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Merge branch 'k2-fsa:master' into dev_zipformer_cn

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zr_jin 2023-08-13 01:20:02 +08:00 committed by GitHub
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134 changed files with 2624 additions and 1741 deletions
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10
.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh vendored
View File

@ -38,7 +38,7 @@ log "Decode with models exported by torch.jit.trace()"
for m in ctc-decoding 1best; do
./conformer_ctc3/jit_pretrained.py \
--model-filename $repo/exp/jit_trace.pt \
--words-file $repo/data/lang_bpe_500/words.txt \
--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 \
--G $repo/data/lm/G_4_gram.pt \
@ -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 \
@ -80,9 +80,9 @@ done
for m in ctc-decoding 1best; do
./conformer_ctc3/pretrained.py \
--checkpoint $repo/exp/pretrained.pt \
--words-file $repo/data/lang_bpe_500/words.txt \
--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 \
@ -93,7 +93,7 @@ done
echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}"
if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode" ]]; then
if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode" ]]; then
mkdir -p conformer_ctc3/exp
ln -s $PWD/$repo/exp/pretrained.pt conformer_ctc3/exp/epoch-999.pt
ln -s $PWD/$repo/data/lang_bpe_500 data/

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

4
.github/scripts/run-pre-trained-conformer-ctc.sh vendored
View File

@ -27,7 +27,7 @@ log "CTC decoding"
--method ctc-decoding \
--num-classes 500 \
--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.flac \
$repo/test_wavs/1221-135766-0001.flac \
$repo/test_wavs/1221-135766-0002.flac
@ -38,7 +38,7 @@ log "HLG decoding"
--method 1best \
--num-classes 500 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
--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.flac \

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

36
.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
@ -104,9 +100,9 @@ for sym in 1 2 3; do
--lang-dir $repo/data/lang_char \
--decoding-method greedy_search \
--max-sym-per-frame $sym \
$repo/test_wavs/DEV_T0000000000.wav \
$repo/test_wavs/DEV_T0000000001.wav \
$repo/test_wavs/DEV_T0000000002.wav
$repo/test_wavs/DEV_T0000000000.wav \
$repo/test_wavs/DEV_T0000000001.wav \
$repo/test_wavs/DEV_T0000000002.wav
done
for method in modified_beam_search beam_search fast_beam_search; do
@ -117,7 +113,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
--beam-size 4 \
--checkpoint $repo/exp/epoch-99.pt \
--lang-dir $repo/data/lang_char \
$repo/test_wavs/DEV_T0000000000.wav \
$repo/test_wavs/DEV_T0000000001.wav \
$repo/test_wavs/DEV_T0000000002.wav
$repo/test_wavs/DEV_T0000000000.wav \
$repo/test_wavs/DEV_T0000000001.wav \
$repo/test_wavs/DEV_T0000000002.wav
done

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 \

76
.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 ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
@ -70,6 +65,7 @@ jobs:
pip install --no-binary protobuf protobuf==3.20.*
pip install --no-deps --force-reinstall https://huggingface.co/csukuangfj/k2/resolve/main/cpu/k2-1.24.3.dev20230508+cpu.torch1.13.1-cp38-cp38-linux_x86_64.whl
pip install kaldifeat==1.25.0.dev20230726+cpu.torch1.13.1 -f https://csukuangfj.github.io/kaldifeat/cpu.html
- name: Run yesno recipe
shell: bash
@ -78,9 +74,75 @@ jobs:
export PYTHONPATH=$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: Show generated files
shell: bash
working-directory: ${{github.workspace}}
run: |
cd egs/yesno/ASR
ls -lh tdnn/exp

54
docs/source/decoding-with-langugage-models/LODR.rst
View File

@ -4,59 +4,59 @@ LODR for RNN Transducer
=======================
As a type of E2E model, neural transducers are usually considered as having an internal
language model, which learns the language level information on the training corpus.
In real-life scenario, there is often a mismatch between the training corpus and the target corpus space.
As a type of E2E model, neural transducers are usually considered as having an internal
language model, which learns the language level information on the training corpus.
In real-life scenario, there is often a mismatch between the training corpus and the target corpus space.
This mismatch can be a problem when decoding for neural transducer models with language models as its internal
language can act "against" the external LM. In this tutorial, we show how to use
`Low-order Density Ratio <https://arxiv.org/abs/2203.16776>`_ to alleviate this effect to further improve the performance
of langugae model integration.
of langugae model integration.
.. note::
This tutorial is based on the recipe
This tutorial is based on the recipe
`pruned_transducer_stateless7_streaming <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless7_streaming>`_,
which is a streaming transducer model trained on `LibriSpeech`_.
which is a streaming transducer model trained on `LibriSpeech`_.
However, you can easily apply LODR to other recipes.
If you encounter any problems, please open an issue here `icefall <https://github.com/k2-fsa/icefall/issues>`__.
.. note::
For simplicity, the training and testing corpus in this tutorial are the same (`LibriSpeech`_). However,
you can change the testing set to any other domains (e.g `GigaSpeech`_) and prepare the language models
For simplicity, the training and testing corpus in this tutorial are the same (`LibriSpeech`_). However,
you can change the testing set to any other domains (e.g `GigaSpeech`_) and prepare the language models
using that corpus.
First, let's have a look at some background information. As the predecessor of LODR, Density Ratio (DR) is first proposed `here <https://arxiv.org/abs/2002.11268>`_
First, let's have a look at some background information. As the predecessor of LODR, Density Ratio (DR) is first proposed `here <https://arxiv.org/abs/2002.11268>`_
to address the language information mismatch between the training
corpus (source domain) and the testing corpus (target domain). Assuming that the source domain and the test domain
are acoustically similar, DR derives the following formular for decoding with Bayes' theorem:
.. math::
\text{score}\left(y_u|\mathit{x},y\right) =
\log p\left(y_u|\mathit{x},y_{1:u-1}\right) +
\lambda_1 \log p_{\text{Target LM}}\left(y_u|\mathit{x},y_{1:u-1}\right) -
\text{score}\left(y_u|\mathit{x},y\right) =
\log p\left(y_u|\mathit{x},y_{1:u-1}\right) +
\lambda_1 \log p_{\text{Target LM}}\left(y_u|\mathit{x},y_{1:u-1}\right) -
\lambda_2 \log p_{\text{Source LM}}\left(y_u|\mathit{x},y_{1:u-1}\right)
where :math:`\lambda_1` and :math:`\lambda_2` are the weights of LM scores for target domain and source domain respectively.
Here, the source domain LM is trained on the training corpus. The only difference in the above formular compared to
where :math:`\lambda_1` and :math:`\lambda_2` are the weights of LM scores for target domain and source domain respectively.
Here, the source domain LM is trained on the training corpus. The only difference in the above formular compared to
shallow fusion is the subtraction of the source domain LM.
Some works treat the predictor and the joiner of the neural transducer as its internal LM. However, the LM is
Some works treat the predictor and the joiner of the neural transducer as its internal LM. However, the LM is
considered to be weak and can only capture low-level language information. Therefore, `LODR <https://arxiv.org/abs/2203.16776>`__ proposed to use
a low-order n-gram LM as an approximation of the ILM of the neural transducer. This leads to the following formula
during decoding for transducer model:
.. math::
\text{score}\left(y_u|\mathit{x},y\right) =
\log p_{rnnt}\left(y_u|\mathit{x},y_{1:u-1}\right) +
\lambda_1 \log p_{\text{Target LM}}\left(y_u|\mathit{x},y_{1:u-1}\right) -
\text{score}\left(y_u|\mathit{x},y\right) =
\log p_{rnnt}\left(y_u|\mathit{x},y_{1:u-1}\right) +
\lambda_1 \log p_{\text{Target LM}}\left(y_u|\mathit{x},y_{1:u-1}\right) -
\lambda_2 \log p_{\text{bi-gram}}\left(y_u|\mathit{x},y_{1:u-1}\right)
In LODR, an additional bi-gram LM estimated on the source domain (e.g training corpus) is required. Comared to DR,
In LODR, an additional bi-gram LM estimated on the source domain (e.g training corpus) is required. Comared to DR,
the only difference lies in the choice of source domain LM. According to the original `paper <https://arxiv.org/abs/2203.16776>`_,
LODR achieves similar performance compared DR in both intra-domain and cross-domain settings.
As a bi-gram is much faster to evaluate, LODR is usually much faster.
@ -85,7 +85,7 @@ 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
@ -99,17 +99,17 @@ The following WERs are achieved on test-clean and test-other:
$ For test-other, WER of different settings are:
$ beam_size_4 7.93 best for test-other
Then, we download the external language model and bi-gram LM that are necessary for LODR.
Then, we download the external language model and bi-gram LM that are necessary for LODR.
Note that the bi-gram is estimated on the LibriSpeech 960 hours' text.
.. code-block:: bash
$ # download the external LM
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm
$ # create a symbolic link so that the checkpoint can be loaded
$ pushd icefall-librispeech-rnn-lm/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt
$ ln -s pretrained.pt epoch-99.pt
$ popd
$
$ # download the bi-gram
@ -122,7 +122,7 @@ Note that the bi-gram is estimated on the LibriSpeech 960 hours' text.
Then, we perform LODR decoding by setting ``--decoding-method`` to ``modified_beam_search_lm_LODR``:
.. code-block:: bash
$ exp_dir=./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ lm_dir=./icefall-librispeech-rnn-lm/exp
$ lm_scale=0.42
@ -135,8 +135,8 @@ 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 \
@ -181,4 +181,4 @@ indeed **further improves** the WER. We can do even better if we increase ``--be
- 6.38
* - 12
- 2.4
- 6.23
- 6.23

6
docs/source/decoding-with-langugage-models/rescoring.rst
View File

@ -48,7 +48,7 @@ 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 +101,7 @@ 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 +173,7 @@ 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 \

4
docs/source/decoding-with-langugage-models/shallow-fusion.rst
View File

@ -46,7 +46,7 @@ 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 +95,7 @@ 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 \

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@ -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

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@ -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.

4
docs/source/index.rst
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@ -21,9 +21,11 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
:caption: Contents:
installation/index
docker/index
faqs
model-export/index
.. toctree::
:maxdepth: 3
@ -38,4 +40,4 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
.. toctree::
:maxdepth: 2
decoding-with-langugage-models/index
decoding-with-langugage-models/index

5
docs/source/installation/index.rst
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@ -3,6 +3,11 @@
Installation
============
.. hint::
We also provide :ref:`icefall_docker` support, which has already setup
the environment for you.
.. hint::
We have a colab notebook guiding you step by step to setup the environment.

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

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
)

2
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

2
egs/aishell/ASR/prepare_aidatatang_200zh.sh
View File

@ -53,7 +53,7 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Process aidatatang_200zh"
if [ ! -f data/fbank/.aidatatang_200zh_fbank.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_fbank.done
fi
fi

321
egs/aishell/ASR/pruned_transducer_stateless7/export.py
View File

@ -1,321 +0,0 @@
#!/usr/bin/env python3
#
# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script converts several saved checkpoints
# to a single one using model averaging.
"""
Usage:
(1) Export to torchscript model using torch.jit.script()
./pruned_transducer_stateless7/export.py \
--exp-dir ./pruned_transducer_stateless7/exp \
--lang-dir data/lang_char \
--epoch 30 \
--avg 9 \
--jit 1
It will generate a file `cpu_jit.pt` in the given `exp_dir`. You can later
load it by `torch.jit.load("cpu_jit.pt")`.
Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
are on CPU. You can use `to("cuda")` to move them to a CUDA device.
Check
https://github.com/k2-fsa/sherpa
for how to use the exported models outside of icefall.
(2) Export `model.state_dict()`
./pruned_transducer_stateless7/export.py \
--exp-dir ./pruned_transducer_stateless7/exp \
--lang-dir data/lang_char \
--epoch 20 \
--avg 10
It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
load it by `icefall.checkpoint.load_checkpoint()`.
To use the generated file with `pruned_transducer_stateless7/decode.py`,
you can do:
cd /path/to/exp_dir
ln -s pretrained.pt epoch-9999.pt
cd /path/to/egs/librispeech/ASR
./pruned_transducer_stateless7/decode.py \
--exp-dir ./pruned_transducer_stateless7/exp \
--epoch 9999 \
--avg 1 \
--max-duration 600 \
--decoding-method greedy_search \
--lang-dir data/lang_char
Check ./pretrained.py for its usage.
Note: If you don't want to train a model from scratch, we have
provided one for you. You can get it at
https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21
with the following commands:
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/marcoyang/icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21
# You will find the pre-trained model in icefall-asr-aishell-zipformer-pruned-transducer-stateless7-2023-03-21exp
"""
import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import torch
import torch.nn as nn
from scaling_converter import convert_scaled_to_non_scaled
from train2 import add_model_arguments, get_params, get_transducer_model
from icefall.checkpoint import (
average_checkpoints,
average_checkpoints_with_averaged_model,
find_checkpoints,
load_checkpoint,
)
from icefall.lexicon import Lexicon
from icefall.utils import str2bool
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--epoch",
type=int,
default=30,
help="""It specifies the checkpoint to use for decoding.
Note: Epoch counts from 1.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
"--avg",
type=int,
default=9,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch' and '--iter'",
)
parser.add_argument(
"--use-averaged-model",
type=str2bool,
default=True,
help="Whether to load averaged model. Currently it only supports "
"using --epoch. If True, it would decode with the averaged model "
"over the epoch range from `epoch-avg` (excluded) to `epoch`."
"Actually only the models with epoch number of `epoch-avg` and "
"`epoch` are loaded for averaging. ",
)
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless7/exp",
help="""It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",
)
parser.add_argument(
"--lang-dir",
type=str,
default="data/lang_char",
help="""The lang dir
It contains language related input files such as
"lexicon.txt"
""",
)
parser.add_argument(
"--jit",
type=str2bool,
default=False,
help="""True to save a model after applying torch.jit.script.
It will generate a file named cpu_jit.pt
Check ./jit_pretrained.py for how to use it.
""",
)
parser.add_argument(
"--context-size",
type=int,
default=1,
help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
)
add_model_arguments(parser)
return parser
@torch.no_grad()
def main():
args = get_parser().parse_args()
args.exp_dir = Path(args.exp_dir)
params = get_params()
params.update(vars(args))
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"device: {device}")
lexicon = Lexicon(params.lang_dir)
params.blank_id = 0
params.vocab_size = max(lexicon.tokens) + 1
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params)
model.to(device)
if not params.use_averaged_model:
if params.iter > 0:
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
elif params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1
filenames = []
for i in range(start, params.epoch + 1):
if i >= 1:
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
else:
if params.iter > 0:
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg + 1
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg + 1:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
filename_start = filenames[-1]
filename_end = filenames[0]
logging.info(
"Calculating the averaged model over iteration checkpoints"
f" from {filename_start} (excluded) to {filename_end}"
)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
)
)
else:
assert params.avg > 0, params.avg
start = params.epoch - params.avg
assert start >= 1, start
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
logging.info(
f"Calculating the averaged model over epoch range from "
f"{start} (excluded) to {params.epoch}"
)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
)
)
model.to("cpu")
model.eval()
if params.jit is True:
convert_scaled_to_non_scaled(model, inplace=True)
# We won't use the forward() method of the model in C++, so just ignore
# it here.
# Otherwise, one of its arguments is a ragged tensor and is not
# torch scriptabe.
model.__class__.forward = torch.jit.ignore(model.__class__.forward)
logging.info("Using torch.jit.script")
model = torch.jit.script(model)
filename = params.exp_dir / "cpu_jit.pt"
model.save(str(filename))
logging.info(f"Saved to {filename}")
else:
logging.info("Not using torchscript. Export model.state_dict()")
# Save it using a format so that it can be loaded
# by :func:`load_checkpoint`
filename = params.exp_dir / "pretrained.pt"
torch.save({"model": model.state_dict()}, str(filename))
logging.info(f"Saved to {filename}")
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
main()

1
egs/aishell/ASR/pruned_transducer_stateless7/export.py Symbolic link
View File

@ -0,0 +1 @@
../../../librispeech/ASR/pruned_transducer_stateless7/export.py

348
egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py
View File

@ -1,348 +0,0 @@
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This script loads a checkpoint and uses it to decode waves.
You can generate the checkpoint with the following command:
./pruned_transducer_stateless7/export.py \
--exp-dir ./pruned_transducer_stateless7/exp \
--lang-dir data/lang_char \
--epoch 20 \
--avg 10
Usage of this script:
(1) greedy search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--lang-dir ./data/lang_char \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
(2) beam search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--lang-dir ./data/lang_char \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
/path/to/bar.wav
(3) modified beam search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--lang-dir ./data/lang_char \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
/path/to/bar.wav
(4) fast beam search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--lang-dir ./data/lang_char \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
/path/to/bar.wav
You can also use `./pruned_transducer_stateless7/exp/epoch-xx.pt`.
Note: ./pruned_transducer_stateless7/exp/pretrained.pt is generated by
./pruned_transducer_stateless7/export.py
"""
import argparse
import logging
import math
from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
beam_search,
fast_beam_search_one_best,
greedy_search,
greedy_search_batch,
modified_beam_search,
)
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.lexicon import Lexicon
from icefall.utils import str2bool
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--checkpoint",
type=str,
required=True,
help="Path to the checkpoint. "
"The checkpoint is assumed to be saved by "
"icefall.checkpoint.save_checkpoint().",
)
parser.add_argument(
"--lang-dir",
type=str,
help="""The lang dir
It contains language related input files such as
"lexicon.txt"
""",
)
parser.add_argument(
"--method",
type=str,
default="greedy_search",
help="""Possible values are:
- greedy_search
- beam_search
- modified_beam_search
- fast_beam_search
""",
)
parser.add_argument(
"sound_files",
type=str,
nargs="+",
help="The input sound file(s) to transcribe. "
"Supported formats are those supported by torchaudio.load(). "
"For example, wav and flac are supported. "
"The sample rate has to be 16kHz.",
)
parser.add_argument(
"--sample-rate",
type=int,
default=16000,
help="The sample rate of the input sound file",
)
parser.add_argument(
"--beam-size",
type=int,
default=4,
help="""An integer indicating how many candidates we will keep for each
frame. Used only when --method is beam_search or
modified_beam_search.""",
)
parser.add_argument(
"--beam",
type=float,
default=4,
help="""A floating point value to calculate the cutoff score during beam
search (i.e., `cutoff = max-score - beam`), which is the same as the
`beam` in Kaldi.
Used only when --method is fast_beam_search""",
)
parser.add_argument(
"--max-contexts",
type=int,
default=4,
help="""Used only when --method is fast_beam_search""",
)
parser.add_argument(
"--max-states",
type=int,
default=8,
help="""Used only when --method is fast_beam_search""",
)
parser.add_argument(
"--context-size",
type=int,
default=1,
help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
)
parser.add_argument(
"--max-sym-per-frame",
type=int,
default=1,
help="""Maximum number of symbols per frame. Used only when
--method is greedy_search.
""",
)
add_model_arguments(parser)
return parser
def read_sound_files(
filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
"""Read a list of sound files into a list 1-D float32 torch tensors.
Args:
filenames:
A list of sound filenames.
expected_sample_rate:
The expected sample rate of the sound files.
Returns:
Return a list of 1-D float32 torch tensors.
"""
ans = []
for f in filenames:
wave, sample_rate = torchaudio.load(f)
assert (
sample_rate == expected_sample_rate
), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
# We use only the first channel
ans.append(wave[0])
return ans
@torch.no_grad()
def main():
parser = get_parser()
args = parser.parse_args()
params = get_params()
params.update(vars(args))
lexicon = Lexicon(params.lang_dir)
params.blank_id = 0
params.vocab_size = max(lexicon.tokens) + 1
token_table = lexicon.token_table
logging.info(f"{params}")
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"device: {device}")
logging.info("Creating model")
model = get_transducer_model(params)
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")
checkpoint = torch.load(args.checkpoint, map_location="cpu")
model.load_state_dict(checkpoint["model"], strict=False)
model.to(device)
model.eval()
model.device = device
logging.info("Constructing Fbank computer")
opts = kaldifeat.FbankOptions()
opts.device = device
opts.frame_opts.dither = 0
opts.frame_opts.snip_edges = False
opts.frame_opts.samp_freq = params.sample_rate
opts.mel_opts.num_bins = params.feature_dim
fbank = kaldifeat.Fbank(opts)
logging.info(f"Reading sound files: {params.sound_files}")
waves = read_sound_files(
filenames=params.sound_files, expected_sample_rate=params.sample_rate
)
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
features = fbank(waves)
feature_lengths = [f.size(0) for f in features]
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
feature_lengths = torch.tensor(feature_lengths, device=device)
encoder_out, encoder_out_lens = model.encoder(x=features, x_lens=feature_lengths)
num_waves = encoder_out.size(0)
hyps = []
msg = f"Using {params.method}"
if params.method == "beam_search":
msg += f" with beam size {params.beam_size}"
logging.info(msg)
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=params.beam,
max_contexts=params.max_contexts,
max_states=params.max_states,
)
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=params.beam_size,
)
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
else:
for i in range(num_waves):
# fmt: off
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
# fmt: on
if params.method == "greedy_search":
hyp_tokens = greedy_search(
model=model,
encoder_out=encoder_out_i,
max_sym_per_frame=params.max_sym_per_frame,
)
elif params.method == "beam_search":
hyp_tokens = beam_search(
model=model,
encoder_out=encoder_out_i,
beam=params.beam_size,
)
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens]
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
logging.info(s)
logging.info("Decoding Done")
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
main()

1
egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py Symbolic link
View File

@ -0,0 +1 @@
../../../librispeech/ASR/pruned_transducer_stateless7/pretrained.py

17
egs/aishell2/ASR/local/compute_fbank_aishell2.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_aishell2(num_mel_bins: int = 80):
def compute_fbank_aishell2(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_aishell2(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)
)
@ -104,6 +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 +121,6 @@ if __name__ == "__main__":
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
compute_fbank_aishell2(num_mel_bins=args.num_mel_bins)
compute_fbank_aishell2(
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
)

2
egs/aishell2/ASR/prepare.sh
View File

@ -101,7 +101,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute fbank for aishell2"
if [ ! -f data/fbank/.aishell2.done ]; then
mkdir -p data/fbank
./local/compute_fbank_aishell2.py
./local/compute_fbank_aishell2.py --perturb-speed True
touch data/fbank/.aishell2.done
fi
fi

18
egs/aishell4/ASR/local/compute_fbank_aishell4.py
View File

@ -32,7 +32,7 @@ import torch
from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig
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_aishell4(num_mel_bins: int = 80):
def compute_fbank_aishell4(num_mel_bins: int = 80, perturb_speed: bool = False):
src_dir = Path("data/manifests/aishell4")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())
@ -83,10 +83,12 @@ def compute_fbank_aishell4(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)
)
cut_set = cut_set.compute_and_store_features(
extractor=extractor,
storage_path=f"{output_dir}/{prefix}_feats_{partition}",
@ -113,6 +115,12 @@ 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()
@ -123,4 +131,6 @@ if __name__ == "__main__":
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
compute_fbank_aishell4(num_mel_bins=args.num_mel_bins)
compute_fbank_aishell4(
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
)

2
egs/aishell4/ASR/prepare.sh
View File

@ -107,7 +107,7 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Compute fbank for aishell4"
if [ ! -f data/fbank/.aishell4.done ]; then
mkdir -p data/fbank
./local/compute_fbank_aishell4.py
./local/compute_fbank_aishell4.py --perturb-speed True
touch data/fbank/.aishell4.done
fi
fi

17
egs/alimeeting/ASR/local/compute_fbank_alimeeting.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_alimeeting(num_mel_bins: int = 80):
def compute_fbank_alimeeting(num_mel_bins: int = 80, perturb_speed: bool = False):
src_dir = Path("data/manifests/alimeeting")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())
@ -82,7 +82,8 @@ def compute_fbank_alimeeting(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)
)
@ -114,6 +115,12 @@ 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()
@ -124,4 +131,6 @@ if __name__ == "__main__":
logging.basicConfig(format=formatter, level=logging.INFO)
args = get_args()
compute_fbank_alimeeting(num_mel_bins=args.num_mel_bins)
compute_fbank_alimeeting(
num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
)

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

@ -97,7 +97,7 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Compute fbank for alimeeting"
if [ ! -f data/fbank/.alimeeting.done ]; then
mkdir -p data/fbank
./local/compute_fbank_alimeeting.py
./local/compute_fbank_alimeeting.py --perturb-speed True
touch data/fbank/.alimeeting.done
fi
fi

32
egs/alimeeting/ASR_v2/local/compute_fbank_alimeeting.py
View File

@ -25,6 +25,7 @@ It looks for manifests in the directory data/manifests.
The generated fbank features are saved in data/fbank.
"""
import argparse
import logging
from pathlib import Path
@ -39,6 +40,8 @@ from lhotse.features.kaldifeat import (
)
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import str2bool
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
# Do this outside of main() in case it needs to take effect
@ -48,7 +51,7 @@ torch.set_num_interop_threads(1)
torch.multiprocessing.set_sharing_strategy("file_system")
def compute_fbank_ami():
def compute_fbank_ami(perturb_speed: bool = False):
src_dir = Path("data/manifests")
output_dir = Path("data/fbank")
@ -84,8 +87,12 @@ def compute_fbank_ami():
suffix="jsonl.gz",
)
def _extract_feats(cuts: CutSet, storage_path: Path, manifest_path: Path) -> None:
cuts = cuts + cuts.perturb_speed(0.9) + cuts.perturb_speed(1.1)
def _extract_feats(
cuts: CutSet, storage_path: Path, manifest_path: Path, speed_perturb: bool
) -> None:
if speed_perturb:
logging.info(f"Doing speed perturb")
cuts = cuts + cuts.perturb_speed(0.9) + cuts.perturb_speed(1.1)
_ = cuts.compute_and_store_features_batch(
extractor=extractor,
storage_path=storage_path,
@ -109,6 +116,7 @@ def compute_fbank_ami():
cuts_ihm,
output_dir / "feats_train_ihm",
src_dir / "cuts_train_ihm.jsonl.gz",
perturb_speed,
)
logging.info("Processing train split IHM + reverberated IHM")
@ -117,6 +125,7 @@ def compute_fbank_ami():
cuts_ihm_rvb,
output_dir / "feats_train_ihm_rvb",
src_dir / "cuts_train_ihm_rvb.jsonl.gz",
perturb_speed,
)
logging.info("Processing train split SDM")
@ -129,6 +138,7 @@ def compute_fbank_ami():
cuts_sdm,
output_dir / "feats_train_sdm",
src_dir / "cuts_train_sdm.jsonl.gz",
perturb_speed,
)
logging.info("Processing train split GSS")
@ -141,6 +151,7 @@ def compute_fbank_ami():
cuts_gss,
output_dir / "feats_train_gss",
src_dir / "cuts_train_gss.jsonl.gz",
perturb_speed,
)
logging.info("Preparing test cuts: IHM, SDM, GSS (optional)")
@ -186,8 +197,21 @@ def compute_fbank_ami():
)
def get_args():
parser = argparse.ArgumentParser()
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()
if __name__ == "__main__":
formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(format=formatter, level=logging.INFO)
compute_fbank_ami()
args = get_args()
compute_fbank_ami(perturb_speed=args.perturb_speed)

2
egs/alimeeting/ASR_v2/prepare.sh
View File

@ -85,7 +85,7 @@ fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Compute fbank for alimeeting"
mkdir -p data/fbank
python local/compute_fbank_alimeeting.py
python local/compute_fbank_alimeeting.py --perturb-speed True
log "Combine features from train splits"
lhotse combine data/manifests/cuts_train_{ihm,ihm_rvb,sdm,gss}.jsonl.gz - | shuf |\
gzip -c > data/manifests/cuts_train_all.jsonl.gz

7
egs/librispeech/ASR/RESULTS.md
View File

@ -90,6 +90,11 @@ You can use <https://github.com/k2-fsa/sherpa> to deploy it.
| greedy_search | 2.23 | 4.96 | --epoch 40 --avg 16 |
| modified_beam_search | 2.21 | 4.91 | --epoch 40 --avg 16 |
| fast_beam_search | 2.24 | 4.93 | --epoch 40 --avg 16 |
| modified_beam_search_shallow_fusion | 2.01 | 4.37 | --epoch 40 --avg 16 --beam-size 12 --lm-scale 0.3 |
| modified_beam_search_LODR | 1.94 | 4.17 | --epoch 40 --avg 16 --beam-size 12 --lm-scale 0.52 --LODR-scale -0.26 |
| modified_beam_search_rescore | 2.04 | 4.39 | --epoch 40 --avg 16 --beam-size 12 |
| modified_beam_search_rescore_LODR | 2.01 | 4.33 | --epoch 40 --avg 16 --beam-size 12 |
The training command is:
```bash
@ -119,6 +124,8 @@ for m in greedy_search modified_beam_search fast_beam_search; do
done
```
To decode with external language models, please refer to the documentation [here](https://k2-fsa.github.io/icefall/decoding-with-langugage-models/index.html).
##### small-scaled model, number of model parameters: 23285615, i.e., 23.3 M
The tensorboard log can be found at

18
egs/librispeech/ASR/conformer_ctc/export.py
View File

@ -23,12 +23,13 @@ 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 +64,10 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
"--tokens",
type=str,
default="data/lang_bpe_500",
help="""It contains language related input files such as "lexicon.txt"
""",
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -98,16 +98,16 @@ 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)
lexicon = Lexicon(params.lang_dir)
max_token_id = max(lexicon.tokens)
num_classes = max_token_id + 1 # +1 for the blank
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
num_classes = num_tokens(token_table) + 1 # +1 for the blank
device = torch.device("cpu")
if torch.cuda.is_available():

40
egs/librispeech/ASR/conformer_ctc/pretrained.py
View File

@ -24,7 +24,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from conformer import Conformer
@ -70,11 +69,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.
Used only when method is ctc-decoding.
""",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -83,10 +80,9 @@ def get_parser():
default="1best",
help="""Decoding method.
Possible values are:
(0) ctc-decoding - Use CTC decoding. It uses a sentence
piece model, i.e., lang_dir/bpe.model, to convert
word pieces to words. It needs neither a lexicon
nor an n-gram LM.
(0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file
to convert tokens to actual words or characters. It needs
neither a lexicon nor an n-gram LM.
(1) 1best - Use the best path as decoding output. Only
the transformer encoder output is used for decoding.
We call it HLG decoding.
@ -297,6 +293,7 @@ def main():
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
hyps = []
features = fbank(waves)
features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
@ -313,10 +310,17 @@ def main():
if params.method == "ctc-decoding":
logging.info("Use CTC decoding")
bpe_model = spm.SentencePieceProcessor()
bpe_model.load(params.bpe_model)
max_token_id = params.num_classes - 1
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
H = k2.ctc_topo(
max_token=max_token_id,
modified=params.num_classes > 500,
@ -337,9 +341,9 @@ def main():
best_path = one_best_decoding(
lattice=lattice, use_double_scores=params.use_double_scores
)
token_ids = get_texts(best_path)
hyps = bpe_model.decode(token_ids)
hyps = [s.split() for s in hyps]
hyp_tokens = get_texts(best_path)
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method in [
"1best",
"whole-lattice-rescoring",
@ -408,16 +412,16 @@ def main():
)
best_path = next(iter(best_path_dict.values()))
hyps = get_texts(best_path)
word_sym_table = k2.SymbolTable.from_file(params.words_file)
hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
hyp_tokens = get_texts(best_path)
for hyp in hyp_tokens:
hyps.append(" ".join([word_sym_table[i] for i in hyp]))
else:
raise ValueError(f"Unsupported decoding method: {params.method}")
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

19
egs/librispeech/ASR/conformer_ctc2/export.py
View File

@ -23,6 +23,7 @@
Usage:
./conformer_ctc2/export.py \
--exp-dir ./conformer_ctc2/exp \
--tokens ./data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -46,6 +47,7 @@ import argparse
import logging
from pathlib import Path
import k2
import torch
from conformer import Conformer
from decode import get_params
@ -56,8 +58,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.lexicon import Lexicon
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -123,10 +124,10 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
"--tokens",
type=str,
default="data/lang_bpe_500",
help="The lang dir",
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -143,14 +144,14 @@ def get_parser():
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))
lexicon = Lexicon(params.lang_dir)
max_token_id = max(lexicon.tokens)
num_classes = max_token_id + 1 # +1 for the blank
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
num_classes = num_tokens(token_table) + 1 # +1 for the blank
device = torch.device("cpu")
if torch.cuda.is_available():

23
egs/librispeech/ASR/conformer_ctc3/export.py
View File

@ -25,7 +25,7 @@ Usage:
./conformer_ctc3/export.py \
--exp-dir ./conformer_ctc3/exp \
--lang-dir data/lang_bpe_500 \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10 \
--jit-trace 1
@ -36,7 +36,7 @@ It will generates the file: `jit_trace.pt`.
./conformer_ctc3/export.py \
--exp-dir ./conformer_ctc3/exp \
--lang-dir data/lang_bpe_500 \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -62,6 +62,7 @@ import argparse
import logging
from pathlib import Path
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_ctc_model, get_params
@ -72,8 +73,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.lexicon import Lexicon
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -130,10 +130,10 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
type=Path,
default="data/lang_bpe_500",
help="The lang dir containing word table and LG graph",
"--tokens",
type=str,
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -171,9 +171,10 @@ def main():
logging.info(f"device: {device}")
lexicon = Lexicon(params.lang_dir)
max_token_id = max(lexicon.tokens)
num_classes = max_token_id + 1 # +1 for the blank
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
num_classes = num_tokens(token_table) + 1 # +1 for the blank
params.vocab_size = num_classes
if params.streaming_model:

42
egs/librispeech/ASR/conformer_ctc3/pretrained.py
View File

@ -24,7 +24,7 @@ Usage (for non-streaming mode):
(1) ctc-decoding
./conformer_ctc3/pretrained.py \
--checkpoint conformer_ctc3/exp/pretrained.pt \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method ctc-decoding \
--sample-rate 16000 \
test_wavs/1089-134686-0001.wav
@ -67,7 +67,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from decode import get_decoding_params
@ -114,11 +113,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.
Used only when method is ctc-decoding.
""",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -127,10 +124,9 @@ def get_parser():
default="1best",
help="""Decoding method.
Possible values are:
(0) ctc-decoding - Use CTC decoding. It uses a sentence
piece model, i.e., lang_dir/bpe.model, to convert
word pieces to words. It needs neither a lexicon
nor an n-gram LM.
(0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file
to convert tokens to actual words or characters. It needs
neither a lexicon nor an n-gram LM.
(1) 1best - Use the best path as decoding output. Only
the transformer encoder output is used for decoding.
We call it HLG decoding.
@ -316,6 +312,7 @@ def main():
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
hyps = []
features = fbank(waves)
feature_lengths = [f.size(0) for f in features]
@ -348,10 +345,17 @@ def main():
if params.method == "ctc-decoding":
logging.info("Use CTC decoding")
bpe_model = spm.SentencePieceProcessor()
bpe_model.load(params.bpe_model)
max_token_id = params.num_classes - 1
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
H = k2.ctc_topo(
max_token=max_token_id,
modified=False,
@ -372,9 +376,9 @@ def main():
best_path = one_best_decoding(
lattice=lattice, use_double_scores=params.use_double_scores
)
token_ids = get_texts(best_path)
hyps = bpe_model.decode(token_ids)
hyps = [s.split() for s in hyps]
hyp_tokens = get_texts(best_path)
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method in [
"1best",
"nbest-rescoring",
@ -439,16 +443,16 @@ def main():
)
best_path = next(iter(best_path_dict.values()))
hyps = get_texts(best_path)
word_sym_table = k2.SymbolTable.from_file(params.words_file)
hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
hyp_tokens = get_texts(best_path)
for hyp in hyp_tokens:
hyps.append(" ".join([word_sym_table[i] for i in hyp]))
else:
raise ValueError(f"Unsupported decoding method: {params.method}")
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

22
egs/librispeech/ASR/conv_emformer_transducer_stateless/export.py
View File

@ -22,7 +22,7 @@
Usage:
./conv_emformer_transducer_stateless/export.py \
--exp-dir ./conv_emformer_transducer_stateless/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 10 \
--use-averaged-model=True \
@ -62,7 +62,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from train import add_model_arguments, get_params, get_transducer_model
@ -72,7 +72,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -118,10 +118,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -166,12 +166,12 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

22
egs/librispeech/ASR/conv_emformer_transducer_stateless2/export-for-ncnn.py
View File

@ -8,7 +8,7 @@ for more details about how to use this file.
Usage:
./conv_emformer_transducer_stateless2/export-for-ncnn.py \
--exp-dir ./conv_emformer_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 10 \
--use-averaged-model=True \
@ -37,7 +37,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train2 import add_model_arguments, get_params, get_transducer_model
@ -48,7 +48,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -94,10 +94,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -217,12 +217,12 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

25
egs/librispeech/ASR/conv_emformer_transducer_stateless2/export-onnx.py
View File

@ -18,7 +18,6 @@ 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
@ -28,7 +27,7 @@ popd
2. Export the model to ONNX
./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 \
@ -55,14 +54,14 @@ import logging
from pathlib import Path
from typing import Dict, Tuple
import k2
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from decoder import Decoder
from emformer import Emformer
from scaling_converter import convert_scaled_to_non_scaled
from train2 import add_model_arguments, get_params, get_transducer_model
from emformer import Emformer
from icefall.checkpoint import (
average_checkpoints,
@ -70,7 +69,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -127,10 +126,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -484,12 +483,12 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

22
egs/librispeech/ASR/conv_emformer_transducer_stateless2/export.py
View File

@ -22,7 +22,7 @@
Usage:
./conv_emformer_transducer_stateless2/export.py \
--exp-dir ./conv_emformer_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 10 \
--use-averaged-model=True \
@ -62,7 +62,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -73,7 +73,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -119,10 +119,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
required=True,
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -167,12 +167,12 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

2
egs/librispeech/ASR/conv_emformer_transducer_stateless2/onnx_pretrained.py
View File

@ -28,7 +28,7 @@ popd
2. Export the model to ONNX
./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 \

25
egs/librispeech/ASR/lstm_transducer_stateless/export.py
View File

@ -26,7 +26,7 @@ Usage:
./lstm_transducer_stateless/export.py \
--exp-dir ./lstm_transducer_stateless/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 35 \
--avg 10 \
--jit-trace 1
@ -38,7 +38,7 @@ It will generate 3 files: `encoder_jit_trace.pt`,
./lstm_transducer_stateless/export.py \
--exp-dir ./lstm_transducer_stateless/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 35 \
--avg 10
@ -79,7 +79,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
import torch.nn as nn
from scaling_converter import convert_scaled_to_non_scaled
@ -91,7 +91,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -148,10 +148,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -266,12 +266,13 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size, <blk> is
# defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

49
egs/librispeech/ASR/lstm_transducer_stateless/pretrained.py
View File

@ -20,7 +20,7 @@ Usage:
(1) greedy search
./lstm_transducer_stateless/pretrained.py \
--checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -28,7 +28,7 @@ Usage:
(2) beam search
./lstm_transducer_stateless/pretrained.py \
--checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -37,7 +37,7 @@ Usage:
(3) modified beam search
./lstm_transducer_stateless/pretrained.py \
--checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage:
(4) fast beam search
./lstm_transducer_stateless/pretrained.py \
--checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -66,7 +66,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -79,6 +78,8 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import num_tokens
def get_parser():
parser = argparse.ArgumentParser(
@ -95,9 +96,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -214,13 +215,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(f"{params}")
@ -275,6 +277,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -286,8 +294,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -296,16 +304,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -326,12 +334,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

23
egs/librispeech/ASR/lstm_transducer_stateless2/export-for-ncnn.py
View File

@ -29,7 +29,7 @@ popd
./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 \
@ -49,7 +49,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -60,7 +60,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -106,10 +106,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -221,12 +221,13 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size, <blk> is
# defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

2
egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx-zh.py
View File

@ -613,7 +613,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

25
egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx.py
View File

@ -28,7 +28,7 @@ popd
2. Export the model to ONNX
./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 \
@ -52,8 +52,8 @@ import logging
from pathlib import Path
from typing import Dict, Optional, Tuple
import k2
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from decoder import Decoder
@ -68,7 +68,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -125,10 +125,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -437,12 +437,13 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size, <blk> is
# defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -607,7 +608,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

25
egs/librispeech/ASR/lstm_transducer_stateless2/export.py
View File

@ -27,7 +27,7 @@ Usage:
./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 \
--epoch 35 \
--avg 10 \
--jit-trace 1
@ -39,7 +39,7 @@ It will generate 3 files: `encoder_jit_trace.pt`,
./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 \
--epoch 35 \
--avg 10
@ -80,7 +80,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
import torch.nn as nn
from scaling_converter import convert_scaled_to_non_scaled
@ -92,7 +92,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -149,10 +149,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -267,12 +267,13 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size, <blk> is
# defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

49
egs/librispeech/ASR/lstm_transducer_stateless2/pretrained.py
View File

@ -20,7 +20,7 @@ Usage:
(1) greedy search
./lstm_transducer_stateless2/pretrained.py \
--checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -28,7 +28,7 @@ Usage:
(2) beam search
./lstm_transducer_stateless2/pretrained.py \
--checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -37,7 +37,7 @@ Usage:
(3) modified beam search
./lstm_transducer_stateless2/pretrained.py \
--checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage:
(4) fast beam search
./lstm_transducer_stateless2/pretrained.py \
--checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -69,7 +69,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -82,6 +81,8 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import num_tokens
def get_parser():
parser = argparse.ArgumentParser(
@ -98,9 +99,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -217,13 +218,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(f"{params}")
@ -278,6 +280,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -289,8 +297,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -299,16 +307,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -329,12 +337,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

25
egs/librispeech/ASR/lstm_transducer_stateless3/export.py
View File

@ -26,7 +26,7 @@ Usage:
./lstm_transducer_stateless3/export.py \
--exp-dir ./lstm_transducer_stateless3/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 40 \
--avg 20 \
--jit-trace 1
@ -38,7 +38,7 @@ It will generate 3 files: `encoder_jit_trace.pt`,
./lstm_transducer_stateless3/export.py \
--exp-dir ./lstm_transducer_stateless3/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 40 \
--avg 20
@ -79,7 +79,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
import torch.nn as nn
from scaling_converter import convert_scaled_to_non_scaled
@ -91,7 +91,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -148,10 +148,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to tokens.txt.",
)
parser.add_argument(
@ -266,12 +266,13 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size, <blk> is
# defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

46
egs/librispeech/ASR/lstm_transducer_stateless3/pretrained.py
View File

@ -20,7 +20,7 @@ Usage:
(1) greedy search
./lstm_transducer_stateless3/pretrained.py \
--checkpoint ./lstm_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -28,7 +28,7 @@ Usage:
(2) beam search
./lstm_transducer_stateless3/pretrained.py \
--checkpoint ./lstm_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -37,7 +37,7 @@ Usage:
(3) modified beam search
./lstm_transducer_stateless3/pretrained.py \
--checkpoint ./lstm_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -79,6 +79,8 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import num_tokens
def get_parser():
parser = argparse.ArgumentParser(
@ -95,9 +97,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -214,13 +216,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(f"{params}")
@ -275,6 +278,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -286,8 +295,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -296,16 +305,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -326,12 +335,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

23
egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py
View File

@ -22,7 +22,7 @@
Usage:
./prunted_stateless_emformer_rnnt/export.py \
--exp-dir ./prunted_stateless_emformer_rnnt/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -48,7 +48,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from train import add_model_arguments, get_params, get_transducer_model
@ -58,7 +58,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -115,10 +115,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -154,13 +154,12 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> and <unk> are defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

2
egs/librispeech/ASR/pruned_transducer_stateless/export-onnx.py
View File

@ -508,7 +508,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

24
egs/librispeech/ASR/pruned_transducer_stateless/export.py
View File

@ -22,7 +22,7 @@
Usage:
./pruned_transducer_stateless/export.py \
--exp-dir ./pruned_transducer_stateless/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -47,12 +47,12 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from train import add_model_arguments, get_params, get_transducer_model
from icefall.checkpoint import average_checkpoints, load_checkpoint
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -87,10 +87,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -135,13 +135,13 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size, <blk> is
# defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.streaming_model:
assert params.causal_convolution

49
egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py
View File

@ -20,7 +20,7 @@ Usage:
(1) greedy search
./pruned_transducer_stateless/pretrained.py \
--checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -28,7 +28,7 @@ Usage:
(2) beam search
./pruned_transducer_stateless/pretrained.py \
--checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -37,7 +37,7 @@ Usage:
(3) modified beam search
./pruned_transducer_stateless/pretrained.py \
--checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage:
(4) fast beam search
./pruned_transducer_stateless/pretrained.py \
--checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -66,7 +66,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -79,7 +78,7 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -97,9 +96,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -237,13 +236,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.simulate_streaming:
assert (
@ -314,6 +314,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -325,8 +331,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -335,16 +341,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -365,12 +371,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

22
egs/librispeech/ASR/pruned_transducer_stateless2/export.py
View File

@ -22,7 +22,7 @@
Usage:
./pruned_transducer_stateless2/export.py \
--exp-dir ./pruned_transducer_stateless2/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -47,12 +47,12 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from train import add_model_arguments, get_params, get_transducer_model
from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -98,10 +98,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -145,12 +145,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.streaming_model:
assert params.causal_convolution

49
egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py
View File

@ -20,7 +20,7 @@ Usage:
(1) greedy search
./pruned_transducer_stateless2/pretrained.py \
--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -28,7 +28,7 @@ Usage:
(2) beam search
./pruned_transducer_stateless2/pretrained.py \
--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -37,7 +37,7 @@ Usage:
(3) modified beam search
./pruned_transducer_stateless2/pretrained.py \
--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage:
(4) fast beam search
./pruned_transducer_stateless2/pretrained.py \
--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -66,7 +66,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -79,7 +78,7 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -97,9 +96,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -238,13 +237,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.simulate_streaming:
assert (
@ -315,6 +315,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -326,8 +332,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -336,16 +342,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -366,12 +372,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

24
egs/librispeech/ASR/pruned_transducer_stateless3/export-onnx.py
View File

@ -28,7 +28,7 @@ popd
2. Export the model to ONNX
./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/
@ -48,8 +48,8 @@ import logging
from pathlib import Path
from typing import Dict, Tuple
import k2
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from conformer import Conformer
@ -59,7 +59,7 @@ from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
from icefall.utils import setup_logger
from icefall.utils import num_tokens, setup_logger
def get_parser():
@ -105,10 +105,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -393,12 +393,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -518,7 +520,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

26
egs/librispeech/ASR/pruned_transducer_stateless3/export.py
View File

@ -26,7 +26,7 @@ Usage:
./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 \
--jit 1
@ -44,7 +44,7 @@ It will also generate 3 other files: `encoder_jit_script.pt`,
./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 \
--jit-trace 1
@ -56,7 +56,7 @@ It will generates 3 files: `encoder_jit_trace.pt`,
./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
@ -97,14 +97,14 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
import torch.nn as nn
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -150,10 +150,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt",
)
parser.add_argument(
@ -342,12 +342,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.streaming_model:
assert params.causal_convolution

51
egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py
View File

@ -20,7 +20,7 @@ You can generate the checkpoint with the following command:
./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
@ -29,7 +29,7 @@ Usage of this script:
(1) greedy search
./pruned_transducer_stateless3/pretrained.py \
--checkpoint ./pruned_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -37,7 +37,7 @@ Usage of this script:
(2) beam search
./pruned_transducer_stateless3/pretrained.py \
--checkpoint ./pruned_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage of this script:
(3) modified beam search
./pruned_transducer_stateless3/pretrained.py \
--checkpoint ./pruned_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -55,7 +55,7 @@ Usage of this script:
(4) fast beam search
./pruned_transducer_stateless3/pretrained.py \
--checkpoint ./pruned_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -75,7 +75,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -88,7 +87,7 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -106,9 +105,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -247,13 +246,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.simulate_streaming:
assert (
@ -324,6 +324,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -335,8 +341,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -345,16 +351,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -375,12 +381,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

22
egs/librispeech/ASR/pruned_transducer_stateless4/export.py
View File

@ -22,7 +22,7 @@
Usage:
./pruned_transducer_stateless4/export.py \
--exp-dir ./pruned_transducer_stateless4/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -48,7 +48,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -59,7 +59,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -116,10 +116,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -164,12 +164,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.streaming_model:
assert params.causal_convolution

26
egs/librispeech/ASR/pruned_transducer_stateless5/export-onnx-streaming.py
View File

@ -28,7 +28,7 @@ popd
2. Export the model to ONNX
./pruned_transducer_stateless5/export-onnx-streaming.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 \
@ -58,13 +58,13 @@ import logging
from pathlib import Path
from typing import Dict, Tuple
import k2
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from conformer import Conformer
from onnxruntime.quantization import QuantType, quantize_dynamic
from decoder import Decoder
from onnxruntime.quantization import QuantType, quantize_dynamic
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -74,7 +74,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -131,10 +131,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -489,12 +489,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -662,7 +664,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

26
egs/librispeech/ASR/pruned_transducer_stateless5/export-onnx.py
View File

@ -28,7 +28,7 @@ popd
2. Export the model to ONNX
./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 \
@ -55,13 +55,13 @@ import logging
from pathlib import Path
from typing import Dict, Tuple
import k2
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from conformer import Conformer
from onnxruntime.quantization import QuantType, quantize_dynamic
from decoder import Decoder
from onnxruntime.quantization import QuantType, quantize_dynamic
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -71,7 +71,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -128,10 +128,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -416,12 +416,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -586,7 +588,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

22
egs/librispeech/ASR/pruned_transducer_stateless5/export.py
View File

@ -22,7 +22,7 @@
Usage:
./pruned_transducer_stateless5/export.py \
--exp-dir ./pruned_transducer_stateless5/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -48,7 +48,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -59,7 +59,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -116,10 +116,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -164,12 +164,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
if params.streaming_model:
assert params.causal_convolution

49
egs/librispeech/ASR/pruned_transducer_stateless5/pretrained.py
View File

@ -20,7 +20,7 @@ Usage:
(1) greedy search
./pruned_transducer_stateless5/pretrained.py \
--checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -28,7 +28,7 @@ Usage:
(2) beam search
./pruned_transducer_stateless5/pretrained.py \
--checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -37,7 +37,7 @@ Usage:
(3) modified beam search
./pruned_transducer_stateless5/pretrained.py \
--checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage:
(4) fast beam search
./pruned_transducer_stateless5/pretrained.py \
--checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -66,7 +66,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -79,6 +78,8 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import num_tokens
def get_parser():
parser = argparse.ArgumentParser(
@ -95,9 +96,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -214,13 +215,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(f"{params}")
@ -275,6 +277,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -286,8 +294,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -296,16 +304,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -326,12 +334,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

22
egs/librispeech/ASR/pruned_transducer_stateless6/export.py
View File

@ -22,7 +22,7 @@
Usage:
./pruned_transducer_stateless6/export.py \
--exp-dir ./pruned_transducer_stateless6/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -47,12 +47,12 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from train import get_params, get_transducer_model
from icefall.checkpoint import average_checkpoints, find_checkpoints, load_checkpoint
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -98,10 +98,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -135,12 +135,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

27
egs/librispeech/ASR/pruned_transducer_stateless7/export-onnx.py
View File

@ -1,6 +1,7 @@
#!/usr/bin/env python3
#
# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang
# Zengrui Jin)
"""
This script exports a transducer model from PyTorch to ONNX.
@ -18,7 +19,6 @@ 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-9.pt"
cd exp
@ -28,7 +28,7 @@ popd
2. Export the model to ONNX
./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 \
@ -50,8 +50,8 @@ import logging
from pathlib import Path
from typing import Dict, Tuple
import k2
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from decoder import Decoder
@ -66,7 +66,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -123,10 +123,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -411,12 +410,12 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -581,7 +580,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

30
egs/librispeech/ASR/pruned_transducer_stateless7/export.py
View File

@ -1,6 +1,7 @@
#!/usr/bin/env python3
#
# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang
# Zengrui Jin)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
@ -26,7 +27,7 @@ Usage:
./pruned_transducer_stateless7/export.py \
--exp-dir ./pruned_transducer_stateless7/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 9 \
--jit 1
@ -45,7 +46,7 @@ for how to use the exported models outside of icefall.
./pruned_transducer_stateless7/export.py \
--exp-dir ./pruned_transducer_stateless7/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -65,7 +66,7 @@ you can do:
--avg 1 \
--max-duration 600 \
--decoding-method greedy_search \
--bpe-model data/lang_bpe_500/bpe.model
--tokens data/lang_bpe_500/tokens.txt \
Check ./pretrained.py for its usage.
@ -86,7 +87,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
import torch.nn as nn
from scaling_converter import convert_scaled_to_non_scaled
@ -98,7 +99,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -155,10 +156,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -198,12 +198,12 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -292,7 +292,7 @@ def main():
model.to("cpu")
model.eval()
if params.jit is True:
if params.jit:
convert_scaled_to_non_scaled(model, inplace=True)
# We won't use the forward() method of the model in C++, so just ignore
# it here.

55
egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py
View File

@ -1,5 +1,6 @@
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang
# Zengrui Jin)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
@ -20,7 +21,7 @@ You can generate the checkpoint with the following command:
./pruned_transducer_stateless7/export.py \
--exp-dir ./pruned_transducer_stateless7/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -29,7 +30,7 @@ Usage of this script:
(1) greedy search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -37,7 +38,7 @@ Usage of this script:
(2) beam search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +47,7 @@ Usage of this script:
(3) modified beam search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -55,7 +56,7 @@ Usage of this script:
(4) fast beam search
./pruned_transducer_stateless7/pretrained.py \
--checkpoint ./pruned_transducer_stateless7/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens ./data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -75,7 +76,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -88,7 +88,7 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import str2bool
from icefall.utils import num_tokens
def get_parser():
@ -106,9 +106,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -225,13 +225,13 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
# Load id of the <blk> token and the vocab size
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(f"{params}")
@ -286,6 +286,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -297,8 +303,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -307,16 +313,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -337,12 +343,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

24
egs/librispeech/ASR/pruned_transducer_stateless7_ctc/export.py
View File

@ -26,7 +26,7 @@ Usage:
./pruned_transducer_stateless7_ctc/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 9 \
--jit 1
@ -45,7 +45,7 @@ for how to use the exported models outside of icefall.
./pruned_transducer_stateless7_ctc/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -86,7 +86,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -97,7 +97,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -154,10 +154,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -197,12 +197,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

51
egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained.py
View File

@ -20,7 +20,7 @@ You can generate the checkpoint with the following command:
./pruned_transducer_stateless7_ctc/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
@ -29,7 +29,7 @@ Usage of this script:
(1) greedy search
./pruned_transducer_stateless7_ctc/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -37,7 +37,7 @@ Usage of this script:
(2) beam search
./pruned_transducer_stateless7_ctc/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage of this script:
(3) modified beam search
./pruned_transducer_stateless7_ctc/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -55,7 +55,7 @@ Usage of this script:
(4) fast beam search
./pruned_transducer_stateless7_ctc/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -75,7 +75,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -88,6 +87,8 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import num_tokens
def get_parser():
parser = argparse.ArgumentParser(
@ -104,9 +105,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -223,13 +224,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(f"{params}")
@ -284,6 +286,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -295,8 +303,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -305,16 +313,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -335,12 +343,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

10
egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained_ctc.py
View File

@ -22,14 +22,14 @@ You can use the following command to get the exported models:
./pruned_transducer_stateless7_ctc/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
Usage of this script:
(1) ctc-decoding
./pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py \
./pruned_transducer_stateless7_ctc/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--bpe-model data/lang_bpe_500/bpe.model \
--method ctc-decoding \
@ -38,7 +38,7 @@ Usage of this script:
/path/to/bar.wav
(2) 1best
./pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py \
./pruned_transducer_stateless7_ctc/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--HLG data/lang_bpe_500/HLG.pt \
--words-file data/lang_bpe_500/words.txt \
@ -48,7 +48,7 @@ Usage of this script:
/path/to/bar.wav
(3) nbest-rescoring
./bruned_transducer_stateless7_ctc/jit_pretrained_ctc.py \
./bruned_transducer_stateless7_ctc/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--HLG data/lang_bpe_500/HLG.pt \
--words-file data/lang_bpe_500/words.txt \
@ -60,7 +60,7 @@ Usage of this script:
(4) whole-lattice-rescoring
./pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py \
./pruned_transducer_stateless7_ctc/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc/exp/pretrained.pt \
--HLG data/lang_bpe_500/HLG.pt \
--words-file data/lang_bpe_500/words.txt \

24
egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/export.py
View File

@ -26,7 +26,7 @@ Usage:
./pruned_transducer_stateless7_ctc_bs/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 13 \
--jit 1
@ -45,7 +45,7 @@ for how to use the exported models outside of icefall.
./pruned_transducer_stateless7_ctc_bs/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 13
@ -86,7 +86,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -97,7 +97,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -154,10 +154,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -197,12 +197,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

26
egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/export_onnx.py
View File

@ -28,7 +28,7 @@ Usage:
./pruned_transducer_stateless7_ctc_bs/export_onnx.py \
--exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 13 \
--onnx 1
@ -48,7 +48,7 @@ Check `onnx_check.py` for how to use them.
(2) Export to ONNX format which can be used in Triton Server
./pruned_transducer_stateless7_ctc_bs/export_onnx.py \
--exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 13 \
--onnx-triton 1
@ -86,9 +86,10 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
import torch.nn as nn
from onnx_wrapper import TritonOnnxDecoder, TritonOnnxJoiner, TritonOnnxLconv
from scaling_converter import convert_scaled_to_non_scaled
from train import add_model_arguments, get_params, get_transducer_model
@ -98,8 +99,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import str2bool
from onnx_wrapper import TritonOnnxDecoder, TritonOnnxJoiner, TritonOnnxLconv
from icefall.utils import num_tokens, str2bool
def get_parser():
@ -156,10 +156,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -728,12 +728,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

51
egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained.py
View File

@ -20,7 +20,7 @@ You can generate the checkpoint with the following command:
./pruned_transducer_stateless7_ctc_bs/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 30 \
--avg 13
@ -29,7 +29,7 @@ Usage of this script:
(1) greedy search
./pruned_transducer_stateless7_ctc_bs/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav
@ -37,7 +37,7 @@ Usage of this script:
(2) beam search
./pruned_transducer_stateless7_ctc_bs/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -46,7 +46,7 @@ Usage of this script:
(3) modified beam search
./pruned_transducer_stateless7_ctc_bs/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method modified_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -55,7 +55,7 @@ Usage of this script:
(4) fast beam search
./pruned_transducer_stateless7_ctc_bs/pretrained.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--method fast_beam_search \
--beam-size 4 \
/path/to/foo.wav \
@ -75,7 +75,6 @@ from typing import List
import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from beam_search import (
@ -88,6 +87,8 @@ from beam_search import (
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_params, get_transducer_model
from icefall.utils import num_tokens
def get_parser():
parser = argparse.ArgumentParser(
@ -104,9 +105,9 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
help="""Path to bpe.model.""",
help="""Path to tokens.txt.""",
)
parser.add_argument(
@ -223,13 +224,14 @@ def main():
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(f"{params}")
@ -284,6 +286,12 @@ def main():
msg += f" with beam size {params.beam_size}"
logging.info(msg)
def token_ids_to_words(token_ids: List[int]) -> str:
text = ""
for i in token_ids:
text += token_table[i]
return text.replace("▁", " ").strip()
if params.method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
hyp_tokens = fast_beam_search_one_best(
@ -295,8 +303,8 @@ def main():
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
@ -305,16 +313,16 @@ def main():
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
for hyp in hyp_tokens:
hyps.append(token_ids_to_words(hyp))
else:
for i in range(num_waves):
# fmt: off
@ -335,12 +343,11 @@ def main():
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
hyps.append(token_ids_to_words(hyp))
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
s += f"{filename}:\n{hyp}\n\n"
logging.info(s)
logging.info("Decoding Done")

10
egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py
View File

@ -22,14 +22,14 @@ You can use the following command to get the exported models:
./pruned_transducer_stateless7_ctc_bs/export.py \
--exp-dir ./pruned_transducer_stateless7_ctc_bs/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--tokens data/lang_bpe_500/tokens.txt \
--epoch 20 \
--avg 10
Usage of this script:
(1) ctc-decoding
./pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py \
./pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--bpe-model data/lang_bpe_500/bpe.model \
--method ctc-decoding \
@ -38,7 +38,7 @@ Usage of this script:
/path/to/bar.wav
(2) 1best
./pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py \
./pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--HLG data/lang_bpe_500/HLG.pt \
--words-file data/lang_bpe_500/words.txt \
@ -48,7 +48,7 @@ Usage of this script:
/path/to/bar.wav
(3) nbest-rescoring
./bruned_transducer_stateless7_ctc/jit_pretrained_ctc.py \
./bruned_transducer_stateless7_ctc/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--HLG data/lang_bpe_500/HLG.pt \
--words-file data/lang_bpe_500/words.txt \
@ -60,7 +60,7 @@ Usage of this script:
(4) whole-lattice-rescoring
./pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py \
./pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py \
--checkpoint ./pruned_transducer_stateless7_ctc_bs/exp/pretrained.pt \
--HLG data/lang_bpe_500/HLG.pt \
--words-file data/lang_bpe_500/words.txt \

7
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py
View File

@ -396,6 +396,12 @@ def decode_one_batch(
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
LM:
A neural network language model.
ngram_lm:
A ngram language model
ngram_lm_scale:
The scale for the ngram language model.
Returns:
Return the decoding result. See above description for the format of
the returned dict.
@ -907,6 +913,7 @@ def main():
ngram_file_name = str(params.lang_dir / f"{params.tokens_ngram}gram.arpa")
logging.info(f"lm filename: {ngram_file_name}")
ngram_lm = kenlm.Model(ngram_file_name)
ngram_lm_scale = None # use a list to search
elif params.decoding_method == "modified_beam_search_LODR":
lm_filename = f"{params.tokens_ngram}gram.fst.txt"

21
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py
View File

@ -66,6 +66,7 @@ import argparse
import logging
from pathlib import Path
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train2 import add_model_arguments, get_params, get_transducer_model
@ -76,8 +77,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.lexicon import Lexicon
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -123,10 +123,10 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
"--tokens",
type=str,
default="data/lang_char",
help="The lang dir",
default="data/lang_char/tokens.txt",
help="The tokens.txt file",
)
parser.add_argument(
@ -246,9 +246,14 @@ def main():
logging.info(f"device: {device}")
lexicon = Lexicon(params.lang_dir)
params.blank_id = 0
params.vocab_size = max(lexicon.tokens) + 1
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

22
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn.py
View File

@ -28,7 +28,7 @@ popd
2. Export to ncnn
./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 \
@ -64,7 +64,7 @@ import argparse
import logging
from pathlib import Path
import sentencepiece as spm
import k2
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train2 import add_model_arguments, get_params, get_transducer_model
@ -75,7 +75,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -121,10 +121,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -244,12 +244,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)

25
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-onnx-zh.py
View File

@ -29,7 +29,7 @@ popd
2. Export the model to ONNX
./pruned_transducer_stateless7_streaming/export-onnx-zh.py \
--lang-dir $repo/data/lang_char_bpe \
--tokens $repo/data/lang_char_bpe/tokens.txt \
--use-averaged-model 0 \
--epoch 99 \
--avg 1 \
@ -60,6 +60,7 @@ import logging
from pathlib import Path
from typing import Dict, List, Tuple
import k2
import onnx
import torch
import torch.nn as nn
@ -76,8 +77,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.lexicon import Lexicon
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -134,10 +134,10 @@ def get_parser():
)
parser.add_argument(
"--lang-dir",
"--tokens",
type=str,
default="data/lang_char",
help="The lang dir",
default="data/lang_char/tokens.txt",
help="The tokens.txt file",
)
parser.add_argument(
@ -493,9 +493,14 @@ def main():
logging.info(f"device: {device}")
lexicon = Lexicon(params.lang_dir)
params.blank_id = 0
params.vocab_size = max(lexicon.tokens) + 1
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -661,7 +666,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

24
egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-onnx.py
View File

@ -27,7 +27,7 @@ popd
2. Export the model to ONNX
./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 \
@ -48,8 +48,8 @@ import logging
from pathlib import Path
from typing import Dict, List, Tuple
import k2
import onnx
import sentencepiece as spm
import torch
import torch.nn as nn
from decoder import Decoder
@ -65,7 +65,7 @@ from icefall.checkpoint import (
find_checkpoints,
load_checkpoint,
)
from icefall.utils import setup_logger, str2bool
from icefall.utils import num_tokens, setup_logger, str2bool
def get_parser():
@ -122,10 +122,10 @@ def get_parser():
)
parser.add_argument(
"--bpe-model",
"--tokens",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
default="data/lang_bpe_500/tokens.txt",
help="Path to the tokens.txt.",
)
parser.add_argument(
@ -481,12 +481,14 @@ def main():
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# Load tokens.txt here
token_table = k2.SymbolTable.from_file(params.tokens)
# Load id of the <blk> token and the vocab size
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
params.blank_id = token_table["<blk>"]
params.unk_id = token_table["<unk>"]
params.vocab_size = num_tokens(token_table) + 1 # +1 for <blk>
logging.info(params)
@ -652,7 +654,7 @@ def main():
quantize_dynamic(
model_input=decoder_filename,
model_output=decoder_filename_int8,
op_types_to_quantize=["MatMul"],
op_types_to_quantize=["MatMul", "Gather"],
weight_type=QuantType.QInt8,
)

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