, load_checkpoint
|
||||
from icefall.env import get_env_info
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import AttributeDict, str2bool
|
||||
from icefall.utils import AttributeDict, num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -92,10 +92,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char",
|
||||
help="The lang dir",
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -192,10 +192,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
@ -226,6 +226,8 @@ class AsrDataModule:
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
buffer_size=self.args.num_buckets * 2000,
|
||||
shuffle_buffer_size=self.args.num_buckets * 5000,
|
||||
drop_last=True,
|
||||
)
|
||||
|
||||
|
@ -372,7 +372,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -384,7 +384,11 @@ def save_results(
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -46,6 +46,7 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from conformer import Conformer
|
||||
@ -56,7 +57,7 @@ from model import Transducer
|
||||
from icefall.checkpoint import average_checkpoints, load_checkpoint
|
||||
from icefall.env import get_env_info
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import AttributeDict, str2bool
|
||||
from icefall.utils import AttributeDict, num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -99,10 +100,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default=Path("data/lang_char"),
|
||||
help="The lang dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -190,10 +191,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
@ -376,7 +376,7 @@ def save_results(
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -388,7 +388,11 @@ def save_results(
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
@ -46,6 +46,7 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from conformer import Conformer
|
||||
@ -55,8 +56,7 @@ from model import Transducer
|
||||
|
||||
from icefall.checkpoint import average_checkpoints, load_checkpoint
|
||||
from icefall.env import get_env_info
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import AttributeDict, str2bool
|
||||
from icefall.utils import AttributeDict, num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -99,10 +99,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default=Path("data/lang_char"),
|
||||
help="The lang dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -190,10 +190,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
1
egs/aishell/ASR/whisper/asr_datamodule.py
Symbolic link
1
egs/aishell/ASR/whisper/asr_datamodule.py
Symbolic link
@ -0,0 +1 @@
|
||||
../tdnn_lstm_ctc/asr_datamodule.py
|
507
egs/aishell/ASR/whisper/decode.py
Executable file
507
egs/aishell/ASR/whisper/decode.py
Executable file
@ -0,0 +1,507 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
|
||||
# Fangjun Kuang,
|
||||
# Wei Kang)
|
||||
# 2024 Yuekai Zhang
|
||||
#
|
||||
# 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:
|
||||
# Command for decoding using fine-tuned models:
|
||||
git lfs install
|
||||
git clone https://huggingface.co/yuekai/icefall_asr_aishell_whisper
|
||||
ln -s icefall_asr_aishell_whisper/exp_large_v2/epoch-10-avg6.pt whisper/exp_large_v2/epoch-999.pt
|
||||
|
||||
python3 ./whisper/decode.py \
|
||||
--exp-dir whisper/exp_large_v2 \
|
||||
--model-name large-v2 \
|
||||
--epoch 999 --avg 1 \
|
||||
--manifest-dir data/fbank_whisper \
|
||||
--beam-size 10 --max-duration 50
|
||||
|
||||
# Command for decoding using pretrained models (before fine-tuning):
|
||||
|
||||
python3 ./whisper/decode.py \
|
||||
--exp-dir whisper/exp_large_v2 \
|
||||
--model-name large-v2 \
|
||||
--epoch -1 --avg 1 \
|
||||
--manifest-dir data/fbank_whisper \
|
||||
--remove-whisper-encoder-input-length-restriction False \
|
||||
--beam-size 10 --max-duration 50
|
||||
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import re
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import k2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import whisper
|
||||
from asr_datamodule import AishellAsrDataModule
|
||||
from tn.chinese.normalizer import Normalizer
|
||||
from whisper.normalizers import BasicTextNormalizer
|
||||
from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
|
||||
from zhconv import convert
|
||||
|
||||
from icefall.checkpoint import average_checkpoints_with_averaged_model, load_checkpoint
|
||||
from icefall.env import get_env_info
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
|
||||
def average_checkpoints(
|
||||
filenames: List[Path], device: torch.device = torch.device("cpu")
|
||||
) -> dict:
|
||||
"""Average a list of checkpoints.
|
||||
The function is mainly used for deepspeed converted checkpoint averaging, which only include model state_dict.
|
||||
|
||||
Args:
|
||||
filenames:
|
||||
Filenames of the checkpoints to be averaged. We assume all
|
||||
checkpoints are saved by :func:`save_checkpoint`.
|
||||
device:
|
||||
Move checkpoints to this device before averaging.
|
||||
Returns:
|
||||
Return a dict (i.e., state_dict) which is the average of all
|
||||
model state dicts contained in the checkpoints.
|
||||
"""
|
||||
n = len(filenames)
|
||||
|
||||
if "model" in torch.load(filenames[0], map_location=device):
|
||||
avg = torch.load(filenames[0], map_location=device)["model"]
|
||||
else:
|
||||
avg = torch.load(filenames[0], map_location=device)
|
||||
|
||||
# Identify shared parameters. Two parameters are said to be shared
|
||||
# if they have the same data_ptr
|
||||
uniqued: Dict[int, str] = dict()
|
||||
|
||||
for k, v in avg.items():
|
||||
v_data_ptr = v.data_ptr()
|
||||
if v_data_ptr in uniqued:
|
||||
continue
|
||||
uniqued[v_data_ptr] = k
|
||||
|
||||
uniqued_names = list(uniqued.values())
|
||||
|
||||
for i in range(1, n):
|
||||
if "model" in torch.load(filenames[i], map_location=device):
|
||||
state_dict = torch.load(filenames[i], map_location=device)["model"]
|
||||
else:
|
||||
state_dict = torch.load(filenames[i], map_location=device)
|
||||
for k in uniqued_names:
|
||||
avg[k] += state_dict[k]
|
||||
|
||||
for k in uniqued_names:
|
||||
if avg[k].is_floating_point():
|
||||
avg[k] /= n
|
||||
else:
|
||||
avg[k] //= n
|
||||
|
||||
return avg
|
||||
|
||||
|
||||
def remove_punctuation(text: str or List[str]):
|
||||
"""Modified from https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py
|
||||
|
||||
Args:
|
||||
text: It can be a string or a list of strings.
|
||||
Returns:
|
||||
Return a string or a list of strings without any punctuation.
|
||||
"""
|
||||
punctuation = "!,.;:?、!,。;:?《》 "
|
||||
if isinstance(text, str):
|
||||
text = re.sub(r"[{}]+".format(punctuation), "", text).strip()
|
||||
return text
|
||||
elif isinstance(text, list):
|
||||
result_text = []
|
||||
for t in text:
|
||||
t = re.sub(r"[{}]+".format(punctuation), "", t).strip()
|
||||
result_text.append(t)
|
||||
return result_text
|
||||
else:
|
||||
raise Exception(f"Not support type {type(text)}")
|
||||
|
||||
|
||||
def to_simple(text: str or List[str]):
|
||||
"""Convert traditional Chinese to simplified Chinese.
|
||||
Args:
|
||||
text: It can be a string or a list of strings.
|
||||
Returns:
|
||||
Return a string or a list of strings converted to simplified Chinese.
|
||||
"""
|
||||
if isinstance(text, str):
|
||||
text = convert(text, "zh-cn")
|
||||
return text
|
||||
elif isinstance(text, list):
|
||||
result_text = []
|
||||
for t in text:
|
||||
t = convert(t, "zh-cn")
|
||||
result_text.append(t)
|
||||
return result_text
|
||||
else:
|
||||
raise Exception(f"Not support type{type(text)}")
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=-1,
|
||||
help="It specifies the checkpoint to use for decoding."
|
||||
"Note: Epoch counts from 0.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--avg",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of checkpoints to average. Automatically select "
|
||||
"consecutive checkpoints before the checkpoint specified by "
|
||||
"'--epoch'. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--method",
|
||||
type=str,
|
||||
default="beam-search",
|
||||
help="""Decoding method.
|
||||
Supported values are:
|
||||
- beam-search
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam-size",
|
||||
type=int,
|
||||
default=1,
|
||||
help="beam size for beam search decoding",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="whisper/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--model-name",
|
||||
type=str,
|
||||
default="large-v2",
|
||||
choices=["large-v2", "large-v3", "medium", "small", "base", "tiny"],
|
||||
help="""The model name to use.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--remove-whisper-encoder-input-length-restriction",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="replace whisper encoder forward method to remove input length restriction",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def get_params() -> AttributeDict:
|
||||
params = AttributeDict(
|
||||
{
|
||||
"env_info": get_env_info(),
|
||||
}
|
||||
)
|
||||
return params
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
batch: dict,
|
||||
) -> Dict[str, List[List[int]]]:
|
||||
"""Decode one batch and return the result in a dict. The dict has the
|
||||
following format:
|
||||
|
||||
- key: "beam-search"
|
||||
- value: A list of lists. Each sublist is a list of token IDs.
|
||||
Args:
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
batch:
|
||||
It is returned by :meth:`torch.utils.data.DataLoader.__iter__`.
|
||||
Returns:
|
||||
Return a dict, whose key may be "beam-search".
|
||||
"""
|
||||
dtype = torch.float16
|
||||
device = torch.device("cuda")
|
||||
|
||||
feature = batch["inputs"]
|
||||
assert feature.ndim == 3
|
||||
feature = feature.to(device, dtype=dtype).transpose(1, 2)
|
||||
if not params.remove_whisper_encoder_input_length_restriction:
|
||||
T = 3000
|
||||
if feature.shape[2] < T:
|
||||
feature = torch.cat(
|
||||
[
|
||||
feature,
|
||||
torch.zeros(
|
||||
feature.shape[0], feature.shape[1], T - feature.shape[2]
|
||||
).to(device, dtype=dtype),
|
||||
],
|
||||
2,
|
||||
)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
feature_len = supervisions["num_frames"]
|
||||
feature_len = feature_len.to(device, dtype=dtype)
|
||||
results = model.decode(feature, params.decoding_options)
|
||||
hyps = [result.text for result in results]
|
||||
|
||||
hyps = remove_punctuation(hyps)
|
||||
hyps = to_simple(hyps)
|
||||
hyps = [params.normalizer.normalize(hyp) for hyp in hyps]
|
||||
|
||||
return {"beam-search": hyps}
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
dl:
|
||||
The dataloader.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
Returns:
|
||||
Return a dict, whose key may be "beam-search".
|
||||
"""
|
||||
results = []
|
||||
|
||||
num_cuts = 0
|
||||
|
||||
try:
|
||||
num_batches = len(dl)
|
||||
except TypeError:
|
||||
num_batches = "?"
|
||||
|
||||
results = defaultdict(list)
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
texts = batch["supervisions"]["text"]
|
||||
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
|
||||
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
batch=batch,
|
||||
)
|
||||
|
||||
for lm_scale, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
assert len(hyps) == len(texts)
|
||||
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
|
||||
ref_words = ref_text.split()
|
||||
this_batch.append((cut_id, ref_words, hyp_words))
|
||||
|
||||
results[lm_scale].extend(this_batch)
|
||||
|
||||
num_cuts += len(batch["supervisions"]["text"])
|
||||
|
||||
if batch_idx % 100 == 0:
|
||||
batch_str = f"{batch_idx}/{num_batches}"
|
||||
|
||||
logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
|
||||
return results
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
|
||||
):
|
||||
|
||||
enable_log = True
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = (
|
||||
params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
if enable_log:
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = (
|
||||
params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
# we compute CER for aishell dataset.
|
||||
results_char = []
|
||||
for res in results:
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results_char,
|
||||
enable_log=enable_log,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
if enable_log:
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = params.exp_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tCER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, CER 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()
|
||||
AishellAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
setup_logger(
|
||||
f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}"
|
||||
)
|
||||
|
||||
options = whisper.DecodingOptions(
|
||||
task="transcribe",
|
||||
language="zh",
|
||||
without_timestamps=True,
|
||||
beam_size=params.beam_size,
|
||||
)
|
||||
params.decoding_options = options
|
||||
params.cleaner = BasicTextNormalizer()
|
||||
params.normalizer = Normalizer()
|
||||
|
||||
logging.info("Decoding started")
|
||||
logging.info(params)
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
if params.remove_whisper_encoder_input_length_restriction:
|
||||
replace_whisper_encoder_forward()
|
||||
model = whisper.load_model(params.model_name, "cpu")
|
||||
if params.epoch > 0:
|
||||
if params.avg > 1:
|
||||
start = params.epoch - params.avg
|
||||
assert start >= 1, start
|
||||
checkpoint = torch.load(
|
||||
f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
|
||||
)
|
||||
if "model" not in checkpoint:
|
||||
# deepspeed converted checkpoint only contains model state_dict
|
||||
filenames = [
|
||||
f"{params.exp_dir}/epoch-{epoch}.pt"
|
||||
for epoch in range(start, params.epoch + 1)
|
||||
]
|
||||
model.load_state_dict(average_checkpoints(filenames))
|
||||
else:
|
||||
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,
|
||||
)
|
||||
)
|
||||
# save checkpoints
|
||||
filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
|
||||
torch.save(model.state_dict(), filename)
|
||||
else:
|
||||
checkpoint = torch.load(
|
||||
f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
|
||||
)
|
||||
if "model" not in checkpoint:
|
||||
model.load_state_dict(checkpoint, strict=True)
|
||||
else:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
# we need cut ids to display recognition results.
|
||||
args.return_cuts = True
|
||||
aishell = AishellAsrDataModule(args)
|
||||
valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())
|
||||
test_dl = aishell.test_dataloaders(aishell.test_cuts())
|
||||
test_sets = ["valid", "test"]
|
||||
test_dls = [valid_dl, test_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dls):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
)
|
||||
|
||||
save_results(params=params, test_set_name=test_set, results_dict=results_dict)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
38
egs/aishell/ASR/whisper/ds_config_zero1.json
Normal file
38
egs/aishell/ASR/whisper/ds_config_zero1.json
Normal file
@ -0,0 +1,38 @@
|
||||
{
|
||||
"fp16": {
|
||||
"enabled": true,
|
||||
"loss_scale": 0,
|
||||
"loss_scale_window": 100,
|
||||
"initial_scale_power": 16,
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 0.01
|
||||
},
|
||||
"zero_optimization": {
|
||||
"stage": 1,
|
||||
"allgather_partitions": true,
|
||||
"allgather_bucket_size": 2e8,
|
||||
"overlap_comm": true,
|
||||
"reduce_scatter": true,
|
||||
"reduce_bucket_size": 2e8,
|
||||
"contiguous_gradients": true
|
||||
},
|
||||
"optimizer": {
|
||||
"type": "Adam",
|
||||
"params": {
|
||||
"lr": 1e-5
|
||||
}
|
||||
},
|
||||
"scheduler": {
|
||||
"type": "WarmupLR",
|
||||
"params": {
|
||||
"warmup_min_lr": 0,
|
||||
"warmup_max_lr": 1e-5,
|
||||
"warmup_num_steps": 100
|
||||
}
|
||||
},
|
||||
"gradient_accumulation_steps": 1,
|
||||
"gradient_clipping": 5,
|
||||
"steps_per_print": 50,
|
||||
"train_micro_batch_size_per_gpu": 1,
|
||||
"wall_clock_breakdown": false
|
||||
}
|
1
egs/aishell/ASR/whisper/label_smoothing.py
Symbolic link
1
egs/aishell/ASR/whisper/label_smoothing.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/conformer_ctc/label_smoothing.py
|
1
egs/aishell/ASR/whisper/optim.py
Symbolic link
1
egs/aishell/ASR/whisper/optim.py
Symbolic link
@ -0,0 +1 @@
|
||||
../../../librispeech/ASR/zipformer/optim.py
|
10
egs/aishell/ASR/whisper/requirements.txt
Executable file
10
egs/aishell/ASR/whisper/requirements.txt
Executable file
@ -0,0 +1,10 @@
|
||||
k2
|
||||
kaldialign
|
||||
git+https://github.com/lhotse-speech/lhotse
|
||||
sentencepiece
|
||||
tensorboard
|
||||
librosa
|
||||
git+https://github.com/yuekaizhang/whisper.git
|
||||
zhconv
|
||||
WeTextProcessing
|
||||
deepspeed
|
927
egs/aishell/ASR/whisper/train.py
Executable file
927
egs/aishell/ASR/whisper/train.py
Executable file
@ -0,0 +1,927 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2023 Xiaomi Corp. (authors: Xiaoyu Yang)
|
||||
# 2024 Yuekai Zhang
|
||||
#
|
||||
# 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:
|
||||
|
||||
#fine-tuning with deepspeed zero stage 1
|
||||
torchrun --nproc_per_node 8 ./whisper/train.py \
|
||||
--max-duration 200 \
|
||||
--exp-dir whisper/exp_large_v2 \
|
||||
--model-name large-v2 \
|
||||
--manifest-dir data/fbank_whisper \
|
||||
--deepspeed \
|
||||
--deepspeed_config ./whisper/ds_config_zero1.json
|
||||
|
||||
# fine-tuning with ddp
|
||||
torchrun --nproc_per_node 8 ./whisper/train.py \
|
||||
--max-duration 200 \
|
||||
--exp-dir whisper/exp_medium \
|
||||
--manifest-dir data/fbank_whisper \
|
||||
--base-lr 1e-5 \
|
||||
--model-name medium
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import copy
|
||||
import logging
|
||||
import random
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from shutil import copyfile
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import deepspeed
|
||||
import k2
|
||||
import optim
|
||||
import torch
|
||||
import torch.multiprocessing as mp
|
||||
import torch.nn as nn
|
||||
import whisper
|
||||
from asr_datamodule import AishellAsrDataModule
|
||||
from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
|
||||
from label_smoothing import LabelSmoothingLoss
|
||||
from lhotse import CutSet, load_manifest
|
||||
from lhotse.cut import Cut
|
||||
from lhotse.dataset.sampling.base import CutSampler
|
||||
from lhotse.utils import fix_random_seed
|
||||
from optim import Eden, ScaledAdam
|
||||
from torch import Tensor
|
||||
from torch.cuda.amp import GradScaler
|
||||
from torch.nn.functional import pad as pad_tensor
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
|
||||
|
||||
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 update_averaged_model
|
||||
from icefall.dist import cleanup_dist, get_rank, get_world_size, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.hooks import register_inf_check_hooks
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
MetricsTracker,
|
||||
filter_uneven_sized_batch,
|
||||
setup_logger,
|
||||
str2bool,
|
||||
)
|
||||
|
||||
LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
|
||||
|
||||
|
||||
def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
|
||||
if isinstance(model, DDP):
|
||||
# get underlying nn.Module
|
||||
model = model.module
|
||||
for module in model.modules():
|
||||
if hasattr(module, "batch_count"):
|
||||
module.batch_count = batch_count
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--tensorboard",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Should various information be logged in tensorboard.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-epochs",
|
||||
type=int,
|
||||
default=10,
|
||||
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="whisper/exp",
|
||||
help="""The experiment dir.
|
||||
It specifies the directory where all training related
|
||||
files, e.g., checkpoints, log, etc, are saved
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--model-name",
|
||||
type=str,
|
||||
default="large-v2",
|
||||
choices=["large-v2", "large-v3", "medium", "small", "base", "tiny"],
|
||||
help="""The model name to use.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--base-lr", type=float, default=1e-5, help="The base learning rate."
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lr-batches",
|
||||
type=float,
|
||||
default=5000,
|
||||
help="""Number of steps that affects how rapidly the learning rate
|
||||
decreases. We suggest not to change this.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lr-epochs",
|
||||
type=float,
|
||||
default=6,
|
||||
help="""Number of epochs that affects how rapidly the learning rate decreases.
|
||||
""",
|
||||
)
|
||||
|
||||
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(
|
||||
"--inf-check",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Add hooks to check for infinite module outputs and gradients.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--keep-last-k",
|
||||
type=int,
|
||||
default=30,
|
||||
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=200,
|
||||
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=True,
|
||||
help="Whether to use half precision training.",
|
||||
)
|
||||
|
||||
parser = deepspeed.add_config_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`:
|
||||
|
||||
- frame_shift_ms: The frame shift in milliseconds.
|
||||
- allowed_excess_duration_ratio: The allowed excess duration ratio.
|
||||
- best_train_loss: The best training loss so far.
|
||||
- best_valid_loss: The best validation loss so far.
|
||||
- best_train_epoch: The epoch where the best training loss is achieved.
|
||||
- best_valid_epoch: The epoch where the best validation loss is achieved.
|
||||
- batch_idx_train: The batch index of the current batch.
|
||||
- log_interval: Log training stats every `log_interval` batches.
|
||||
- reset_interval: Reset the stats every `reset_interval` batches.
|
||||
- valid_interval: Run validation every `valid_interval` batches.
|
||||
- env_info: The environment information.
|
||||
"""
|
||||
params = AttributeDict(
|
||||
{
|
||||
"frame_shift_ms": 10.0,
|
||||
"subsampling_factor": 2,
|
||||
"allowed_excess_duration_ratio": 0.1,
|
||||
"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": 5000,
|
||||
"env_info": get_env_info(),
|
||||
}
|
||||
)
|
||||
|
||||
return params
|
||||
|
||||
|
||||
def load_checkpoint_if_available(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
model_avg: nn.Module = None,
|
||||
optimizer: Optional[torch.optim.Optimizer] = None,
|
||||
scheduler: Optional[LRSchedulerType] = 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.
|
||||
scheduler:
|
||||
The scheduler 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,
|
||||
scheduler=scheduler,
|
||||
)
|
||||
|
||||
keys = [
|
||||
"best_train_epoch",
|
||||
"best_valid_epoch",
|
||||
"batch_idx_train",
|
||||
"best_train_loss",
|
||||
"best_valid_loss",
|
||||
]
|
||||
for k in keys:
|
||||
params[k] = saved_params[k]
|
||||
|
||||
if params.start_batch > 0:
|
||||
if "cur_epoch" in saved_params:
|
||||
params["start_epoch"] = saved_params["cur_epoch"]
|
||||
|
||||
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,
|
||||
scheduler: Optional[LRSchedulerType] = None,
|
||||
sampler: Optional[CutSampler] = None,
|
||||
scaler: Optional[GradScaler] = None,
|
||||
rank: int = 0,
|
||||
) -> None:
|
||||
"""Save model, optimizer, scheduler and training stats to file.
|
||||
|
||||
Args:
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The training model.
|
||||
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,
|
||||
scheduler=scheduler,
|
||||
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,
|
||||
tokenizer: whisper.tokenizer.Tokenizer,
|
||||
model: Union[nn.Module, DDP],
|
||||
batch: dict,
|
||||
is_training: bool,
|
||||
) -> Tuple[Tensor, MetricsTracker]:
|
||||
"""
|
||||
Compute the loss for the given batch.
|
||||
Args:
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
tokenizer:
|
||||
The tokenizer used to encode the text.
|
||||
model:
|
||||
The model for training.
|
||||
batch:
|
||||
A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
|
||||
for the content in it.
|
||||
is_training:
|
||||
Whether it is training.
|
||||
Returns:
|
||||
Return a tuple of two elements. The first element is the loss tensor.
|
||||
"""
|
||||
# For the uneven-sized batch, the total duration after padding would possibly
|
||||
# cause OOM. Hence, for each batch, which is sorted descendingly by length,
|
||||
# we simply drop the last few shortest samples, so that the retained total frames
|
||||
# (after padding) would not exceed `allowed_max_frames`:
|
||||
# `allowed_max_frames = int(max_frames * (1.0 + allowed_excess_duration_ratio))`,
|
||||
# where `max_frames = max_duration * 1000 // frame_shift_ms`.
|
||||
# We set allowed_excess_duration_ratio=0.1.
|
||||
if isinstance(model, DDP):
|
||||
# get underlying nn.Module
|
||||
model = model.module
|
||||
|
||||
def _batch_tensors(tensors: List[Tensor], pad_value: Any) -> Tensor:
|
||||
padding_size = max(tensor.shape[0] for tensor in tensors)
|
||||
dims = len(tensors[0].shape)
|
||||
padded_tensors = []
|
||||
for tensor in tensors:
|
||||
padding = [0] * 2 * dims
|
||||
padding[-1] = padding_size - tensor.shape[0]
|
||||
padded_tensors.append(pad_tensor(tensor, padding, "constant", pad_value))
|
||||
return torch.stack([tensor for tensor in padded_tensors], dim=0)
|
||||
|
||||
max_frames = params.max_duration * 1000 // params.frame_shift_ms
|
||||
allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
|
||||
batch = filter_uneven_sized_batch(batch, allowed_max_frames)
|
||||
|
||||
device = model.device if isinstance(model, DDP) else next(model.parameters()).device
|
||||
feature = batch["inputs"]
|
||||
|
||||
assert feature.ndim == 3
|
||||
feature = feature.to(device)
|
||||
feature = feature.transpose(1, 2) # (N, C, T)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
feature_lens = supervisions["num_frames"].to(device)
|
||||
|
||||
batch_idx_train = params.batch_idx_train
|
||||
|
||||
texts = batch["supervisions"]["text"]
|
||||
# remove spaces in texts
|
||||
texts = [text.replace(" ", "") for text in texts]
|
||||
|
||||
text_tokens_list = [
|
||||
list(tokenizer.sot_sequence_including_notimestamps)
|
||||
+ tokenizer.encode(text)
|
||||
+ [tokenizer.eot]
|
||||
for text in texts
|
||||
]
|
||||
# convert it to torch tensor
|
||||
text_tokens_list = [
|
||||
torch.LongTensor(text_tokens) for text_tokens in text_tokens_list
|
||||
]
|
||||
|
||||
# 50256 is the index of <pad> for all whisper models
|
||||
prev_outputs_tokens = _batch_tensors(
|
||||
[tokens[:-1] for tokens in text_tokens_list], pad_value=50256
|
||||
)
|
||||
target_tokens = _batch_tensors(
|
||||
[tokens[1:] for tokens in text_tokens_list], pad_value=50256
|
||||
)
|
||||
target_lengths = torch.LongTensor(
|
||||
[tokens.shape[0] - 1 for tokens in text_tokens_list]
|
||||
)
|
||||
|
||||
decoder_criterion = LabelSmoothingLoss(
|
||||
ignore_index=50256, label_smoothing=0.1, reduction="sum"
|
||||
)
|
||||
|
||||
# ignore the first 3 tokens, which are always <|lang_id|>, <|transcibe|>, <|notimestampes|>
|
||||
ignore_prefix_size = 3
|
||||
with torch.set_grad_enabled(is_training):
|
||||
encoder_out = model.encoder(feature)
|
||||
text_logits = model.decoder(prev_outputs_tokens.to(device), encoder_out)
|
||||
text_logits = text_logits[:, ignore_prefix_size:, :]
|
||||
target_tokens = target_tokens[:, ignore_prefix_size:]
|
||||
loss = decoder_criterion(text_logits, target_tokens.to(device))
|
||||
|
||||
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()
|
||||
|
||||
return loss, info
|
||||
|
||||
|
||||
def compute_validation_loss(
|
||||
params: AttributeDict,
|
||||
tokenizer: whisper.tokenizer.Tokenizer,
|
||||
model: Union[nn.Module, DDP],
|
||||
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):
|
||||
with torch.cuda.amp.autocast(enabled=params.use_fp16):
|
||||
loss, loss_info = compute_loss(
|
||||
params=params,
|
||||
tokenizer=tokenizer,
|
||||
model=model,
|
||||
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,
|
||||
tokenizer: whisper.tokenizer.Tokenizer,
|
||||
model: Union[nn.Module, DDP],
|
||||
optimizer: torch.optim.Optimizer,
|
||||
scheduler: LRSchedulerType,
|
||||
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.
|
||||
scheduler:
|
||||
The learning rate scheduler, we call step() every step.
|
||||
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()
|
||||
|
||||
for batch_idx, batch in enumerate(train_dl):
|
||||
params.batch_idx_train += 1
|
||||
batch_size = len(batch["supervisions"]["text"])
|
||||
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
|
||||
logging.info("Computing validation loss")
|
||||
valid_info = compute_validation_loss(
|
||||
params=params,
|
||||
tokenizer=tokenizer,
|
||||
model=model,
|
||||
valid_dl=valid_dl,
|
||||
world_size=world_size,
|
||||
)
|
||||
model.train()
|
||||
logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
|
||||
logging.info(
|
||||
f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
|
||||
)
|
||||
if tb_writer is not None:
|
||||
valid_info.write_summary(
|
||||
tb_writer, "train/valid_", params.batch_idx_train
|
||||
)
|
||||
|
||||
try:
|
||||
with torch.cuda.amp.autocast(enabled=params.use_fp16):
|
||||
loss, loss_info = compute_loss(
|
||||
params=params,
|
||||
tokenizer=tokenizer,
|
||||
model=model,
|
||||
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.
|
||||
if params.deepspeed:
|
||||
# deepspeed's backward() is different from torch's backward()
|
||||
# in that it does not accept a loss tensor as input.
|
||||
# It computes the loss internally.
|
||||
model.backward(loss)
|
||||
model.step()
|
||||
else:
|
||||
scaler.scale(loss).backward()
|
||||
set_batch_count(model, params.batch_idx_train)
|
||||
scheduler.step_batch(params.batch_idx_train)
|
||||
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
except: # noqa
|
||||
display_and_save_batch(batch, params=params)
|
||||
raise
|
||||
|
||||
if params.print_diagnostics and batch_idx == 5:
|
||||
return
|
||||
|
||||
if (
|
||||
rank == 0
|
||||
and params.batch_idx_train > 0
|
||||
and params.batch_idx_train % params.average_period == 0
|
||||
and not params.deepspeed
|
||||
):
|
||||
update_averaged_model(
|
||||
params=params,
|
||||
model_cur=model,
|
||||
model_avg=model_avg,
|
||||
)
|
||||
|
||||
if batch_idx % 100 == 0 and params.use_fp16 and not params.deepspeed:
|
||||
# If the grad scale was less than 1, try increasing it. The _growth_interval
|
||||
# of the grad scaler is configurable, but we can't configure it to have different
|
||||
# behavior depending on the current grad scale.
|
||||
cur_grad_scale = scaler._scale.item()
|
||||
if cur_grad_scale < 1.0 or (cur_grad_scale < 8.0 and batch_idx % 400 == 0):
|
||||
scaler.update(cur_grad_scale * 2.0)
|
||||
if cur_grad_scale < 0.01:
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
if batch_idx % params.log_interval == 0:
|
||||
try:
|
||||
cur_lr = scheduler.get_last_lr()[0]
|
||||
except: # noqa
|
||||
cur_lr = 0.0
|
||||
cur_grad_scale = (
|
||||
scaler._scale.item()
|
||||
if (params.use_fp16 and not params.deepspeed)
|
||||
else 1.0
|
||||
)
|
||||
|
||||
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}, "
|
||||
+ (
|
||||
f"grad_scale: {scaler._scale.item()}"
|
||||
if (params.use_fp16 and not params.deepspeed)
|
||||
else ""
|
||||
)
|
||||
)
|
||||
|
||||
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 params.use_fp16:
|
||||
tb_writer.add_scalar(
|
||||
"train/grad_scale",
|
||||
cur_grad_scale,
|
||||
params.batch_idx_train,
|
||||
)
|
||||
|
||||
loss_value = tot_loss["loss"] / tot_loss["frames"]
|
||||
params.train_loss = loss_value
|
||||
if params.train_loss < params.best_train_loss:
|
||||
params.best_train_epoch = params.cur_epoch
|
||||
params.best_train_loss = params.train_loss
|
||||
|
||||
|
||||
def run(rank, world_size, args):
|
||||
"""
|
||||
Args:
|
||||
rank:
|
||||
It is a value between 0 and `world_size-1`, which is
|
||||
passed automatically by `mp.spawn()` in :func:`main`.
|
||||
The node with rank 0 is responsible for saving checkpoint.
|
||||
world_size:
|
||||
Number of GPUs for DDP training.
|
||||
args:
|
||||
The return value of get_parser().parse_args()
|
||||
"""
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
fix_random_seed(params.seed)
|
||||
|
||||
setup_logger(f"{params.exp_dir}/log/log-train")
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
|
||||
replace_whisper_encoder_forward()
|
||||
model = whisper.load_model(params.model_name, "cpu")
|
||||
del model.alignment_heads
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
tokenizer = whisper.tokenizer.get_tokenizer(
|
||||
model.is_multilingual,
|
||||
num_languages=model.num_languages,
|
||||
language="zh",
|
||||
task="transcribe",
|
||||
)
|
||||
|
||||
model_avg: Optional[nn.Module] = None
|
||||
if rank == 0:
|
||||
# model_avg is only used with rank 0
|
||||
model_avg = copy.deepcopy(model).to(torch.float64)
|
||||
|
||||
assert params.start_epoch > 0, params.start_epoch
|
||||
checkpoints = load_checkpoint_if_available(
|
||||
params=params, model=model, model_avg=model_avg
|
||||
)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", rank)
|
||||
else:
|
||||
device = torch.device("cpu")
|
||||
logging.info(f"Device: {device}")
|
||||
model.to(device)
|
||||
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr=params.base_lr)
|
||||
scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
|
||||
|
||||
if checkpoints and "optimizer" in checkpoints:
|
||||
logging.info("Loading optimizer state dict")
|
||||
optimizer.load_state_dict(checkpoints["optimizer"])
|
||||
|
||||
if (
|
||||
checkpoints
|
||||
and "scheduler" in checkpoints
|
||||
and checkpoints["scheduler"] is not None
|
||||
):
|
||||
logging.info("Loading scheduler state dict")
|
||||
scheduler.load_state_dict(checkpoints["scheduler"])
|
||||
|
||||
if world_size > 1:
|
||||
if params.deepspeed:
|
||||
logging.info("Using DeepSpeed")
|
||||
model, optimizer, _, scheduler = deepspeed.initialize(
|
||||
args=params, model=model, model_parameters=model.parameters()
|
||||
)
|
||||
else:
|
||||
logging.info("Using DDP")
|
||||
setup_dist(use_ddp_launch=True)
|
||||
model = DDP(model, device_ids=[rank], find_unused_parameters=True)
|
||||
|
||||
if params.print_diagnostics:
|
||||
opts = diagnostics.TensorDiagnosticOptions(
|
||||
512
|
||||
) # allow 4 megabytes per sub-module
|
||||
diagnostic = diagnostics.attach_diagnostics(model, opts)
|
||||
|
||||
if params.inf_check:
|
||||
register_inf_check_hooks(model)
|
||||
|
||||
aishell = AishellAsrDataModule(args)
|
||||
|
||||
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 = aishell.train_dataloaders(aishell.train_cuts())
|
||||
valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())
|
||||
|
||||
scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
|
||||
if checkpoints and "grad_scaler" in checkpoints:
|
||||
logging.info("Loading grad scaler state dict")
|
||||
scaler.load_state_dict(checkpoints["grad_scaler"])
|
||||
|
||||
if args.tensorboard and rank == 0:
|
||||
tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
|
||||
else:
|
||||
tb_writer = None
|
||||
|
||||
logging.info(f"start training from epoch {params.start_epoch}")
|
||||
for epoch in range(params.start_epoch, params.num_epochs + 1):
|
||||
if not params.deepspeed:
|
||||
scheduler.step_epoch(epoch - 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,
|
||||
tokenizer=tokenizer,
|
||||
model=model,
|
||||
model_avg=model_avg,
|
||||
optimizer=optimizer,
|
||||
scheduler=scheduler,
|
||||
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
|
||||
|
||||
if params.deepspeed:
|
||||
model.save_checkpoint(
|
||||
save_dir=params.exp_dir,
|
||||
tag=f"epoch-{params.cur_epoch}",
|
||||
client_state={},
|
||||
)
|
||||
if rank == 0:
|
||||
convert_zero_checkpoint_to_fp32_state_dict(
|
||||
params.exp_dir,
|
||||
f"{params.exp_dir}/epoch-{params.cur_epoch}.pt",
|
||||
tag=f"epoch-{params.cur_epoch}",
|
||||
)
|
||||
else:
|
||||
save_checkpoint(
|
||||
params=params,
|
||||
model=model,
|
||||
model_avg=model_avg,
|
||||
optimizer=optimizer,
|
||||
scheduler=scheduler,
|
||||
sampler=train_dl.sampler,
|
||||
scaler=scaler,
|
||||
rank=rank,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
if world_size > 1 and not params.deepspeed:
|
||||
torch.distributed.barrier()
|
||||
cleanup_dist()
|
||||
|
||||
|
||||
def display_and_save_batch(
|
||||
batch: dict,
|
||||
params: AttributeDict,
|
||||
) -> None:
|
||||
"""Display the batch statistics and save the batch into disk.
|
||||
|
||||
Args:
|
||||
batch:
|
||||
A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
|
||||
for the content in it.
|
||||
params:
|
||||
Parameters for training. See :func:`get_params`.
|
||||
"""
|
||||
from lhotse.utils import uuid4
|
||||
|
||||
filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
|
||||
logging.info(f"Saving batch to {filename}")
|
||||
torch.save(batch, filename)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
features = batch["inputs"]
|
||||
|
||||
logging.info(f"features shape: {features.shape}")
|
||||
|
||||
|
||||
def main():
|
||||
parser = get_parser()
|
||||
AishellAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
world_size = get_world_size()
|
||||
rank = get_rank()
|
||||
|
||||
torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
run(rank=rank, world_size=world_size, args=args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
@ -0,0 +1,29 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import whisper
|
||||
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
"""
|
||||
x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
|
||||
the mel spectrogram of the audio
|
||||
"""
|
||||
x = F.gelu(self.conv1(x))
|
||||
x = F.gelu(self.conv2(x))
|
||||
x = x.permute(0, 2, 1)
|
||||
|
||||
x = (x + self.positional_embedding[: x.shape[1], :]).to(x.dtype)
|
||||
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
|
||||
x = self.ln_post(x)
|
||||
return x
|
||||
|
||||
|
||||
def replace_whisper_encoder_forward():
|
||||
"""
|
||||
This function monkey patches the forward method of the whisper encoder.
|
||||
To be called before the model is loaded, it changes whisper to process audio with any length < 30s.
|
||||
"""
|
||||
whisper.model.AudioEncoder.forward = forward
|
@ -560,7 +560,7 @@ def save_results(
|
||||
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
@ -570,7 +570,11 @@ def save_results(
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results,
|
||||
enable_log=True,
|
||||
compute_CER=True,
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
|
840
egs/aishell/ASR/zipformer/decode_bbpe.py
Executable file
840
egs/aishell/ASR/zipformer/decode_bbpe.py
Executable file
@ -0,0 +1,840 @@
|
||||
#!/usr/bin/env python3
|
||||
#
|
||||
# Copyright 2021-2024 Xiaomi Corporation (Author: Fangjun Kuang,
|
||||
# Zengwei Yao,
|
||||
# Mingshuang Luo,
|
||||
# Zengrui Jin,)
|
||||
#
|
||||
# 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
|
||||
./zipformer/decode_bbpe.py \
|
||||
--epoch 35 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp_bbpe \
|
||||
--lang-dir data/lang_bbpe_500 \
|
||||
--bpe-model data/lang_bbpe_500/bbpe.model \
|
||||
--max-duration 600 \
|
||||
--decoding-method greedy_search
|
||||
|
||||
(2) modified beam search
|
||||
./zipformer/decode_bbpe.py \
|
||||
--epoch 35 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp_bbpe \
|
||||
--lang-dir data/lang_bbpe_500 \
|
||||
--bpe-model data/lang_bbpe_500/bbpe.model \
|
||||
--max-duration 600 \
|
||||
--decoding-method modified_beam_search \
|
||||
--beam-size 4
|
||||
|
||||
(3) fast beam search (trivial_graph)
|
||||
./zipformer/decode_bbpe.py \
|
||||
--epoch 35 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp_bbpe \
|
||||
--lang-dir data/lang_bbpe_500 \
|
||||
--bpe-model data/lang_bbpe_500/bbpe.model \
|
||||
--max-duration 600 \
|
||||
--decoding-method fast_beam_search \
|
||||
--beam 20.0 \
|
||||
--max-contexts 8 \
|
||||
--max-states 64
|
||||
|
||||
(4) fast beam search (LG)
|
||||
./zipformer/decode_bbpe.py \
|
||||
--epoch 30 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp_bbpe \
|
||||
--lang-dir data/lang_bbpe_500 \
|
||||
--bpe-model data/lang_bbpe_500/bbpe.model \
|
||||
--max-duration 600 \
|
||||
--decoding-method fast_beam_search_LG \
|
||||
--beam 20.0 \
|
||||
--max-contexts 8 \
|
||||
--max-states 64
|
||||
|
||||
(5) fast beam search (nbest oracle WER)
|
||||
./zipformer/decode_bbpe.py \
|
||||
--epoch 35 \
|
||||
--avg 15 \
|
||||
--exp-dir ./zipformer/exp_bbpe \
|
||||
--lang-dir data/lang_bbpe_500 \
|
||||
--bpe-model data/lang_bbpe_500/bbpe.model \
|
||||
--max-duration 600 \
|
||||
--decoding-method fast_beam_search_nbest_oracle \
|
||||
--beam 20.0 \
|
||||
--max-contexts 8 \
|
||||
--max-states 64 \
|
||||
--num-paths 200 \
|
||||
--nbest-scale 0.5
|
||||
"""
|
||||
|
||||
|
||||
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 AishellAsrDataModule
|
||||
from beam_search import (
|
||||
beam_search,
|
||||
fast_beam_search_nbest_oracle,
|
||||
fast_beam_search_one_best,
|
||||
greedy_search,
|
||||
greedy_search_batch,
|
||||
modified_beam_search,
|
||||
)
|
||||
from lhotse.cut import Cut
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
|
||||
from icefall import byte_encode, smart_byte_decode, tokenize_by_CJK_char
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
average_checkpoints_with_averaged_model,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
make_pad_mask,
|
||||
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=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. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="zipformer_bbpe/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
default="data/lang_bbpe_500/bbpe.model",
|
||||
help="Path to the byte BPE model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
type=Path,
|
||||
default="data/lang_bbpe_500/",
|
||||
help="The lang dir containing word table and LG graph",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Possible values are:
|
||||
- greedy_search
|
||||
- modified_beam_search
|
||||
- fast_beam_search
|
||||
- fast_beam_search_LG
|
||||
- fast_beam_search_nbest_oracle
|
||||
If you use fast_beam_search_LG, you have to specify
|
||||
`--lang-dir`, which should contain `LG.pt`.
|
||||
""",
|
||||
)
|
||||
|
||||
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=20.0,
|
||||
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,
|
||||
fast_beam_search, fast_beam_search_LG,
|
||||
and fast_beam_search_nbest_oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ngram-lm-scale",
|
||||
type=float,
|
||||
default=0.01,
|
||||
help="""
|
||||
Used only when --decoding_method is fast_beam_search_LG.
|
||||
It specifies the scale for n-gram LM scores.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ilme-scale",
|
||||
type=float,
|
||||
default=0.2,
|
||||
help="""
|
||||
Used only when --decoding_method is fast_beam_search_LG.
|
||||
It specifies the scale for the internal language model estimation.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=8,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search, fast_beam_search, fast_beam_search_LG,
|
||||
and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=64,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search, fast_beam_search, fast_beam_search_LG,
|
||||
and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
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""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=200,
|
||||
help="""Number of paths for nbest decoding.
|
||||
Used only when the decoding method is fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=0.5,
|
||||
help="""Scale applied to lattice scores when computing nbest paths.
|
||||
Used only when the decoding method is and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--blank-penalty",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="""
|
||||
The penalty applied on blank symbol during decoding.
|
||||
Note: It is a positive value that would be applied to logits like
|
||||
this `logits[:, 0] -= blank_penalty` (suppose logits.shape is
|
||||
[batch_size, vocab] and blank id is 0).
|
||||
""",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
lexicon: Lexicon,
|
||||
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.
|
||||
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 LG, Used
|
||||
only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
|
||||
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)
|
||||
|
||||
if params.causal:
|
||||
# this seems to cause insertions at the end of the utterance if used with zipformer.
|
||||
pad_len = 30
|
||||
feature_lens += pad_len
|
||||
feature = torch.nn.functional.pad(
|
||||
feature,
|
||||
pad=(0, 0, 0, pad_len),
|
||||
value=LOG_EPS,
|
||||
)
|
||||
|
||||
x, x_lens = model.encoder_embed(feature, feature_lens)
|
||||
|
||||
src_key_padding_mask = make_pad_mask(x_lens)
|
||||
x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C)
|
||||
|
||||
encoder_out, encoder_out_lens = model.encoder(x, x_lens, src_key_padding_mask)
|
||||
encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
|
||||
|
||||
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,
|
||||
blank_penalty=params.blank_penalty,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(smart_byte_decode(hyp).split())
|
||||
elif params.decoding_method == "fast_beam_search_LG":
|
||||
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,
|
||||
blank_penalty=params.blank_penalty,
|
||||
ilme_scale=params.ilme_scale,
|
||||
)
|
||||
for hyp in hyp_tokens:
|
||||
hyps.append([lexicon.word_table[i] for i in hyp])
|
||||
elif params.decoding_method == "fast_beam_search_nbest_oracle":
|
||||
ref_texts = []
|
||||
for tx in supervisions["text"]:
|
||||
ref_texts.append(byte_encode(tokenize_by_CJK_char(tx)))
|
||||
|
||||
hyp_tokens = fast_beam_search_nbest_oracle(
|
||||
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,
|
||||
num_paths=params.num_paths,
|
||||
ref_texts=sp.encode(ref_texts),
|
||||
nbest_scale=params.nbest_scale,
|
||||
blank_penalty=params.blank_penalty,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(smart_byte_decode(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,
|
||||
blank_penalty=params.blank_penalty,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(smart_byte_decode(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,
|
||||
blank_penalty=params.blank_penalty,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(smart_byte_decode(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,
|
||||
blank_penalty=params.blank_penalty,
|
||||
)
|
||||
elif params.decoding_method == "beam_search":
|
||||
hyp = beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
beam=params.beam_size,
|
||||
blank_penalty=params.blank_penalty,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
hyps.append(smart_byte_decode(sp.decode(hyp)).split())
|
||||
|
||||
key = f"blank_penalty_{params.blank_penalty}"
|
||||
if params.decoding_method == "greedy_search":
|
||||
return {"greedy_search_" + key: hyps}
|
||||
elif "fast_beam_search" in params.decoding_method:
|
||||
key += f"_beam_{params.beam}_"
|
||||
key += f"max_contexts_{params.max_contexts}_"
|
||||
key += f"max_states_{params.max_states}"
|
||||
if "nbest" in params.decoding_method:
|
||||
key += f"_num_paths_{params.num_paths}_"
|
||||
key += f"nbest_scale_{params.nbest_scale}"
|
||||
if "LG" in params.decoding_method:
|
||||
key += f"_ilme_scale_{params.ilme_scale}"
|
||||
key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
|
||||
|
||||
return {key: hyps}
|
||||
else:
|
||||
return {f"beam_size_{params.beam_size}_" + key: hyps}
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
lexicon: Lexicon,
|
||||
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.
|
||||
lexicon:
|
||||
directory containing the lexicon.
|
||||
sp:
|
||||
SentencePiece model.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or LG, Used
|
||||
only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
|
||||
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 = 20
|
||||
|
||||
results = defaultdict(list)
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
texts = batch["supervisions"]["text"]
|
||||
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
|
||||
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
lexicon=lexicon,
|
||||
decoding_graph=decoding_graph,
|
||||
batch=batch,
|
||||
)
|
||||
|
||||
for name, hyps in hyps_dict.items():
|
||||
this_batch = []
|
||||
assert len(hyps) == len(texts)
|
||||
for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
|
||||
ref_words = "".join(ref_text.split())
|
||||
|
||||
this_batch.append((cut_id, 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"
|
||||
)
|
||||
results = sorted(results)
|
||||
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"
|
||||
)
|
||||
|
||||
results_char = []
|
||||
for res in results:
|
||||
results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
|
||||
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results_char, 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()
|
||||
AishellAsrDataModule.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",
|
||||
"modified_beam_search",
|
||||
"fast_beam_search",
|
||||
"fast_beam_search_LG",
|
||||
"fast_beam_search_nbest_oracle",
|
||||
)
|
||||
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 params.causal:
|
||||
assert (
|
||||
"," not in params.chunk_size
|
||||
), "chunk_size should be one value in decoding."
|
||||
assert (
|
||||
"," not in params.left_context_frames
|
||||
), "left_context_frames should be one value in decoding."
|
||||
params.suffix += f"-chunk-{params.chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context_frames}"
|
||||
|
||||
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}"
|
||||
if "nbest" in params.decoding_method:
|
||||
params.suffix += f"-nbest-scale-{params.nbest_scale}"
|
||||
params.suffix += f"-num-paths-{params.num_paths}"
|
||||
if "LG" in params.decoding_method:
|
||||
params.suffix += f"_ilme_scale_{params.ilme_scale}"
|
||||
params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
|
||||
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}"
|
||||
params.suffix += f"-blank-penalty-{params.blank_penalty}"
|
||||
|
||||
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)
|
||||
|
||||
# <blk> and <unk> are defined in local/train_bbpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_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 "fast_beam_search" in params.decoding_method:
|
||||
if "LG" in params.decoding_method:
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
lg_filename = params.lang_dir / "LG.pt"
|
||||
logging.info(f"Loading {lg_filename}")
|
||||
decoding_graph = k2.Fsa.from_dict(
|
||||
torch.load(lg_filename, map_location=device)
|
||||
)
|
||||
decoding_graph.scores *= params.ngram_lm_scale
|
||||
else:
|
||||
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}")
|
||||
|
||||
# we need cut ids to display recognition results.
|
||||
args.return_cuts = True
|
||||
aishell = AishellAsrDataModule(args)
|
||||
|
||||
def remove_short_utt(c: Cut):
|
||||
T = ((c.num_frames - 7) // 2 + 1) // 2
|
||||
if T <= 0:
|
||||
logging.warning(
|
||||
f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}."
|
||||
)
|
||||
return T > 0
|
||||
|
||||
dev_cuts = aishell.valid_cuts()
|
||||
dev_cuts = dev_cuts.filter(remove_short_utt)
|
||||
dev_dl = aishell.valid_dataloaders(dev_cuts)
|
||||
|
||||
test_cuts = aishell.test_cuts()
|
||||
test_cuts = test_cuts.filter(remove_short_utt)
|
||||
test_dl = aishell.test_dataloaders(test_cuts)
|
||||
|
||||
test_sets = ["dev", "test"]
|
||||
test_dls = [dev_dl, test_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dls):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
lexicon=lexicon,
|
||||
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()
|
279
egs/aishell/ASR/zipformer/jit_pretrained_bbpe.py
Executable file
279
egs/aishell/ASR/zipformer/jit_pretrained_bbpe.py
Executable file
@ -0,0 +1,279 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021-2024 Xiaomi Corporation (Author: Fangjun Kuang,
|
||||
# Zengwei Yao,
|
||||
# Zengrui Jin,)
|
||||
#
|
||||
# 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 loads torchscript models, exported by `torch.jit.script()`
|
||||
and uses them to decode waves.
|
||||
You can use the following command to get the exported models:
|
||||
|
||||
./zipformer/export.py \
|
||||
--exp-dir ./zipformer_bbpe/exp \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
--epoch 30 \
|
||||
--avg 9 \
|
||||
--jit 1
|
||||
|
||||
Usage of this script:
|
||||
|
||||
./zipformer/jit_pretrained.py \
|
||||
--nn-model-filename ./zipformer_bbpe/exp/cpu_jit.pt \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from typing import List
|
||||
|
||||
import kaldifeat
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torchaudio
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
from icefall import smart_byte_decode
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nn-model-filename",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the torchscript model cpu_jit.pt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
required=True,
|
||||
help="""Path to the bbpe.model.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"sound_files",
|
||||
type=str,
|
||||
nargs="+",
|
||||
help="The input sound file(s) to transcribe. "
|
||||
"Supported formats are those supported by torchaudio.load(). "
|
||||
"For example, wav and flac are supported. "
|
||||
"The sample rate has to be 16kHz.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float = 16000
|
||||
) -> List[torch.Tensor]:
|
||||
"""Read a list of sound files into a list 1-D float32 torch tensors.
|
||||
Args:
|
||||
filenames:
|
||||
A list of sound filenames.
|
||||
expected_sample_rate:
|
||||
The expected sample rate of the sound files.
|
||||
Returns:
|
||||
Return a list of 1-D float32 torch tensors.
|
||||
"""
|
||||
ans = []
|
||||
for f in filenames:
|
||||
wave, sample_rate = torchaudio.load(f)
|
||||
assert (
|
||||
sample_rate == expected_sample_rate
|
||||
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0].contiguous())
|
||||
return ans
|
||||
|
||||
|
||||
def greedy_search(
|
||||
model: torch.jit.ScriptModule,
|
||||
encoder_out: torch.Tensor,
|
||||
encoder_out_lens: torch.Tensor,
|
||||
) -> List[List[int]]:
|
||||
"""Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
|
||||
Args:
|
||||
model:
|
||||
The transducer model.
|
||||
encoder_out:
|
||||
A 3-D tensor of shape (N, T, C)
|
||||
encoder_out_lens:
|
||||
A 1-D tensor of shape (N,).
|
||||
Returns:
|
||||
Return the decoded results for each utterance.
|
||||
"""
|
||||
assert encoder_out.ndim == 3
|
||||
assert encoder_out.size(0) >= 1, encoder_out.size(0)
|
||||
|
||||
packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
|
||||
input=encoder_out,
|
||||
lengths=encoder_out_lens.cpu(),
|
||||
batch_first=True,
|
||||
enforce_sorted=False,
|
||||
)
|
||||
|
||||
device = encoder_out.device
|
||||
blank_id = model.decoder.blank_id
|
||||
|
||||
batch_size_list = packed_encoder_out.batch_sizes.tolist()
|
||||
N = encoder_out.size(0)
|
||||
|
||||
assert torch.all(encoder_out_lens > 0), encoder_out_lens
|
||||
assert N == batch_size_list[0], (N, batch_size_list)
|
||||
|
||||
context_size = model.decoder.context_size
|
||||
hyps = [[blank_id] * context_size for _ in range(N)]
|
||||
|
||||
decoder_input = torch.tensor(
|
||||
hyps,
|
||||
device=device,
|
||||
dtype=torch.int64,
|
||||
) # (N, context_size)
|
||||
|
||||
decoder_out = model.decoder(
|
||||
decoder_input,
|
||||
need_pad=torch.tensor([False]),
|
||||
).squeeze(1)
|
||||
|
||||
offset = 0
|
||||
for batch_size in batch_size_list:
|
||||
start = offset
|
||||
end = offset + batch_size
|
||||
current_encoder_out = packed_encoder_out.data[start:end]
|
||||
current_encoder_out = current_encoder_out
|
||||
# current_encoder_out's shape: (batch_size, encoder_out_dim)
|
||||
offset = end
|
||||
|
||||
decoder_out = decoder_out[:batch_size]
|
||||
|
||||
logits = model.joiner(
|
||||
current_encoder_out,
|
||||
decoder_out,
|
||||
)
|
||||
# logits'shape (batch_size, vocab_size)
|
||||
|
||||
assert logits.ndim == 2, logits.shape
|
||||
y = logits.argmax(dim=1).tolist()
|
||||
emitted = False
|
||||
for i, v in enumerate(y):
|
||||
if v != blank_id:
|
||||
hyps[i].append(v)
|
||||
emitted = True
|
||||
if emitted:
|
||||
# update decoder output
|
||||
decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
|
||||
decoder_input = torch.tensor(
|
||||
decoder_input,
|
||||
device=device,
|
||||
dtype=torch.int64,
|
||||
)
|
||||
decoder_out = model.decoder(
|
||||
decoder_input,
|
||||
need_pad=torch.tensor([False]),
|
||||
)
|
||||
decoder_out = decoder_out.squeeze(1)
|
||||
|
||||
sorted_ans = [h[context_size:] for h in hyps]
|
||||
ans = []
|
||||
unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
|
||||
for i in range(N):
|
||||
ans.append(sorted_ans[unsorted_indices[i]])
|
||||
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
logging.info(vars(args))
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
model = torch.jit.load(args.nn_model_filename)
|
||||
|
||||
model.eval()
|
||||
|
||||
model.to(device)
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(args.bpe_model)
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = 16000
|
||||
opts.mel_opts.num_bins = 80
|
||||
opts.mel_opts.high_freq = -400
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {args.sound_files}")
|
||||
waves = read_sound_files(
|
||||
filenames=args.sound_files,
|
||||
)
|
||||
waves = [w.to(device) for w in waves]
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
|
||||
features = pad_sequence(
|
||||
features,
|
||||
batch_first=True,
|
||||
padding_value=math.log(1e-10),
|
||||
)
|
||||
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
encoder_out, encoder_out_lens = model.encoder(
|
||||
features=features,
|
||||
feature_lengths=feature_lengths,
|
||||
)
|
||||
|
||||
hyps = greedy_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
)
|
||||
|
||||
s = "\n"
|
||||
for filename, hyp in zip(args.sound_files, hyps):
|
||||
words = smart_byte_decode(sp.decode(hyp))
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
403
egs/aishell/ASR/zipformer/pretrained_bbpe.py
Executable file
403
egs/aishell/ASR/zipformer/pretrained_bbpe.py
Executable file
@ -0,0 +1,403 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021-2024 Xiaomi Corporation (Author: Fangjun Kuang,
|
||||
# Zengwei Yao,
|
||||
# Zengrui Jin,)
|
||||
#
|
||||
# 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 loads a checkpoint and uses it to decode waves.
|
||||
You can generate the checkpoint with the following command:
|
||||
|
||||
Note: This is a example for librispeech dataset, if you are using different
|
||||
dataset, you should change the argument values according to your dataset.
|
||||
|
||||
- For non-streaming model:
|
||||
|
||||
./zipformer/export.py \
|
||||
--exp-dir ./zipformer/exp_bbpe \
|
||||
--tokens ./data/lang_bbpe_500/tokens.txt \
|
||||
--epoch 30 \
|
||||
--avg 9
|
||||
|
||||
- For streaming model:
|
||||
|
||||
./zipformer/export.py \
|
||||
--exp-dir ./zipformer/exp_bbpe \
|
||||
--causal 1 \
|
||||
--tokens ./data/lang_bbpe_500/tokens.txt \
|
||||
--epoch 30 \
|
||||
--avg 9
|
||||
|
||||
Usage of this script:
|
||||
|
||||
- For non-streaming model:
|
||||
|
||||
(1) greedy search
|
||||
./zipformer/pretrained_bbpe.py \
|
||||
--checkpoint ./zipformer/exp_bbpe/pretrained.pt \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
--method greedy_search \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(2) modified beam search
|
||||
./zipformer/pretrained_bbpe.py \
|
||||
--checkpoint ./zipformer/exp_bbpe/pretrained.pt \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
--method modified_beam_search \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(3) fast beam search
|
||||
./zipformer/pretrained_bbpe.py \
|
||||
--checkpoint ./zipformer/exp_bbpe/pretrained.pt \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
--method fast_beam_search \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
- For streaming model:
|
||||
|
||||
(1) greedy search
|
||||
./zipformer/pretrained_bbpe.py \
|
||||
--checkpoint ./zipformer/exp_bbpe/pretrained.pt \
|
||||
--causal 1 \
|
||||
--chunk-size 16 \
|
||||
--left-context-frames 128 \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
--method greedy_search \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(2) modified beam search
|
||||
./zipformer/pretrained_bbpe.py \
|
||||
--checkpoint ./zipformer/exp_bbpe/pretrained.pt \
|
||||
--causal 1 \
|
||||
--chunk-size 16 \
|
||||
--left-context-frames 128 \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
--method modified_beam_search \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
(3) fast beam search
|
||||
./zipformer/pretrained_bbpe.py \
|
||||
--checkpoint ./zipformer/exp_bbpe/pretrained.pt \
|
||||
--causal 1 \
|
||||
--chunk-size 16 \
|
||||
--left-context-frames 128 \
|
||||
--bpe ./data/lang_bbpe_500/bbpe.model \
|
||||
--method fast_beam_search \
|
||||
/path/to/foo.wav \
|
||||
/path/to/bar.wav
|
||||
|
||||
|
||||
You can also use `./zipformer/exp_bbpe/epoch-xx.pt`.
|
||||
|
||||
Note: ./zipformer/exp_bbpe/pretrained.pt is generated by ./zipformer/export_bbpe.py
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from typing import List
|
||||
|
||||
import k2
|
||||
import kaldifeat
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torchaudio
|
||||
from beam_search import (
|
||||
beam_search,
|
||||
fast_beam_search_one_best,
|
||||
greedy_search,
|
||||
greedy_search_batch,
|
||||
modified_beam_search,
|
||||
)
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from train import add_model_arguments, get_model, get_params
|
||||
|
||||
from icefall import smart_byte_decode
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--checkpoint",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the checkpoint. "
|
||||
"The checkpoint is assumed to be saved by "
|
||||
"icefall.checkpoint.save_checkpoint().",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
required=True,
|
||||
help="""Path to the bbpe.model.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Possible values are:
|
||||
- greedy_search
|
||||
- modified_beam_search
|
||||
- fast_beam_search
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"sound_files",
|
||||
type=str,
|
||||
nargs="+",
|
||||
help="The input sound file(s) to transcribe. "
|
||||
"Supported formats are those supported by torchaudio.load(). "
|
||||
"For example, wav and flac are supported. "
|
||||
"The sample rate has to be 16kHz.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--sample-rate",
|
||||
type=int,
|
||||
default=16000,
|
||||
help="The sample rate of the input sound file",
|
||||
)
|
||||
|
||||
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 --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 --method is fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""Used only when --method is fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=8,
|
||||
help="""Used only when --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
|
||||
--method is greedy_search.
|
||||
""",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def read_sound_files(
|
||||
filenames: List[str], expected_sample_rate: float
|
||||
) -> List[torch.Tensor]:
|
||||
"""Read a list of sound files into a list 1-D float32 torch tensors.
|
||||
Args:
|
||||
filenames:
|
||||
A list of sound filenames.
|
||||
expected_sample_rate:
|
||||
The expected sample rate of the sound files.
|
||||
Returns:
|
||||
Return a list of 1-D float32 torch tensors.
|
||||
"""
|
||||
ans = []
|
||||
for f in filenames:
|
||||
wave, sample_rate = torchaudio.load(f)
|
||||
assert (
|
||||
sample_rate == expected_sample_rate
|
||||
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
|
||||
# We use only the first channel
|
||||
ans.append(wave[0].contiguous())
|
||||
return ans
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
params = get_params()
|
||||
|
||||
params.update(vars(args))
|
||||
|
||||
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.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
logging.info(f"{params}")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
if params.causal:
|
||||
assert (
|
||||
"," not in params.chunk_size
|
||||
), "chunk_size should be one value in decoding."
|
||||
assert (
|
||||
"," not in params.left_context_frames
|
||||
), "left_context_frames should be one value in decoding."
|
||||
|
||||
logging.info("Creating model")
|
||||
model = get_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
checkpoint = torch.load(args.checkpoint, map_location="cpu")
|
||||
model.load_state_dict(checkpoint["model"], strict=False)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = params.sample_rate
|
||||
opts.mel_opts.num_bins = params.feature_dim
|
||||
opts.mel_opts.high_freq = -400
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {params.sound_files}")
|
||||
waves = read_sound_files(
|
||||
filenames=params.sound_files, expected_sample_rate=params.sample_rate
|
||||
)
|
||||
waves = [w.to(device) for w in waves]
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
|
||||
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
# model forward
|
||||
encoder_out, encoder_out_lens = model.forward_encoder(features, feature_lengths)
|
||||
|
||||
num_waves = encoder_out.size(0)
|
||||
hyps = []
|
||||
msg = f"Using {params.method}"
|
||||
logging.info(msg)
|
||||
|
||||
if params.method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
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(smart_byte_decode(hyp).split())
|
||||
elif params.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(smart_byte_decode(hyp).split())
|
||||
elif params.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(smart_byte_decode(hyp).split())
|
||||
else:
|
||||
for i in range(num_waves):
|
||||
# fmt: off
|
||||
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
|
||||
# fmt: on
|
||||
if params.method == "greedy_search":
|
||||
hyp = greedy_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
max_sym_per_frame=params.max_sym_per_frame,
|
||||
)
|
||||
elif params.method == "beam_search":
|
||||
hyp = beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported method: {params.method}")
|
||||
|
||||
hyps.append(smart_byte_decode(sp.decode(hyp)).split())
|
||||
|
||||
s = "\n"
|
||||
for filename, hyp in zip(params.sound_files, hyps):
|
||||
words = " ".join(hyp)
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
@ -86,6 +86,7 @@ from icefall.checkpoint import (
|
||||
)
|
||||
from icefall.dist import cleanup_dist, setup_dist
|
||||
from icefall.env import get_env_info
|
||||
from icefall.err import raise_grad_scale_is_too_small_error
|
||||
from icefall.hooks import register_inf_check_hooks
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import (
|
||||
@ -985,9 +986,7 @@ def train_one_epoch(
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
save_bad_model()
|
||||
raise RuntimeError(
|
||||
f"grad_scale is too small, exiting: {cur_grad_scale}"
|
||||
)
|
||||
raise_grad_scale_is_too_small_error(cur_grad_scale)
|
||||
|
||||
if batch_idx % params.log_interval == 0:
|
||||
cur_lr = max(scheduler.get_last_lr())
|
||||
|
941
egs/aishell/ASR/zipformer/train_bbpe.py
Executable file
941
egs/aishell/ASR/zipformer/train_bbpe.py
Executable file
@ -0,0 +1,941 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021-2024 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Wei Kang,
|
||||
# Mingshuang Luo,
|
||||
# Zengwei Yao,
|
||||
# Daniel Povey,
|
||||
# Zengrui Jin,)
|
||||
#
|
||||
# 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="0,1,2,3,4,5,6,7"
|
||||
|
||||
./zipformer/train_bbpe.py \
|
||||
--world-size 8 \
|
||||
--num-epochs 12 \
|
||||
--start-epoch 1 \
|
||||
--exp-dir zipformer/exp_bbpe \
|
||||
--max-duration 350
|
||||
|
||||
# For mix precision training:
|
||||
|
||||
./zipformer/train_bbpe.py \
|
||||
--world-size 8 \
|
||||
--num-epochs 12 \
|
||||
--start-epoch 1 \
|
||||
--use-fp16 1 \
|
||||
--exp-dir zipformer/exp_bbpe \
|
||||
--max-duration 750
|
||||
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import copy
|
||||
import logging
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from typing import 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 AishellAsrDataModule
|
||||
from lhotse.cut import Cut
|
||||
from lhotse.utils import fix_random_seed
|
||||
from optim import Eden, ScaledAdam
|
||||
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 train import (
|
||||
LRSchedulerType,
|
||||
add_model_arguments,
|
||||
get_adjusted_batch_count,
|
||||
get_model,
|
||||
get_params,
|
||||
load_checkpoint_if_available,
|
||||
save_checkpoint,
|
||||
set_batch_count,
|
||||
)
|
||||
|
||||
from icefall import byte_encode, diagnostics
|
||||
from icefall.checkpoint import 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.err import raise_grad_scale_is_too_small_error
|
||||
from icefall.hooks import register_inf_check_hooks
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
MetricsTracker,
|
||||
get_parameter_groups_with_lrs,
|
||||
setup_logger,
|
||||
str2bool,
|
||||
tokenize_by_CJK_char,
|
||||
)
|
||||
|
||||
|
||||
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="zipformer_bbpe/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_bbpe_500/bbpe.model",
|
||||
help="Path to the Byte BPE model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--base-lr", type=float, default=0.045, help="The base learning rate."
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lr-batches",
|
||||
type=float,
|
||||
default=7500,
|
||||
help="""Number of steps that affects how rapidly the learning rate
|
||||
decreases. We suggest not to change this.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lr-epochs",
|
||||
type=float,
|
||||
default=3.5,
|
||||
help="""Number of epochs that affects how rapidly the learning rate decreases.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ref-duration",
|
||||
type=float,
|
||||
default=600,
|
||||
help="""Reference batch duration for purposes of adjusting batch counts for setting various schedules inside the model""",
|
||||
)
|
||||
|
||||
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(
|
||||
"--inf-check",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Add hooks to check for infinite module outputs and gradients.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--save-every-n",
|
||||
type=int,
|
||||
default=4000,
|
||||
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=30,
|
||||
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=200,
|
||||
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 compute_loss(
|
||||
params: AttributeDict,
|
||||
model: Union[nn.Module, DDP],
|
||||
sp: spm.SentencePieceProcessor,
|
||||
batch: dict,
|
||||
is_training: bool,
|
||||
) -> Tuple[Tensor, MetricsTracker]:
|
||||
"""
|
||||
Compute CTC loss given the model and its inputs.
|
||||
|
||||
Args:
|
||||
params:
|
||||
Parameters for training. See :func:`get_params`.
|
||||
model:
|
||||
The model for training. It is an instance of Zipformer 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.
|
||||
warmup: a floating point value which increases throughout training;
|
||||
values >= 1.0 are fully warmed up and have all modules present.
|
||||
"""
|
||||
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)
|
||||
|
||||
batch_idx_train = params.batch_idx_train
|
||||
warm_step = params.warm_step
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
s = params.simple_loss_scale
|
||||
# take down the scale on the simple loss from 1.0 at the start
|
||||
# to params.simple_loss scale by warm_step.
|
||||
simple_loss_scale = (
|
||||
s
|
||||
if batch_idx_train >= warm_step
|
||||
else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
|
||||
)
|
||||
pruned_loss_scale = (
|
||||
1.0
|
||||
if batch_idx_train >= warm_step
|
||||
else 0.1 + 0.9 * (batch_idx_train / warm_step)
|
||||
)
|
||||
|
||||
loss = simple_loss_scale * simple_loss + pruned_loss_scale * 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,
|
||||
scheduler: LRSchedulerType,
|
||||
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.
|
||||
scheduler:
|
||||
The learning rate scheduler, we call step() every step.
|
||||
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)
|
||||
|
||||
saved_bad_model = False
|
||||
|
||||
def save_bad_model(suffix: str = ""):
|
||||
save_checkpoint_impl(
|
||||
filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
|
||||
model=model,
|
||||
model_avg=model_avg,
|
||||
params=params,
|
||||
optimizer=optimizer,
|
||||
scheduler=scheduler,
|
||||
sampler=train_dl.sampler,
|
||||
scaler=scaler,
|
||||
rank=0,
|
||||
)
|
||||
|
||||
for batch_idx, batch in enumerate(train_dl):
|
||||
if batch_idx % 10 == 0:
|
||||
set_batch_count(model, get_adjusted_batch_count(params))
|
||||
if batch_idx < cur_batch_idx:
|
||||
continue
|
||||
cur_batch_idx = batch_idx
|
||||
|
||||
params.batch_idx_train += 1
|
||||
batch_size = len(batch["supervisions"]["text"])
|
||||
|
||||
try:
|
||||
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()
|
||||
scheduler.step_batch(params.batch_idx_train)
|
||||
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
except: # noqa
|
||||
save_bad_model()
|
||||
display_and_save_batch(batch, params=params, sp=sp)
|
||||
raise
|
||||
|
||||
if params.print_diagnostics and batch_idx == 5:
|
||||
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,
|
||||
scheduler=scheduler,
|
||||
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 % 100 == 0 and params.use_fp16:
|
||||
# If the grad scale was less than 1, try increasing it. The _growth_interval
|
||||
# of the grad scaler is configurable, but we can't configure it to have different
|
||||
# behavior depending on the current grad scale.
|
||||
cur_grad_scale = scaler._scale.item()
|
||||
|
||||
if cur_grad_scale < 8.0 or (cur_grad_scale < 32.0 and batch_idx % 400 == 0):
|
||||
scaler.update(cur_grad_scale * 2.0)
|
||||
if cur_grad_scale < 0.01:
|
||||
if not saved_bad_model:
|
||||
save_bad_model(suffix="-first-warning")
|
||||
saved_bad_model = True
|
||||
logging.warning(f"Grad scale is small: {cur_grad_scale}")
|
||||
if cur_grad_scale < 1.0e-05:
|
||||
save_bad_model()
|
||||
raise_grad_scale_is_too_small_error(cur_grad_scale)
|
||||
|
||||
if batch_idx % params.log_interval == 0:
|
||||
cur_lr = max(scheduler.get_last_lr())
|
||||
cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
|
||||
|
||||
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}, "
|
||||
+ (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
|
||||
)
|
||||
|
||||
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 params.use_fp16:
|
||||
tb_writer.add_scalar(
|
||||
"train/grad_scale", cur_grad_scale, params.batch_idx_train
|
||||
)
|
||||
|
||||
if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
|
||||
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}")
|
||||
logging.info(
|
||||
f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
|
||||
)
|
||||
if tb_writer is not None:
|
||||
valid_info.write_summary(
|
||||
tb_writer, "train/valid_", params.batch_idx_train
|
||||
)
|
||||
|
||||
loss_value = tot_loss["loss"] / tot_loss["frames"]
|
||||
params.train_loss = loss_value
|
||||
if params.train_loss < params.best_train_loss:
|
||||
params.best_train_epoch = params.cur_epoch
|
||||
params.best_train_loss = params.train_loss
|
||||
|
||||
|
||||
def run(rank, world_size, args):
|
||||
"""
|
||||
Args:
|
||||
rank:
|
||||
It is a value between 0 and `world_size-1`, which is
|
||||
passed automatically by `mp.spawn()` in :func:`main`.
|
||||
The node with rank 0 is responsible for saving checkpoint.
|
||||
world_size:
|
||||
Number of GPUs for DDP training.
|
||||
args:
|
||||
The return value of get_parser().parse_args()
|
||||
"""
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
fix_random_seed(params.seed)
|
||||
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)
|
||||
|
||||
# <blk> is defined in local/train_bbpe_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_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).to(torch.float64)
|
||||
|
||||
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], find_unused_parameters=True)
|
||||
|
||||
optimizer = ScaledAdam(
|
||||
get_parameter_groups_with_lrs(model, lr=params.base_lr, include_names=True),
|
||||
lr=params.base_lr, # should have no effect
|
||||
clipping_scale=2.0,
|
||||
)
|
||||
|
||||
scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
|
||||
|
||||
if checkpoints and "optimizer" in checkpoints:
|
||||
logging.info("Loading optimizer state dict")
|
||||
optimizer.load_state_dict(checkpoints["optimizer"])
|
||||
|
||||
if (
|
||||
checkpoints
|
||||
and "scheduler" in checkpoints
|
||||
and checkpoints["scheduler"] is not None
|
||||
):
|
||||
logging.info("Loading scheduler state dict")
|
||||
scheduler.load_state_dict(checkpoints["scheduler"])
|
||||
|
||||
if params.print_diagnostics:
|
||||
opts = diagnostics.TensorDiagnosticOptions(
|
||||
512
|
||||
) # allow 4 megabytes per sub-module
|
||||
diagnostic = diagnostics.attach_diagnostics(model, opts)
|
||||
|
||||
if params.inf_check:
|
||||
register_inf_check_hooks(model)
|
||||
|
||||
aishell = AishellAsrDataModule(args)
|
||||
|
||||
train_cuts = aishell.train_cuts()
|
||||
valid_cuts = aishell.valid_cuts()
|
||||
|
||||
def remove_short_and_long_utt(c: Cut):
|
||||
# Keep only utterances with duration between 1 second and 15 seconds
|
||||
#
|
||||
# Caution: There is a reason to select 15.0 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
|
||||
if c.duration < 1.0 or c.duration > 15.0:
|
||||
# logging.warning(
|
||||
# f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
|
||||
# )
|
||||
return False
|
||||
|
||||
# In pruned RNN-T, we require that T >= S
|
||||
# where T is the number of feature frames after subsampling
|
||||
# and S is the number of tokens in the utterance
|
||||
|
||||
# In ./zipformer.py, the conv module uses the following expression
|
||||
# for subsampling
|
||||
T = ((c.num_frames - 7) // 2 + 1) // 2
|
||||
tokens = sp.encode(c.supervisions[0].text, out_type=str)
|
||||
|
||||
if T < len(tokens):
|
||||
logging.warning(
|
||||
f"Exclude cut with ID {c.id} from training. "
|
||||
f"Number of frames (before subsampling): {c.num_frames}. "
|
||||
f"Number of frames (after subsampling): {T}. "
|
||||
f"Text: {c.supervisions[0].text}. "
|
||||
f"Tokens: {tokens}. "
|
||||
f"Number of tokens: {len(tokens)}"
|
||||
)
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def tokenize_and_encode_text(c: Cut):
|
||||
# Text normalize for each sample
|
||||
text = c.supervisions[0].text
|
||||
text = byte_encode(tokenize_by_CJK_char(text))
|
||||
c.supervisions[0].text = text
|
||||
return c
|
||||
|
||||
train_cuts = train_cuts.filter(remove_short_and_long_utt)
|
||||
|
||||
train_cuts = train_cuts.map(tokenize_and_encode_text)
|
||||
|
||||
valid_cuts = valid_cuts.map(tokenize_and_encode_text)
|
||||
|
||||
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 = aishell.train_dataloaders(
|
||||
train_cuts, sampler_state_dict=sampler_state_dict
|
||||
)
|
||||
|
||||
valid_dl = aishell.valid_dataloaders(valid_cuts)
|
||||
|
||||
if False and 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, init_scale=1.0)
|
||||
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):
|
||||
scheduler.step_epoch(epoch - 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,
|
||||
scheduler=scheduler,
|
||||
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,
|
||||
scheduler=scheduler,
|
||||
sampler=train_dl.sampler,
|
||||
scaler=scaler,
|
||||
rank=rank,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
if world_size > 1:
|
||||
torch.distributed.barrier()
|
||||
cleanup_dist()
|
||||
|
||||
|
||||
def display_and_save_batch(
|
||||
batch: dict,
|
||||
params: AttributeDict,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
) -> None:
|
||||
"""Display the batch statistics and save the batch into disk.
|
||||
|
||||
Args:
|
||||
batch:
|
||||
A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
|
||||
for the content in it.
|
||||
params:
|
||||
Parameters for training. See :func:`get_params`.
|
||||
sp:
|
||||
The sentence piece model.
|
||||
"""
|
||||
from lhotse.utils import uuid4
|
||||
|
||||
filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
|
||||
logging.info(f"Saving batch to {filename}")
|
||||
torch.save(batch, filename)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
features = batch["inputs"]
|
||||
|
||||
logging.info(f"features shape: {features.shape}")
|
||||
|
||||
y = sp.encode(supervisions["text"], out_type=int)
|
||||
num_tokens = sum(len(i) for i in y)
|
||||
logging.info(f"num tokens: {num_tokens}")
|
||||
|
||||
|
||||
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.zero_grad()
|
||||
except Exception 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]}) ..."
|
||||
)
|
||||
display_and_save_batch(batch, params=params, sp=sp)
|
||||
raise
|
||||
logging.info(
|
||||
f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
parser = get_parser()
|
||||
AishellAsrDataModule.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()
|
@ -29,7 +29,14 @@ import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
|
||||
from lhotse import (
|
||||
CutSet,
|
||||
Fbank,
|
||||
FbankConfig,
|
||||
LilcomChunkyWriter,
|
||||
WhisperFbank,
|
||||
WhisperFbankConfig,
|
||||
)
|
||||
from lhotse.recipes.utils import read_manifests_if_cached
|
||||
|
||||
from icefall.utils import get_executor, str2bool
|
||||
@ -42,10 +49,12 @@ torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
|
||||
|
||||
def compute_fbank_aishell2(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
def compute_fbank_aishell2(
|
||||
num_mel_bins: int = 80, perturb_speed: bool = False, whisper_fbank: bool = False
|
||||
):
|
||||
src_dir = Path("data/manifests")
|
||||
output_dir = Path("data/fbank")
|
||||
num_jobs = min(15, os.cpu_count())
|
||||
num_jobs = min(8, os.cpu_count())
|
||||
|
||||
dataset_parts = (
|
||||
"train",
|
||||
@ -68,8 +77,12 @@ def compute_fbank_aishell2(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
list(manifests.keys()),
|
||||
dataset_parts,
|
||||
)
|
||||
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
if whisper_fbank:
|
||||
extractor = WhisperFbank(
|
||||
WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
|
||||
)
|
||||
else:
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
for partition, m in manifests.items():
|
||||
@ -82,7 +95,7 @@ def compute_fbank_aishell2(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
supervisions=m["supervisions"],
|
||||
)
|
||||
if "train" in partition and perturb_speed:
|
||||
logging.info(f"Doing speed perturb")
|
||||
logging.info("Doing speed perturb")
|
||||
cut_set = (
|
||||
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
|
||||
)
|
||||
@ -111,7 +124,12 @@ def get_args():
|
||||
default=False,
|
||||
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--whisper-fbank",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Use WhisperFbank instead of Fbank. Default: False.",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
@ -122,5 +140,7 @@ if __name__ == "__main__":
|
||||
|
||||
args = get_args()
|
||||
compute_fbank_aishell2(
|
||||
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
|
||||
num_mel_bins=args.num_mel_bins,
|
||||
perturb_speed=args.perturb_speed,
|
||||
whisper_fbank=args.whisper_fbank,
|
||||
)
|
||||
|
@ -108,6 +108,16 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
|
||||
fi
|
||||
fi
|
||||
|
||||
whisper_mel_bins=80
|
||||
if [ $stage -le 30 ] && [ $stop_stage -ge 30 ]; then
|
||||
log "Stage 30: Compute whisper fbank for aishell2"
|
||||
if [ ! -f data/fbank/.aishell2.whisper.done ]; then
|
||||
mkdir -p data/fbank
|
||||
./local/compute_fbank_aishell2.py --perturb-speed ${perturb_speed} --num-mel-bins ${whisper_mel_bins} --whisper-fbank true
|
||||
touch data/fbank/.aishell2.whisper.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
log "Stage 4: Compute fbank for musan"
|
||||
if [ ! -f data/fbank/.msuan.done ]; then
|
||||
|
@ -296,6 +296,8 @@ class AiShell2AsrDataModule:
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
buffer_size=self.args.num_buckets * 2000,
|
||||
shuffle_buffer_size=self.args.num_buckets * 5000,
|
||||
drop_last=self.args.drop_last,
|
||||
)
|
||||
else:
|
||||
|
@ -22,7 +22,7 @@
|
||||
Usage:
|
||||
./pruned_transducer_stateless5/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless5/exp \
|
||||
--lang-dir data/lang_char
|
||||
--tokens ./data/lang_char/tokens.txt \
|
||||
--epoch 25 \
|
||||
--avg 5
|
||||
|
||||
@ -48,6 +48,7 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
@ -57,8 +58,7 @@ from icefall.checkpoint import (
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import str2bool
|
||||
from icefall.utils import num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -115,10 +115,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char",
|
||||
help="The lang dir",
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -154,10 +154,10 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
params.blank_id = lexicon.token_table["<blk>"]
|
||||
params.unk_id = lexicon.token_table["<unk>"]
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.unk_id = token_table["<unk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
@ -29,7 +29,14 @@ import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig
|
||||
from lhotse import (
|
||||
CutSet,
|
||||
Fbank,
|
||||
FbankConfig,
|
||||
LilcomChunkyWriter,
|
||||
WhisperFbank,
|
||||
WhisperFbankConfig,
|
||||
)
|
||||
from lhotse.recipes.utils import read_manifests_if_cached
|
||||
|
||||
from icefall.utils import get_executor, str2bool
|
||||
@ -42,10 +49,12 @@ torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
|
||||
|
||||
def compute_fbank_aishell4(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
def compute_fbank_aishell4(
|
||||
num_mel_bins: int = 80, perturb_speed: bool = False, whisper_fbank: bool = False
|
||||
):
|
||||
src_dir = Path("data/manifests/aishell4")
|
||||
output_dir = Path("data/fbank")
|
||||
num_jobs = min(15, os.cpu_count())
|
||||
num_jobs = min(8, os.cpu_count())
|
||||
|
||||
dataset_parts = (
|
||||
"train_S",
|
||||
@ -70,7 +79,12 @@ def compute_fbank_aishell4(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
dataset_parts,
|
||||
)
|
||||
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
if whisper_fbank:
|
||||
extractor = WhisperFbank(
|
||||
WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
|
||||
)
|
||||
else:
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
for partition, m in manifests.items():
|
||||
@ -84,7 +98,7 @@ def compute_fbank_aishell4(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
supervisions=m["supervisions"],
|
||||
)
|
||||
if "train" in partition and perturb_speed:
|
||||
logging.info(f"Doing speed perturb")
|
||||
logging.info("Doing speed perturb")
|
||||
cut_set = (
|
||||
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
|
||||
)
|
||||
@ -95,7 +109,7 @@ def compute_fbank_aishell4(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
# when an executor is specified, make more partitions
|
||||
num_jobs=num_jobs if ex is None else 80,
|
||||
executor=ex,
|
||||
storage_type=ChunkedLilcomHdf5Writer,
|
||||
storage_type=LilcomChunkyWriter,
|
||||
)
|
||||
|
||||
logging.info("About splitting cuts into smaller chunks")
|
||||
@ -121,7 +135,12 @@ def get_args():
|
||||
default=False,
|
||||
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--whisper-fbank",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Use WhisperFbank instead of Fbank. Default: False.",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
@ -132,5 +151,7 @@ if __name__ == "__main__":
|
||||
|
||||
args = get_args()
|
||||
compute_fbank_aishell4(
|
||||
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
|
||||
num_mel_bins=args.num_mel_bins,
|
||||
perturb_speed=args.perturb_speed,
|
||||
whisper_fbank=args.whisper_fbank,
|
||||
)
|
||||
|
@ -6,7 +6,7 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
||||
set -eou pipefail
|
||||
|
||||
stage=-1
|
||||
stop_stage=100
|
||||
stop_stage=7
|
||||
perturb_speed=true
|
||||
|
||||
|
||||
@ -76,11 +76,21 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
|
||||
log "Stage 2: Process aishell4"
|
||||
log "Stage 2: Compute fbank for aishell4"
|
||||
if [ ! -f data/fbank/aishell4/.fbank.done ]; then
|
||||
mkdir -p data/fbank/aishell4
|
||||
mkdir -p data/fbank
|
||||
./local/compute_fbank_aishell4.py --perturb-speed ${perturb_speed}
|
||||
touch data/fbank/aishell4/.fbank.done
|
||||
touch data/fbank/.fbank.done
|
||||
fi
|
||||
fi
|
||||
|
||||
whisper_mel_bins=80
|
||||
if [ $stage -le 20 ] && [ $stop_stage -ge 20 ]; then
|
||||
log "Stage 20: Compute whisper fbank for aishell4"
|
||||
if [ ! -f data/fbank/aishell4/.fbank.done ]; then
|
||||
mkdir -p data/fbank
|
||||
./local/compute_fbank_aishell4.py --perturb-speed ${perturb_speed} --num-mel-bins ${whisper_mel_bins} --whisper-fbank true
|
||||
touch data/fbank/.fbank.done
|
||||
fi
|
||||
fi
|
||||
|
||||
@ -106,16 +116,7 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
||||
log "Stage 5: Compute fbank for aishell4"
|
||||
if [ ! -f data/fbank/.aishell4.done ]; then
|
||||
mkdir -p data/fbank
|
||||
./local/compute_fbank_aishell4.py --perturb-speed ${perturb_speed}
|
||||
touch data/fbank/.aishell4.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
log "Stage 6: Prepare char based lang"
|
||||
log "Stage 5: Prepare char based lang"
|
||||
lang_char_dir=data/lang_char
|
||||
mkdir -p $lang_char_dir
|
||||
|
||||
|
@ -306,7 +306,8 @@ class Aishell4AsrDataModule:
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
buffer_size=100000,
|
||||
buffer_size=self.args.num_buckets * 2000,
|
||||
shuffle_buffer_size=self.args.num_buckets * 5000,
|
||||
drop_last=self.args.drop_last,
|
||||
)
|
||||
else:
|
||||
|
@ -48,6 +48,7 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from train import add_model_arguments, get_params, get_transducer_model
|
||||
|
||||
@ -57,8 +58,7 @@ from icefall.checkpoint import (
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import str2bool
|
||||
from icefall.utils import num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -115,13 +115,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char",
|
||||
help="""The lang dir
|
||||
It contains language related input files such as
|
||||
"lexicon.txt"
|
||||
""",
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -157,9 +154,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
params.blank_id = lexicon.token_table["<blk>"]
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
@ -29,7 +29,14 @@ import os
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
|
||||
from lhotse import (
|
||||
CutSet,
|
||||
Fbank,
|
||||
FbankConfig,
|
||||
LilcomChunkyWriter,
|
||||
WhisperFbank,
|
||||
WhisperFbankConfig,
|
||||
)
|
||||
from lhotse.recipes.utils import read_manifests_if_cached
|
||||
|
||||
from icefall.utils import get_executor, str2bool
|
||||
@ -42,10 +49,12 @@ torch.set_num_threads(1)
|
||||
torch.set_num_interop_threads(1)
|
||||
|
||||
|
||||
def compute_fbank_alimeeting(num_mel_bins: int = 80, perturb_speed: bool = False):
|
||||
def compute_fbank_alimeeting(
|
||||
num_mel_bins: int = 80, perturb_speed: bool = False, whisper_fbank: bool = False
|
||||
):
|
||||
src_dir = Path("data/manifests/alimeeting")
|
||||
output_dir = Path("data/fbank")
|
||||
num_jobs = min(15, os.cpu_count())
|
||||
num_jobs = min(8, os.cpu_count())
|
||||
|
||||
dataset_parts = (
|
||||
"train",
|
||||
@ -53,7 +62,7 @@ def compute_fbank_alimeeting(num_mel_bins: int = 80, perturb_speed: bool = False
|
||||
"test",
|
||||
)
|
||||
|
||||
prefix = "alimeeting"
|
||||
prefix = "alimeeting-far"
|
||||
suffix = "jsonl.gz"
|
||||
manifests = read_manifests_if_cached(
|
||||
dataset_parts=dataset_parts,
|
||||
@ -70,7 +79,12 @@ def compute_fbank_alimeeting(num_mel_bins: int = 80, perturb_speed: bool = False
|
||||
dataset_parts,
|
||||
)
|
||||
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
if whisper_fbank:
|
||||
extractor = WhisperFbank(
|
||||
WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
|
||||
)
|
||||
else:
|
||||
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
|
||||
|
||||
with get_executor() as ex: # Initialize the executor only once.
|
||||
for partition, m in manifests.items():
|
||||
@ -83,7 +97,7 @@ def compute_fbank_alimeeting(num_mel_bins: int = 80, perturb_speed: bool = False
|
||||
supervisions=m["supervisions"],
|
||||
)
|
||||
if "train" in partition and perturb_speed:
|
||||
logging.info(f"Doing speed perturb")
|
||||
logging.info("Doing speed perturb")
|
||||
cut_set = (
|
||||
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
|
||||
)
|
||||
@ -121,7 +135,12 @@ def get_args():
|
||||
default=False,
|
||||
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--whisper-fbank",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Use the Whisper Fbank feature extractor. Default: False.",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
@ -132,5 +151,7 @@ if __name__ == "__main__":
|
||||
|
||||
args = get_args()
|
||||
compute_fbank_alimeeting(
|
||||
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
|
||||
num_mel_bins=args.num_mel_bins,
|
||||
perturb_speed=args.perturb_speed,
|
||||
whisper_fbank=args.whisper_fbank,
|
||||
)
|
||||
|
@ -6,7 +6,7 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
|
||||
set -eou pipefail
|
||||
|
||||
stage=-1
|
||||
stop_stage=100
|
||||
stop_stage=7
|
||||
perturb_speed=true
|
||||
|
||||
# We assume dl_dir (download dir) contains the following
|
||||
@ -15,7 +15,7 @@ perturb_speed=true
|
||||
#
|
||||
# - $dl_dir/alimeeting
|
||||
# This directory contains the following files downloaded from
|
||||
# https://openslr.org/62/
|
||||
# https://openslr.org/119/
|
||||
#
|
||||
# - Train_Ali_far.tar.gz
|
||||
# - Train_Ali_near.tar.gz
|
||||
@ -66,10 +66,21 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
|
||||
fi
|
||||
|
||||
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
|
||||
log "Stage 2: Process alimeeting"
|
||||
if [ ! -f data/fbank/alimeeting/.fbank.done ]; then
|
||||
mkdir -p data/fbank/alimeeting
|
||||
log "Stage 2: compute fbank for alimeeting"
|
||||
if [ ! -f data/fbank/.fbank.done ]; then
|
||||
mkdir -p data/fbank
|
||||
./local/compute_fbank_alimeeting.py --perturb-speed ${perturb_speed}
|
||||
touch data/fbank/.fbank.done
|
||||
fi
|
||||
fi
|
||||
|
||||
whisper_mel_bins=80
|
||||
if [ $stage -le 20 ] && [ $stop_stage -ge 20 ]; then
|
||||
log "Stage 20: compute whisper fbank for alimeeting"
|
||||
if [ ! -f data/fbank/.fbank.done ]; then
|
||||
mkdir -p data/fbank
|
||||
./local/compute_fbank_alimeeting.py --perturb-speed ${perturb_speed} --num-mel-bins ${whisper_mel_bins} --whisper-fbank true
|
||||
touch data/fbank/.fbank.done
|
||||
fi
|
||||
fi
|
||||
|
||||
@ -95,16 +106,7 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
|
||||
fi
|
||||
|
||||
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
|
||||
log "Stage 5: Compute fbank for alimeeting"
|
||||
if [ ! -f data/fbank/.alimeeting.done ]; then
|
||||
mkdir -p data/fbank
|
||||
./local/compute_fbank_alimeeting.py --perturb-speed True
|
||||
touch data/fbank/.alimeeting.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
log "Stage 6: Prepare char based lang"
|
||||
log "Stage 5: Prepare char based lang"
|
||||
lang_char_dir=data/lang_char
|
||||
mkdir -p $lang_char_dir
|
||||
|
||||
|
@ -288,7 +288,8 @@ class AlimeetingAsrDataModule:
|
||||
max_duration=self.args.max_duration,
|
||||
shuffle=self.args.shuffle,
|
||||
num_buckets=self.args.num_buckets,
|
||||
buffer_size=30000,
|
||||
buffer_size=self.args.num_buckets * 2000,
|
||||
shuffle_buffer_size=self.args.num_buckets * 5000,
|
||||
drop_last=True,
|
||||
)
|
||||
else:
|
||||
|
@ -20,7 +20,7 @@
|
||||
Usage:
|
||||
./pruned_transducer_stateless2/export.py \
|
||||
--exp-dir ./pruned_transducer_stateless2/exp \
|
||||
--lang-dir data/lang_char \
|
||||
--tokens ./data/lang_char/tokens.txt \
|
||||
--epoch 29 \
|
||||
--avg 18
|
||||
|
||||
@ -45,12 +45,12 @@ import argparse
|
||||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import k2
|
||||
import torch
|
||||
from train import get_params, get_transducer_model
|
||||
|
||||
from icefall.checkpoint import average_checkpoints, load_checkpoint
|
||||
from icefall.lexicon import Lexicon
|
||||
from icefall.utils import str2bool
|
||||
from icefall.utils import num_tokens, str2bool
|
||||
|
||||
|
||||
def get_parser():
|
||||
@ -85,10 +85,10 @@ def get_parser():
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--lang-dir",
|
||||
"--tokens",
|
||||
type=str,
|
||||
default="data/lang_char",
|
||||
help="The lang dir",
|
||||
default="data/lang_char/tokens.txt",
|
||||
help="Path to the tokens.txt",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
@ -122,10 +122,9 @@ def main():
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
lexicon = Lexicon(params.lang_dir)
|
||||
|
||||
params.blank_id = 0
|
||||
params.vocab_size = max(lexicon.tokens) + 1
|
||||
token_table = k2.SymbolTable.from_file(params.tokens)
|
||||
params.blank_id = token_table["<blk>"]
|
||||
params.vocab_size = num_tokens(token_table) + 1
|
||||
|
||||
logging.info(params)
|
||||
|
||||
|
Some files were not shown because too many files have changed in this diff Show More
Loading…
x
Reference in New Issue
Block a user