Merge branch 'k2-fsa:master' into master

.github/scripts/run-gigaspeech-pruned-transducer-stateless2-2022-05-12.sh

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

.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh

@@ -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/

.github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh

@@ -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

.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh

@@ -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

.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh

@@ -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

.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh

@@ -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

.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh

@@ -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

.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh

@@ -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 \

.github/scripts/run-librispeech-pruned-transducer-stateless7-2022-11-11.sh

@@ -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

.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh

@@ -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

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

@@ -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

.github/scripts/run-librispeech-pruned-transducer-stateless7-streaming-2022-12-29.sh

@@ -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 \

.github/scripts/run-librispeech-pruned-transducer-stateless8-2022-11-14.sh

@@ -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

.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh

@@ -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 \

.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh

@@ -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

.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh

@@ -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

.github/scripts/run-multi-zh_hans-zipformer.sh

@@ -0,0 +1,51 @@
+#!/usr/bin/env bash
+
+set -e
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/multi_zh-hans/ASR
+
+repo_url=https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2/
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+git clone $repo_url
+repo=$(basename $repo_url)
+
+
+log "Display test files"
+tree $repo/
+ls -lh $repo/test_wavs/*.wav
+
+pushd $repo/exp
+ln -s epoch-20.pt epoch-99.pt
+popd
+
+ls -lh $repo/exp/*.pt
+
+
+./zipformer/pretrained.py \
+  --checkpoint $repo/exp/epoch-99.pt \
+  --tokens $repo/data/lang_bpe_2000/tokens.txt \
+  --method greedy_search \
+$repo/test_wavs/DEV_T0000000000.wav \
+$repo/test_wavs/DEV_T0000000001.wav \
+$repo/test_wavs/DEV_T0000000002.wav
+
+for method in modified_beam_search fast_beam_search; do
+  log "$method"
+
+  ./zipformer/pretrained.py \
+    --method $method \
+    --beam-size 4 \
+    --checkpoint $repo/exp/epoch-99.pt \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+  $repo/test_wavs/DEV_T0000000000.wav \
+  $repo/test_wavs/DEV_T0000000001.wav \
+  $repo/test_wavs/DEV_T0000000002.wav
+done

.github/scripts/run-pre-trained-conformer-ctc.sh

@@ -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 \

.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh

@@ -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

.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh

@@ -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

.github/scripts/run-pre-trained-transducer-stateless.sh

@@ -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

.github/scripts/run-pre-trained-transducer.sh

@@ -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

.github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh

@@ -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 \
+  --avg 1
-  --onnx 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-encoder-filename $repo/exp/encoder-epoch-10-avg-2.onnx \
-  --onnx-decoder-filename $repo/exp/decoder.onnx \
+  --onnx-decoder-filename $repo/exp/decoder-epoch-10-avg-2.onnx \
-  --onnx-joiner-filename $repo/exp/joiner.onnx \
+  --onnx-joiner-filename $repo/exp/joiner-epoch-10-avg-2.onnx \
-  --onnx-joiner-encoder-proj-filename $repo/exp/joiner_encoder_proj.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.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 \
+  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
-  --decoder-model-filename $repo/exp/decoder.onnx \
+  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
-  --joiner-model-filename $repo/exp/joiner.onnx \
+  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
-  --joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
-  --joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.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_T0000000000.wav \
-  $repo/test_wavs/DEV_T0000000001.wav \
+    $repo/test_wavs/DEV_T0000000001.wav \
-  $repo/test_wavs/DEV_T0000000002.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_T0000000000.wav \
-  $repo/test_wavs/DEV_T0000000001.wav \
+    $repo/test_wavs/DEV_T0000000001.wav \
-  $repo/test_wavs/DEV_T0000000002.wav
+    $repo/test_wavs/DEV_T0000000002.wav
 done

.github/scripts/test-ncnn-export.sh

@@ -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 \

.github/scripts/test-onnx-export.sh

@@ -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 \

.github/workflows/run-aishell-2022-06-20.yml

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

.github/workflows/run-gigaspeech-2022-05-13.yml

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

.github/workflows/run-librispeech-2022-03-12.yml

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

.github/workflows/run-librispeech-2022-04-29.yml

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

.github/workflows/run-librispeech-2022-05-13.yml

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

.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml

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

.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml

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

.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml

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

.github/workflows/run-multi-zh_hans-zipformer.yml

@@ -0,0 +1,84 @@
+# Copyright      2023   Xiaomi Corp.    (author: Zengrui Jin)
+
+# See ../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: run-multi-zh_hans-zipformer
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+concurrency:
+  group: run_multi-zh_hans_zipformer-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  run_multi-zh_hans_zipformer:
+    if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'multi-zh_hans'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: [3.8]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
+
+      - name: Install Python dependencies
+        run: |
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
+          pip uninstall -y protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
+
+      - name: Cache kaldifeat
+        id: my-cache
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}-2023-05-22
+
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/install-kaldifeat.sh
+
+      - name: Inference with pre-trained model
+        shell: bash
+        env:
+          GITHUB_EVENT_NAME: ${{ github.event_name }}
+          GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
+        run: |
+          sudo apt-get -qq install git-lfs tree
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
+          .github/scripts/run-multi-zh_hans-zipformer.sh

.github/workflows/run-pretrained-conformer-ctc.yml

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

.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml

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

.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml

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

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

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

.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml

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

.github/workflows/run-pretrained-transducer-stateless.yml

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

.github/workflows/run-pretrained-transducer.yml

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

.github/workflows/run-yesno-recipe.yml

@@ -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

README.md

@@ -338,7 +338,7 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder
 
 #### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
 
-The best results for Chinese CER(%) and English WER(%) respectivly (zh: Chinese, en: English):
+The best results for Chinese CER(%) and English WER(%) respectively (zh: Chinese, en: English):
 |decoding-method | dev | dev_zh | dev_en | test | test_zh | test_en |
 |--|--|--|--|--|--|--|
 |greedy_search| 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13|

docs/source/conf.py

@@ -95,4 +95,7 @@ rst_epilog = """
 .. _k2: https://github.com/k2-fsa/k2
 .. _lhotse: https://github.com/lhotse-speech/lhotse
 .. _yesno: https://www.openslr.org/1/
+.. _Next-gen Kaldi: https://github.com/k2-fsa
+.. _Kaldi: https://github.com/kaldi-asr/kaldi
+.. _lilcom: https://github.com/danpovey/lilcom
 """

docs/source/decoding-with-langugage-models/LODR.rst

@@ -71,9 +71,12 @@ As the initial step, let's download the pre-trained model.
 .. code-block:: bash
 
     $ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
-    $ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
+    $ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
     $ git lfs pull --include "pretrained.pt"
     $ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
+    $ cd ../data/lang_bpe_500
+    $ git lfs pull --include bpe.model
+    $ cd ../../..
 
 To test the model, let's have a look at the decoding results **without** using LM. This can be done via the following command:
 

docs/source/decoding-with-langugage-models/rescoring.rst

@@ -34,9 +34,12 @@ As the initial step, let's download the pre-trained model.
 .. code-block:: bash
 
     $ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
-    $ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
+    $ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
     $ git lfs pull --include "pretrained.pt"
     $ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
+    $ cd ../data/lang_bpe_500
+    $ git lfs pull --include bpe.model
+    $ cd ../../..
 
 As usual, we first test the model's performance without external LM. This can be done via the following command:
 

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

@@ -32,9 +32,12 @@ As the initial step, let's download the pre-trained model.
 .. code-block:: bash
 
     $ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
-    $ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
+    $ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
     $ git lfs pull --include "pretrained.pt"
     $ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
+    $ cd ../data/lang_bpe_500
+    $ git lfs pull --include bpe.model
+    $ cd ../../..
 
 To test the model, let's have a look at the decoding results without using LM. This can be done via the following command:
 

docs/source/for-dummies/data-preparation.rst

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

docs/source/for-dummies/decoding.rst

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

docs/source/for-dummies/environment-setup.rst

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

docs/source/for-dummies/index.rst

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

docs/source/for-dummies/model-export.rst

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

docs/source/for-dummies/training.rst

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

docs/source/index.rst

@@ -20,6 +20,7 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
    :maxdepth: 2
    :caption: Contents:
 
+   for-dummies/index.rst
    installation/index
    docker/index
    faqs

docs/source/model-export/export-model-state-dict.rst

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

docs/source/model-export/export-ncnn-conv-emformer.rst

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

docs/source/model-export/export-ncnn-lstm.rst

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

docs/source/model-export/export-ncnn-zipformer.rst

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

docs/source/model-export/export-onnx.rst

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

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

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

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

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

egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -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
+    )

egs/aidatatang_200zh/ASR/prepare.sh

@@ -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

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -37,7 +37,7 @@ from lhotse.dataset import (
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import OnTheFlyFeatures
@@ -291,8 +291,8 @@ class Aidatatang_200zhAsrDataModule:
                 drop_last=True,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -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
+    )

egs/aishell/ASR/local/compute_fbank_aishell.py

@@ -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
+    )

egs/aishell/ASR/prepare.sh

@@ -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

egs/aishell/ASR/prepare_aidatatang_200zh.sh

@@ -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

egs/aishell/ASR/pruned_transducer_stateless7/export.py

@@ -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()

egs/aishell/ASR/pruned_transducer_stateless7/export.py

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

egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py

@@ -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()

egs/aishell/ASR/pruned_transducer_stateless7/pretrained.py

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

egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -30,7 +30,7 @@ from lhotse.dataset import (
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import OnTheFlyFeatures
@@ -278,8 +278,8 @@ class AishellAsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/aishell2/ASR/local/compute_fbank_aishell2.py

@@ -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
+    )

egs/aishell2/ASR/prepare.sh

@@ -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

egs/aishell2/ASR/pruned_transducer_stateless5/asr_datamodule.py

@@ -31,7 +31,7 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
@@ -299,8 +299,8 @@ class AiShell2AsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/aishell4/ASR/local/compute_fbank_aishell4.py

@@ -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
+    )

egs/aishell4/ASR/prepare.sh

@@ -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

egs/aishell4/ASR/pruned_transducer_stateless5/asr_datamodule.py

@@ -30,7 +30,7 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 for AudioSamples
@@ -310,8 +310,8 @@ class Aishell4AsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/alimeeting/ASR/local/compute_fbank_alimeeting.py

@@ -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
+    )

egs/alimeeting/ASR/prepare.sh

@@ -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

egs/alimeeting/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -37,7 +37,7 @@ from lhotse.dataset import (
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import OnTheFlyFeatures
@@ -292,8 +292,8 @@ class AlimeetingAsrDataModule:
                 drop_last=True,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/alimeeting/ASR_v2/local/compute_fbank_alimeeting.py

@@ -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:
+    def _extract_feats(
-        cuts = cuts + cuts.perturb_speed(0.9) + cuts.perturb_speed(1.1)
+        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)

egs/alimeeting/ASR_v2/prepare.sh

@@ -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

egs/ami/SURT/dprnn_zipformer/asr_datamodule.py

@@ -257,7 +257,7 @@ class AmiAsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
             train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,

egs/commonvoice/ASR/pruned_transducer_stateless7/asr_datamodule.py

@@ -30,7 +30,7 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
@@ -311,8 +311,8 @@ class CommonVoiceAsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/csj/ASR/local/utils/asr_datamodule.py

@@ -31,7 +31,7 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
@@ -339,8 +339,8 @@ class CSJAsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/gigaspeech/ASR/conformer_ctc/asr_datamodule.py

@@ -27,7 +27,7 @@ from lhotse.dataset import (
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import OnTheFlyFeatures
@@ -264,8 +264,8 @@ class GigaSpeechAsrDataModule:
                 drop_last=True,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -30,7 +30,7 @@ from lhotse.dataset import (
     DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
-    SingleCutSampler,
+    SimpleCutSampler,
     SpecAugment,
 )
 from lhotse.dataset.input_strategies import OnTheFlyFeatures
@@ -297,8 +297,8 @@ class GigaSpeechAsrDataModule:
                 drop_last=True,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
-            train_sampler = SingleCutSampler(
+            train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,

egs/libricss/SURT/dprnn_zipformer/asr_datamodule.py

@@ -259,7 +259,7 @@ class LibriCssAsrDataModule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SingleCutSampler.")
+            logging.info("Using SimpleCutSampler.")
             train_sampler = SimpleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,

egs/libricss/SURT/dprnn_zipformer/scaling.py

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

egs/libricss/SURT/prepare.sh

@@ -79,7 +79,7 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
   #   ln -sfv /path/to/rirs_noises $dl_dir/
   #
   if [ ! -d $dl_dir/rirs_noises ]; then
-    lhotse download rirs_noises $dl_dir
+    lhotse download rir-noise $dl_dir/rirs_noises
   fi
 fi
 
@@ -89,6 +89,7 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
   # to $dl_dir/librispeech. We perform text normalization for the transcripts.
   # NOTE: Alignments are required for this recipe.
   mkdir -p data/manifests
+
   lhotse prepare librispeech -p train-clean-100 -p train-clean-360 -p train-other-500 -p dev-clean \
     -j 4 --alignments-dir $dl_dir/libri_alignments/LibriSpeech $dl_dir/librispeech data/manifests/
 fi
@@ -112,7 +113,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
 
   # We assume that you have downloaded the RIRS_NOISES corpus
   # to $dl_dir/rirs_noises
-  lhotse prepare rir-noise -p real_rir -p iso_noise $dl_dir/rirs_noises data/manifests
+  lhotse prepare rir-noise -p real_rir -p iso_noise $dl_dir/rirs_noises/RIRS_NOISES data/manifests
 fi
 
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then

egs/librispeech/ASR/conformer_ctc/export.py

@@ -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",
+        required=True,
-        help="""It contains language related input files such as "lexicon.txt"
+        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)
+    # Load tokens.txt here
-    max_token_id = max(lexicon.tokens)
+    token_table = k2.SymbolTable.from_file(params.tokens)
-    num_classes = max_token_id + 1  # +1 for the blank
+
+    num_classes = num_tokens(token_table) + 1  # +1 for the blank
 
     device = torch.device("cpu")
     if torch.cuda.is_available():

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -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.
+        help="Path to the tokens.txt.",
-        Used only when method is ctc-decoding.
-        """,
     )
 
     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
+        (0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file 
-            piece model, i.e., lang_dir/bpe.model, to convert
+            to convert tokens to actual words or characters. It needs 
-            word pieces to words. It needs neither a lexicon
+            neither a lexicon nor an n-gram LM.
-            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)
+        hyp_tokens = get_texts(best_path)
-        hyps = bpe_model.decode(token_ids)
+        for hyp in hyp_tokens:
-        hyps = [s.split() for s in hyps]
+            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{hyp}\n\n"
-        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/conformer_ctc2/export.py

@@ -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 num_tokens, str2bool
-from icefall.utils import str2bool
 
 
 def get_parser():
@@ -123,10 +124,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500",
+        required=True,
-        help="The lang dir",
+        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)
+    # Load tokens.txt here
-    max_token_id = max(lexicon.tokens)
+    token_table = k2.SymbolTable.from_file(params.tokens)
-    num_classes = max_token_id + 1  # +1 for the blank
+
+    num_classes = num_tokens(token_table) + 1  # +1 for the blank
 
     device = torch.device("cpu")
     if torch.cuda.is_available():

egs/librispeech/ASR/conformer_ctc3/export.py

@@ -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 num_tokens, str2bool
-from icefall.utils import str2bool
 
 
 def get_parser():
@@ -130,10 +130,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--tokens",
-        type=Path,
+        type=str,
-        default="data/lang_bpe_500",
+        required=True,
-        help="The lang dir containing word table and LG graph",
+        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)
+    # Load tokens.txt here
-    max_token_id = max(lexicon.tokens)
+    token_table = k2.SymbolTable.from_file(params.tokens)
-    num_classes = max_token_id + 1  # +1 for the blank
+
+    num_classes = num_tokens(token_table) + 1  # +1 for the blank
     params.vocab_size = num_classes
 
     if params.streaming_model:

egs/librispeech/ASR/conformer_ctc3/pretrained.py

@@ -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.
+        help="Path to the tokens.txt.",
-        Used only when method is ctc-decoding.
-        """,
     )
 
     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
+        (0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file 
-            piece model, i.e., lang_dir/bpe.model, to convert
+            to convert tokens to actual words or characters. It needs 
-            word pieces to words. It needs neither a lexicon
+            neither a lexicon nor an n-gram LM.
-            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)
+        hyp_tokens = get_texts(best_path)
-        hyps = bpe_model.decode(token_ids)
+        for hyp in hyp_tokens:
-        hyps = [s.split() for s in hyps]
+            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{hyp}\n\n"
-        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/conv_emformer_transducer_stateless/export.py

@@ -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",
+        required=True,
-        help="Path to the BPE model",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -166,12 +166,12 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
+    # Load id of the <blk> token and the vocab size
-    params.blank_id = sp.piece_to_id("<blk>")
+    params.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/conv_emformer_transducer_stateless2/export-for-ncnn.py

@@ -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",
+        required=True,
-        help="Path to the BPE model",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -217,12 +217,12 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
+    # Load id of the <blk> token and the vocab size
-    params.blank_id = sp.piece_to_id("<blk>")
+    params.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)