mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-09-05 07:04:18 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'master' into diagnostics_fix

Browse Source
This commit is contained in:
zr_jin 2023-10-19 22:24:50 +08:00 committed by GitHub
commit d36efe14c7
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
549 changed files with 51433 additions and 3479 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.flake8
View File

@ -24,6 +24,7 @@ exclude =
**/data/**,
icefall/shared/make_kn_lm.py,
icefall/__init__.py
icefall/ctc/__init__.py
ignore =
# E203 white space before ":"

3
.github/scripts/run-gigaspeech-pruned-transducer-stateless2-2022-05-12.sh vendored
View File

@ -29,6 +29,9 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" ==
ls -lh data/fbank
ls -lh pruned_transducer_stateless2/exp
ln -s data/fbank/cuts_DEV.jsonl.gz data/fbank/gigaspeech_cuts_DEV.jsonl.gz
ln -s data/fbank/cuts_TEST.jsonl.gz data/fbank/gigaspeech_cuts_TEST.jsonl.gz
log "Decoding dev and test"
# use a small value for decoding with CPU

4
.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh vendored
View File

@ -53,7 +53,7 @@ log "Export to torchscript model"
./conformer_ctc3/export.py \
--exp-dir $repo/exp \
--lang-dir $repo/data/lang_bpe_500 \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--jit-trace 1 \
--epoch 99 \
--avg 1 \
@ -82,7 +82,7 @@ for m in ctc-decoding 1best; do
--checkpoint $repo/exp/pretrained.pt \
--words-file $repo/data/lang_bpe_500/words.txt \
--HLG $repo/data/lang_bpe_500/HLG.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--G $repo/data/lm/G_4_gram.pt \
--method $m \
--sample-rate 16000 \

6
.github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh vendored
View File

@ -31,7 +31,7 @@ log "Test exporting with torch.jit.trace()"
./lstm_transducer_stateless2/export.py \
--exp-dir $repo/exp \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--use-averaged-model 0 \
@ -55,7 +55,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -68,7 +68,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh vendored
View File

@ -28,7 +28,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -41,7 +41,7 @@ for method in fast_beam_search modified_beam_search beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh vendored
View File

@ -36,7 +36,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -49,7 +49,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh vendored
View File

@ -35,7 +35,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -48,7 +48,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

8
.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh vendored
View File

@ -30,14 +30,14 @@ popd
log "Export to torchscript model"
./pruned_transducer_stateless3/export.py \
--exp-dir $repo/exp \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--jit 1
./pruned_transducer_stateless3/export.py \
--exp-dir $repo/exp \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--jit-trace 1
@ -74,7 +74,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -87,7 +87,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh vendored
View File

@ -32,7 +32,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--num-encoder-layers 18 \
--dim-feedforward 2048 \
--nhead 8 \
@ -51,7 +51,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav \

6
.github/scripts/run-librispeech-pruned-transducer-stateless7-2022-11-11.sh vendored
View File

@ -33,7 +33,7 @@ log "Export to torchscript model"
./pruned_transducer_stateless7/export.py \
--exp-dir $repo/exp \
--use-averaged-model false \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--jit 1
@ -56,7 +56,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -69,7 +69,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

6
.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh vendored
View File

@ -37,7 +37,7 @@ log "Export to torchscript model"
./pruned_transducer_stateless7_ctc/export.py \
--exp-dir $repo/exp \
--use-averaged-model false \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--jit 1
@ -74,7 +74,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -87,7 +87,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

6
.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2023-01-29.sh vendored
View File

@ -36,7 +36,7 @@ log "Export to torchscript model"
./pruned_transducer_stateless7_ctc_bs/export.py \
--exp-dir $repo/exp \
--use-averaged-model false \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--jit 1
@ -72,7 +72,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -85,7 +85,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

6
.github/scripts/run-librispeech-pruned-transducer-stateless7-streaming-2022-12-29.sh vendored
View File

@ -37,7 +37,7 @@ log "Export to torchscript model"
./pruned_transducer_stateless7_streaming/export.py \
--exp-dir $repo/exp \
--use-averaged-model false \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--decode-chunk-len 32 \
--epoch 99 \
--avg 1 \
@ -81,7 +81,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--decode-chunk-len 32 \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
@ -95,7 +95,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--decode-chunk-len 32 \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \

6
.github/scripts/run-librispeech-pruned-transducer-stateless8-2022-11-14.sh vendored
View File

@ -41,7 +41,7 @@ log "Decode with models exported by torch.jit.script()"
log "Export to torchscript model"
./pruned_transducer_stateless8/export.py \
--exp-dir $repo/exp \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model false \
--epoch 99 \
--avg 1 \
@ -65,7 +65,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -78,7 +78,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh vendored
View File

@ -32,7 +32,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--simulate-streaming 1 \
--causal-convolution 1 \
$repo/test_wavs/1089-134686-0001.wav \
@ -47,7 +47,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--simulate-streaming 1 \
--causal-convolution 1 \
$repo/test_wavs/1089-134686-0001.wav \

4
.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh vendored
View File

@ -28,7 +28,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -41,7 +41,7 @@ for method in fast_beam_search modified_beam_search beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh vendored
View File

@ -37,7 +37,7 @@ log "Export to torchscript model"
./zipformer_mmi/export.py \
--exp-dir $repo/exp \
--use-averaged-model false \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--jit 1
@ -61,7 +61,7 @@ for method in 1best nbest nbest-rescoring-LG nbest-rescoring-3-gram nbest-rescor
--method $method \
--checkpoint $repo/exp/pretrained.pt \
--lang-dir $repo/data/lang_bpe_500 \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

51
.github/scripts/run-multi-zh_hans-zipformer.sh vendored Executable file
View File

@ -0,0 +1,51 @@
#!/usr/bin/env bash
set -e
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/multi_zh-hans/ASR
repo_url=https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2/
log "Downloading pre-trained model from $repo_url"
git lfs install
git clone $repo_url
repo=$(basename $repo_url)
log "Display test files"
tree $repo/
ls -lh $repo/test_wavs/*.wav
pushd $repo/exp
ln -s epoch-20.pt epoch-99.pt
popd
ls -lh $repo/exp/*.pt
./zipformer/pretrained.py \
--checkpoint $repo/exp/epoch-99.pt \
--tokens $repo/data/lang_bpe_2000/tokens.txt \
--method greedy_search \
$repo/test_wavs/DEV_T0000000000.wav \
$repo/test_wavs/DEV_T0000000001.wav \
$repo/test_wavs/DEV_T0000000002.wav
for method in modified_beam_search fast_beam_search; do
log "$method"
./zipformer/pretrained.py \
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/epoch-99.pt \
--tokens $repo/data/lang_bpe_2000/tokens.txt \
$repo/test_wavs/DEV_T0000000000.wav \
$repo/test_wavs/DEV_T0000000001.wav \
$repo/test_wavs/DEV_T0000000002.wav
done

46
.github/scripts/run-pre-trained-conformer-ctc.sh vendored
View File

@ -1,46 +0,0 @@
#!/usr/bin/env bash
set -e
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/librispeech/ASR
repo_url=https://github.com/csukuangfj/icefall-asr-conformer-ctc-bpe-500
git lfs install
log "Downloading pre-trained model from $repo_url"
git clone $repo_url
repo=$(basename $repo_url)
log "Display test files"
tree $repo/
ls -lh $repo/test_wavs/*.flac
log "CTC decoding"
./conformer_ctc/pretrained.py \
--method ctc-decoding \
--num-classes 500 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
$repo/test_wavs/1089-134686-0001.flac \
$repo/test_wavs/1221-135766-0001.flac \
$repo/test_wavs/1221-135766-0002.flac
log "HLG decoding"
./conformer_ctc/pretrained.py \
--method 1best \
--num-classes 500 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--words-file $repo/data/lang_bpe_500/words.txt \
--HLG $repo/data/lang_bpe_500/HLG.pt \
$repo/test_wavs/1089-134686-0001.flac \
$repo/test_wavs/1221-135766-0001.flac \
$repo/test_wavs/1221-135766-0002.flac

240
.github/scripts/run-pre-trained-ctc.sh vendored Executable file
View File

@ -0,0 +1,240 @@
#!/usr/bin/env bash
set -e
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]}) $*"
}
pushd egs/librispeech/ASR
repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-zipformer-ctc-en-2023-10-02
log "Downloading pre-trained model from $repo_url"
git lfs install
git clone $repo_url
repo=$(basename $repo_url)
log "Display test files"
tree $repo/
ls -lh $repo/test_wavs/*.wav
log "CTC greedy search"
./zipformer/onnx_pretrained_ctc.py \
--nn-model $repo/model.onnx \
--tokens $repo/tokens.txt \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav
log "CTC H decoding"
./zipformer/onnx_pretrained_ctc_H.py \
--nn-model $repo/model.onnx \
--tokens $repo/tokens.txt \
--H $repo/H.fst \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav
log "CTC HL decoding"
./zipformer/onnx_pretrained_ctc_HL.py \
--nn-model $repo/model.onnx \
--words $repo/words.txt \
--HL $repo/HL.fst \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav
log "CTC HLG decoding"
./zipformer/onnx_pretrained_ctc_HLG.py \
--nn-model $repo/model.onnx \
--words $repo/words.txt \
--HLG $repo/HLG.fst \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav
rm -rf $repo
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09
log "Downloading pre-trained model from $repo_url"
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "exp/pretrained.pt"
git lfs pull --include "data/lang_bpe_500/HLG.pt"
git lfs pull --include "data/lang_bpe_500/L.pt"
git lfs pull --include "data/lang_bpe_500/L_disambig.pt"
git lfs pull --include "data/lang_bpe_500/Linv.pt"
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "data/lang_bpe_500/lexicon.txt"
git lfs pull --include "data/lang_bpe_500/lexicon_disambig.txt"
git lfs pull --include "data/lang_bpe_500/tokens.txt"
git lfs pull --include "data/lang_bpe_500/words.txt"
git lfs pull --include "data/lm/G_3_gram.fst.txt"
popd
log "Display test files"
tree $repo/
ls -lh $repo/test_wavs/*.wav
log "CTC decoding"
./conformer_ctc/pretrained.py \
--method ctc-decoding \
--num-classes 500 \
--checkpoint $repo/exp/pretrained.pt \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
log "HLG decoding"
./conformer_ctc/pretrained.py \
--method 1best \
--num-classes 500 \
--checkpoint $repo/exp/pretrained.pt \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--words-file $repo/data/lang_bpe_500/words.txt \
--HLG $repo/data/lang_bpe_500/HLG.pt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
log "CTC decoding on CPU with kaldi decoders using OpenFst"
log "Exporting model with torchscript"
pushd $repo/exp
ln -s pretrained.pt epoch-99.pt
popd
./conformer_ctc/export.py \
--epoch 99 \
--avg 1 \
--exp-dir $repo/exp \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--jit 1
ls -lh $repo/exp
log "Generating H.fst, HL.fst"
./local/prepare_lang_fst.py --lang-dir $repo/data/lang_bpe_500 --ngram-G $repo/data/lm/G_3_gram.fst.txt
ls -lh $repo/data/lang_bpe_500
log "Decoding with H on CPU with OpenFst"
./conformer_ctc/jit_pretrained_decode_with_H.py \
--nn-model $repo/exp/cpu_jit.pt \
--H $repo/data/lang_bpe_500/H.fst \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
log "Decoding with HL on CPU with OpenFst"
./conformer_ctc/jit_pretrained_decode_with_HL.py \
--nn-model $repo/exp/cpu_jit.pt \
--HL $repo/data/lang_bpe_500/HL.fst \
--words $repo/data/lang_bpe_500/words.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
log "Decoding with HLG on CPU with OpenFst"
./conformer_ctc/jit_pretrained_decode_with_HLG.py \
--nn-model $repo/exp/cpu_jit.pt \
--HLG $repo/data/lang_bpe_500/HLG.fst \
--words $repo/data/lang_bpe_500/words.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
rm -rf $repo
popd
log "Test aishell"
pushd egs/aishell/ASR
repo_url=https://huggingface.co/csukuangfj/icefall_asr_aishell_conformer_ctc
log "Downloading pre-trained model from $repo_url"
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "exp/pretrained.pt"
git lfs pull --include "data/lang_char/H.fst"
git lfs pull --include "data/lang_char/HL.fst"
git lfs pull --include "data/lang_char/HLG.fst"
popd
log "Display test files"
tree $repo/
ls -lh $repo/test_wavs/*.wav
log "CTC decoding"
log "Exporting model with torchscript"
pushd $repo/exp
ln -s pretrained.pt epoch-99.pt
popd
./conformer_ctc/export.py \
--epoch 99 \
--avg 1 \
--exp-dir $repo/exp \
--tokens $repo/data/lang_char/tokens.txt \
--jit 1
ls -lh $repo/exp
ls -lh $repo/data/lang_char
log "Decoding with H on CPU with OpenFst"
./conformer_ctc/jit_pretrained_decode_with_H.py \
--nn-model $repo/exp/cpu_jit.pt \
--H $repo/data/lang_char/H.fst \
--tokens $repo/data/lang_char/tokens.txt \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav
log "Decoding with HL on CPU with OpenFst"
./conformer_ctc/jit_pretrained_decode_with_HL.py \
--nn-model $repo/exp/cpu_jit.pt \
--HL $repo/data/lang_char/HL.fst \
--words $repo/data/lang_char/words.txt \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav
log "Decoding with HLG on CPU with OpenFst"
./conformer_ctc/jit_pretrained_decode_with_HLG.py \
--nn-model $repo/exp/cpu_jit.pt \
--HLG $repo/data/lang_char/HLG.fst \
--words $repo/data/lang_char/words.txt \
$repo/test_wavs/0.wav \
$repo/test_wavs/1.wav \
$repo/test_wavs/2.wav
rm -rf $repo

4
.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh vendored
View File

@ -28,7 +28,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -41,7 +41,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh vendored
View File

@ -28,7 +28,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -41,7 +41,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

4
.github/scripts/run-pre-trained-transducer-stateless.sh vendored
View File

@ -28,7 +28,7 @@ for sym in 1 2 3; do
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
@ -41,7 +41,7 @@ for method in fast_beam_search modified_beam_search beam_search; do
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

2
.github/scripts/run-pre-trained-transducer.sh vendored
View File

@ -27,7 +27,7 @@ log "Beam search decoding"
--method beam_search \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav

44
.github/scripts/run-swbd-conformer-ctc-2023-08-26.sh vendored Executable file
View File

@ -0,0 +1,44 @@
#!/usr/bin/env bash
set -e
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/swbd/ASR
repo_url=https://huggingface.co/zrjin/icefall-asr-swbd-conformer-ctc-2023-8-26
log "Downloading pre-trained model from $repo_url"
git lfs install
git clone $repo_url
repo=$(basename $repo_url)
log "Display test files"
tree $repo/
ls -lh $repo/test_wavs/*.wav
pushd $repo/exp
ln -s epoch-98.pt epoch-99.pt
popd
ls -lh $repo/exp/*.pt
for method in ctc-decoding 1best; do
log "$method"
./conformer_ctc/pretrained.py \
--method $method \
--checkpoint $repo/exp/epoch-99.pt \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--words-file $repo/data/lang_bpe_500/words.txt \
--HLG $repo/data/lang_bpe_500/HLG.pt \
--G $repo/data/lm/G_4_gram.pt \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
done

24
.github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh vendored
View File

@ -17,7 +17,6 @@ git lfs install
git clone $repo_url
repo=$(basename $repo_url)
log "Display test files"
tree $repo/
ls -lh $repo/test_wavs/*.wav
@ -29,12 +28,11 @@ popd
log "Test exporting to ONNX format"
./pruned_transducer_stateless2/export.py \
./pruned_transducer_stateless2/export-onnx.py \
--exp-dir $repo/exp \
--lang-dir $repo/data/lang_char \
--epoch 99 \
--avg 1 \
--onnx 1
--avg 1
log "Export to torchscript model"
@ -59,19 +57,17 @@ log "Decode with ONNX models"
./pruned_transducer_stateless2/onnx_check.py \
--jit-filename $repo/exp/cpu_jit.pt \
--onnx-encoder-filename $repo/exp/encoder.onnx \
--onnx-decoder-filename $repo/exp/decoder.onnx \
--onnx-joiner-filename $repo/exp/joiner.onnx \
--onnx-joiner-encoder-proj-filename $repo/exp/joiner_encoder_proj.onnx \
--onnx-joiner-decoder-proj-filename $repo/exp/joiner_decoder_proj.onnx
--onnx-encoder-filename $repo/exp/encoder-epoch-10-avg-2.onnx \
--onnx-decoder-filename $repo/exp/decoder-epoch-10-avg-2.onnx \
--onnx-joiner-filename $repo/exp/joiner-epoch-10-avg-2.onnx \
--onnx-joiner-encoder-proj-filename $repo/exp/joiner_encoder_proj-epoch-10-avg-2.onnx \
--onnx-joiner-decoder-proj-filename $repo/exp/joiner_decoder_proj-epoch-10-avg-2.onnx
./pruned_transducer_stateless2/onnx_pretrained.py \
--tokens $repo/data/lang_char/tokens.txt \
--encoder-model-filename $repo/exp/encoder.onnx \
--decoder-model-filename $repo/exp/decoder.onnx \
--joiner-model-filename $repo/exp/joiner.onnx \
--joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
--joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.onnx \
--encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
--decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
--joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
$repo/test_wavs/DEV_T0000000000.wav \
$repo/test_wavs/DEV_T0000000001.wav \
$repo/test_wavs/DEV_T0000000002.wav

12
.github/scripts/test-ncnn-export.sh vendored
View File

@ -45,7 +45,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained-epoch-30-avg-10-averaged.pt"
cd exp
@ -56,11 +55,10 @@ log "Export via torch.jit.trace()"
./conv_emformer_transducer_stateless2/export-for-ncnn.py \
--exp-dir $repo/exp \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--epoch 99 \
--avg 1 \
--use-averaged-model 0 \
\
--tokens $repo/data/lang_bpe_500/tokens.txt \
--num-encoder-layers 12 \
--chunk-length 32 \
--cnn-module-kernel 31 \
@ -91,7 +89,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained-iter-468000-avg-16.pt"
cd exp
@ -102,7 +99,7 @@ log "Export via torch.jit.trace()"
./lstm_transducer_stateless2/export-for-ncnn.py \
--exp-dir $repo/exp \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--use-averaged-model 0
@ -140,7 +137,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained.pt"
cd exp
@ -148,7 +144,7 @@ ln -s pretrained.pt epoch-99.pt
popd
./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--exp-dir $repo/exp \
--use-averaged-model 0 \
--epoch 99 \
@ -199,7 +195,7 @@ ln -s pretrained.pt epoch-9999.pt
popd
./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \
--lang-dir $repo/data/lang_char_bpe \
--tokens $repo/data/lang_char_bpe/tokens.txt \
--exp-dir $repo/exp \
--use-averaged-model 0 \
--epoch 9999 \

138
.github/scripts/test-onnx-export.sh vendored
View File

@ -10,7 +10,123 @@ log() {
cd egs/librispeech/ASR
log "=========================================================================="
repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
log "Downloading pre-trained model from $repo_url"
git lfs install
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "exp/pretrained.pt"
cd exp
ln -s pretrained.pt epoch-99.pt
popd
log "Export via torch.jit.script()"
./zipformer/export.py \
--exp-dir $repo/exp \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--jit 1
log "Test export to ONNX format"
./zipformer/export-onnx.py \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
--exp-dir $repo/exp \
--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"
ls -lh $repo/exp
log "Run onnx_check.py"
./zipformer/onnx_check.py \
--jit-filename $repo/exp/jit_script.pt \
--onnx-encoder-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
--onnx-decoder-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
--onnx-joiner-filename $repo/exp/joiner-epoch-99-avg-1.onnx
log "Run onnx_pretrained.py"
./zipformer/onnx_pretrained.py \
--encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
--decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
--joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav
rm -rf $repo
repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17
log "Downloading pre-trained model from $repo_url"
git lfs install
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "exp/pretrained.pt"
cd exp
ln -s pretrained.pt epoch-99.pt
popd
log "Test export streaming model to ONNX format"
./zipformer/export-onnx-streaming.py \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
--exp-dir $repo/exp \
--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 True \
--chunk-size 16 \
--left-context-frames 64
ls -lh $repo/exp
log "Run onnx_pretrained-streaming.py"
./zipformer/onnx_pretrained-streaming.py \
--encoder-model-filename $repo/exp/encoder-epoch-99-avg-1-chunk-16-left-64.onnx \
--decoder-model-filename $repo/exp/decoder-epoch-99-avg-1-chunk-16-left-64.onnx \
--joiner-model-filename $repo/exp/joiner-epoch-99-avg-1-chunk-16-left-64.onnx \
--tokens $repo/data/lang_bpe_500/tokens.txt \
$repo/test_wavs/1089-134686-0001.wav
rm -rf $repo
log "--------------------------------------------------------------------------"
log "=========================================================================="
repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
@ -39,7 +155,7 @@ log "Export via torch.jit.trace()"
log "Test exporting to ONNX format"
./pruned_transducer_stateless7_streaming/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
@ -88,7 +204,7 @@ popd
log "Export via torch.jit.script()"
./pruned_transducer_stateless3/export.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/ \
@ -97,7 +213,7 @@ log "Export via torch.jit.script()"
log "Test exporting to ONNX format"
./pruned_transducer_stateless3/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 9999 \
--avg 1 \
--exp-dir $repo/exp/
@ -126,7 +242,6 @@ log "Run onnx_pretrained.py"
rm -rf $repo
log "--------------------------------------------------------------------------"
log "=========================================================================="
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-2022-05-13
GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
@ -143,7 +258,7 @@ popd
log "Export via torch.jit.script()"
./pruned_transducer_stateless5/export.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--use-averaged-model 0 \
@ -159,7 +274,7 @@ log "Export via torch.jit.script()"
log "Test exporting to ONNX format"
./pruned_transducer_stateless5/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--use-averaged-model 0 \
@ -205,7 +320,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
repo=$(basename $repo_url)
pushd $repo
git lfs pull --include "data/lang_bpe_500/bpe.model"
git lfs pull --include "exp/pretrained.pt"
cd exp
@ -215,7 +329,7 @@ popd
log "Export via torch.jit.script()"
./pruned_transducer_stateless7/export.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
@ -226,7 +340,7 @@ log "Export via torch.jit.script()"
log "Test exporting to ONNX format"
./pruned_transducer_stateless7/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
@ -270,7 +384,7 @@ popd
log "Test exporting to ONNX format"
./conv_emformer_transducer_stateless2/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
@ -310,7 +424,7 @@ popd
log "Export via torch.jit.trace()"
./lstm_transducer_stateless2/export.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
@ -320,7 +434,7 @@ log "Export via torch.jit.trace()"
log "Test exporting to ONNX format"
./lstm_transducer_stateless2/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \

45
.github/workflows/build-docker-image.yml vendored Normal file
View File

@ -0,0 +1,45 @@
# see also
# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
name: Build docker image
on:
workflow_dispatch:
concurrency:
group: build_docker-${{ github.ref }}
cancel-in-progress: true
jobs:
build-docker-image:
name: ${{ matrix.image }}
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest]
image: ["torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
steps:
# refer to https://github.com/actions/checkout
- uses: actions/checkout@v2
with:
fetch-depth: 0
- name: Rename
shell: bash
run: |
image=${{ matrix.image }}
mv -v ./docker/$image.dockerfile ./Dockerfile
- name: Log in to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_PASSWORD }}
- name: Build and push
uses: docker/build-push-action@v4
with:
context: .
file: ./Dockerfile
push: true
tags: k2fsa/icefall:${{ matrix.image }}

2
.github/workflows/run-aishell-2022-06-20.yml vendored
View File

@ -45,7 +45,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

92
.github/workflows/run-docker-image.yml vendored Normal file
View File

@ -0,0 +1,92 @@
name: Run docker image
on:
workflow_dispatch:
concurrency:
group: run_docker_image-${{ github.ref }}
cancel-in-progress: true
jobs:
run-docker-image:
name: ${{ matrix.image }}
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest]
image: ["torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
steps:
# refer to https://github.com/actions/checkout
- uses: actions/checkout@v2
with:
fetch-depth: 0
- name: Run the build process with Docker
uses: addnab/docker-run-action@v3
with:
image: k2fsa/icefall:${{ matrix.image }}
shell: bash
run: |
uname -a
cat /etc/*release
nvcc --version
# For torch1.9.0-cuda10.2
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/compat:$LD_LIBRARY_PATH
# For torch1.12.1-cuda11.3
export LD_LIBRARY_PATH=/usr/local/cuda-11.3/compat:$LD_LIBRARY_PATH
# For torch2.0.0-cuda11.7
export LD_LIBRARY_PATH=/usr/local/cuda-11.7/compat:$LD_LIBRARY_PATH
which nvcc
cuda_dir=$(dirname $(which nvcc))
echo "cuda_dir: $cuda_dir"
find $cuda_dir -name libcuda.so*
echo "--------------------"
find / -name libcuda.so* 2>/dev/null
# for torch1.13.0-cuda11.6
if [ -e /opt/conda/lib/stubs/libcuda.so ]; then
cd /opt/conda/lib/stubs && ln -s libcuda.so libcuda.so.1 && cd -
export LD_LIBRARY_PATH=/opt/conda/lib/stubs:$LD_LIBRARY_PATH
fi
find / -name libcuda.so* 2>/dev/null
echo "LD_LIBRARY_PATH: $LD_LIBRARY_PATH"
python3 --version
which python3
python3 -m pip list
echo "----------torch----------"
python3 -m torch.utils.collect_env
echo "----------k2----------"
python3 -c "import k2; print(k2.__file__)"
python3 -c "import k2; print(k2.__dev_version__)"
python3 -m k2.version
echo "----------lhotse----------"
python3 -c "import lhotse; print(lhotse.__file__)"
python3 -c "import lhotse; print(lhotse.__version__)"
echo "----------kaldifeat----------"
python3 -c "import kaldifeat; print(kaldifeat.__file__)"
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
echo "Test yesno recipe"
cd egs/yesno/ASR
./prepare.sh
./tdnn/train.py
./tdnn/decode.py

2
.github/workflows/run-gigaspeech-2022-05-13.yml vendored
View File

@ -44,7 +44,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-librispeech-2022-03-12.yml vendored
View File

@ -44,7 +44,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-librispeech-2022-04-29.yml vendored
View File

@ -44,7 +44,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-librispeech-2022-05-13.yml vendored
View File

@ -44,7 +44,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml vendored
View File

@ -44,7 +44,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml vendored
View File

@ -44,7 +44,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml vendored
View File

@ -44,7 +44,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

84
.github/workflows/run-multi-zh_hans-zipformer.yml vendored Normal file
View File

@ -0,0 +1,84 @@
# Copyright 2023 Xiaomi Corp. (author: 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.
name: run-multi-zh_hans-zipformer
on:
push:
branches:
- master
pull_request:
types: [labeled]
concurrency:
group: run_multi-zh_hans_zipformer-${{ github.ref }}
cancel-in-progress: true
jobs:
run_multi-zh_hans_zipformer:
if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'multi-zh_hans'
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.8]
fail-fast: false
steps:
- uses: actions/checkout@v2
with:
fetch-depth: 0
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
cache-dependency-path: '**/requirements-ci.txt'
- name: Install Python dependencies
run: |
grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install
pip uninstall -y protobuf
pip install --no-binary protobuf protobuf==3.20.*
- name: Cache kaldifeat
id: my-cache
uses: actions/cache@v2
with:
path: |
~/tmp/kaldifeat
key: cache-tmp-${{ matrix.python-version }}-2023-05-22
- name: Install kaldifeat
if: steps.my-cache.outputs.cache-hit != 'true'
shell: bash
run: |
.github/scripts/install-kaldifeat.sh
- name: Inference with pre-trained model
shell: bash
env:
GITHUB_EVENT_NAME: ${{ github.event_name }}
GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
run: |
sudo apt-get -qq install git-lfs tree
export PYTHONPATH=$PWD:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
.github/scripts/run-multi-zh_hans-zipformer.sh

19
.github/workflows/run-pretrained-conformer-ctc.yml → .github/workflows/run-pretrained-ctc.yml vendored
View File

@ -14,7 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
name: run-pre-trained-conformer-ctc
name: run-pre-trained-ctc
on:
push:
@ -23,18 +23,25 @@ on:
pull_request:
types: [labeled]
workflow_dispatch:
inputs:
test-run:
description: 'Test (y/n)?'
required: true
default: 'y'
concurrency:
group: run_pre_trained_conformer_ctc-${{ github.ref }}
group: run_pre_trained_ctc-${{ github.ref }}
cancel-in-progress: true
jobs:
run_pre_trained_conformer_ctc:
if: github.event.label.name == 'ready' || github.event_name == 'push'
run_pre_trained_ctc:
if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event.inputs.test-run == 'y' || github.event.label.name == 'ctc'
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false
@ -77,4 +84,4 @@ jobs:
export PYTHONPATH=$PWD:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
.github/scripts/run-pre-trained-conformer-ctc.sh
.github/scripts/run-pre-trained-ctc.sh

2
.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml vendored
View File

@ -43,7 +43,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml vendored
View File

@ -43,7 +43,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml vendored
View File

@ -34,7 +34,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml vendored
View File

@ -34,7 +34,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-pretrained-transducer-stateless.yml vendored
View File

@ -43,7 +43,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

2
.github/workflows/run-pretrained-transducer.yml vendored
View File

@ -34,7 +34,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.7, 3.8, 3.9]
python-version: [3.8]
fail-fast: false

84
.github/workflows/run-swbd-conformer-ctc.yml vendored Normal file
View File

@ -0,0 +1,84 @@
# Copyright 2023 Xiaomi Corp. (author: 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.
name: run-swbd-conformer_ctc
on:
push:
branches:
- master
pull_request:
types: [labeled]
concurrency:
group: run-swbd-conformer_ctc-${{ github.ref }}
cancel-in-progress: true
jobs:
run-swbd-conformer_ctc:
if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'swbd'
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-latest]
python-version: [3.8]
fail-fast: false
steps:
- uses: actions/checkout@v2
with:
fetch-depth: 0
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
cache-dependency-path: '**/requirements-ci.txt'
- name: Install Python dependencies
run: |
grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install
pip uninstall -y protobuf
pip install --no-binary protobuf protobuf==3.20.*
- name: Cache kaldifeat
id: my-cache
uses: actions/cache@v2
with:
path: |
~/tmp/kaldifeat
key: cache-tmp-${{ matrix.python-version }}-2023-05-22
- name: Install kaldifeat
if: steps.my-cache.outputs.cache-hit != 'true'
shell: bash
run: |
.github/scripts/install-kaldifeat.sh
- name: Inference with pre-trained model
shell: bash
env:
GITHUB_EVENT_NAME: ${{ github.event_name }}
GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
run: |
sudo apt-get -qq install git-lfs tree
export PYTHONPATH=$PWD:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
.github/scripts/run-swbd-conformer-ctc-2023-08-26.sh

115
.github/workflows/run-yesno-recipe.yml vendored
View File

@ -44,11 +44,6 @@ jobs:
with:
fetch-depth: 0
- name: Install graphviz
shell: bash
run: |
sudo apt-get -qq install graphviz
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
@ -65,11 +60,12 @@ jobs:
- name: Install Python dependencies
run: |
grep -v '^#' ./requirements-ci.txt | grep -v kaldifst | xargs -n 1 -L 1 pip install
grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install
pip uninstall -y protobuf
pip install --no-binary protobuf protobuf==3.20.*
pip install --no-deps --force-reinstall https://huggingface.co/csukuangfj/k2/resolve/main/cpu/k2-1.24.3.dev20230508+cpu.torch1.13.1-cp38-cp38-linux_x86_64.whl
pip install kaldifeat==1.25.0.dev20230726+cpu.torch1.13.1 -f https://csukuangfj.github.io/kaldifeat/cpu.html
- name: Run yesno recipe
shell: bash
@ -78,9 +74,112 @@ jobs:
export PYTHONPATH=$PWD:$PYTHONPATH
echo $PYTHONPATH
cd egs/yesno/ASR
./prepare.sh
python3 ./tdnn/train.py
python3 ./tdnn/decode.py
# TODO: Check that the WER is less than some value
- name: Test exporting to pretrained.pt
shell: bash
working-directory: ${{github.workspace}}
run: |
export PYTHONPATH=$PWD:$PYTHONPATH
echo $PYTHONPATH
cd egs/yesno/ASR
python3 ./tdnn/export.py --epoch 14 --avg 2
python3 ./tdnn/pretrained.py \
--checkpoint ./tdnn/exp/pretrained.pt \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
- name: Test exporting to torchscript
shell: bash
working-directory: ${{github.workspace}}
run: |
export PYTHONPATH=$PWD:$PYTHONPATH
echo $PYTHONPATH
cd egs/yesno/ASR
python3 ./tdnn/export.py --epoch 14 --avg 2 --jit 1
python3 ./tdnn/jit_pretrained.py \
--nn-model ./tdnn/exp/cpu_jit.pt \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
- name: Test exporting to onnx
shell: bash
working-directory: ${{github.workspace}}
run: |
export PYTHONPATH=$PWD:$PYTHONPATH
echo $PYTHONPATH
cd egs/yesno/ASR
python3 ./tdnn/export_onnx.py --epoch 14 --avg 2
echo "Test float32 model"
python3 ./tdnn/onnx_pretrained.py \
--nn-model ./tdnn/exp/model-epoch-14-avg-2.onnx \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
echo "Test int8 model"
python3 ./tdnn/onnx_pretrained.py \
--nn-model ./tdnn/exp/model-epoch-14-avg-2.int8.onnx \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
- name: Test decoding with H
shell: bash
working-directory: ${{github.workspace}}
run: |
export PYTHONPATH=$PWD:$PYTHONPATH
echo $PYTHONPATH
cd egs/yesno/ASR
python3 ./tdnn/export.py --epoch 14 --avg 2 --jit 1
python3 ./tdnn/jit_pretrained_decode_with_H.py \
--nn-model ./tdnn/exp/cpu_jit.pt \
--H ./data/lang_phone/H.fst \
--tokens ./data/lang_phone/tokens.txt \
./download/waves_yesno/0_0_0_1_0_0_0_1.wav \
./download/waves_yesno/0_0_1_0_0_0_1_0.wav \
./download/waves_yesno/0_0_1_0_0_1_1_1.wav
- name: Test decoding with HL
shell: bash
working-directory: ${{github.workspace}}
run: |
export PYTHONPATH=$PWD:$PYTHONPATH
echo $PYTHONPATH
cd egs/yesno/ASR
python3 ./tdnn/export.py --epoch 14 --avg 2 --jit 1
python3 ./tdnn/jit_pretrained_decode_with_HL.py \
--nn-model ./tdnn/exp/cpu_jit.pt \
--HL ./data/lang_phone/HL.fst \
--words ./data/lang_phone/words.txt \
./download/waves_yesno/0_0_0_1_0_0_0_1.wav \
./download/waves_yesno/0_0_1_0_0_0_1_0.wav \
./download/waves_yesno/0_0_1_0_0_1_1_1.wav
- name: Show generated files
shell: bash
working-directory: ${{github.workspace}}
run: |
cd egs/yesno/ASR
ls -lh tdnn/exp
ls -lh data/lang_phone

57
.github/workflows/test.yml vendored
View File

@ -35,9 +35,9 @@ jobs:
matrix:
os: [ubuntu-latest]
python-version: ["3.8"]
torch: ["1.10.0"]
torchaudio: ["0.10.0"]
k2-version: ["1.23.2.dev20221201"]
torch: ["1.13.0"]
torchaudio: ["0.13.0"]
k2-version: ["1.24.3.dev20230719"]
fail-fast: false
@ -66,14 +66,14 @@ jobs:
pip install torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
pip install torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.github.io/k2/cpu.html
pip install git+https://github.com/lhotse-speech/lhotse
# icefall requirements
pip uninstall -y protobuf
pip install --no-binary protobuf protobuf==3.20.*
pip install kaldifst
pip install onnxruntime
pip install onnxruntime matplotlib
pip install -r requirements.txt
- name: Install graphviz
@ -83,13 +83,6 @@ jobs:
python3 -m pip install -qq graphviz
sudo apt-get -qq install graphviz
- name: Install graphviz
if: startsWith(matrix.os, 'macos')
shell: bash
run: |
python3 -m pip install -qq graphviz
brew install -q graphviz
- name: Run tests
if: startsWith(matrix.os, 'ubuntu')
run: |
@ -129,40 +122,10 @@ jobs:
cd ../transducer_lstm
pytest -v -s
- name: Run tests
if: startsWith(matrix.os, 'macos')
run: |
ls -lh
export PYTHONPATH=$PWD:$PWD/lhotse:$PYTHONPATH
lib_path=$(python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())")
echo "lib_path: $lib_path"
export DYLD_LIBRARY_PATH=$lib_path:$DYLD_LIBRARY_PATH
pytest -v -s ./test
# run tests for conformer ctc
cd egs/librispeech/ASR/conformer_ctc
cd ../zipformer
pytest -v -s
cd ../pruned_transducer_stateless
pytest -v -s
cd ../pruned_transducer_stateless2
pytest -v -s
cd ../pruned_transducer_stateless3
pytest -v -s
cd ../pruned_transducer_stateless4
pytest -v -s
cd ../transducer_stateless
pytest -v -s
# cd ../transducer
# pytest -v -s
cd ../transducer_stateless2
pytest -v -s
cd ../transducer_lstm
pytest -v -s
- uses: actions/upload-artifact@v2
with:
path: egs/librispeech/ASR/zipformer/swoosh.pdf
name: swoosh.pdf

2
.gitignore vendored
View File

@ -34,3 +34,5 @@ node_modules
*.param
*.bin
.DS_Store
*.fst
*.arpa

12
README.md
View File

@ -29,6 +29,7 @@ We provide the following recipes:
- [yesno][yesno]
- [LibriSpeech][librispeech]
- [GigaSpeech][gigaspeech]
- [AMI][ami]
- [Aishell][aishell]
- [Aishell2][aishell2]
- [Aishell4][aishell4]
@ -37,6 +38,7 @@ We provide the following recipes:
- [Aidatatang_200zh][aidatatang_200zh]
- [WenetSpeech][wenetspeech]
- [Alimeeting][alimeeting]
- [Switchboard][swbd]
- [TAL_CSASR][tal_csasr]
### yesno
@ -118,9 +120,9 @@ We provide a Colab notebook to run a pre-trained transducer conformer + stateles
| Encoder | Params | test-clean | test-other |
|-----------------|--------|------------|------------|
| zipformer | 65.5M | 2.21 | 4.91 |
| zipformer-small | 23.2M | 2.46 | 5.83 |
| zipformer-large | 148.4M | 2.11 | 4.77 |
| zipformer | 65.5M | 2.21 | 4.79 |
| zipformer-small | 23.2M | 2.42 | 5.73 |
| zipformer-large | 148.4M | 2.06 | 4.63 |
Note: No auxiliary losses are used in the training and no LMs are used
in the decoding.
@ -338,7 +340,7 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder
#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
The best results for Chinese CER(%) and English WER(%) respectivly (zh: Chinese, en: English):
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|
@ -393,4 +395,6 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
[wenetspeech]: egs/wenetspeech/ASR
[alimeeting]: egs/alimeeting/ASR
[tal_csasr]: egs/tal_csasr/ASR
[ami]: egs/ami
[swbd]: egs/swbd/ASR
[k2]: https://github.com/k2-fsa/k2

15
docker/README.md
View File

@ -1,5 +1,20 @@
# icefall dockerfile
## Download from dockerhub
You can find pre-built docker image for icefall at the following address:
<https://hub.docker.com/r/k2fsa/icefall/tags>
Example usage:
```bash
docker run --gpus all --rm -it k2fsa/icefall:torch1.13.0-cuda11.6 /bin/bash
```
## Build from dockerfile
2 sets of configuration are provided - (a) Ubuntu18.04-pytorch1.12.1-cuda11.3-cudnn8, and (b) Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8.
If your NVIDIA driver supports CUDA Version: 11.3, please go for case (a) Ubuntu18.04-pytorch1.12.1-cuda11.3-cudnn8.

70
docker/torch1.12.1-cuda11.3.dockerfile Normal file
View File

@ -0,0 +1,70 @@
FROM pytorch/pytorch:1.12.1-cuda11.3-cudnn8-devel
ENV LC_ALL C.UTF-8
ARG DEBIAN_FRONTEND=noninteractive
ARG K2_VERSION="1.24.3.dev20230725+cuda11.3.torch1.12.1"
ARG KALDIFEAT_VERSION="1.25.0.dev20230726+cuda11.3.torch1.12.1"
ARG TORCHAUDIO_VERSION="0.12.1+cu113"
LABEL authors="Fangjun Kuang <csukuangfj@gmail.com>"
LABEL k2_version=${K2_VERSION}
LABEL kaldifeat_version=${KALDIFEAT_VERSION}
LABEL github_repo="https://github.com/k2-fsa/icefall"
RUN apt-get update && \
apt-get install -y --no-install-recommends \
curl \
vim \
libssl-dev \
autoconf \
automake \
bzip2 \
ca-certificates \
ffmpeg \
g++ \
gfortran \
git \
libtool \
make \
patch \
sox \
subversion \
unzip \
valgrind \
wget \
zlib1g-dev \
&& rm -rf /var/lib/apt/lists/*
# Install dependencies
RUN pip install --no-cache-dir \
torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/torch_stable.html \
k2==${K2_VERSION} -f https://k2-fsa.github.io/k2/cuda.html \
git+https://github.com/lhotse-speech/lhotse \
kaldifeat==${KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cuda.html \
\
kaldi_native_io \
kaldialign \
kaldifst \
kaldilm \
sentencepiece>=0.1.96 \
tensorboard \
typeguard \
dill \
onnx \
onnxruntime \
onnxmltools \
multi_quantization \
typeguard \
numpy \
pytest \
graphviz
RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
cd /workspace/icefall && \
pip install --no-cache-dir -r requirements.txt
ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
WORKDIR /workspace/icefall

72
docker/torch1.13.0-cuda11.6.dockerfile Normal file
View File

@ -0,0 +1,72 @@
FROM pytorch/pytorch:1.13.0-cuda11.6-cudnn8-runtime
ENV LC_ALL C.UTF-8
ARG DEBIAN_FRONTEND=noninteractive
ARG K2_VERSION="1.24.3.dev20230725+cuda11.6.torch1.13.0"
ARG KALDIFEAT_VERSION="1.25.0.dev20230726+cuda11.6.torch1.13.0"
ARG TORCHAUDIO_VERSION="0.13.0+cu116"
LABEL authors="Fangjun Kuang <csukuangfj@gmail.com>"
LABEL k2_version=${K2_VERSION}
LABEL kaldifeat_version=${KALDIFEAT_VERSION}
LABEL github_repo="https://github.com/k2-fsa/icefall"
RUN apt-get update && \
apt-get install -y --no-install-recommends \
curl \
vim \
libssl-dev \
autoconf \
automake \
bzip2 \
ca-certificates \
ffmpeg \
g++ \
gfortran \
git \
libtool \
make \
patch \
sox \
subversion \
unzip \
valgrind \
wget \
zlib1g-dev \
&& rm -rf /var/lib/apt/lists/*
# Install dependencies
RUN pip install --no-cache-dir \
torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/torch_stable.html \
k2==${K2_VERSION} -f https://k2-fsa.github.io/k2/cuda.html \
git+https://github.com/lhotse-speech/lhotse \
kaldifeat==${KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cuda.html \
\
kaldi_native_io \
kaldialign \
kaldifst \
kaldilm \
sentencepiece>=0.1.96 \
tensorboard \
typeguard \
dill \
onnx \
onnxruntime \
onnxmltools \
multi_quantization \
typeguard \
numpy \
pytest \
graphviz
RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
cd /workspace/icefall && \
pip install --no-cache-dir -r requirements.txt
ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
ENV LD_LIBRARY_PATH /opt/conda/lib/stubs:$LD_LIBRARY_PATH
WORKDIR /workspace/icefall

86
docker/torch1.9.0-cuda10.2.dockerfile Normal file
View File

@ -0,0 +1,86 @@
FROM pytorch/pytorch:1.9.0-cuda10.2-cudnn7-devel
ENV LC_ALL C.UTF-8
ARG DEBIAN_FRONTEND=noninteractive
ARG K2_VERSION="1.24.3.dev20230726+cuda10.2.torch1.9.0"
ARG KALDIFEAT_VERSION="1.25.0.dev20230726+cuda10.2.torch1.9.0"
ARG TORCHAUDIO_VERSION="0.9.0"
LABEL authors="Fangjun Kuang <csukuangfj@gmail.com>"
LABEL k2_version=${K2_VERSION}
LABEL kaldifeat_version=${KALDIFEAT_VERSION}
LABEL github_repo="https://github.com/k2-fsa/icefall"
# see https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/
RUN rm /etc/apt/sources.list.d/cuda.list && \
rm /etc/apt/sources.list.d/nvidia-ml.list && \
apt-key del 7fa2af80
RUN apt-get update && \
apt-get install -y --no-install-recommends \
curl \
vim \
libssl-dev \
autoconf \
automake \
bzip2 \
ca-certificates \
ffmpeg \
g++ \
gfortran \
git \
libtool \
make \
patch \
sox \
subversion \
unzip \
valgrind \
wget \
zlib1g-dev \
&& rm -rf /var/lib/apt/lists/*
RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb && \
dpkg -i cuda-keyring_1.0-1_all.deb && \
rm -v cuda-keyring_1.0-1_all.deb && \
apt-get update && \
rm -rf /var/lib/apt/lists/*
# Install dependencies
RUN pip uninstall -y tqdm && \
pip install -U --no-cache-dir \
torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/torch_stable.html \
k2==${K2_VERSION} -f https://k2-fsa.github.io/k2/cuda.html \
kaldifeat==${KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cuda.html \
git+https://github.com/lhotse-speech/lhotse \
\
kaldi_native_io \
kaldialign \
kaldifst \
kaldilm \
sentencepiece>=0.1.96 \
tensorboard \
typeguard \
dill \
onnx \
onnxruntime \
onnxmltools \
multi_quantization \
typeguard \
numpy \
pytest \
graphviz \
tqdm>=4.63.0
RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
cd /workspace/icefall && \
pip install --no-cache-dir -r requirements.txt
ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
WORKDIR /workspace/icefall

70
docker/torch2.0.0-cuda11.7.dockerfile Normal file
View File

@ -0,0 +1,70 @@
FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
ENV LC_ALL C.UTF-8
ARG DEBIAN_FRONTEND=noninteractive
ARG K2_VERSION="1.24.3.dev20230718+cuda11.7.torch2.0.0"
ARG KALDIFEAT_VERSION="1.25.0.dev20230726+cuda11.7.torch2.0.0"
ARG TORCHAUDIO_VERSION="2.0.0+cu117"
LABEL authors="Fangjun Kuang <csukuangfj@gmail.com>"
LABEL k2_version=${K2_VERSION}
LABEL kaldifeat_version=${KALDIFEAT_VERSION}
LABEL github_repo="https://github.com/k2-fsa/icefall"
RUN apt-get update && \
apt-get install -y --no-install-recommends \
curl \
vim \
libssl-dev \
autoconf \
automake \
bzip2 \
ca-certificates \
ffmpeg \
g++ \
gfortran \
git \
libtool \
make \
patch \
sox \
subversion \
unzip \
valgrind \
wget \
zlib1g-dev \
&& rm -rf /var/lib/apt/lists/*
# Install dependencies
RUN pip install --no-cache-dir \
torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/torch_stable.html \
k2==${K2_VERSION} -f https://k2-fsa.github.io/k2/cuda.html \
git+https://github.com/lhotse-speech/lhotse \
kaldifeat==${KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cuda.html \
\
kaldi_native_io \
kaldialign \
kaldifst \
kaldilm \
sentencepiece>=0.1.96 \
tensorboard \
typeguard \
dill \
onnx \
onnxruntime \
onnxmltools \
multi_quantization \
typeguard \
numpy \
pytest \
graphviz
RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
cd /workspace/icefall && \
pip install --no-cache-dir -r requirements.txt
ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
WORKDIR /workspace/icefall

8
docs/source/conf.py
View File

@ -90,4 +90,12 @@ rst_epilog = """
.. _musan: http://www.openslr.org/17/
.. _ONNX: https://github.com/onnx/onnx
.. _onnxruntime: https://github.com/microsoft/onnxruntime
.. _torch: https://github.com/pytorch/pytorch
.. _torchaudio: https://github.com/pytorch/audio
.. _k2: https://github.com/k2-fsa/k2
.. _lhotse: https://github.com/lhotse-speech/lhotse
.. _yesno: https://www.openslr.org/1/
.. _Next-gen Kaldi: https://github.com/k2-fsa
.. _Kaldi: https://github.com/kaldi-asr/kaldi
.. _lilcom: https://github.com/danpovey/lilcom
"""

2
docs/source/contributing/code-style.rst
View File

@ -38,7 +38,7 @@ Please fix any issues reported by the check tools.
.. HINT::
Some of the check tools, i.e., ``black`` and ``isort`` will modify
the files to be commited **in-place**. So please run ``git status``
the files to be committed **in-place**. So please run ``git status``
after failure to see which file has been modified by the tools
before you make any further changes.

11
docs/source/decoding-with-langugage-models/LODR.rst
View File

@ -71,9 +71,12 @@ As the initial step, let's download the pre-trained model.
.. code-block:: bash
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
$ cd ../data/lang_bpe_500
$ git lfs pull --include bpe.model
$ cd ../../..
To test the model, let's have a look at the decoding results **without** using LM. This can be done via the following command:
@ -85,7 +88,7 @@ To test the model, let's have a look at the decoding results **without** using L
--avg 1 \
--use-averaged-model False \
--exp-dir $exp_dir \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search
@ -135,8 +138,8 @@ Then, we perform LODR decoding by setting ``--decoding-method`` to ``modified_be
--exp-dir $exp_dir \
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search_lm_LODR \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
--decoding-method modified_beam_search_LODR \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--use-shallow-fusion 1 \
--lm-type rnn \
--lm-exp-dir $lm_dir \

24
docs/source/decoding-with-langugage-models/index.rst
View File

@ -2,7 +2,29 @@ Decoding with language models
=============================
This section describes how to use external langugage models
during decoding to improve the WER of transducer models.
during decoding to improve the WER of transducer models. To train an external language model,
please refer to this tutorial: :ref:`train_nnlm`.
The following decoding methods with external langugage models are available:
.. list-table::
:widths: 25 50
:header-rows: 1
* - Decoding method
- beam=4
* - ``modified_beam_search``
- Beam search (i.e. really n-best decoding, the "beam" is the value of n), similar to the original RNN-T paper. Note, this method does not use language model.
* - ``modified_beam_search_lm_shallow_fusion``
- As ``modified_beam_search``, but interpolate RNN-T scores with language model scores, also known as shallow fusion
* - ``modified_beam_search_LODR``
- As ``modified_beam_search_lm_shallow_fusion``, but subtract score of a (BPE-symbol-level) bigram backoff language model used as an approximation to the internal language model of RNN-T.
* - ``modified_beam_search_lm_rescore``
- As ``modified_beam_search``, but rescore the n-best hypotheses with external language model (e.g. RNNLM) and re-rank them.
* - ``modified_beam_search_lm_rescore_LODR``
- As ``modified_beam_search_lm_rescore``, but also subtract the score of a (BPE-symbol-level) bigram backoff language model during re-ranking.
.. toctree::
:maxdepth: 2

23
docs/source/decoding-with-langugage-models/rescoring.rst
View File

@ -4,7 +4,7 @@ LM rescoring for Transducer
=================================
LM rescoring is a commonly used approach to incorporate external LM information. Unlike shallow-fusion-based
methods (see :ref:`shallow-fusion`, :ref:`LODR`), rescoring is usually performed to re-rank the n-best hypotheses after beam search.
methods (see :ref:`shallow_fusion`, :ref:`LODR`), rescoring is usually performed to re-rank the n-best hypotheses after beam search.
Rescoring is usually more efficient than shallow fusion since less computation is performed on the external LM.
In this tutorial, we will show you how to use external LM to rescore the n-best hypotheses decoded from neural transducer models in
`icefall <https://github.com/k2-fsa/icefall>`__.
@ -34,9 +34,12 @@ As the initial step, let's download the pre-trained model.
.. code-block:: bash
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
$ cd ../data/lang_bpe_500
$ git lfs pull --include bpe.model
$ cd ../../..
As usual, we first test the model's performance without external LM. This can be done via the following command:
@ -48,7 +51,7 @@ As usual, we first test the model's performance without external LM. This can be
--avg 1 \
--use-averaged-model False \
--exp-dir $exp_dir \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search
@ -101,7 +104,7 @@ is set to `False`.
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search_lm_rescore \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--use-shallow-fusion 0 \
--lm-type rnn \
--lm-exp-dir $lm_dir \
@ -173,7 +176,7 @@ Then we can performn LM rescoring + LODR by changing the decoding method to `mod
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search_lm_rescore_LODR \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--use-shallow-fusion 0 \
--lm-type rnn \
--lm-exp-dir $lm_dir \
@ -225,23 +228,23 @@ Here, we benchmark the WERs and decoding speed of them:
- beam=4
- beam=8
- beam=12
* - `modified_beam_search`
* - ``modified_beam_search``
- 3.11/7.93; 132s
- 3.1/7.95; 177s
- 3.1/7.96; 210s
* - `modified_beam_search_lm_shallow_fusion`
* - ``modified_beam_search_lm_shallow_fusion``
- 2.77/7.08; 262s
- 2.62/6.65; 352s
- 2.58/6.65; 488s
* - LODR
* - ``modified_beam_search_LODR``
- 2.61/6.74; 400s
- 2.45/6.38; 610s
- 2.4/6.23; 870s
* - `modified_beam_search_lm_rescore`
* - ``modified_beam_search_lm_rescore``
- 2.93/7.6; 156s
- 2.67/7.11; 203s
- 2.59/6.86; 255s
* - `modified_beam_search_lm_rescore_LODR`
* - ``modified_beam_search_lm_rescore_LODR``
- 2.9/7.57; 160s
- 2.63/7.04; 203s
- 2.52/6.73; 263s

9
docs/source/decoding-with-langugage-models/shallow-fusion.rst
View File

@ -32,9 +32,12 @@ As the initial step, let's download the pre-trained model.
.. code-block:: bash
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
$ cd ../data/lang_bpe_500
$ git lfs pull --include bpe.model
$ cd ../../..
To test the model, let's have a look at the decoding results without using LM. This can be done via the following command:
@ -46,7 +49,7 @@ To test the model, let's have a look at the decoding results without using LM. T
--avg 1 \
--use-averaged-model False \
--exp-dir $exp_dir \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search
@ -95,7 +98,7 @@ To use shallow fusion for decoding, we can execute the following command:
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search_lm_shallow_fusion \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--use-shallow-fusion 1 \
--lm-type rnn \
--lm-exp-dir $lm_dir \

BIN
docs/source/docker/img/docker-hub.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 356 KiB

17
docs/source/docker/index.rst Normal file
View File

@ -0,0 +1,17 @@
.. _icefall_docker:
Docker
======
This section describes how to use pre-built docker images to run `icefall`_.
.. hint::
If you only have CPUs available, you can still use the pre-built docker
images.
.. toctree::
:maxdepth: 2
./intro.rst

171
docs/source/docker/intro.rst Normal file
View File

@ -0,0 +1,171 @@
Introduction
=============
We have pre-built docker images hosted at the following address:
`<https://hub.docker.com/repository/docker/k2fsa/icefall/general>`_
.. figure:: img/docker-hub.png
:width: 600
:align: center
You can find the ``Dockerfile`` at `<https://github.com/k2-fsa/icefall/tree/master/docker>`_.
We describe the following items in this section:
- How to view available tags
- How to download pre-built docker images
- How to run the `yesno`_ recipe within a docker container on ``CPU``
View available tags
===================
You can use the following command to view available tags:
.. code-block:: bash
curl -s 'https://registry.hub.docker.com/v2/repositories/k2fsa/icefall/tags/'|jq '."results"[]["name"]'
which will give you something like below:
.. code-block:: bash
"torch2.0.0-cuda11.7"
"torch1.12.1-cuda11.3"
"torch1.9.0-cuda10.2"
"torch1.13.0-cuda11.6"
.. hint::
Available tags will be updated when there are new releases of `torch`_.
Please select an appropriate combination of `torch`_ and CUDA.
Download a docker image
=======================
Suppose that you select the tag ``torch1.13.0-cuda11.6``, you can use
the following command to download it:
.. code-block:: bash
sudo docker image pull k2fsa/icefall:torch1.13.0-cuda11.6
Run a docker image with GPU
===========================
.. code-block:: bash
sudo docker run --gpus all --rm -it k2fsa/icefall:torch1.13.0-cuda11.6 /bin/bash
Run a docker image with CPU
===========================
.. code-block:: bash
sudo docker run --rm -it k2fsa/icefall:torch1.13.0-cuda11.6 /bin/bash
Run yesno within a docker container
===================================
After starting the container, the following interface is presented:
.. code-block:: bash
root@60c947eac59c:/workspace/icefall#
It shows the current user is ``root`` and the current working directory
is ``/workspace/icefall``.
Update the code
---------------
Please first run:
.. code-block:: bash
root@60c947eac59c:/workspace/icefall# git pull
so that your local copy contains the latest code.
Data preparation
----------------
Now we can use
.. code-block:: bash
root@60c947eac59c:/workspace/icefall# cd egs/yesno/ASR/
to switch to the ``yesno`` recipe and run
.. code-block:: bash
root@60c947eac59c:/workspace/icefall/egs/yesno/ASR# ./prepare.sh
.. hint::
If you are running without GPU, it may report the following error:
.. code-block:: bash
File "/opt/conda/lib/python3.9/site-packages/k2/__init__.py", line 23, in <module>
from _k2 import DeterminizeWeightPushingType
ImportError: libcuda.so.1: cannot open shared object file: No such file or directory
We can use the following command to fix it:
.. code-block:: bash
root@60c947eac59c:/workspace/icefall/egs/yesno/ASR# ln -s /opt/conda/lib/stubs/libcuda.so /opt/conda/lib/stubs/libcuda.so.1
The logs of running ``./prepare.sh`` are listed below:
.. literalinclude:: ./log/log-preparation.txt
Training
--------
After preparing the data, we can start training with the following command
.. code-block:: bash
root@60c947eac59c:/workspace/icefall/egs/yesno/ASR# ./tdnn/train.py
All of the training logs are given below:
.. hint::
It is running on CPU and it takes only 16 seconds for this run.
.. literalinclude:: ./log/log-train-2023-08-01-01-55-27
Decoding
--------
After training, we can decode the trained model with
.. code-block:: bash
root@60c947eac59c:/workspace/icefall/egs/yesno/ASR# ./tdnn/decode.py
The decoding logs are given below:
.. code-block:: bash
2023-08-01 02:06:22,400 INFO [decode.py:263] Decoding started
2023-08-01 02:06:22,400 INFO [decode.py:264] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '4c05309499a08454997adf500b56dcc629e35ae5', 'k2-git-date': 'Tue Jul 25 16:23:36 2023', 'lhotse-version': '1.16.0.dev+git.7640d663.clean', 'torch-version': '1.13.0', 'torch-cuda-available': False, 'torch-cuda-version': '11.6', 'python-version': '3.9', 'icefall-git-branch': 'master', 'icefall-git-sha1': '375520d-clean', 'icefall-git-date': 'Fri Jul 28 07:43:08 2023', 'icefall-path': '/workspace/icefall', 'k2-path': '/opt/conda/lib/python3.9/site-packages/k2/__init__.py', 'lhotse-path': '/opt/conda/lib/python3.9/site-packages/lhotse/__init__.py', 'hostname': '60c947eac59c', 'IP address': '172.17.0.2'}}
2023-08-01 02:06:22,401 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-08-01 02:06:22,403 INFO [decode.py:273] device: cpu
2023-08-01 02:06:22,406 INFO [decode.py:291] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
2023-08-01 02:06:22,424 INFO [asr_datamodule.py:218] About to get test cuts
2023-08-01 02:06:22,425 INFO [asr_datamodule.py:252] About to get test cuts
2023-08-01 02:06:22,504 INFO [decode.py:204] batch 0/?, cuts processed until now is 4
[W NNPACK.cpp:53] Could not initialize NNPACK! Reason: Unsupported hardware.
2023-08-01 02:06:22,687 INFO [decode.py:241] The transcripts are stored in tdnn/exp/recogs-test_set.txt
2023-08-01 02:06:22,688 INFO [utils.py:564] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
2023-08-01 02:06:22,690 INFO [decode.py:249] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
2023-08-01 02:06:22,690 INFO [decode.py:316] Done!
Congratulations! You have finished successfully running `icefall`_ within a docker container.

180
docs/source/for-dummies/data-preparation.rst Normal file
View File

@ -0,0 +1,180 @@
.. _dummies_tutorial_data_preparation:
Data Preparation
================
After :ref:`dummies_tutorial_environment_setup`, we can start preparing the
data for training and decoding.
The first step is to prepare the data for training. We have already provided
`prepare.sh <https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/prepare.sh>`_
that would prepare everything required for training.
.. code-block::
cd /tmp/icefall
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
cd egs/yesno/ASR
./prepare.sh
Note that in each recipe from `icefall`_, there exists a file ``prepare.sh``,
which you should run before you run anything else.
That is all you need for data preparation.
For the more curious
--------------------
If you are wondering how to prepare your own dataset, please refer to the following
URLs for more details:
- `<https://github.com/lhotse-speech/lhotse/tree/master/lhotse/recipes>`_
It contains recipes for a variety of dataset. If you want to add your own
dataset, please read recipes in this folder first.
- `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py>`_
The `yesno`_ recipe in `lhotse`_.
If you already have a `Kaldi`_ dataset directory, which contains files like
``wav.scp``, ``feats.scp``, then you can refer to `<https://lhotse.readthedocs.io/en/latest/kaldi.html#example>`_.
A quick look to the generated files
-----------------------------------
``./prepare.sh`` puts generated files into two directories:
- ``download``
- ``data``
download
^^^^^^^^
The ``download`` directory contains downloaded dataset files:
.. code-block:: bas
tree -L 1 ./download/
./download/
|-- waves_yesno
`-- waves_yesno.tar.gz
.. hint::
Please refer to `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py#L41>`_
for how the data is downloaded and extracted.
data
^^^^
.. code-block:: bash
tree ./data/
./data/
|-- fbank
| |-- yesno_cuts_test.jsonl.gz
| |-- yesno_cuts_train.jsonl.gz
| |-- yesno_feats_test.lca
| `-- yesno_feats_train.lca
|-- lang_phone
| |-- HLG.pt
| |-- L.pt
| |-- L_disambig.pt
| |-- Linv.pt
| |-- lexicon.txt
| |-- lexicon_disambig.txt
| |-- tokens.txt
| `-- words.txt
|-- lm
| |-- G.arpa
| `-- G.fst.txt
`-- manifests
|-- yesno_recordings_test.jsonl.gz
|-- yesno_recordings_train.jsonl.gz
|-- yesno_supervisions_test.jsonl.gz
`-- yesno_supervisions_train.jsonl.gz
4 directories, 18 files
**data/manifests**:
This directory contains manifests. They are used to generate files in
``data/fbank``.
To give you an idea of what it contains, we examine the first few lines of
the manifests related to the ``train`` dataset.
.. code-block:: bash
cd data/manifests
gunzip -c yesno_recordings_train.jsonl.gz | head -n 3
The output is given below:
.. code-block:: bash
{"id": "0_0_0_0_1_1_1_1", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_0_1_1_1_1.wav"}], "sampling_rate": 8000, "num_samples": 50800, "duration": 6.35, "channel_ids": [0]}
{"id": "0_0_0_1_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_1_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48880, "duration": 6.11, "channel_ids": [0]}
{"id": "0_0_1_0_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_1_0_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48160, "duration": 6.02, "channel_ids": [0]}
Please refer to `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/audio.py#L300>`_
for the meaning of each field per line.
.. code-block:: bash
gunzip -c yesno_supervisions_train.jsonl.gz | head -n 3
The output is given below:
.. code-block:: bash
{"id": "0_0_0_0_1_1_1_1", "recording_id": "0_0_0_0_1_1_1_1", "start": 0.0, "duration": 6.35, "channel": 0, "text": "NO NO NO NO YES YES YES YES", "language": "Hebrew"}
{"id": "0_0_0_1_0_1_1_0", "recording_id": "0_0_0_1_0_1_1_0", "start": 0.0, "duration": 6.11, "channel": 0, "text": "NO NO NO YES NO YES YES NO", "language": "Hebrew"}
{"id": "0_0_1_0_0_1_1_0", "recording_id": "0_0_1_0_0_1_1_0", "start": 0.0, "duration": 6.02, "channel": 0, "text": "NO NO YES NO NO YES YES NO", "language": "Hebrew"}
Please refer to `<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/supervision.py#L510>`_
for the meaning of each field per line.
**data/fbank**:
This directory contains everything from ``data/manifests``. Furthermore, it also contains features
for training.
``data/fbank/yesno_feats_train.lca`` contains the features for the train dataset.
Features are compressed using `lilcom`_.
``data/fbank/yesno_cuts_train.jsonl.gz`` stores the `CutSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/cut/set.py#L72>`_,
which stores `RecordingSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/audio.py#L928>`_,
`SupervisionSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/supervision.py#L510>`_,
and `FeatureSet <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/features/base.py#L593>`_.
To give you an idea about what it looks like, we can run the following command:
.. code-block:: bash
cd data/fbank
gunzip -c yesno_cuts_train.jsonl.gz | head -n 3
The output is given below:
.. code-block:: bash
{"id": "0_0_0_0_1_1_1_1-0", "start": 0, "duration": 6.35, "channel": 0, "supervisions": [{"id": "0_0_0_0_1_1_1_1", "recording_id": "0_0_0_0_1_1_1_1", "start": 0.0, "duration": 6.35, "channel": 0, "text": "NO NO NO NO YES YES YES YES", "language": "Hebrew"}], "features": {"type": "kaldi-fbank", "num_frames": 635, "num_features": 23, "frame_shift": 0.01, "sampling_rate": 8000, "start": 0, "duration": 6.35, "storage_type": "lilcom_chunky", "storage_path": "data/fbank/yesno_feats_train.lca", "storage_key": "0,13000,3570", "channels": 0}, "recording": {"id": "0_0_0_0_1_1_1_1", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_0_1_1_1_1.wav"}], "sampling_rate": 8000, "num_samples": 50800, "duration": 6.35, "channel_ids": [0]}, "type": "MonoCut"}
{"id": "0_0_0_1_0_1_1_0-1", "start": 0, "duration": 6.11, "channel": 0, "supervisions": [{"id": "0_0_0_1_0_1_1_0", "recording_id": "0_0_0_1_0_1_1_0", "start": 0.0, "duration": 6.11, "channel": 0, "text": "NO NO NO YES NO YES YES NO", "language": "Hebrew"}], "features": {"type": "kaldi-fbank", "num_frames": 611, "num_features": 23, "frame_shift": 0.01, "sampling_rate": 8000, "start": 0, "duration": 6.11, "storage_type": "lilcom_chunky", "storage_path": "data/fbank/yesno_feats_train.lca", "storage_key": "16570,12964,2929", "channels": 0}, "recording": {"id": "0_0_0_1_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_0_1_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48880, "duration": 6.11, "channel_ids": [0]}, "type": "MonoCut"}
{"id": "0_0_1_0_0_1_1_0-2", "start": 0, "duration": 6.02, "channel": 0, "supervisions": [{"id": "0_0_1_0_0_1_1_0", "recording_id": "0_0_1_0_0_1_1_0", "start": 0.0, "duration": 6.02, "channel": 0, "text": "NO NO YES NO NO YES YES NO", "language": "Hebrew"}], "features": {"type": "kaldi-fbank", "num_frames": 602, "num_features": 23, "frame_shift": 0.01, "sampling_rate": 8000, "start": 0, "duration": 6.02, "storage_type": "lilcom_chunky", "storage_path": "data/fbank/yesno_feats_train.lca", "storage_key": "32463,12936,2696", "channels": 0}, "recording": {"id": "0_0_1_0_0_1_1_0", "sources": [{"type": "file", "channels": [0], "source": "/tmp/icefall/egs/yesno/ASR/download/waves_yesno/0_0_1_0_0_1_1_0.wav"}], "sampling_rate": 8000, "num_samples": 48160, "duration": 6.02, "channel_ids": [0]}, "type": "MonoCut"}
Note that ``yesno_cuts_train.jsonl.gz`` only stores the information about how to read the features.
The actual features are stored separately in ``data/fbank/yesno_feats_train.lca``.
**data/lang**:
This directory contains the lexicon.
**data/lm**:
This directory contains language models.

39
docs/source/for-dummies/decoding.rst Normal file
View File

@ -0,0 +1,39 @@
.. _dummies_tutorial_decoding:
Decoding
========
After :ref:`dummies_tutorial_training`, we can start decoding.
The command to start the decoding is quite simple:
.. code-block:: bash
cd /tmp/icefall
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
cd egs/yesno/ASR
# We use CPU for decoding by setting the following environment variable
export CUDA_VISIBLE_DEVICES=""
./tdnn/decode.py
The output logs are given below:
.. literalinclude:: ./code/decoding-yesno.txt
For the more curious
--------------------
.. code-block:: bash
./tdnn/decode.py --help
will print the usage information about ``./tdnn/decode.py``. For instance, you
can specify:
- ``--epoch`` to use which checkpoint for decoding
- ``--avg`` to select how many checkpoints to use for model averaging
You usually try different combinations of ``--epoch`` and ``--avg`` and select
one that leads to the lowest WER (`Word Error Rate <https://en.wikipedia.org/wiki/Word_error_rate>`_).

121
docs/source/for-dummies/environment-setup.rst Normal file
View File

@ -0,0 +1,121 @@
.. _dummies_tutorial_environment_setup:
Environment setup
=================
We will create an environment for `Next-gen Kaldi`_ that runs on ``CPU``
in this tutorial.
.. note::
Since the `yesno`_ dataset used in this tutorial is very tiny, training on
``CPU`` works very well for it.
If your dataset is very large, e.g., hundreds or thousands of hours of
training data, please follow :ref:`install icefall` to install `icefall`_
that works with ``GPU``.
Create a virtual environment
----------------------------
.. code-block:: bash
virtualenv -p python3 /tmp/icefall_env
The above command creates a virtual environment in the directory ``/tmp/icefall_env``.
You can select any directory you want.
The output of the above command is given below:
.. code-block:: bash
Already using interpreter /usr/bin/python3
Using base prefix '/usr'
New python executable in /tmp/icefall_env/bin/python3
Also creating executable in /tmp/icefall_env/bin/python
Installing setuptools, pkg_resources, pip, wheel...done.
Now we can activate the environment using:
.. code-block:: bash
source /tmp/icefall_env/bin/activate
Install dependencies
--------------------
.. warning::
Remeber to activate your virtual environment before you continue!
After activating the virtual environment, we can use the following command
to install dependencies of `icefall`_:
.. hint::
Remeber that we will run this tutorial on ``CPU``, so we install
dependencies required only by running on ``CPU``.
.. code-block:: bash
# Caution: Installation order matters!
# We use torch 2.0.0 and torchaduio 2.0.0 in this tutorial.
# Other versions should also work.
pip install torch==2.0.0+cpu torchaudio==2.0.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
# If you are using macOS or Windows, please use the following command to install torch and torchaudio
# pip install torch==2.0.0 torchaudio==2.0.0 -f https://download.pytorch.org/whl/torch_stable.html
# Now install k2
# Please refer to https://k2-fsa.github.io/k2/installation/from_wheels.html#linux-cpu-example
pip install k2==1.24.3.dev20230726+cpu.torch2.0.0 -f https://k2-fsa.github.io/k2/cpu.html
# Install the latest version of lhotse
pip install git+https://github.com/lhotse-speech/lhotse
Install icefall
---------------
We will put the source code of `icefall`_ into the directory ``/tmp``
You can select any directory you want.
.. code-block:: bash
cd /tmp
git clone https://github.com/k2-fsa/icefall
cd icefall
pip install -r ./requirements.txt
.. code-block:: bash
# Anytime we want to use icefall, we have to set the following
# environment variable
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
.. hint::
If you get the following error during this tutorial:
.. code-block:: bash
ModuleNotFoundError: No module named 'icefall'
please set the above environment variable to fix it.
Congratulations! You have installed `icefall`_ successfully.
For the more curious
--------------------
`icefall`_ contains a collection of Python scripts and you don't need to
use ``python3 setup.py install`` or ``pip install icefall`` to install it.
All you need to do is to download the code and set the environment variable
``PYTHONPATH``.

34
docs/source/for-dummies/index.rst Normal file
View File

@ -0,0 +1,34 @@
Icefall for dummies tutorial
============================
This tutorial walks you step by step about how to create a simple
ASR (`Automatic Speech Recognition <https://en.wikipedia.org/wiki/Speech_recognition>`_)
system with `Next-gen Kaldi`_.
We use the `yesno`_ dataset for demonstration. We select it out of two reasons:
- It is quite tiny, containing only about 12 minutes of data
- The training can be finished within 20 seconds on ``CPU``.
That also means you don't need a ``GPU`` to run this tutorial.
Let's get started!
Please follow items below **sequentially**.
.. note::
The :ref:`dummies_tutorial_data_preparation` runs only on Linux and on macOS.
All other parts run on Linux, macOS, and Windows.
Help from the community is appreciated to port the :ref:`dummies_tutorial_data_preparation`
to Windows.
.. toctree::
:maxdepth: 2
./environment-setup.rst
./data-preparation.rst
./training.rst
./decoding.rst
./model-export.rst

310
docs/source/for-dummies/model-export.rst Normal file
View File

@ -0,0 +1,310 @@
Model Export
============
There are three ways to export a pre-trained model.
- Export the model parameters via `model.state_dict() <https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=load_state_dict#torch.nn.Module.state_dict>`_
- Export via `torchscript <https://pytorch.org/docs/stable/jit.html>`_: either `torch.jit.script() <https://pytorch.org/docs/stable/generated/torch.jit.script.html#torch.jit.script>`_ or `torch.jit.trace() <https://pytorch.org/docs/stable/generated/torch.jit.trace.html>`_
- Export to `ONNX`_ via `torch.onnx.export() <https://pytorch.org/docs/stable/onnx.html>`_
Each method is explained below in detail.
Export the model parameters via model.state_dict()
---------------------------------------------------
The command for this kind of export is
.. code-block:: bash
cd /tmp/icefall
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
cd egs/yesno/ASR
# assume that "--epoch 14 --avg 2" produces the lowest WER.
./tdnn/export.py --epoch 14 --avg 2
The output logs are given below:
.. code-block:: bash
2023-08-16 20:42:03,912 INFO [export.py:76] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'jit': False}
2023-08-16 20:42:03,913 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-08-16 20:42:03,950 INFO [export.py:93] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
2023-08-16 20:42:03,971 INFO [export.py:106] Not using torch.jit.script
2023-08-16 20:42:03,974 INFO [export.py:111] Saved to tdnn/exp/pretrained.pt
We can see from the logs that the exported model is saved to the file ``tdnn/exp/pretrained.pt``.
To give you an idea of what ``tdnn/exp/pretrained.pt`` contains, we can use the following command:
.. code-block:: python3
>>> import torch
>>> m = torch.load("tdnn/exp/pretrained.pt")
>>> list(m.keys())
['model']
>>> list(m["model"].keys())
['tdnn.0.weight', 'tdnn.0.bias', 'tdnn.2.running_mean', 'tdnn.2.running_var', 'tdnn.2.num_batches_tracked', 'tdnn.3.weight', 'tdnn.3.bias', 'tdnn.5.running_mean', 'tdnn.5.running_var', 'tdnn.5.num_batches_tracked', 'tdnn.6.weight', 'tdnn.6.bias', 'tdnn.8.running_mean', 'tdnn.8.running_var', 'tdnn.8.num_batches_tracked', 'output_linear.weight', 'output_linear.bias']
We can use ``tdnn/exp/pretrained.pt`` in the following way with ``./tdnn/decode.py``:
.. code-block:: bash
cd tdnn/exp
ln -s pretrained.pt epoch-99.pt
cd ../..
./tdnn/decode.py --epoch 99 --avg 1
The output logs of the above command are given below:
.. code-block:: bash
2023-08-16 20:45:48,089 INFO [decode.py:262] Decoding started
2023-08-16 20:45:48,090 INFO [decode.py:263] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 99, 'avg': 1, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': False, 'k2-git-sha1': 'ad79f1c699c684de9785ed6ca5edb805a41f78c3', 'k2-git-date': 'Wed Jul 26 09:30:42 2023', 'lhotse-version': '1.16.0.dev+git.aa073f6.clean', 'torch-version': '2.0.0', 'torch-cuda-available': False, 'torch-cuda-version': None, 'python-version': '3.1', 'icefall-git-branch': 'master', 'icefall-git-sha1': '9a47c08-clean', 'icefall-git-date': 'Mon Aug 14 22:10:50 2023', 'icefall-path': '/private/tmp/icefall', 'k2-path': '/private/tmp/icefall_env/lib/python3.11/site-packages/k2/__init__.py', 'lhotse-path': '/private/tmp/icefall_env/lib/python3.11/site-packages/lhotse/__init__.py', 'hostname': 'fangjuns-MacBook-Pro.local', 'IP address': '127.0.0.1'}}
2023-08-16 20:45:48,092 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-08-16 20:45:48,103 INFO [decode.py:272] device: cpu
2023-08-16 20:45:48,109 INFO [checkpoint.py:112] Loading checkpoint from tdnn/exp/epoch-99.pt
2023-08-16 20:45:48,115 INFO [asr_datamodule.py:218] About to get test cuts
2023-08-16 20:45:48,115 INFO [asr_datamodule.py:253] About to get test cuts
2023-08-16 20:45:50,386 INFO [decode.py:203] batch 0/?, cuts processed until now is 4
2023-08-16 20:45:50,556 INFO [decode.py:240] The transcripts are stored in tdnn/exp/recogs-test_set.txt
2023-08-16 20:45:50,557 INFO [utils.py:564] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
2023-08-16 20:45:50,558 INFO [decode.py:248] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
2023-08-16 20:45:50,559 INFO [decode.py:315] Done!
We can see that it produces an identical WER as before.
We can also use it to decode files with the following command:
.. code-block:: bash
# ./tdnn/pretrained.py requires kaldifeat
#
# Please refer to https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html
# for how to install kaldifeat
pip install kaldifeat==1.25.0.dev20230726+cpu.torch2.0.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
./tdnn/pretrained.py \
--checkpoint ./tdnn/exp/pretrained.pt \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
The output is given below:
.. code-block:: bash
2023-08-16 20:53:19,208 INFO [pretrained.py:136] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './tdnn/exp/pretrained.pt', 'words_file': './data/lang_phone/words.txt', 'HLG': './data/lang_phone/HLG.pt', 'sound_files': ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']}
2023-08-16 20:53:19,208 INFO [pretrained.py:142] device: cpu
2023-08-16 20:53:19,208 INFO [pretrained.py:144] Creating model
2023-08-16 20:53:19,212 INFO [pretrained.py:156] Loading HLG from ./data/lang_phone/HLG.pt
2023-08-16 20:53:19,213 INFO [pretrained.py:160] Constructing Fbank computer
2023-08-16 20:53:19,213 INFO [pretrained.py:170] Reading sound files: ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']
2023-08-16 20:53:19,224 INFO [pretrained.py:176] Decoding started
2023-08-16 20:53:19,304 INFO [pretrained.py:212]
download/waves_yesno/0_0_0_1_0_0_0_1.wav:
NO NO NO YES NO NO NO YES
download/waves_yesno/0_0_1_0_0_0_1_0.wav:
NO NO YES NO NO NO YES NO
2023-08-16 20:53:19,304 INFO [pretrained.py:214] Decoding Done
Export via torch.jit.script()
-----------------------------
The command for this kind of export is
.. code-block:: bash
cd /tmp/icefall
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
cd egs/yesno/ASR
# assume that "--epoch 14 --avg 2" produces the lowest WER.
./tdnn/export.py --epoch 14 --avg 2 --jit true
The output logs are given below:
.. code-block:: bash
2023-08-16 20:47:44,666 INFO [export.py:76] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'jit': True}
2023-08-16 20:47:44,667 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-08-16 20:47:44,670 INFO [export.py:93] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
2023-08-16 20:47:44,677 INFO [export.py:100] Using torch.jit.script
2023-08-16 20:47:44,843 INFO [export.py:104] Saved to tdnn/exp/cpu_jit.pt
From the output logs we can see that the generated file is saved to ``tdnn/exp/cpu_jit.pt``.
Don't be confused by the name ``cpu_jit.pt``. The ``cpu`` part means the model is moved to
CPU before exporting. That means, when you load it with:
.. code-block:: bash
torch.jit.load()
you don't need to specify the argument `map_location <https://pytorch.org/docs/stable/generated/torch.jit.load.html#torch.jit.load>`_
and it resides on CPU by default.
To use ``tdnn/exp/cpu_jit.pt`` with `icefall`_ to decode files, we can use:
.. code-block:: bash
# ./tdnn/jit_pretrained.py requires kaldifeat
#
# Please refer to https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html
# for how to install kaldifeat
pip install kaldifeat==1.25.0.dev20230726+cpu.torch2.0.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
./tdnn/jit_pretrained.py \
--nn-model ./tdnn/exp/cpu_jit.pt \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
The output is given below:
.. code-block:: bash
2023-08-16 20:56:00,603 INFO [jit_pretrained.py:121] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'nn_model': './tdnn/exp/cpu_jit.pt', 'words_file': './data/lang_phone/words.txt', 'HLG': './data/lang_phone/HLG.pt', 'sound_files': ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']}
2023-08-16 20:56:00,603 INFO [jit_pretrained.py:127] device: cpu
2023-08-16 20:56:00,603 INFO [jit_pretrained.py:129] Loading torchscript model
2023-08-16 20:56:00,640 INFO [jit_pretrained.py:134] Loading HLG from ./data/lang_phone/HLG.pt
2023-08-16 20:56:00,641 INFO [jit_pretrained.py:138] Constructing Fbank computer
2023-08-16 20:56:00,641 INFO [jit_pretrained.py:148] Reading sound files: ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']
2023-08-16 20:56:00,642 INFO [jit_pretrained.py:154] Decoding started
2023-08-16 20:56:00,727 INFO [jit_pretrained.py:190]
download/waves_yesno/0_0_0_1_0_0_0_1.wav:
NO NO NO YES NO NO NO YES
download/waves_yesno/0_0_1_0_0_0_1_0.wav:
NO NO YES NO NO NO YES NO
2023-08-16 20:56:00,727 INFO [jit_pretrained.py:192] Decoding Done
.. hint::
We provide only code for ``torch.jit.script()``. You can try ``torch.jit.trace()``
if you want.
Export via torch.onnx.export()
------------------------------
The command for this kind of export is
.. code-block:: bash
cd /tmp/icefall
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
cd egs/yesno/ASR
# tdnn/export_onnx.py requires onnx and onnxruntime
pip install onnx onnxruntime
# assume that "--epoch 14 --avg 2" produces the lowest WER.
./tdnn/export_onnx.py \
--epoch 14 \
--avg 2
The output logs are given below:
.. code-block:: bash
2023-08-16 20:59:20,888 INFO [export_onnx.py:83] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'epoch': 14, 'avg': 2}
2023-08-16 20:59:20,888 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-08-16 20:59:20,892 INFO [export_onnx.py:100] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
================ Diagnostic Run torch.onnx.export version 2.0.0 ================
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
2023-08-16 20:59:21,047 INFO [export_onnx.py:127] Saved to tdnn/exp/model-epoch-14-avg-2.onnx
2023-08-16 20:59:21,047 INFO [export_onnx.py:136] meta_data: {'model_type': 'tdnn', 'version': '1', 'model_author': 'k2-fsa', 'comment': 'non-streaming tdnn for the yesno recipe', 'vocab_size': 4}
2023-08-16 20:59:21,049 INFO [export_onnx.py:140] Generate int8 quantization models
2023-08-16 20:59:21,075 INFO [onnx_quantizer.py:538] Quantization parameters for tensor:"/Transpose_1_output_0" not specified
2023-08-16 20:59:21,081 INFO [export_onnx.py:151] Saved to tdnn/exp/model-epoch-14-avg-2.int8.onnx
We can see from the logs that it generates two files:
- ``tdnn/exp/model-epoch-14-avg-2.onnx`` (ONNX model with ``float32`` weights)
- ``tdnn/exp/model-epoch-14-avg-2.int8.onnx`` (ONNX model with ``int8`` weights)
To use the generated ONNX model files for decoding with `onnxruntime`_, we can use
.. code-block:: bash
# ./tdnn/onnx_pretrained.py requires kaldifeat
#
# Please refer to https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html
# for how to install kaldifeat
pip install kaldifeat==1.25.0.dev20230726+cpu.torch2.0.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
./tdnn/onnx_pretrained.py \
--nn-model ./tdnn/exp/model-epoch-14-avg-2.onnx \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
The output is given below:
.. code-block:: bash
2023-08-16 21:03:24,260 INFO [onnx_pretrained.py:166] {'feature_dim': 23, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'nn_model': './tdnn/exp/model-epoch-14-avg-2.onnx', 'words_file': './data/lang_phone/words.txt', 'HLG': './data/lang_phone/HLG.pt', 'sound_files': ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']}
2023-08-16 21:03:24,260 INFO [onnx_pretrained.py:171] device: cpu
2023-08-16 21:03:24,260 INFO [onnx_pretrained.py:173] Loading onnx model ./tdnn/exp/model-epoch-14-avg-2.onnx
2023-08-16 21:03:24,267 INFO [onnx_pretrained.py:176] Loading HLG from ./data/lang_phone/HLG.pt
2023-08-16 21:03:24,270 INFO [onnx_pretrained.py:180] Constructing Fbank computer
2023-08-16 21:03:24,273 INFO [onnx_pretrained.py:190] Reading sound files: ['download/waves_yesno/0_0_0_1_0_0_0_1.wav', 'download/waves_yesno/0_0_1_0_0_0_1_0.wav']
2023-08-16 21:03:24,279 INFO [onnx_pretrained.py:196] Decoding started
2023-08-16 21:03:24,318 INFO [onnx_pretrained.py:232]
download/waves_yesno/0_0_0_1_0_0_0_1.wav:
NO NO NO YES NO NO NO YES
download/waves_yesno/0_0_1_0_0_0_1_0.wav:
NO NO YES NO NO NO YES NO
2023-08-16 21:03:24,318 INFO [onnx_pretrained.py:234] Decoding Done
.. note::
To use the ``int8`` ONNX model for decoding, please use:
.. code-block:: bash
./tdnn/onnx_pretrained.py \
--nn-model ./tdnn/exp/model-epoch-14-avg-2.onnx \
--HLG ./data/lang_phone/HLG.pt \
--words-file ./data/lang_phone/words.txt \
download/waves_yesno/0_0_0_1_0_0_0_1.wav \
download/waves_yesno/0_0_1_0_0_0_1_0.wav
For the more curious
--------------------
If you are wondering how to deploy the model without ``torch``, please
continue reading. We will show how to use `sherpa-onnx`_ to run the
exported ONNX models, which depends only on `onnxruntime`_ and does not
depend on ``torch``.
In this tutorial, we will only demonstrate the usage of `sherpa-onnx`_ with the
pre-trained model of the `yesno`_ recipe. There are also other two frameworks
available:
- `sherpa`_. It works with torchscript models.
- `sherpa-ncnn`_. It works with models exported using :ref:`icefall_export_to_ncnn` with `ncnn`_
Please see `<https://k2-fsa.github.io/sherpa/>`_ for further details.

39
docs/source/for-dummies/training.rst Normal file
View File

@ -0,0 +1,39 @@
.. _dummies_tutorial_training:
Training
========
After :ref:`dummies_tutorial_data_preparation`, we can start training.
The command to start the training is quite simple:
.. code-block:: bash
cd /tmp/icefall
export PYTHONPATH=/tmp/icefall:$PYTHONPATH
cd egs/yesno/ASR
# We use CPU for training by setting the following environment variable
export CUDA_VISIBLE_DEVICES=""
./tdnn/train.py
That's it!
You can find the training logs below:
.. literalinclude:: ./code/train-yesno.txt
For the more curious
--------------------
.. code-block:: bash
./tdnn/train.py --help
will print the usage information about ``./tdnn/train.py``. For instance, you
can specify the number of epochs to train and the location to save the training
results.
The training text logs are saved in ``tdnn/exp/log`` while the tensorboard
logs are in ``tdnn/exp/tensorboard``.

3
docs/source/index.rst
View File

@ -20,10 +20,13 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
:maxdepth: 2
:caption: Contents:
for-dummies/index.rst
installation/index
docker/index
faqs
model-export/index
.. toctree::
:maxdepth: 3

682
docs/source/installation/index.rst
View File

@ -3,40 +3,28 @@
Installation
============
.. hint::
We also provide :ref:`icefall_docker` support, which has already setup
the environment for you.
``icefall`` depends on `k2 <https://github.com/k2-fsa/k2>`_ and
`lhotse <https://github.com/lhotse-speech/lhotse>`_.
.. hint::
We have a colab notebook guiding you step by step to setup the environment.
|yesno colab notebook|
.. |yesno colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing
`icefall`_ depends on `k2`_ and `lhotse`_.
We recommend that you use the following steps to install the dependencies.
- (0) Install CUDA toolkit and cuDNN
- (1) Install PyTorch and torchaudio
- (2) Install k2
- (3) Install lhotse
.. caution::
99% users who have issues about the installation are using conda.
.. caution::
99% users who have issues about the installation are using conda.
.. caution::
99% users who have issues about the installation are using conda.
.. hint::
We suggest that you use ``pip install`` to install PyTorch.
You can use the following command to create a virutal environment in Python:
.. code-block:: bash
python3 -m venv ./my_env
source ./my_env/bin/activate
- (1) Install `torch`_ and `torchaudio`_
- (2) Install `k2`_
- (3) Install `lhotse`_
.. caution::
@ -50,27 +38,20 @@ Please refer to
to install CUDA and cuDNN.
(1) Install PyTorch and torchaudio
----------------------------------
(1) Install torch and torchaudio
--------------------------------
Please refer `<https://pytorch.org/>`_ to install PyTorch
and torchaudio.
.. hint::
You can also go to `<https://download.pytorch.org/whl/torch_stable.html>`_
to download pre-compiled wheels and install them.
Please refer `<https://pytorch.org/>`_ to install `torch`_ and `torchaudio`_.
.. caution::
Please install torch and torchaudio at the same time.
(2) Install k2
--------------
Please refer to `<https://k2-fsa.github.io/k2/installation/index.html>`_
to install ``k2``.
to install `k2`_.
.. caution::
@ -78,21 +59,18 @@ to install ``k2``.
.. note::
We suggest that you install k2 from source by following
`<https://k2-fsa.github.io/k2/installation/from_source.html>`_
or
`<https://k2-fsa.github.io/k2/installation/for_developers.html>`_.
We suggest that you install k2 from pre-compiled wheels by following
`<https://k2-fsa.github.io/k2/installation/from_wheels.html>`_
.. hint::
Please always install the latest version of k2.
Please always install the latest version of `k2`_.
(3) Install lhotse
------------------
Please refer to `<https://lhotse.readthedocs.io/en/latest/getting-started.html#installation>`_
to install ``lhotse``.
to install `lhotse`_.
.. hint::
@ -100,17 +78,16 @@ to install ``lhotse``.
pip install git+https://github.com/lhotse-speech/lhotse
to install the latest version of lhotse.
to install the latest version of `lhotse`_.
(4) Download icefall
--------------------
``icefall`` is a collection of Python scripts; what you need is to download it
`icefall`_ is a collection of Python scripts; what you need is to download it
and set the environment variable ``PYTHONPATH`` to point to it.
Assume you want to place ``icefall`` in the folder ``/tmp``. The
following commands show you how to setup ``icefall``:
Assume you want to place `icefall`_ in the folder ``/tmp``. The
following commands show you how to setup `icefall`_:
.. code-block:: bash
@ -122,285 +99,334 @@ following commands show you how to setup ``icefall``:
.. HINT::
You can put several versions of ``icefall`` in the same virtual environment.
To switch among different versions of ``icefall``, just set ``PYTHONPATH``
You can put several versions of `icefall`_ in the same virtual environment.
To switch among different versions of `icefall`_, just set ``PYTHONPATH``
to point to the version you want.
Installation example
--------------------
The following shows an example about setting up the environment.
(1) Create a virtual environment
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code-block:: bash
$ virtualenv -p python3.8 test-icefall
kuangfangjun:~$ virtualenv -p python3.8 test-icefall
created virtual environment CPython3.8.0.final.0-64 in 9422ms
creator CPython3Posix(dest=/star-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False)
seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/star-fj/fangjun/.local/share/virtualenv)
added seed packages: pip==22.3.1, setuptools==65.6.3, wheel==0.38.4
activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator
created virtual environment CPython3.8.6.final.0-64 in 1540ms
creator CPython3Posix(dest=/ceph-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False)
seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/fangjun/.local/share/v
irtualenv)
added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2
activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
kuangfangjun:~$ source test-icefall/bin/activate
(test-icefall) kuangfangjun:~$
(2) Activate your virtual environment
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
(2) Install CUDA toolkit and cuDNN
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You need to determine the version of CUDA toolkit to install.
.. code-block:: bash
$ source test-icefall/bin/activate
(test-icefall) kuangfangjun:~$ nvidia-smi | head -n 4
(3) Install k2
Wed Jul 26 21:57:49 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
You can choose any CUDA version that is ``not`` greater than the version printed by ``nvidia-smi``.
In our case, we can choose any version ``<= 11.6``.
We will use ``CUDA 11.6`` in this example. Please follow
`<https://k2-fsa.github.io/k2/installation/cuda-cudnn.html#cuda-11-6>`_
to install CUDA toolkit and cuDNN if you have not done that before.
After installing CUDA toolkit, you can use the following command to verify it:
.. code-block:: bash
(test-icefall) kuangfangjun:~$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
(3) Install torch and torchaudio
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Since we have selected CUDA toolkit ``11.6``, we have to install a version of `torch`_
that is compiled against CUDA ``11.6``. We select ``torch 1.13.0+cu116`` in this
example.
After selecting the version of `torch`_ to install, we need to also install
a compatible version of `torchaudio`_, which is ``0.13.0+cu116`` in our case.
Please refer to `<https://pytorch.org/audio/stable/installation.html#compatibility-matrix>`_
to select an appropriate version of `torchaudio`_ to install if you use a different
version of `torch`_.
.. code-block:: bash
(test-icefall) kuangfangjun:~$ pip install torch==1.13.0+cu116 torchaudio==0.13.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html
Looking in links: https://download.pytorch.org/whl/torch_stable.html
Collecting torch==1.13.0+cu116
Downloading https://download.pytorch.org/whl/cu116/torch-1.13.0%2Bcu116-cp38-cp38-linux_x86_64.whl (1983.0 MB)
________________________________________ 2.0/2.0 GB 764.4 kB/s eta 0:00:00
Collecting torchaudio==0.13.0+cu116
Downloading https://download.pytorch.org/whl/cu116/torchaudio-0.13.0%2Bcu116-cp38-cp38-linux_x86_64.whl (4.2 MB)
________________________________________ 4.2/4.2 MB 1.3 MB/s eta 0:00:00
Requirement already satisfied: typing-extensions in /star-fj/fangjun/test-icefall/lib/python3.8/site-packages (from torch==1.13.0+cu116) (4.7.1)
Installing collected packages: torch, torchaudio
Successfully installed torch-1.13.0+cu116 torchaudio-0.13.0+cu116
Verify that `torch`_ and `torchaudio`_ are successfully installed:
.. code-block:: bash
(test-icefall) kuangfangjun:~$ python3 -c "import torch; print(torch.__version__)"
1.13.0+cu116
(test-icefall) kuangfangjun:~$ python3 -c "import torchaudio; print(torchaudio.__version__)"
0.13.0+cu116
(4) Install k2
~~~~~~~~~~~~~~
We will install `k2`_ from pre-compiled wheels by following
`<https://k2-fsa.github.io/k2/installation/from_wheels.html>`_
.. code-block:: bash
$ pip install k2==1.4.dev20210822+cpu.torch1.9.0 -f https://k2-fsa.org/nightly/index.html
(test-icefall) kuangfangjun:~$ pip install k2==1.24.3.dev20230725+cuda11.6.torch1.13.0 -f https://k2-fsa.github.io/k2/cuda.html
Looking in links: https://k2-fsa.org/nightly/index.html
Collecting k2==1.4.dev20210822+cpu.torch1.9.0
Downloading https://k2-fsa.org/nightly/whl/k2-1.4.dev20210822%2Bcpu.torch1.9.0-cp38-cp38-linux_x86_64.whl (1.6 MB)
|________________________________| 1.6 MB 185 kB/s
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Looking in links: https://k2-fsa.github.io/k2/cuda.html
Collecting k2==1.24.3.dev20230725+cuda11.6.torch1.13.0
Downloading https://huggingface.co/csukuangfj/k2/resolve/main/ubuntu-cuda/k2-1.24.3.dev20230725%2Bcuda11.6.torch1.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (104.3 MB)
________________________________________ 104.3/104.3 MB 5.1 MB/s eta 0:00:00
Requirement already satisfied: torch==1.13.0 in /star-fj/fangjun/test-icefall/lib/python3.8/site-packages (from k2==1.24.3.dev20230725+cuda11.6.torch1.13.0) (1.13.0+cu116)
Collecting graphviz
Downloading graphviz-0.17-py3-none-any.whl (18 kB)
Collecting torch==1.9.0
Using cached torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB)
Collecting typing-extensions
Using cached typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)
Installing collected packages: typing-extensions, torch, graphviz, k2
Successfully installed graphviz-0.17 k2-1.4.dev20210822+cpu.torch1.9.0 torch-1.9.0 typing-extensions-3.10.0.0
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/de/5e/fcbb22c68208d39edff467809d06c9d81d7d27426460ebc598e55130c1aa/graphviz-0.20.1-py3-none-any.whl (47 kB)
Requirement already satisfied: typing-extensions in /star-fj/fangjun/test-icefall/lib/python3.8/site-packages (from torch==1.13.0->k2==1.24.3.dev20230725+cuda11.6.torch1.13.0) (4.7.1)
Installing collected packages: graphviz, k2
Successfully installed graphviz-0.20.1 k2-1.24.3.dev20230725+cuda11.6.torch1.13.0
.. WARNING::
.. hint::
We choose to install a CPU version of k2 for testing. You would probably want to install
a CUDA version of k2.
Please refer to `<https://k2-fsa.github.io/k2/cuda.html>`_ for the available
pre-compiled wheels about `k2`_.
Verify that `k2`_ has been installed successfully:
(4) Install lhotse
.. code-block:: bash
(test-icefall) kuangfangjun:~$ python3 -m k2.version
Collecting environment information...
k2 version: 1.24.3
Build type: Release
Git SHA1: 4c05309499a08454997adf500b56dcc629e35ae5
Git date: Tue Jul 25 16:23:36 2023
Cuda used to build k2: 11.6
cuDNN used to build k2: 8.3.2
Python version used to build k2: 3.8
OS used to build k2: CentOS Linux release 7.9.2009 (Core)
CMake version: 3.27.0
GCC version: 9.3.1
CMAKE_CUDA_FLAGS: -Wno-deprecated-gpu-targets -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_35,code=sm_35 -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_50,code=sm_50 -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_60,code=sm_60 -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_61,code=sm_61 -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_70,code=sm_70 -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_75,code=sm_75 -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_80,code=sm_80 -lineinfo --expt-extended-lambda -use_fast_math -Xptxas=-w --expt-extended-lambda -gencode arch=compute_86,code=sm_86 -DONNX_NAMESPACE=onnx_c2 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_86,code=compute_86 -Xcudafe --diag_suppress=cc_clobber_ignored,--diag_suppress=integer_sign_change,--diag_suppress=useless_using_declaration,--diag_suppress=set_but_not_used,--diag_suppress=field_without_dll_interface,--diag_suppress=base_class_has_different_dll_interface,--diag_suppress=dll_interface_conflict_none_assumed,--diag_suppress=dll_interface_conflict_dllexport_assumed,--diag_suppress=implicit_return_from_non_void_function,--diag_suppress=unsigned_compare_with_zero,--diag_suppress=declared_but_not_referenced,--diag_suppress=bad_friend_decl --expt-relaxed-constexpr --expt-extended-lambda -D_GLIBCXX_USE_CXX11_ABI=0 --compiler-options -Wall --compiler-options -Wno-strict-overflow --compiler-options -Wno-unknown-pragmas
CMAKE_CXX_FLAGS: -D_GLIBCXX_USE_CXX11_ABI=0 -Wno-unused-variable -Wno-strict-overflow
PyTorch version used to build k2: 1.13.0+cu116
PyTorch is using Cuda: 11.6
NVTX enabled: True
With CUDA: True
Disable debug: True
Sync kernels : False
Disable checks: False
Max cpu memory allocate: 214748364800 bytes (or 200.0 GB)
k2 abort: False
__file__: /star-fj/fangjun/test-icefall/lib/python3.8/site-packages/k2/version/version.py
_k2.__file__: /star-fj/fangjun/test-icefall/lib/python3.8/site-packages/_k2.cpython-38-x86_64-linux-gnu.so
(5) Install lhotse
~~~~~~~~~~~~~~~~~~
.. code-block::
.. code-block:: bash
$ pip install git+https://github.com/lhotse-speech/lhotse
(test-icefall) kuangfangjun:~$ pip install git+https://github.com/lhotse-speech/lhotse
Collecting git+https://github.com/lhotse-speech/lhotse
Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-7b1b76ge
Running command git clone -q https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-7b1b76ge
Collecting audioread>=2.1.9
Using cached audioread-2.1.9-py3-none-any.whl
Collecting SoundFile>=0.10
Using cached SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB)
Collecting click>=7.1.1
Using cached click-8.0.1-py3-none-any.whl (97 kB)
Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-vq12fd5i
Running command git clone --filter=blob:none --quiet https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-vq12fd5i
Resolved https://github.com/lhotse-speech/lhotse to commit 7640d663469b22cd0b36f3246ee9b849cd25e3b7
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Collecting cytoolz>=0.10.1
Using cached cytoolz-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB)
Collecting dataclasses
Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
Collecting h5py>=2.10.0
Downloading h5py-3.4.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
|________________________________| 4.5 MB 684 kB/s
Collecting intervaltree>=3.1.0
Using cached intervaltree-3.1.0-py2.py3-none-any.whl
Collecting lilcom>=1.1.0
Using cached lilcom-1.1.1-cp38-cp38-linux_x86_64.whl
Collecting numpy>=1.18.1
Using cached numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.8 MB)
Collecting packaging
Using cached packaging-21.0-py3-none-any.whl (40 kB)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1e/3b/a7828d575aa17fb7acaf1ced49a3655aa36dad7e16eb7e6a2e4df0dda76f/cytoolz-0.12.2-cp38-cp38-
manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)
________________________________________ 2.0/2.0 MB 33.2 MB/s eta 0:00:00
Collecting pyyaml>=5.3.1
Using cached PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/6b/6600ac24725c7388255b2f5add93f91e58a5d7efaf4af244fdbcc11a541b/PyYAML-6.0.1-cp38-cp38-ma
nylinux_2_17_x86_64.manylinux2014_x86_64.whl (736 kB)
________________________________________ 736.6/736.6 kB 38.6 MB/s eta 0:00:00
Collecting dataclasses
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/26/2f/1095cdc2868052dd1e64520f7c0d5c8c550ad297e944e641dbf1ffbb9a5d/dataclasses-0.6-py3-none-
any.whl (14 kB)
Requirement already satisfied: torchaudio in ./test-icefall/lib/python3.8/site-packages (from lhotse==1.16.0.dev0+git.7640d66.clean) (0.13.0+cu116)
Collecting lilcom>=1.1.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a8/65/df0a69c52bd085ca1ad4e5c4c1a5c680e25f9477d8e49316c4ff1e5084a4/lilcom-1.7-cp38-cp38-many
linux_2_17_x86_64.manylinux2014_x86_64.whl (87 kB)
________________________________________ 87.1/87.1 kB 8.7 MB/s eta 0:00:00
Collecting tqdm
Downloading tqdm-4.62.1-py2.py3-none-any.whl (76 kB)
|________________________________| 76 kB 2.7 MB/s
Collecting torchaudio==0.9.0
Downloading torchaudio-0.9.0-cp38-cp38-manylinux1_x86_64.whl (1.9 MB)
|________________________________| 1.9 MB 73.1 MB/s
Requirement already satisfied: torch==1.9.0 in ./test-icefall/lib/python3.8/site-packages (from torchaudio==0.9.0->lhotse===0.8.0.dev
-2a1410b-clean) (1.9.0)
Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch==1.9.0->torchaudio==0.9.0-
>lhotse===0.8.0.dev-2a1410b-clean) (3.10.0.0)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/e6/02/a2cff6306177ae6bc73bc0665065de51dfb3b9db7373e122e2735faf0d97/tqdm-4.65.0-py3-none-any
.whl (77 kB)
Requirement already satisfied: numpy>=1.18.1 in ./test-icefall/lib/python3.8/site-packages (from lhotse==1.16.0.dev0+git.7640d66.clean) (1.24.4)
Collecting audioread>=2.1.9
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5d/cb/82a002441902dccbe427406785db07af10182245ee639ea9f4d92907c923/audioread-3.0.0.tar.gz (
377 kB)
Preparing metadata (setup.py) ... done
Collecting tabulate>=0.8.1
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-
any.whl (35 kB)
Collecting click>=7.1.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/1a/70/e63223f8116931d365993d4a6b7ef653a4d920b41d03de7c59499962821f/click-8.1.6-py3-none-any.
whl (97 kB)
________________________________________ 97.9/97.9 kB 8.4 MB/s eta 0:00:00
Collecting packaging
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ab/c3/57f0601a2d4fe15de7a553c00adbc901425661bf048f2a22dfc500caf121/packaging-23.1-py3-none-
any.whl (48 kB)
Collecting intervaltree>=3.1.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/50/fb/396d568039d21344639db96d940d40eb62befe704ef849b27949ded5c3bb/intervaltree-3.1.0.tar.gz
(32 kB)
Preparing metadata (setup.py) ... done
Requirement already satisfied: torch in ./test-icefall/lib/python3.8/site-packages (from lhotse==1.16.0.dev0+git.7640d66.clean) (1.13.0+cu116)
Collecting SoundFile>=0.10
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ad/bd/0602167a213d9184fc688b1086dc6d374b7ae8c33eccf169f9b50ce6568c/soundfile-0.12.1-py2.py3-
none-manylinux_2_17_x86_64.whl (1.3 MB)
________________________________________ 1.3/1.3 MB 46.5 MB/s eta 0:00:00
Collecting toolz>=0.8.0
Using cached toolz-0.11.1-py3-none-any.whl (55 kB)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7f/5c/922a3508f5bda2892be3df86c74f9cf1e01217c2b1f8a0ac4841d903e3e9/toolz-0.12.0-py3-none-any.whl (55 kB)
Collecting sortedcontainers<3.0,>=2.0
Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/32/46/9cb0e58b2deb7f82b84065f37f3bffeb12413f947f9388e4cac22c4621ce/sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
Collecting cffi>=1.0
Using cached cffi-1.14.6-cp38-cp38-manylinux1_x86_64.whl (411 kB)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b7/8b/06f30caa03b5b3ac006de4f93478dbd0239e2a16566d81a106c322dc4f79/cffi-1.15.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (442 kB)
Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch->lhotse==1.16.0.dev0+git.7640d66.clean) (4.7.1)
Collecting pycparser
Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB)
Collecting pyparsing>=2.0.2
Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
Building wheels for collected packages: lhotse
Building wheel for lhotse (setup.py) ... done
Created wheel for lhotse: filename=lhotse-0.8.0.dev_2a1410b_clean-py3-none-any.whl size=342242 sha256=f683444afa4dc0881133206b4646a
9d0f774224cc84000f55d0a67f6e4a37997
Stored in directory: /tmp/pip-ephem-wheel-cache-ftu0qysz/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f
WARNING: Built wheel for lhotse is invalid: Metadata 1.2 mandates PEP 440 version, but '0.8.0.dev-2a1410b-clean' is not
Failed to build lhotse
Installing collected packages: pycparser, toolz, sortedcontainers, pyparsing, numpy, cffi, tqdm, torchaudio, SoundFile, pyyaml, packa
ging, lilcom, intervaltree, h5py, dataclasses, cytoolz, click, audioread, lhotse
Running setup.py install for lhotse ... done
DEPRECATION: lhotse was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible
replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/is
sues/8368.
Successfully installed SoundFile-0.10.3.post1 audioread-2.1.9 cffi-1.14.6 click-8.0.1 cytoolz-0.11.0 dataclasses-0.6 h5py-3.4.0 inter
valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor
tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/62/d5/5f610ebe421e85889f2e55e33b7f9a6795bd982198517d912eb1c76e1a53/pycparser-2.21-py2.py3-none-any.whl (118 kB)
Building wheels for collected packages: lhotse, audioread, intervaltree
Building wheel for lhotse (pyproject.toml) ... done
Created wheel for lhotse: filename=lhotse-1.16.0.dev0+git.7640d66.clean-py3-none-any.whl size=687627 sha256=cbf0a4d2d0b639b33b91637a4175bc251d6a021a069644ecb1a9f2b3a83d072a
Stored in directory: /tmp/pip-ephem-wheel-cache-wwtk90_m/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f
Building wheel for audioread (setup.py) ... done
Created wheel for audioread: filename=audioread-3.0.0-py3-none-any.whl size=23704 sha256=5e2d3537c96ce9cf0f645a654c671163707bf8cb8d9e358d0e2b0939a85ff4c2
Stored in directory: /star-fj/fangjun/.cache/pip/wheels/e2/c3/9c/f19ae5a03f8862d9f0776b0c0570f1fdd60a119d90954e3f39
Building wheel for intervaltree (setup.py) ... done
Created wheel for intervaltree: filename=intervaltree-3.1.0-py2.py3-none-any.whl size=26098 sha256=2604170976cfffe0d2f678cb1a6e5b525f561cd50babe53d631a186734fec9f9
Stored in directory: /star-fj/fangjun/.cache/pip/wheels/f3/ed/2b/c179ebfad4e15452d6baef59737f27beb9bfb442e0620f7271
Successfully built lhotse audioread intervaltree
Installing collected packages: sortedcontainers, dataclasses, tqdm, toolz, tabulate, pyyaml, pycparser, packaging, lilcom, intervaltree, click, audioread, cytoolz, cffi, SoundFile, lhotse
Successfully installed SoundFile-0.12.1 audioread-3.0.0 cffi-1.15.1 click-8.1.6 cytoolz-0.12.2 dataclasses-0.6 intervaltree-3.1.0 lhotse-1.16.0.dev0+git.7640d66.clean lilcom-1.7 packaging-23.1 pycparser-2.21 pyyaml-6.0.1 sortedcontainers-2.4.0 tabulate-0.9.0 toolz-0.12.0 tqdm-4.65.0
(5) Download icefall
Verify that `lhotse`_ has been installed successfully:
.. code-block:: bash
(test-icefall) kuangfangjun:~$ python3 -c "import lhotse; print(lhotse.__version__)"
1.16.0.dev+git.7640d66.clean
(6) Download icefall
~~~~~~~~~~~~~~~~~~~~
.. code-block::
.. code-block:: bash
$ cd /tmp
$ git clone https://github.com/k2-fsa/icefall
(test-icefall) kuangfangjun:~$ cd /tmp/
(test-icefall) kuangfangjun:tmp$ git clone https://github.com/k2-fsa/icefall
Cloning into 'icefall'...
remote: Enumerating objects: 500, done.
remote: Counting objects: 100% (500/500), done.
remote: Compressing objects: 100% (308/308), done.
remote: Total 500 (delta 263), reused 307 (delta 102), pack-reused 0
Receiving objects: 100% (500/500), 172.49 KiB | 385.00 KiB/s, done.
Resolving deltas: 100% (263/263), done.
remote: Enumerating objects: 12942, done.
remote: Counting objects: 100% (67/67), done.
remote: Compressing objects: 100% (56/56), done.
remote: Total 12942 (delta 17), reused 35 (delta 6), pack-reused 12875
Receiving objects: 100% (12942/12942), 14.77 MiB | 9.29 MiB/s, done.
Resolving deltas: 100% (8835/8835), done.
$ cd icefall
$ pip install -r requirements.txt
Collecting kaldilm
Downloading kaldilm-1.8.tar.gz (48 kB)
|________________________________| 48 kB 574 kB/s
Collecting kaldialign
Using cached kaldialign-0.2-cp38-cp38-linux_x86_64.whl
Collecting sentencepiece>=0.1.96
Using cached sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
Collecting tensorboard
Using cached tensorboard-2.6.0-py3-none-any.whl (5.6 MB)
Requirement already satisfied: setuptools>=41.0.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r
requirements.txt (line 4)) (57.4.0)
Collecting absl-py>=0.4
Using cached absl_py-0.13.0-py3-none-any.whl (132 kB)
Collecting google-auth-oauthlib<0.5,>=0.4.1
Using cached google_auth_oauthlib-0.4.5-py2.py3-none-any.whl (18 kB)
Collecting grpcio>=1.24.3
Using cached grpcio-1.39.0-cp38-cp38-manylinux2014_x86_64.whl (4.3 MB)
Requirement already satisfied: wheel>=0.26 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r require
ments.txt (line 4)) (0.36.2)
Requirement already satisfied: numpy>=1.12.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r requi
rements.txt (line 4)) (1.21.2)
Collecting protobuf>=3.6.0
Using cached protobuf-3.17.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)
Collecting werkzeug>=0.11.15
Using cached Werkzeug-2.0.1-py3-none-any.whl (288 kB)
Collecting tensorboard-data-server<0.7.0,>=0.6.0
Using cached tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)
Collecting google-auth<2,>=1.6.3
Downloading google_auth-1.35.0-py2.py3-none-any.whl (152 kB)
|________________________________| 152 kB 1.4 MB/s
Collecting requests<3,>=2.21.0
Using cached requests-2.26.0-py2.py3-none-any.whl (62 kB)
Collecting tensorboard-plugin-wit>=1.6.0
Using cached tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB)
Collecting markdown>=2.6.8
Using cached Markdown-3.3.4-py3-none-any.whl (97 kB)
Collecting six
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting cachetools<5.0,>=2.0.0
Using cached cachetools-4.2.2-py3-none-any.whl (11 kB)
Collecting rsa<5,>=3.1.4
Using cached rsa-4.7.2-py3-none-any.whl (34 kB)
Collecting pyasn1-modules>=0.2.1
Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
Collecting requests-oauthlib>=0.7.0
Using cached requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)
Collecting pyasn1<0.5.0,>=0.4.6
Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
Collecting urllib3<1.27,>=1.21.1
Using cached urllib3-1.26.6-py2.py3-none-any.whl (138 kB)
Collecting certifi>=2017.4.17
Using cached certifi-2021.5.30-py2.py3-none-any.whl (145 kB)
Collecting charset-normalizer~=2.0.0
Using cached charset_normalizer-2.0.4-py3-none-any.whl (36 kB)
Collecting idna<4,>=2.5
Using cached idna-3.2-py3-none-any.whl (59 kB)
Collecting oauthlib>=3.0.0
Using cached oauthlib-3.1.1-py2.py3-none-any.whl (146 kB)
Building wheels for collected packages: kaldilm
Building wheel for kaldilm (setup.py) ... done
Created wheel for kaldilm: filename=kaldilm-1.8-cp38-cp38-linux_x86_64.whl size=897233 sha256=eccb906cafcd45bf9a7e1a1718e4534254bfb
f4c0d0cbc66eee6c88d68a63862
Stored in directory: /root/fangjun/.cache/pip/wheels/85/7d/63/f2dd586369b8797cb36d213bf3a84a789eeb92db93d2e723c9
Successfully built kaldilm
Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, six, rsa, requests, pyasn1-modules, oauthlib, cach
etools, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google
-auth-oauthlib, absl-py, tensorboard, sentencepiece, kaldilm, kaldialign
Successfully installed absl-py-0.13.0 cachetools-4.2.2 certifi-2021.5.30 charset-normalizer-2.0.4 google-auth-1.35.0 google-auth-oaut
hlib-0.4.5 grpcio-1.39.0 idna-3.2 kaldialign-0.2 kaldilm-1.8 markdown-3.3.4 oauthlib-3.1.1 protobuf-3.17.3 pyasn1-0.4.8 pyasn1-module
s-0.2.8 requests-2.26.0 requests-oauthlib-1.3.0 rsa-4.7.2 sentencepiece-0.1.96 six-1.16.0 tensorboard-2.6.0 tensorboard-data-server-0
.6.1 tensorboard-plugin-wit-1.8.0 urllib3-1.26.6 werkzeug-2.0.1
(test-icefall) kuangfangjun:tmp$ cd icefall/
(test-icefall) kuangfangjun:icefall$ pip install -r ./requirements.txt
Test Your Installation
----------------------
To test that your installation is successful, let us run
the `yesno recipe <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR>`_
on CPU.
on ``CPU``.
Data preparation
~~~~~~~~~~~~~~~~
.. code-block:: bash
$ export PYTHONPATH=/tmp/icefall:$PYTHONPATH
$ cd /tmp/icefall
$ cd egs/yesno/ASR
$ ./prepare.sh
(test-icefall) kuangfangjun:icefall$ export PYTHONPATH=/tmp/icefall:$PYTHONPATH
(test-icefall) kuangfangjun:icefall$ cd /tmp/icefall
(test-icefall) kuangfangjun:icefall$ cd egs/yesno/ASR
(test-icefall) kuangfangjun:ASR$ ./prepare.sh
The log of running ``./prepare.sh`` is:
.. code-block::
2023-05-12 17:55:21 (prepare.sh:27:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download
2023-05-12 17:55:21 (prepare.sh:30:main) Stage 0: Download data
/tmp/icefall/egs/yesno/ASR/download/waves_yesno.tar.gz: 100%|_______________________________________________________________| 4.70M/4.70M [06:54<00:00, 11.4kB/s]
2023-05-12 18:02:19 (prepare.sh:39:main) Stage 1: Prepare yesno manifest
2023-05-12 18:02:21 (prepare.sh:45:main) Stage 2: Compute fbank for yesno
2023-05-12 18:02:23,199 INFO [compute_fbank_yesno.py:65] Processing train
Extracting and storing features: 100%|_______________________________________________________________| 90/90 [00:00<00:00, 212.60it/s]
2023-05-12 18:02:23,640 INFO [compute_fbank_yesno.py:65] Processing test
Extracting and storing features: 100%|_______________________________________________________________| 30/30 [00:00<00:00, 304.53it/s]
2023-05-12 18:02:24 (prepare.sh:51:main) Stage 3: Prepare lang
2023-05-12 18:02:26 (prepare.sh:66:main) Stage 4: Prepare G
2023-07-27 12:41:39 (prepare.sh:27:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download
2023-07-27 12:41:39 (prepare.sh:30:main) Stage 0: Download data
/tmp/icefall/egs/yesno/ASR/download/waves_yesno.tar.gz: 100%|___________________________________________________| 4.70M/4.70M [00:00<00:00, 11.1MB/s]
2023-07-27 12:41:46 (prepare.sh:39:main) Stage 1: Prepare yesno manifest
2023-07-27 12:41:50 (prepare.sh:45:main) Stage 2: Compute fbank for yesno
2023-07-27 12:41:55,718 INFO [compute_fbank_yesno.py:65] Processing train
Extracting and storing features: 100%|_______________________________________________________________________________| 90/90 [00:01<00:00, 87.82it/s]
2023-07-27 12:41:56,778 INFO [compute_fbank_yesno.py:65] Processing test
Extracting and storing features: 100%|______________________________________________________________________________| 30/30 [00:00<00:00, 256.92it/s]
2023-07-27 12:41:57 (prepare.sh:51:main) Stage 3: Prepare lang
2023-07-27 12:42:02 (prepare.sh:66:main) Stage 4: Prepare G
/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):79
[I] Reading \data\ section.
/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):140
[I] Reading \1-grams: section.
2023-05-12 18:02:26 (prepare.sh:92:main) Stage 5: Compile HLG
2023-05-12 18:02:28,581 INFO [compile_hlg.py:124] Processing data/lang_phone
2023-05-12 18:02:28,582 INFO [lexicon.py:171] Converting L.pt to Linv.pt
2023-05-12 18:02:28,609 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3
2023-05-12 18:02:28,610 INFO [compile_hlg.py:52] Loading G.fst.txt
2023-05-12 18:02:28,611 INFO [compile_hlg.py:62] Intersecting L and G
2023-05-12 18:02:28,613 INFO [compile_hlg.py:64] LG shape: (4, None)
2023-05-12 18:02:28,613 INFO [compile_hlg.py:66] Connecting LG
2023-05-12 18:02:28,614 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None)
2023-05-12 18:02:28,614 INFO [compile_hlg.py:70] <class 'torch.Tensor'>
2023-05-12 18:02:28,614 INFO [compile_hlg.py:71] Determinizing LG
2023-05-12 18:02:28,615 INFO [compile_hlg.py:74] <class '_k2.ragged.RaggedTensor'>
2023-05-12 18:02:28,615 INFO [compile_hlg.py:76] Connecting LG after k2.determinize
2023-05-12 18:02:28,615 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG
2023-05-12 18:02:28,616 INFO [compile_hlg.py:91] LG shape after k2.remove_epsilon: (6, None)
2023-05-12 18:02:28,617 INFO [compile_hlg.py:96] Arc sorting LG
2023-05-12 18:02:28,617 INFO [compile_hlg.py:99] Composing H and LG
2023-05-12 18:02:28,619 INFO [compile_hlg.py:106] Connecting LG
2023-05-12 18:02:28,619 INFO [compile_hlg.py:109] Arc sorting LG
2023-05-12 18:02:28,619 INFO [compile_hlg.py:111] HLG.shape: (8, None)
2023-05-12 18:02:28,619 INFO [compile_hlg.py:127] Saving HLG.pt to data/lang_phone
2023-07-27 12:42:02 (prepare.sh:92:main) Stage 5: Compile HLG
2023-07-27 12:42:07,275 INFO [compile_hlg.py:124] Processing data/lang_phone
2023-07-27 12:42:07,276 INFO [lexicon.py:171] Converting L.pt to Linv.pt
2023-07-27 12:42:07,309 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3
2023-07-27 12:42:07,310 INFO [compile_hlg.py:52] Loading G.fst.txt
2023-07-27 12:42:07,314 INFO [compile_hlg.py:62] Intersecting L and G
2023-07-27 12:42:07,323 INFO [compile_hlg.py:64] LG shape: (4, None)
2023-07-27 12:42:07,323 INFO [compile_hlg.py:66] Connecting LG
2023-07-27 12:42:07,323 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None)
2023-07-27 12:42:07,323 INFO [compile_hlg.py:70] <class 'torch.Tensor'>
2023-07-27 12:42:07,323 INFO [compile_hlg.py:71] Determinizing LG
2023-07-27 12:42:07,341 INFO [compile_hlg.py:74] <class '_k2.ragged.RaggedTensor'>
2023-07-27 12:42:07,341 INFO [compile_hlg.py:76] Connecting LG after k2.determinize
2023-07-27 12:42:07,341 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG
2023-07-27 12:42:07,354 INFO [compile_hlg.py:91] LG shape after k2.remove_epsilon: (6, None)
2023-07-27 12:42:07,445 INFO [compile_hlg.py:96] Arc sorting LG
2023-07-27 12:42:07,445 INFO [compile_hlg.py:99] Composing H and LG
2023-07-27 12:42:07,446 INFO [compile_hlg.py:106] Connecting LG
2023-07-27 12:42:07,446 INFO [compile_hlg.py:109] Arc sorting LG
2023-07-27 12:42:07,447 INFO [compile_hlg.py:111] HLG.shape: (8, None)
2023-07-27 12:42:07,447 INFO [compile_hlg.py:127] Saving HLG.pt to data/lang_phone
Training
~~~~~~~~
@ -409,12 +435,13 @@ Now let us run the training part:
.. code-block::
$ export CUDA_VISIBLE_DEVICES=""
$ ./tdnn/train.py
(test-icefall) kuangfangjun:ASR$ export CUDA_VISIBLE_DEVICES=""
(test-icefall) kuangfangjun:ASR$ ./tdnn/train.py
.. CAUTION::
We use ``export CUDA_VISIBLE_DEVICES=""`` so that ``icefall`` uses CPU
We use ``export CUDA_VISIBLE_DEVICES=""`` so that `icefall`_ uses CPU
even if there are GPUs available.
.. hint::
@ -432,53 +459,52 @@ The training log is given below:
.. code-block::
2023-05-12 18:04:59,759 INFO [train.py:481] Training started
2023-05-12 18:04:59,759 INFO [train.py:482] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0,
'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10,
'reduction': 'sum', 'use_double_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'seed': 42, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0,
'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 'num_workers': 2,
'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b7f09fa35e72589914f67089c0da9f196a92ca4', 'k2-git-date': 'Mon May 8 22:58:45 2023',
'lhotse-version': '1.15.0.dev+git.6fcfced.clean', 'torch-version': '2.0.0+cu118', 'torch-cuda-available': False, 'torch-cuda-version': '11.8', 'python-version': '3.1', 'icefall-git-branch': 'master',
'icefall-git-sha1': '30bde4b-clean', 'icefall-git-date': 'Thu May 11 17:37:47 2023', 'icefall-path': '/tmp/icefall',
'k2-path': 'tmp/lib/python3.10/site-packages/k2-1.24.3.dev20230512+cuda11.8.torch2.0.0-py3.10-linux-x86_64.egg/k2/__init__.py',
'lhotse-path': 'tmp/lib/python3.10/site-packages/lhotse/__init__.py', 'hostname': 'host', 'IP address': '0.0.0.0'}}
2023-05-12 18:04:59,761 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-05-12 18:04:59,764 INFO [train.py:495] device: cpu
2023-05-12 18:04:59,791 INFO [asr_datamodule.py:146] About to get train cuts
2023-05-12 18:04:59,791 INFO [asr_datamodule.py:244] About to get train cuts
2023-05-12 18:04:59,852 INFO [asr_datamodule.py:149] About to create train dataset
2023-05-12 18:04:59,852 INFO [asr_datamodule.py:199] Using SingleCutSampler.
2023-05-12 18:04:59,852 INFO [asr_datamodule.py:205] About to create train dataloader
2023-05-12 18:04:59,853 INFO [asr_datamodule.py:218] About to get test cuts
2023-05-12 18:04:59,853 INFO [asr_datamodule.py:252] About to get test cuts
2023-05-12 18:04:59,986 INFO [train.py:422] Epoch 0, batch 0, loss[loss=1.065, over 2436.00 frames. ], tot_loss[loss=1.065, over 2436.00 frames. ], batch size: 4
2023-05-12 18:05:00,352 INFO [train.py:422] Epoch 0, batch 10, loss[loss=0.4561, over 2828.00 frames. ], tot_loss[loss=0.7076, over 22192.90 frames. ], batch size: 4
2023-05-12 18:05:00,691 INFO [train.py:444] Epoch 0, validation loss=0.9002, over 18067.00 frames.
2023-05-12 18:05:00,996 INFO [train.py:422] Epoch 0, batch 20, loss[loss=0.2555, over 2695.00 frames. ], tot_loss[loss=0.484, over 34971.47 frames. ], batch size: 5
2023-05-12 18:05:01,217 INFO [train.py:444] Epoch 0, validation loss=0.4688, over 18067.00 frames.
2023-05-12 18:05:01,251 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-0.pt
2023-05-12 18:05:01,389 INFO [train.py:422] Epoch 1, batch 0, loss[loss=0.2532, over 2436.00 frames. ], tot_loss[loss=0.2532, over 2436.00 frames. ], batch size: 4
2023-05-12 18:05:01,637 INFO [train.py:422] Epoch 1, batch 10, loss[loss=0.1139, over 2828.00 frames. ], tot_loss[loss=0.1592, over 22192.90 frames. ], batch size: 4
2023-05-12 18:05:01,859 INFO [train.py:444] Epoch 1, validation loss=0.1629, over 18067.00 frames.
2023-05-12 18:05:02,094 INFO [train.py:422] Epoch 1, batch 20, loss[loss=0.0767, over 2695.00 frames. ], tot_loss[loss=0.118, over 34971.47 frames. ], batch size: 5
2023-05-12 18:05:02,350 INFO [train.py:444] Epoch 1, validation loss=0.06778, over 18067.00 frames.
2023-05-12 18:05:02,395 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-1.pt
2023-07-27 12:50:51,936 INFO [train.py:481] Training started
2023-07-27 12:50:51,936 INFO [train.py:482] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'seed': 42, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '4c05309499a08454997adf500b56dcc629e35ae5', 'k2-git-date': 'Tue Jul 25 16:23:36 2023', 'lhotse-version': '1.16.0.dev+git.7640d66.clean', 'torch-version': '1.13.0+cu116', 'torch-cuda-available': False, 'torch-cuda-version': '11.6', 'python-version': '3.8', 'icefall-git-branch': 'master', 'icefall-git-sha1': '3fb0a43-clean', 'icefall-git-date': 'Thu Jul 27 12:36:05 2023', 'icefall-path': '/tmp/icefall', 'k2-path': '/star-fj/fangjun/test-icefall/lib/python3.8/site-packages/k2/__init__.py', 'lhotse-path': '/star-fj/fangjun/test-icefall/lib/python3.8/site-packages/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-1-1220091118-57c4d55446-sph26', 'IP address': '10.177.77.20'}}
2023-07-27 12:50:51,941 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-07-27 12:50:51,949 INFO [train.py:495] device: cpu
2023-07-27 12:50:51,965 INFO [asr_datamodule.py:146] About to get train cuts
2023-07-27 12:50:51,965 INFO [asr_datamodule.py:244] About to get train cuts
2023-07-27 12:50:51,967 INFO [asr_datamodule.py:149] About to create train dataset
2023-07-27 12:50:51,967 INFO [asr_datamodule.py:199] Using SingleCutSampler.
2023-07-27 12:50:51,967 INFO [asr_datamodule.py:205] About to create train dataloader
2023-07-27 12:50:51,968 INFO [asr_datamodule.py:218] About to get test cuts
2023-07-27 12:50:51,968 INFO [asr_datamodule.py:252] About to get test cuts
2023-07-27 12:50:52,565 INFO [train.py:422] Epoch 0, batch 0, loss[loss=1.065, over 2436.00 frames. ], tot_loss[loss=1.065, over 2436.00 frames. ], batch size: 4
2023-07-27 12:50:53,681 INFO [train.py:422] Epoch 0, batch 10, loss[loss=0.4561, over 2828.00 frames. ], tot_loss[loss=0.7076, over 22192.90 frames.], batch size: 4
2023-07-27 12:50:54,167 INFO [train.py:444] Epoch 0, validation loss=0.9002, over 18067.00 frames.
2023-07-27 12:50:55,011 INFO [train.py:422] Epoch 0, batch 20, loss[loss=0.2555, over 2695.00 frames. ], tot_loss[loss=0.484, over 34971.47 frames. ], batch size: 5
2023-07-27 12:50:55,331 INFO [train.py:444] Epoch 0, validation loss=0.4688, over 18067.00 frames.
2023-07-27 12:50:55,368 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-0.pt
2023-07-27 12:50:55,633 INFO [train.py:422] Epoch 1, batch 0, loss[loss=0.2532, over 2436.00 frames. ], tot_loss[loss=0.2532, over 2436.00 frames. ],
batch size: 4
2023-07-27 12:50:56,242 INFO [train.py:422] Epoch 1, batch 10, loss[loss=0.1139, over 2828.00 frames. ], tot_loss[loss=0.1592, over 22192.90 frames.], batch size: 4
2023-07-27 12:50:56,522 INFO [train.py:444] Epoch 1, validation loss=0.1627, over 18067.00 frames.
2023-07-27 12:50:57,209 INFO [train.py:422] Epoch 1, batch 20, loss[loss=0.07055, over 2695.00 frames. ], tot_loss[loss=0.1175, over 34971.47 frames.], batch size: 5
2023-07-27 12:50:57,600 INFO [train.py:444] Epoch 1, validation loss=0.07091, over 18067.00 frames.
2023-07-27 12:50:57,640 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-1.pt
2023-07-27 12:50:57,847 INFO [train.py:422] Epoch 2, batch 0, loss[loss=0.07731, over 2436.00 frames. ], tot_loss[loss=0.07731, over 2436.00 frames.], batch size: 4
2023-07-27 12:50:58,427 INFO [train.py:422] Epoch 2, batch 10, loss[loss=0.04391, over 2828.00 frames. ], tot_loss[loss=0.05341, over 22192.90 frames. ], batch size: 4
2023-07-27 12:50:58,884 INFO [train.py:444] Epoch 2, validation loss=0.04384, over 18067.00 frames.
2023-07-27 12:50:59,387 INFO [train.py:422] Epoch 2, batch 20, loss[loss=0.03458, over 2695.00 frames. ], tot_loss[loss=0.04616, over 34971.47 frames. ], batch size: 5
2023-07-27 12:50:59,707 INFO [train.py:444] Epoch 2, validation loss=0.03379, over 18067.00 frames.
2023-07-27 12:50:59,758 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-2.pt
... ...
2023-05-12 18:05:14,789 INFO [train.py:422] Epoch 13, batch 0, loss[loss=0.01056, over 2436.00 frames. ], tot_loss[loss=0.01056, over 2436.00 frames. ], batch size: 4
2023-05-12 18:05:15,016 INFO [train.py:422] Epoch 13, batch 10, loss[loss=0.009022, over 2828.00 frames. ], tot_loss[loss=0.009985, over 22192.90 frames. ], batch size: 4
2023-05-12 18:05:15,271 INFO [train.py:444] Epoch 13, validation loss=0.01088, over 18067.00 frames.
2023-05-12 18:05:15,497 INFO [train.py:422] Epoch 13, batch 20, loss[loss=0.01174, over 2695.00 frames. ], tot_loss[loss=0.01077, over 34971.47 frames. ], batch size: 5
2023-05-12 18:05:15,747 INFO [train.py:444] Epoch 13, validation loss=0.01087, over 18067.00 frames.
2023-05-12 18:05:15,783 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-13.pt
2023-05-12 18:05:15,921 INFO [train.py:422] Epoch 14, batch 0, loss[loss=0.01045, over 2436.00 frames. ], tot_loss[loss=0.01045, over 2436.00 frames. ], batch size: 4
2023-05-12 18:05:16,146 INFO [train.py:422] Epoch 14, batch 10, loss[loss=0.008957, over 2828.00 frames. ], tot_loss[loss=0.009903, over 22192.90 frames. ], batch size: 4
2023-05-12 18:05:16,374 INFO [train.py:444] Epoch 14, validation loss=0.01092, over 18067.00 frames.
2023-05-12 18:05:16,598 INFO [train.py:422] Epoch 14, batch 20, loss[loss=0.01169, over 2695.00 frames. ], tot_loss[loss=0.01065, over 34971.47 frames. ], batch size: 5
2023-05-12 18:05:16,824 INFO [train.py:444] Epoch 14, validation loss=0.01077, over 18067.00 frames.
2023-05-12 18:05:16,862 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-14.pt
2023-05-12 18:05:16,865 INFO [train.py:555] Done!
2023-07-27 12:51:23,433 INFO [train.py:422] Epoch 13, batch 0, loss[loss=0.01054, over 2436.00 frames. ], tot_loss[loss=0.01054, over 2436.00 frames. ], batch size: 4
2023-07-27 12:51:23,980 INFO [train.py:422] Epoch 13, batch 10, loss[loss=0.009014, over 2828.00 frames. ], tot_loss[loss=0.009974, over 22192.90 frames. ], batch size: 4
2023-07-27 12:51:24,489 INFO [train.py:444] Epoch 13, validation loss=0.01085, over 18067.00 frames.
2023-07-27 12:51:25,258 INFO [train.py:422] Epoch 13, batch 20, loss[loss=0.01172, over 2695.00 frames. ], tot_loss[loss=0.01055, over 34971.47 frames. ], batch size: 5
2023-07-27 12:51:25,621 INFO [train.py:444] Epoch 13, validation loss=0.01074, over 18067.00 frames.
2023-07-27 12:51:25,699 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-13.pt
2023-07-27 12:51:25,866 INFO [train.py:422] Epoch 14, batch 0, loss[loss=0.01044, over 2436.00 frames. ], tot_loss[loss=0.01044, over 2436.00 frames. ], batch size: 4
2023-07-27 12:51:26,844 INFO [train.py:422] Epoch 14, batch 10, loss[loss=0.008942, over 2828.00 frames. ], tot_loss[loss=0.01, over 22192.90 frames. ], batch size: 4
2023-07-27 12:51:27,221 INFO [train.py:444] Epoch 14, validation loss=0.01082, over 18067.00 frames.
2023-07-27 12:51:27,970 INFO [train.py:422] Epoch 14, batch 20, loss[loss=0.01169, over 2695.00 frames. ], tot_loss[loss=0.01054, over 34971.47 frames. ], batch size: 5
2023-07-27 12:51:28,247 INFO [train.py:444] Epoch 14, validation loss=0.01073, over 18067.00 frames.
2023-07-27 12:51:28,323 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-14.pt
2023-07-27 12:51:28,326 INFO [train.py:555] Done!
Decoding
~~~~~~~~
@ -487,42 +513,32 @@ Let us use the trained model to decode the test set:
.. code-block::
$ ./tdnn/decode.py
(test-icefall) kuangfangjun:ASR$ ./tdnn/decode.py
The decoding log is:
2023-07-27 12:55:12,840 INFO [decode.py:263] Decoding started
2023-07-27 12:55:12,840 INFO [decode.py:264] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'export': False, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True, 'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '4c05309499a08454997adf500b56dcc629e35ae5', 'k2-git-date': 'Tue Jul 25 16:23:36 2023', 'lhotse-version': '1.16.0.dev+git.7640d66.clean', 'torch-version': '1.13.0+cu116', 'torch-cuda-available': False, 'torch-cuda-version': '11.6', 'python-version': '3.8', 'icefall-git-branch': 'master', 'icefall-git-sha1': '3fb0a43-clean', 'icefall-git-date': 'Thu Jul 27 12:36:05 2023', 'icefall-path': '/tmp/icefall', 'k2-path': '/star-fj/fangjun/test-icefall/lib/python3.8/site-packages/k2/__init__.py', 'lhotse-path': '/star-fj/fangjun/test-icefall/lib/python3.8/site-packages/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-1-1220091118-57c4d55446-sph26', 'IP address': '10.177.77.20'}}
2023-07-27 12:55:12,841 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-07-27 12:55:12,855 INFO [decode.py:273] device: cpu
2023-07-27 12:55:12,868 INFO [decode.py:291] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
2023-07-27 12:55:12,882 INFO [asr_datamodule.py:218] About to get test cuts
2023-07-27 12:55:12,883 INFO [asr_datamodule.py:252] About to get test cuts
2023-07-27 12:55:13,157 INFO [decode.py:204] batch 0/?, cuts processed until now is 4
2023-07-27 12:55:13,701 INFO [decode.py:241] The transcripts are stored in tdnn/exp/recogs-test_set.txt
2023-07-27 12:55:13,702 INFO [utils.py:564] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
2023-07-27 12:55:13,704 INFO [decode.py:249] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
2023-07-27 12:55:13,704 INFO [decode.py:316] Done!
.. code-block::
2023-05-12 18:08:30,482 INFO [decode.py:263] Decoding started
2023-05-12 18:08:30,483 INFO [decode.py:264] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23,
'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'export': False, 'feature_dir': PosixPath('data/fbank'),
'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': False, 'return_cuts': True,
'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b7f09fa35e72589914f67089c0da9f196a92ca4', 'k2-git-date': 'Mon May 8 22:58:45 2023',
'lhotse-version': '1.15.0.dev+git.6fcfced.clean', 'torch-version': '2.0.0+cu118', 'torch-cuda-available': False, 'torch-cuda-version': '11.8', 'python-version': '3.1', 'icefall-git-branch': 'master',
'icefall-git-sha1': '30bde4b-clean', 'icefall-git-date': 'Thu May 11 17:37:47 2023', 'icefall-path': '/tmp/icefall',
'k2-path': '/tmp/lib/python3.10/site-packages/k2-1.24.3.dev20230512+cuda11.8.torch2.0.0-py3.10-linux-x86_64.egg/k2/__init__.py',
'lhotse-path': '/tmp/lib/python3.10/site-packages/lhotse/__init__.py', 'hostname': 'host', 'IP address': '0.0.0.0'}}
2023-05-12 18:08:30,483 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-05-12 18:08:30,487 INFO [decode.py:273] device: cpu
2023-05-12 18:08:30,513 INFO [decode.py:291] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
2023-05-12 18:08:30,521 INFO [asr_datamodule.py:218] About to get test cuts
2023-05-12 18:08:30,521 INFO [asr_datamodule.py:252] About to get test cuts
2023-05-12 18:08:30,675 INFO [decode.py:204] batch 0/?, cuts processed until now is 4
2023-05-12 18:08:30,923 INFO [decode.py:241] The transcripts are stored in tdnn/exp/recogs-test_set.txt
2023-05-12 18:08:30,924 INFO [utils.py:558] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
2023-05-12 18:08:30,925 INFO [decode.py:249] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
2023-05-12 18:08:30,925 INFO [decode.py:316] Done!
**Congratulations!** You have successfully setup the environment and have run the first recipe in ``icefall``.
**Congratulations!** You have successfully setup the environment and have run the first recipe in `icefall`_.
Have fun with ``icefall``!
YouTube Video
-------------
We provide the following YouTube video showing how to install ``icefall``.
We provide the following YouTube video showing how to install `icefall`_.
It also shows how to debug various problems that you may encounter while
using ``icefall``.
using `icefall`_.
.. note::

4
docs/source/model-export/export-model-state-dict.rst
View File

@ -41,7 +41,7 @@ as an example.
./pruned_transducer_stateless3/export.py \
--exp-dir ./pruned_transducer_stateless3/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -78,7 +78,7 @@ In each recipe, there is also a file ``pretrained.py``, which can use
./pruned_transducer_stateless3/pretrained.py \
--checkpoint ./icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/exp/pretrained-iter-1224000-avg-14.pt \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/data/lang_bpe_500/bpe.model \
--tokens ./icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/data/lang_bpe_500/tokens.txt \
--method greedy_search \
./icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/test_wavs/1089-134686-0001.wav \
./icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/test_wavs/1221-135766-0001.wav \

3
docs/source/model-export/export-ncnn-conv-emformer.rst
View File

@ -153,11 +153,10 @@ Next, we use the following code to export our model:
./conv_emformer_transducer_stateless2/export-for-ncnn.py \
--exp-dir $dir/exp \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--tokens $dir/data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 1 \
--use-averaged-model 0 \
\
--num-encoder-layers 12 \
--chunk-length 32 \
--cnn-module-kernel 31 \

2
docs/source/model-export/export-ncnn-lstm.rst
View File

@ -73,7 +73,7 @@ Next, we use the following code to export our model:
./lstm_transducer_stateless2/export-for-ncnn.py \
--exp-dir $dir/exp \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--tokens $dir/data/lang_bpe_500/tokens.txt \
--epoch 99 \
--avg 1 \
--use-averaged-model 0 \

3
docs/source/model-export/export-ncnn-zipformer.rst
View File

@ -72,12 +72,11 @@ Next, we use the following code to export our model:
dir=./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
--bpe-model $dir/data/lang_bpe_500/bpe.model \
--tokens $dir/data/lang_bpe_500/tokens.txt \
--exp-dir $dir/exp \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
\
--decode-chunk-len 32 \
--num-left-chunks 4 \
--num-encoder-layers "2,4,3,2,4" \

2
docs/source/model-export/export-ncnn.rst
View File

@ -1,3 +1,5 @@
.. _icefall_export_to_ncnn:
Export to ncnn
==============

2
docs/source/model-export/export-onnx.rst
View File

@ -71,7 +71,7 @@ Export the model to ONNX
.. code-block:: bash
./pruned_transducer_stateless7_streaming/export-onnx.py \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--tokens $repo/data/lang_bpe_500/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \

2
docs/source/model-export/export-with-torch-jit-script.rst
View File

@ -32,7 +32,7 @@ as an example in the following.
./pruned_transducer_stateless3/export.py \
--exp-dir ./pruned_transducer_stateless3/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch $epoch \
--avg $avg \
--jit 1

2
docs/source/model-export/export-with-torch-jit-trace.rst
View File

@ -33,7 +33,7 @@ as an example in the following.
./lstm_transducer_stateless2/export.py \
--exp-dir ./lstm_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--iter $iter \
--avg $avg \
--jit-trace 1

16
docs/source/recipes/Non-streaming-ASR/librispeech/distillation.rst
View File

@ -47,7 +47,7 @@ The data preparation contains several stages, you can use the following two
options:
- ``--stage``
- ``--stop-stage``
- ``--stop_stage``
to control which stage(s) should be run. By default, all stages are executed.
@ -56,8 +56,8 @@ For example,
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./prepare.sh --stage 0 --stop-stage 0 # run only stage 0
$ ./prepare.sh --stage 2 --stop-stage 5 # run from stage 2 to stage 5
$ ./prepare.sh --stage 0 --stop_stage 0 # run only stage 0
$ ./prepare.sh --stage 2 --stop_stage 5 # run from stage 2 to stage 5
.. HINT::
@ -108,15 +108,15 @@ As usual, you can control the stages you want to run by specifying the following
two options:
- ``--stage``
- ``--stop-stage``
- ``--stop_stage``
For example,
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./distillation_with_hubert.sh --stage 0 --stop-stage 0 # run only stage 0
$ ./distillation_with_hubert.sh --stage 2 --stop-stage 4 # run from stage 2 to stage 5
$ ./distillation_with_hubert.sh --stage 0 --stop_stage 0 # run only stage 0
$ ./distillation_with_hubert.sh --stage 2 --stop_stage 4 # run from stage 2 to stage 5
Here are a few options in `./distillation_with_hubert.sh <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/distillation_with_hubert.sh>`_
you need to know before you proceed.
@ -134,7 +134,7 @@ and prepares MVQ-augmented training manifests.
.. code-block:: bash
$ ./distillation_with_hubert.sh --stage 2 --stop-stage 2 # run only stage 2
$ ./distillation_with_hubert.sh --stage 2 --stop_stage 2 # run only stage 2
Please see the
following screenshot for the output of an example execution.
@ -172,7 +172,7 @@ To perform training, please run stage 3 by executing the following command.
.. code-block:: bash
$ ./prepare.sh --stage 3 --stop-stage 3 # run MVQ training
$ ./prepare.sh --stage 3 --stop_stage 3 # run MVQ training
Here is the code snippet for training:

7
docs/source/recipes/RNN-LM/index.rst Normal file
View File

@ -0,0 +1,7 @@
RNN-LM
======
.. toctree::
:maxdepth: 2
librispeech/lm-training

104
docs/source/recipes/RNN-LM/librispeech/lm-training.rst Normal file
View File

@ -0,0 +1,104 @@
.. _train_nnlm:
Train an RNN langugage model
======================================
If you have enough text data, you can train a neural network language model (NNLM) to improve
the WER of your E2E ASR system. This tutorial shows you how to train an RNNLM from
scratch.
.. HINT::
For how to use an NNLM during decoding, please refer to the following tutorials:
:ref:`shallow_fusion`, :ref:`LODR`, :ref:`rescoring`
.. note::
This tutorial is based on the LibriSpeech recipe. Please check it out for the necessary
python scripts for this tutorial. We use the LibriSpeech LM-corpus as the LM training set
for illustration purpose. You can also collect your own data. The data format is quite simple:
each line should contain a complete sentence, and words should be separated by space.
First, let's download the training data for the RNNLM. This can be done via the
following command:
.. code-block:: bash
$ wget https://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz
$ gzip -d librispeech-lm-norm.txt.gz
As we are training a BPE-level RNNLM, we need to tokenize the training text, which requires a
BPE tokenizer. This can be achieved by executing the following command:
.. code-block:: bash
$ # if you don't have the BPE
$ 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/data/lang_bpe_500
$ git lfs pull --include bpe.model
$ cd ../../..
$ ./local/prepare_lm_training_data.py \
--bpe-model icefall-asr-librispeech-zipformer-2023-05-15/data/lang_bpe_500/bpe.model \
--lm-data librispeech-lm-norm.txt \
--lm-archive data/lang_bpe_500/lm_data.pt
Now, you should have a file name ``lm_data.pt`` file store under the directory ``data/lang_bpe_500``.
This is the packed training data for the RNNLM. We then sort the training data according to its
sentence length.
.. code-block:: bash
$ # This could take a while (~ 20 minutes), feel free to grab a cup of coffee :)
$ ./local/sort_lm_training_data.py \
--in-lm-data data/lang_bpe_500/lm_data.pt \
--out-lm-data data/lang_bpe_500/sorted_lm_data.pt \
--out-statistics data/lang_bpe_500/lm_data_stats.txt
The aforementioned steps can be repeated to create a a validation set for you RNNLM. Let's say
you have a validation set in ``valid.txt``, you can just set ``--lm-data valid.txt``
and ``--lm-archive data/lang_bpe_500/lm-data-valid.pt`` when calling ``./local/prepare_lm_training_data.py``.
After completing the previous steps, the training and testing sets for training RNNLM are ready.
The next step is to train the RNNLM model. The training command is as follows:
.. code-block:: bash
$ # assume you are in the icefall root directory
$ cd rnn_lm
$ ln -s ../../egs/librispeech/ASR/data .
$ cd ..
$ ./rnn_lm/train.py \
--world-size 4 \
--exp-dir ./rnn_lm/exp \
--start-epoch 0 \
--num-epochs 10 \
--use-fp16 0 \
--tie-weights 1 \
--embedding-dim 2048 \
--hidden_dim 2048 \
--num-layers 3 \
--batch-size 300 \
--lm-data rnn_lm/data/lang_bpe_500/sorted_lm_data.pt \
--lm-data-valid rnn_lm/data/lang_bpe_500/sorted_lm_data.pt
.. note::
You can adjust the RNNLM hyper parameters to control the size of the RNNLM,
such as embedding dimension and hidden state dimension. For more details, please
run ``./rnn_lm/train.py --help``.
.. note::
The training of RNNLM can take a long time (usually a couple of days).

1
docs/source/recipes/index.rst
View File

@ -15,3 +15,4 @@ We may add recipes for other tasks as well in the future.
Non-streaming-ASR/index
Streaming-ASR/index
RNN-LM/index

18
egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py
View File

@ -32,7 +32,7 @@ import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import get_executor
from icefall.utils import get_executor, str2bool
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
@ -42,7 +42,7 @@ torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80, perturb_speed: bool = False):
src_dir = Path("data/manifests/aidatatang_200zh")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())
@ -85,7 +85,8 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
recordings=m["recordings"],
supervisions=m["supervisions"],
)
if "train" in partition:
if "train" in partition and perturb_speed:
logging.info(f"Doing speed perturb")
cut_set = (
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
)
@ -109,7 +110,12 @@ def get_args():
default=80,
help="""The number of mel bins for Fbank""",
)
parser.add_argument(
"--perturb-speed",
type=str2bool,
default=False,
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
)
return parser.parse_args()
@ -119,4 +125,6 @@ if __name__ == "__main__":
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
compute_fbank_aidatatang_200zh(num_mel_bins=args.num_mel_bins)
compute_fbank_aidatatang_200zh(
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
)

2
egs/aidatatang_200zh/ASR/prepare.sh
View File

@ -77,7 +77,7 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for aidatatang_200zh"
if [ ! -f data/fbank/.aidatatang_200zh.done ]; then
mkdir -p data/fbank
./local/compute_fbank_aidatatang_200zh.py
./local/compute_fbank_aidatatang_200zh.py --perturb-speed True
touch data/fbank/.aidatatang_200zh.done
fi
fi

8
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py
View File

@ -37,7 +37,7 @@ from lhotse.dataset import (
DynamicBucketingSampler,
K2SpeechRecognitionDataset,
PrecomputedFeatures,
SingleCutSampler,
SimpleCutSampler,
SpecAugment,
)
from lhotse.dataset.input_strategies import OnTheFlyFeatures
@ -211,7 +211,7 @@ class Aidatatang_200zhAsrDataModule:
if self.args.enable_musan:
logging.info("Enable MUSAN")
transforms.append(
CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
)
else:
logging.info("Disable MUSAN")
@ -291,8 +291,8 @@ class Aidatatang_200zhAsrDataModule:
drop_last=True,
)
else:
logging.info("Using SingleCutSampler.")
train_sampler = SingleCutSampler(
logging.info("Using SimpleCutSampler.")
train_sampler = SimpleCutSampler(
cuts_train,
max_duration=self.args.max_duration,
shuffle=self.args.shuffle,

3
egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/train.py
View File

@ -635,7 +635,6 @@ def train_one_epoch(
tot_loss = MetricsTracker()
for batch_idx, batch in enumerate(train_dl):
params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"])
@ -800,7 +799,7 @@ def run(rank, world_size, args):
if params.print_diagnostics:
opts = diagnostics.TensorDiagnosticOptions(
2**22
512
) # allow 4 megabytes per sub-module
diagnostic = diagnostics.attach_diagnostics(model, opts)

50
egs/aishell/ASR/RESULTS.md
View File

@ -2,6 +2,56 @@
### Aishell training result(Stateless Transducer)
#### Pruned transducer stateless 7 streaming
[./pruned_transducer_stateless7_streaming](./pruned_transducer_stateless7_streaming)
It's Streaming version of Zipformer1 with Pruned RNNT loss.
| | test | dev | comment |
|------------------------|------|------|---------------------------------------|
| greedy search | 6.95 | 6.29 | --epoch 44 --avg 15 --max-duration 600 |
| modified beam search | 6.51 | 5.90 | --epoch 44 --avg 15 --max-duration 600 |
| fast beam search | 6.73 | 6.09 | --epoch 44 --avg 15 --max-duration 600 |
Training command is:
```bash
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1"
./pruned_transducer_stateless7_streaming/train.py \
--world-size 2 \
--num-epochs 50 \
--use-fp16 1 \
--context-size 1 \
--max-duration 800 \
--exp-dir ./pruned_transducer_stateless7_streaming/exp \
--enable-musan 0 \
--spec-aug-time-warp-factor 20
```
**Caution**: It uses `--context-size=1`.
The decoding command is:
```bash
for m in greedy_search modified_beam_search fast_beam_search ; do
./pruned_transducer_stateless7_streaming/decode.py \
--epoch 44 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless7_streaming/exp \
--lang-dir data/lang_char \
--context-size 1 \
--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-pruned-transducer-stateless7-streaming-2023-10-16/>
#### Pruned transducer stateless 7
[./pruned_transducer_stateless7](./pruned_transducer_stateless7)

21
egs/aishell/ASR/conformer_ctc/export.py Normal file → Executable file
View File

@ -23,12 +23,12 @@ import argparse
import logging
from pathlib import Path
import k2
import torch
from conformer import Conformer
from icefall.checkpoint import average_checkpoints, load_checkpoint
from icefall.lexicon import Lexicon
from icefall.utils import AttributeDict, str2bool
from icefall.utils import AttributeDict, num_tokens, str2bool
def get_parser():
@ -63,11 +63,10 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
"--tokens",
type=str,
default="data/lang_char",
help="""It contains language related input files such as "lexicon.txt"
""",
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -98,16 +97,16 @@ def get_params() -> AttributeDict:
def main():
args = get_parser().parse_args()
args.exp_dir = Path(args.exp_dir)
args.lang_dir = Path(args.lang_dir)
params = get_params()
params.update(vars(args))
logging.info(params)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
lexicon = Lexicon(params.lang_dir)
max_token_id = max(lexicon.tokens)
num_classes = max_token_id + 1 # +1 for the blank
num_classes = num_tokens(token_table) + 1 # +1 for the blank
logging.info(params)
device = torch.device("cpu")
if torch.cuda.is_available():

1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_H.py Symbolic link
View File

@ -0,0 +1 @@
../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_H.py

1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py Symbolic link
View File

@ -0,0 +1 @@
../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py

1
egs/aishell/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py Symbolic link
View File

@ -0,0 +1 @@
../../../librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HLG.py

0
egs/aishell/ASR/conformer_ctc/test_transformer.py Normal file → Executable file
View File

18
egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py
View File

@ -32,7 +32,7 @@ import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import get_executor
from icefall.utils import get_executor, str2bool
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
@ -42,7 +42,7 @@ torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80, perturb_speed: bool = False):
src_dir = Path("data/manifests")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())
@ -85,7 +85,8 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
recordings=m["recordings"],
supervisions=m["supervisions"],
)
if "train" in partition:
if "train" in partition and perturb_speed:
logging.info(f"Doing speed perturb")
cut_set = (
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
)
@ -109,7 +110,12 @@ def get_args():
default=80,
help="""The number of mel bins for Fbank""",
)
parser.add_argument(
"--perturb-speed",
type=str2bool,
default=False,
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
)
return parser.parse_args()
@ -119,4 +125,6 @@ if __name__ == "__main__":
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
compute_fbank_aidatatang_200zh(num_mel_bins=args.num_mel_bins)
compute_fbank_aidatatang_200zh(
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
)

18
egs/aishell/ASR/local/compute_fbank_aishell.py
View File

@ -32,7 +32,7 @@ import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import get_executor
from icefall.utils import get_executor, str2bool
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
@ -42,7 +42,7 @@ torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def compute_fbank_aishell(num_mel_bins: int = 80):
def compute_fbank_aishell(num_mel_bins: int = 80, perturb_speed: bool = False):
src_dir = Path("data/manifests")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())
@ -81,7 +81,8 @@ def compute_fbank_aishell(num_mel_bins: int = 80):
recordings=m["recordings"],
supervisions=m["supervisions"],
)
if "train" in partition:
if "train" in partition and perturb_speed:
logging.info(f"Doing speed perturb")
cut_set = (
cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
)
@ -104,7 +105,12 @@ def get_args():
default=80,
help="""The number of mel bins for Fbank""",
)
parser.add_argument(
"--perturb-speed",
type=str2bool,
default=False,
help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
)
return parser.parse_args()
@ -114,4 +120,6 @@ if __name__ == "__main__":
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
compute_fbank_aishell(num_mel_bins=args.num_mel_bins)
compute_fbank_aishell(
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
)

1
egs/aishell/ASR/local/prepare_lang_fst.py Symbolic link
View File

@ -0,0 +1 @@
../../../librispeech/ASR/local/prepare_lang_fst.py

13
egs/aishell/ASR/local/train_bbpe_model.py
View File

@ -15,7 +15,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# You can install sentencepiece via:
#
# pip install sentencepiece
@ -26,12 +25,12 @@
# Please install a version >=0.1.96
import argparse
import re
import shutil
import tempfile
from pathlib import Path
import sentencepiece as spm
from icefall import byte_encode, tokenize_by_CJK_char
@ -74,6 +73,11 @@ def main():
model_type = "unigram"
model_prefix = f"{lang_dir}/{model_type}_{vocab_size}"
model_file = Path(model_prefix + ".model")
if model_file.is_file():
print(f"{model_file} exists - skipping")
return
character_coverage = 1.0
input_sentence_size = 100000000
@ -88,8 +92,6 @@ def main():
_convert_to_bchar(args.transcript, train_text)
model_file = Path(model_prefix + ".model")
if not model_file.is_file():
spm.SentencePieceTrainer.train(
input=train_text,
vocab_size=vocab_size,
@ -102,9 +104,6 @@ def main():
bos_id=-1,
eos_id=-1,
)
else:
print(f"{model_file} exists - skipping")
return
shutil.copyfile(model_file, f"{lang_dir}/bbpe.model")

10
egs/aishell/ASR/prepare.sh
View File

@ -114,7 +114,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute fbank for aishell"
if [ ! -f data/fbank/.aishell.done ]; then
mkdir -p data/fbank
./local/compute_fbank_aishell.py
./local/compute_fbank_aishell.py --perturb-speed True
touch data/fbank/.aishell.done
fi
fi
@ -143,6 +143,7 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
./local/prepare_lang.py --lang-dir $lang_phone_dir
fi
# Train a bigram P for MMI training
if [ ! -f $lang_phone_dir/transcript_words.txt ]; then
log "Generate data to train phone based bigram P"
@ -257,6 +258,13 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
--max-order=3 \
data/lm/3-gram.unpruned.arpa > data/lm/G_3_gram_char.fst.txt
fi
if [ ! -f $lang_char_dir/HLG.fst ]; then
lang_phone_dir=data/lang_phone
./local/prepare_lang_fst.py \
--lang-dir $lang_phone_dir \
--ngram-G ./data/lm/G_3_gram.fst.txt
fi
fi
if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then

Some files were not shown because too many files have changed in this diff Show More