Merge branch 'master' into einichi

.github/scripts/baker_zh/TTS/run-matcha.sh

@@ -0,0 +1,167 @@
+#!/usr/bin/env bash
+
+set -ex
+
+apt-get update
+apt-get install -y sox
+
+python3 -m pip install numba conformer==0.3.2 diffusers librosa
+python3 -m pip install jieba
+
+
+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/baker_zh/TTS
+
+sed -i.bak s/600/8/g ./prepare.sh
+sed -i.bak s/"first 100"/"first 3"/g ./prepare.sh
+sed -i.bak s/500/5/g ./prepare.sh
+git diff
+
+function prepare_data() {
+  # We have created a subset of the data for testing
+  #
+  mkdir -p download
+  pushd download
+  wget -q https://huggingface.co/csukuangfj/tmp-files/resolve/main/BZNSYP-samples.tar.bz2
+  tar xvf BZNSYP-samples.tar.bz2
+  mv BZNSYP-samples BZNSYP
+  rm BZNSYP-samples.tar.bz2
+  popd
+
+  ./prepare.sh
+  tree .
+}
+
+function train() {
+  pushd ./matcha
+  sed -i.bak s/1500/3/g ./train.py
+  git diff .
+  popd
+
+  ./matcha/train.py \
+    --exp-dir matcha/exp \
+    --num-epochs 1 \
+    --save-every-n 1 \
+    --num-buckets 2 \
+    --tokens data/tokens.txt \
+    --max-duration 20
+
+    ls -lh matcha/exp
+}
+
+function infer() {
+  curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
+
+  ./matcha/infer.py \
+    --num-buckets 2 \
+    --epoch 1 \
+    --exp-dir ./matcha/exp \
+    --tokens data/tokens.txt \
+    --cmvn ./data/fbank/cmvn.json \
+    --vocoder ./generator_v2 \
+    --input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
+    --output-wav ./generated.wav
+
+  ls -lh *.wav
+  soxi ./generated.wav
+  rm -v ./generated.wav
+  rm -v generator_v2
+}
+
+function export_onnx() {
+  pushd matcha/exp
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27/resolve/main/epoch-2000.pt
+  popd
+
+  pushd data/fbank
+  rm -v *.json
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27/resolve/main/cmvn.json
+  popd
+
+  ./matcha/export_onnx.py \
+    --exp-dir ./matcha/exp \
+    --epoch 2000 \
+    --tokens ./data/tokens.txt \
+    --cmvn ./data/fbank/cmvn.json
+
+  ls -lh *.onnx
+
+  if false; then
+    # The CI machine does not have enough memory to run it
+    #
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v3
+    python3 ./matcha/export_onnx_hifigan.py
+  else
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v1.onnx
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v2.onnx
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v3.onnx
+  fi
+
+  ls -lh *.onnx
+
+  python3 ./matcha/generate_lexicon.py
+
+  for v in v1 v2 v3; do
+    python3 ./matcha/onnx_pretrained.py \
+     --acoustic-model ./model-steps-6.onnx \
+     --vocoder ./hifigan_$v.onnx \
+     --tokens ./data/tokens.txt \
+     --lexicon ./lexicon.txt \
+     --input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
+     --output-wav /icefall/generated-matcha-tts-steps-6-$v.wav
+  done
+
+  ls -lh /icefall/*.wav
+  soxi /icefall/generated-matcha-tts-steps-6-*.wav
+  cp ./model-steps-*.onnx /icefall
+
+  d=matcha-icefall-zh-baker
+  mkdir $d
+  cp -v data/tokens.txt $d
+  cp -v lexicon.txt $d
+  cp model-steps-3.onnx $d
+  pushd $d
+  curl -SL -O https://github.com/csukuangfj/cppjieba/releases/download/sherpa-onnx-2024-04-19/dict.tar.bz2
+  tar xvf dict.tar.bz2
+  rm dict.tar.bz2
+
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/date.fst
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/number.fst
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-aishell3-vits-low-2024-04-06/resolve/main/data/phone.fst
+
+cat >README.md <<EOF
+# Introduction
+
+This model is trained using the dataset from
+https://en.data-baker.com/datasets/freeDatasets/
+
+The dataset contains 10000 Chinese sentences of a native Chinese female speaker,
+which is about 12 hours.
+
+**Note**: The dataset is for non-commercial use only.
+
+You can find the training code at
+https://github.com/k2-fsa/icefall/tree/master/egs/baker_zh/TTS
+EOF
+
+  ls -lh
+  popd
+  tar cvjf $d.tar.bz2 $d
+  mv $d.tar.bz2 /icefall
+  mv $d /icefall
+}
+
+prepare_data
+train
+infer
+export_onnx
+
+rm -rfv generator_v* matcha/exp
+git checkout .

.github/scripts/docker/Dockerfile

@@ -31,12 +31,15 @@ LABEL github_repo="https://github.com/k2-fsa/icefall"
 
 # Install dependencies
 RUN pip install --no-cache-dir \
-      torch==${TORCH_VERSION} torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/cpu/torch_stable.html \
+      torch==${TORCH_VERSION}+cpu -f https://download.pytorch.org/whl/torch \
+      torchaudio==${TORCHAUDIO_VERSION}+cpu -f https://download.pytorch.org/whl/torchaudio \
       k2==${_K2_VERSION} -f https://k2-fsa.github.io/k2/cpu.html \
       \
       git+https://github.com/lhotse-speech/lhotse \
       kaldifeat==${_KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cpu.html \
+      conformer==0.3.2 \
       cython \
+      diffusers \
       dill \
       espnet_tts_frontend \
       graphviz \
@@ -45,10 +48,11 @@ RUN pip install --no-cache-dir \
       kaldialign \
       kaldifst \
       kaldilm \
-      matplotlib \
+      librosa \
+      "matplotlib<=3.9.4" \
       multi_quantization \
       numba \
-      numpy \
+      "numpy<2.0" \
       onnxoptimizer \
       onnxsim \
       onnx \

.github/scripts/docker/generate_build_matrix.py

@@ -2,9 +2,19 @@
 # Copyright    2023  Xiaomi Corp.        (authors: Fangjun Kuang)
 
 
+import argparse
 import json
 
 
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--min-torch-version",
+        help="Minimu torch version",
+    )
+    return parser.parse_args()
+
+
 def version_gt(a, b):
     a_major, a_minor = list(map(int, a.split(".")))[:2]
     b_major, b_minor = list(map(int, b.split(".")))[:2]
@@ -42,22 +52,34 @@ def get_torchaudio_version(torch_version):
         return torch_version
 
 
-def get_matrix():
+
-    k2_version = "1.24.4.dev20240223"
+def get_matrix(min_torch_version):
-    kaldifeat_version = "1.25.4.dev20240223"
+    k2_version = "1.24.4.dev20241029"
-    version = "20240725"
+    kaldifeat_version = "1.25.5.dev20241029"
+    version = "20241218"
+
+    # torchaudio 2.5.0 does not support python 3.13
+
     python_version = ["3.8", "3.9", "3.10", "3.11", "3.12"]
     torch_version = []
     torch_version += ["1.13.0", "1.13.1"]
     torch_version += ["2.0.0", "2.0.1"]
-    torch_version += ["2.1.0", "2.1.1", "2.1.2"]
+    #  torch_version += ["2.1.0", "2.1.1", "2.1.2"]
-    torch_version += ["2.2.0", "2.2.1", "2.2.2"]
+    #  torch_version += ["2.2.0", "2.2.1", "2.2.2"]
+    # Test only torch >= 2.3.0
     torch_version += ["2.3.0", "2.3.1"]
     torch_version += ["2.4.0"]
 
+    torch_version += ["2.4.1"]
+    torch_version += ["2.5.0"]
+    torch_version += ["2.5.1"]
+
     matrix = []
     for p in python_version:
         for t in torch_version:
+            if min_torch_version and version_gt(min_torch_version, t):
+                continue
+
             # torchaudio <= 1.13.x supports only python <= 3.10
 
             if version_gt(p, "3.10") and not version_gt(t, "2.0"):
@@ -67,21 +89,20 @@ def get_matrix():
             if version_gt(p, "3.11") and not version_gt(t, "2.1"):
                 continue
 
-            k2_version_2 = k2_version
+            if version_gt(p, "3.12") and not version_gt(t, "2.4"):
-            kaldifeat_version_2 = kaldifeat_version
+                continue
 
-            if t == "2.2.2":
+            if version_gt(t, "2.4") and version_gt("3.10", p):
-                k2_version_2 = "1.24.4.dev20240328"
+                # torch>=2.5 requires python 3.10
-                kaldifeat_version_2 = "1.25.4.dev20240329"
+                continue
-            elif t == "2.3.0":
+
-                k2_version_2 = "1.24.4.dev20240425"
+
-                kaldifeat_version_2 = "1.25.4.dev20240425"
+            if t == "2.5.1":
-            elif t == "2.3.1":
+                k2_version_2 = "1.24.4.dev20241122"
-                k2_version_2 = "1.24.4.dev20240606"
+                kaldifeat_version_2 = "1.25.5.dev20241126"
-                kaldifeat_version_2 = "1.25.4.dev20240606"
+            else:
-            elif t == "2.4.0":
+                k2_version_2 = k2_version
-                k2_version_2 = "1.24.4.dev20240725"
+                kaldifeat_version_2 = kaldifeat_version
-                kaldifeat_version_2 = "1.25.4.dev20240725"
 
             matrix.append(
                 {
@@ -97,7 +118,8 @@ def get_matrix():
 
 
 def main():
-    matrix = get_matrix()
+    args = get_args()
+    matrix = get_matrix(min_torch_version=args.min_torch_version)
     print(json.dumps({"include": matrix}))
 
 

.github/scripts/ljspeech/TTS/run-matcha.sh

@@ -0,0 +1,157 @@
+#!/usr/bin/env bash
+
+set -ex
+
+apt-get update
+apt-get install -y sox
+
+python3 -m pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html
+python3 -m pip install espnet_tts_frontend
+python3 -m pip install numba conformer==0.3.2 diffusers librosa
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/ljspeech/TTS
+
+sed -i.bak s/600/8/g ./prepare.sh
+sed -i.bak s/"first 100"/"first 3"/g ./prepare.sh
+sed -i.bak s/500/5/g ./prepare.sh
+git diff
+
+function prepare_data() {
+  # We have created a subset of the data for testing
+  #
+  mkdir -p download
+  pushd download
+  wget -q https://huggingface.co/csukuangfj/ljspeech-subset-for-ci-test/resolve/main/LJSpeech-1.1.tar.bz2
+  tar xvf LJSpeech-1.1.tar.bz2
+  popd
+
+  ./prepare.sh
+  tree .
+}
+
+function train() {
+  pushd ./matcha
+  sed -i.bak s/1500/3/g ./train.py
+  git diff .
+  popd
+
+  ./matcha/train.py \
+    --exp-dir matcha/exp \
+    --num-epochs 1 \
+    --save-every-n 1 \
+    --num-buckets 2 \
+    --tokens data/tokens.txt \
+    --max-duration 20
+
+    ls -lh matcha/exp
+}
+
+function infer() {
+
+  curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
+
+  ./matcha/infer.py \
+    --num-buckets 2 \
+    --epoch 1 \
+    --exp-dir ./matcha/exp \
+    --tokens data/tokens.txt \
+    --vocoder ./generator_v1 \
+    --input-text "how are you doing?" \
+    --output-wav ./generated.wav
+
+  ls -lh *.wav
+  soxi ./generated.wav
+  rm -v ./generated.wav
+  rm -v generator_v1
+}
+
+function export_onnx() {
+  pushd matcha/exp
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/epoch-4000.pt
+  popd
+
+  pushd data/fbank
+  rm -fv *.json
+  curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/data/cmvn.json
+  popd
+
+  ./matcha/export_onnx.py \
+    --exp-dir ./matcha/exp \
+    --epoch 4000 \
+    --tokens ./data/tokens.txt \
+    --cmvn ./data/fbank/cmvn.json
+
+  ls -lh *.onnx
+
+  if false; then
+    # The CI machine does not have enough memory to run it
+    #
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
+    curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v3
+    python3 ./matcha/export_onnx_hifigan.py
+  else
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v1.onnx
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v2.onnx
+    curl -SL -O https://huggingface.co/csukuangfj/icefall-tts-ljspeech-matcha-en-2024-10-28/resolve/main/exp/hifigan_v3.onnx
+  fi
+
+  ls -lh *.onnx
+
+  for v in v1 v2 v3; do
+    python3 ./matcha/onnx_pretrained.py \
+     --acoustic-model ./model-steps-6.onnx \
+     --vocoder ./hifigan_$v.onnx \
+     --tokens ./data/tokens.txt \
+     --input-text "how are you doing?" \
+     --output-wav /icefall/generated-matcha-tts-steps-6-$v.wav
+  done
+
+  ls -lh /icefall/*.wav
+  soxi /icefall/generated-matcha-tts-steps-6-*.wav
+
+  cp ./model-steps-*.onnx /icefall
+
+  d=matcha-icefall-en_US-ljspeech
+  mkdir $d
+  cp -v data/tokens.txt $d
+  cp model-steps-3.onnx $d
+  pushd $d
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
+  tar xf espeak-ng-data.tar.bz2
+  rm espeak-ng-data.tar.bz2
+
+cat >README.md <<EOF
+# Introduction
+
+This model is trained using the dataset from
+https://keithito.com/LJ-Speech-Dataset/
+
+The dataset contains only 1 female speaker.
+
+You can find the training code at
+https://github.com/k2-fsa/icefall/tree/master/egs/ljspeech/TTS#matcha
+EOF
+
+  ls -lh
+
+  popd
+
+  tar cvjf $d.tar.bz2 $d
+  mv $d.tar.bz2 /icefall
+  mv $d /icefall
+}
+
+prepare_data
+train
+infer
+export_onnx
+
+rm -rfv generator_v* matcha/exp
+git checkout .

.github/scripts/ljspeech/TTS/run.sh

@@ -22,7 +22,7 @@ git diff
 function prepare_data() {
   # We have created a subset of the data for testing
   #
-  mkdir download
+  mkdir -p download
   pushd download
   wget -q https://huggingface.co/csukuangfj/ljspeech-subset-for-ci-test/resolve/main/LJSpeech-1.1.tar.bz2
   tar xvf LJSpeech-1.1.tar.bz2

.github/scripts/multi-zh-hans.sh

@@ -16,6 +16,48 @@ log "pwd: $PWD"
 
 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_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+pushd $repo
+cd exp
+git lfs pull --include pretrained.pt
+ln -s pretrained.pt epoch-99.pt
+cd ../data/lang_bpe_2000
+ls -lh
+git lfs pull --include L.pt L_disambig.pt Linv.pt bpe.model
+git lfs pull --include "*.model"
+ls -lh
+popd
+
+log "--------------------------------------------"
+log "Export non-streaming ONNX transducer models "
+log "--------------------------------------------"
+./zipformer/export-onnx.py \
+  --tokens $repo/data/lang_bpe_2000/tokens.txt \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --causal False
+
+ls -lh $repo/exp
+
+./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_2000/tokens.txt \
+  $repo/test_wavs/DEV_T0000000000.wav \
+  $repo/test_wavs/DEV_T0000000001.wav \
+  $repo/test_wavs/DEV_T0000000002.wav \
+  $repo/test_wavs/TEST_MEETING_T0000000113.wav \
+  $repo/test_wavs/TEST_MEETING_T0000000219.wav \
+  $repo/test_wavs/TEST_MEETING_T0000000351.wav
+
+rm -rf $repo
+
 repo_url=https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-ctc-streaming-2023-11-05
 log "Downloading pre-trained model from $repo_url"
 GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url

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

@@ -19,7 +19,7 @@ repo=$(basename $repo_url)
 
 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"${GITHUB_EVENT_NAME}" == x"workflow_dispatch" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode"  ]]; then
   mkdir -p pruned_transducer_stateless2/exp
   ln -s $PWD/$repo/exp/pretrained-iter-3488000-avg-20.pt pruned_transducer_stateless2/exp/epoch-999.pt
   ln -s $PWD/$repo/data/lang_bpe_500 data/
@@ -29,8 +29,16 @@ 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
+  pushd data/fbank
-  ln -s data/fbank/cuts_TEST.jsonl.gz data/fbank/gigaspeech_cuts_TEST.jsonl.gz
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/cuts_DEV.jsonl.gz
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/cuts_TEST.jsonl.gz
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/feats_DEV.lca
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/feats_TEST.lca
+
+  ln -sf cuts_DEV.jsonl.gz gigaspeech_cuts_DEV.jsonl.gz
+  ln -sf cuts_TEST.jsonl.gz gigaspeech_cuts_TEST.jsonl.gz
+  popd
+
 
   log "Decoding dev and test"
 

.github/scripts/run-gigaspeech-zipformer-2023-10-17.sh

@@ -129,20 +129,34 @@ 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"${GITHUB_EVENT_NAME}" == x"workflow_dispatch" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode"  ]]; then
   mkdir -p zipformer/exp
   ln -s $PWD/$repo/exp/pretrained.pt zipformer/exp/epoch-30.pt
+  mkdir -p data
   ln -s $PWD/$repo/data/lang_bpe_500 data/
 
   ls -lh data
   ls -lh zipformer/exp
 
+  mkdir -p data/fbank
+  pushd data/fbank
+
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/cuts_DEV.jsonl.gz
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/cuts_TEST.jsonl.gz
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/feats_DEV.lca
+  curl -SL -O https://huggingface.co/csukuangfj/giga-dev-dataset-fbank/resolve/main/data/fbank/feats_TEST.lca
+
+  ln -sf cuts_DEV.jsonl.gz gigaspeech_cuts_DEV.jsonl.gz
+  ln -sf cuts_TEST.jsonl.gz gigaspeech_cuts_TEST.jsonl.gz
+
+  popd
+
   log "Decoding test-clean and test-other"
 
   # use a small value for decoding with CPU
   max_duration=100
 
-  for method in greedy_search fast_beam_search modified_beam_search; do
+  for method in greedy_search; do
     log "Decoding with $method"
 
     ./zipformer/decode.py \

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

@@ -162,7 +162,7 @@ if [[ x"${GITHUB_EVENT_LABEL_NAME}" == x"LODR" ]]; then
     --ngram-lm-scale -0.16
 fi
 
-if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_NAME}" == x"workflow_dispatch" ]]; then
   mkdir -p lstm_transducer_stateless2/exp
   ln -s $PWD/$repo/exp/pretrained.pt lstm_transducer_stateless2/exp/epoch-999.pt
   ln -s $PWD/$repo/data/lang_bpe_500 data/
@@ -175,7 +175,7 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
   # use a small value for decoding with CPU
   max_duration=100
 
-  for method in greedy_search fast_beam_search modified_beam_search; do
+  for method in greedy_search fast_beam_search; do
     log "Decoding with $method"
 
     ./lstm_transducer_stateless2/decode.py \

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

@@ -25,6 +25,7 @@ popd
 
 log "Export via torch.jit.script()"
 ./zipformer/export.py \
+  --use-averaged-model 0 \
   --exp-dir $repo/exp \
   --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \

.github/workflows/audioset.yml

@@ -83,7 +83,7 @@ jobs:
           ls -lh ./model-onnx/*
 
       - name: Upload model to huggingface
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
         uses: nick-fields/retry@v3
@@ -116,7 +116,7 @@ jobs:
             rm -rf huggingface
 
       - name: Prepare for release
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         shell: bash
         run: |
           d=sherpa-onnx-zipformer-audio-tagging-2024-04-09
@@ -125,7 +125,7 @@ jobs:
           ls -lh
 
       - name: Release exported onnx models
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         uses: svenstaro/upload-release-action@v2
         with:
           file_glob: true

.github/workflows/baker_zh.yml

@@ -0,0 +1,152 @@
+name: baker_zh
+
+on:
+  push:
+    branches:
+      - master
+      - baker-matcha-2
+
+  pull_request:
+    branches:
+      - master
+
+  workflow_dispatch:
+
+concurrency:
+  group: baker-zh-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  generate_build_matrix:
+    if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
+    # see https://github.com/pytorch/pytorch/pull/50633
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Generating build matrix
+        id: set-matrix
+        run: |
+          # outputting for debugging purposes
+          python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3")
+          echo "::set-output name=matrix::${MATRIX}"
+
+  baker_zh:
+    needs: generate_build_matrix
+    name: py${{ matrix.python-version }} torch${{ matrix.torch-version }} v${{ matrix.version }}
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        ${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Free space
+        shell: bash
+        run: |
+          ls -lh
+          df -h
+          rm -rf /opt/hostedtoolcache
+          df -h
+          echo "pwd: $PWD"
+          echo "github.workspace ${{ github.workspace }}"
+
+      - name: Run tests
+        uses: addnab/docker-run-action@v3
+        with:
+            image: ghcr.io/${{ github.repository_owner }}/icefall:cpu-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}-v${{ matrix.version }}
+            options: |
+              --volume ${{ github.workspace }}/:/icefall
+            shell: bash
+            run: |
+              export PYTHONPATH=/icefall:$PYTHONPATH
+              cd /icefall
+
+              pip install onnx==1.17.0
+
+              pip list
+
+              git config --global --add safe.directory /icefall
+
+              .github/scripts/baker_zh/TTS/run-matcha.sh
+
+      - name: display files
+        shell: bash
+        run: |
+          ls -lh
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: generated-test-files-${{ matrix.python-version }}-${{ matrix.torch-version }}
+          path: ./*.wav
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-2
+          path: ./model-steps-2.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-3
+          path: ./model-steps-3.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-4
+          path: ./model-steps-4.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-5
+          path: ./model-steps-5.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-6
+          path: ./model-steps-6.onnx
+
+      - name: Upload models to huggingface
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        shell: bash
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          d=matcha-icefall-zh-baker
+
+          GIT_LFS_SKIP_SMUDGE=1  git clone https://huggingface.co/csukuangfj/$d hf
+          cp -av $d/* hf/
+
+          pushd hf
+          git add .
+
+          git config --global user.name "csukuangfj"
+          git config --global user.email "csukuangfj@gmail.com"
+          git config --global lfs.allowincompletepush true
+
+          git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$d main || true
+          popd
+
+      - name: Release exported onnx models
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        uses: svenstaro/upload-release-action@v2
+        with:
+          file_glob: true
+          overwrite: true
+          file: matcha-icefall-*.tar.bz2
+          repo_name: k2-fsa/sherpa-onnx
+          repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
+          tag: tts-models

.github/workflows/build-doc.yml

@@ -26,6 +26,8 @@ on:
   pull_request:
     types: [labeled]
 
+  workflow_dispatch:
+
 concurrency:
   group: build_doc-${{ github.ref }}
   cancel-in-progress: true

.github/workflows/build-docker-image.yml

@@ -16,7 +16,9 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        image: ["torch2.4.0-cuda12.4", "torch2.4.0-cuda12.1", "torch2.4.0-cuda11.8", "torch2.3.1-cuda12.1", "torch2.3.1-cuda11.8", "torch2.2.2-cuda12.1", "torch2.2.2-cuda11.8", "torch2.2.1-cuda12.1", "torch2.2.1-cuda11.8", "torch2.2.0-cuda12.1", "torch2.2.0-cuda11.8", "torch2.1.0-cuda12.1", "torch2.1.0-cuda11.8", "torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
+
+        image: ["torch2.4.1-cuda12.4", "torch2.4.1-cuda12.1", "torch2.4.1-cuda11.8", "torch2.4.0-cuda12.4", "torch2.4.0-cuda12.1", "torch2.4.0-cuda11.8", "torch2.3.1-cuda12.1", "torch2.3.1-cuda11.8", "torch2.2.2-cuda12.1", "torch2.2.2-cuda11.8", "torch2.2.1-cuda12.1", "torch2.2.1-cuda11.8", "torch2.2.0-cuda12.1", "torch2.2.0-cuda11.8", "torch2.1.0-cuda12.1", "torch2.1.0-cuda11.8", "torch2.0.0-cuda11.7", "torch1.13.0-cuda11.6", "torch1.12.1-cuda11.3", "torch1.9.0-cuda10.2"]
+
 
     steps:
       # refer to https://github.com/actions/checkout

.github/workflows/ljspeech.yml

@@ -30,8 +30,8 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py
+          python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3"
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3")
           echo "::set-output name=matrix::${MATRIX}"
 
   ljspeech:
@@ -70,6 +70,11 @@ jobs:
               cd /icefall
               git config --global --add safe.directory /icefall
 
+              pip install "matplotlib<=3.9.4"
+
+              pip list
+
+              .github/scripts/ljspeech/TTS/run-matcha.sh
               .github/scripts/ljspeech/TTS/run.sh
 
       - name: display files
@@ -78,19 +83,13 @@ jobs:
           ls -lh
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0'
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
         with:
           name: generated-test-files-${{ matrix.python-version }}-${{ matrix.torch-version }}
           path: ./*.wav
 
-      - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0'
-        with:
-          name: generated-models-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}
-          path: ./*.wav
-
       - name: Release exported onnx models
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.2.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         uses: svenstaro/upload-release-action@v2
         with:
           file_glob: true
@@ -100,3 +99,68 @@ jobs:
           repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
           tag: tts-models
 
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-2
+          path: ./model-steps-2.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-3
+          path: ./model-steps-3.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-4
+          path: ./model-steps-4.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-5
+          path: ./model-steps-5.onnx
+
+      - uses: actions/upload-artifact@v4
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        with:
+          name: step-6
+          path: ./model-steps-6.onnx
+
+      - name: Upload models to huggingface
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        shell: bash
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          d=matcha-icefall-en_US-ljspeech
+
+          GIT_LFS_SKIP_SMUDGE=1  git clone https://huggingface.co/csukuangfj/$d hf
+          cp -av $d/* hf/
+
+          pushd hf
+
+          git lfs track "cmn_dict"
+          git lfs track "ru_dict"
+
+          git add .
+
+          git config --global user.name "csukuangfj"
+          git config --global user.email "csukuangfj@gmail.com"
+          git config --global lfs.allowincompletepush true
+
+          git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$d main || true
+          popd
+
+      - name: Release exported onnx models
+        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        uses: svenstaro/upload-release-action@v2
+        with:
+          file_glob: true
+          overwrite: true
+          file: matcha-icefall-*.tar.bz2
+          repo_name: k2-fsa/sherpa-onnx
+          repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
+          tag: tts-models

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

@@ -33,13 +33,15 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+  workflow_dispatch:
+
 concurrency:
   group: run_gigaspeech_2022_05_13-${{ github.ref }}
   cancel-in-progress: true
 
 jobs:
   run_gigaspeech_2022_05_13:
-    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event_name == 'workflow_dispatch' || github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -104,7 +106,7 @@ jobs:
           .github/scripts/run-gigaspeech-pruned-transducer-stateless2-2022-05-12.sh
 
       - name: Display decoding results for gigaspeech pruned_transducer_stateless2
-        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' || github.event.label.name == 'run-decode'
         shell: bash
         run: |
           cd egs/gigaspeech/ASR/
@@ -119,8 +121,8 @@ jobs:
           find exp/greedy_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2
 
       - name: Upload decoding results for gigaspeech pruned_transducer_stateless2
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
-        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' || github.event.label.name == 'run-decode'
         with:
           name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-latest-cpu-gigaspeech-pruned_transducer_stateless2-2022-05-12
           path: egs/gigaspeech/ASR/pruned_transducer_stateless2/exp/

.github/workflows/run-gigaspeech-zipformer-2023-10-17.yml

@@ -42,7 +42,7 @@ concurrency:
 
 jobs:
   run_gigaspeech_2023_10_17_zipformer:
-    if: github.event.label.name == 'zipformer' ||github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'zipformer' ||github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -90,10 +90,6 @@ jobs:
           GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
         run: |
-          mkdir -p egs/gigaspeech/ASR/data
-          ln -sfv ~/tmp/fbank-libri egs/gigaspeech/ASR/data/fbank
-          ls -lh egs/gigaspeech/ASR/data/*
-
           sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
@@ -112,7 +108,7 @@ jobs:
           tag: asr-models
 
       - name: Display decoding results for gigaspeech zipformer
-        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' || github.event_name == 'workflow_dispatch'
         shell: bash
         run: |
           cd egs/gigaspeech/ASR/
@@ -124,17 +120,17 @@ jobs:
           find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
           find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
 
-          echo "===fast_beam_search==="
+          # echo "===fast_beam_search==="
-          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          # find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
-          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+          # find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
-
+          #
-          echo "===modified beam search==="
+          # echo "===modified beam search==="
-          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          # find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
-          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+          # find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
 
       - name: Upload decoding results for gigaspeech zipformer
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
-        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' || github.event_name == 'workflow_dispatch'
         with:
           name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-latest-cpu-zipformer-2022-11-11
           path: egs/gigaspeech/ASR/zipformer/exp/

.github/workflows/run-librispeech-lstm-transducer-stateless2-2022-09-03.yml

@@ -16,13 +16,15 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+  workflow_dispatch:
+
 concurrency:
   group: run_librispeech_lstm_transducer_stateless2_2022_09_03-${{ github.ref }}
   cancel-in-progress: true
 
 jobs:
   run_librispeech_lstm_transducer_stateless2_2022_09_03:
-    if: github.event.label.name == 'ready' || github.event.label.name == 'LODR' || github.event.label.name == 'shallow-fusion' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'LODR' || github.event.label.name == 'shallow-fusion' || github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -114,7 +116,7 @@ jobs:
           .github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh
 
       - name: Display decoding results for lstm_transducer_stateless2
-        if: github.event_name == 'schedule'
+        if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch'
         shell: bash
         run: |
           cd egs/librispeech/ASR
@@ -128,9 +130,9 @@ jobs:
           find fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
           find fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
 
-          echo "===modified beam search==="
+          # echo "===modified beam search==="
-          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          # find modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
-          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+          # find modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
 
       - name: Display decoding results for lstm_transducer_stateless2
         if: github.event.label.name == 'shallow-fusion'
@@ -156,8 +158,8 @@ jobs:
           find modified_beam_search_LODR  -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
 
       - name: Upload decoding results for lstm_transducer_stateless2
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
-        if: github.event_name == 'schedule' || github.event.label.name == 'shallow-fusion' || github.event.label.name == 'LODR'
+        if: github.event_name == 'schedule' || github.event.label.name == 'shallow-fusion' || github.event.label.name == 'LODR' || github.event_name == 'workflow_dispatch'
         with:
           name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-latest-cpu-lstm_transducer_stateless2-2022-09-03
           path: egs/librispeech/ASR/lstm_transducer_stateless2/exp/

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

@@ -23,6 +23,8 @@ on:
   pull_request:
     types: [labeled]
 
+  workflow_dispatch:
+
 concurrency:
   group: run_multi-corpora_zipformer-${{ github.ref }}
   cancel-in-progress: true

.github/workflows/run-ptb-rnn-lm.yml

@@ -16,6 +16,8 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+  workflow_dispatch:
+
 concurrency:
   group: run_ptb_rnn_lm_training-${{ github.ref }}
   cancel-in-progress: true
@@ -64,7 +66,7 @@ jobs:
           ./train-rnn-lm.sh --world-size 1 --num-epochs 5 --use-epoch 4 --use-avg 2
 
       - name: Upload pretrained models
-        uses: actions/upload-artifact@v2
+        uses: actions/upload-artifact@v4
         if: github.event.label.name == 'ready' || github.event.label.name == 'rnnlm' || github.event_name == 'push' || github.event_name == 'schedule'
         with:
           name: python-${{ matrix.python-version }}-ubuntu-rnn-lm-ptb

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

@@ -23,6 +23,8 @@ on:
   pull_request:
     types: [labeled]
 
+  workflow_dispatch:
+
 concurrency:
   group: run-swbd-conformer_ctc-${{ github.ref }}
   cancel-in-progress: true

.github/workflows/run-wenetspeech-pruned-transducer-stateless2.yml

@@ -23,6 +23,8 @@ on:
   pull_request:
     types: [labeled]
 
+  workflow_dispatch:
+
 concurrency:
   group: run_wenetspeech_pruned_transducer_stateless2-${{ github.ref }}
   cancel-in-progress: true

.github/workflows/style_check.yml

@@ -24,6 +24,8 @@ on:
     branches:
       - master
 
+  workflow_dispatch:
+
 concurrency:
   group: style_check-${{ github.ref }}
   cancel-in-progress: true
@@ -34,7 +36,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python-version: [3.8]
+        python-version: [3.10.15]
       fail-fast: false
 
     steps:

.github/workflows/test-ncnn-export.yml

@@ -16,6 +16,8 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+  workflow_dispatch:
+
 concurrency:
   group: test_ncnn_export-${{ github.ref }}
   cancel-in-progress: true

.github/workflows/test-onnx-export.yml

@@ -16,6 +16,8 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+  workflow_dispatch:
+
 concurrency:
   group: test_onnx_export-${{ github.ref }}
   cancel-in-progress: true

.github/workflows/test.yml

@@ -105,7 +105,7 @@ jobs:
               cd ../zipformer
               pytest -v -s
 
-      - uses: actions/upload-artifact@v2
+      - uses: actions/upload-artifact@v4
         with:
           path: egs/librispeech/ASR/zipformer/swoosh.pdf
-          name: swoosh.pdf
+          name: swoosh-${{ matrix.python-version }}-${{ matrix.torch-version }}

.github/workflows/yesno.yml

@@ -61,5 +61,6 @@ jobs:
 
               python3 -m torch.utils.collect_env
               python3 -m k2.version
+              pip list
 
               .github/scripts/yesno/ASR/run.sh

README.md

@@ -42,7 +42,6 @@ for more details.
   - [LibriSpeech][librispeech]
   - [Libriheavy][libriheavy]
   - [Multi-Dialect Broadcast News Arabic Speech Recognition][mgb2]
-  - [PeopleSpeech][peoplespeech]
   - [SPGISpeech][spgispeech]
   - [Switchboard][swbd]
   - [TIMIT][timit]
@@ -334,6 +333,7 @@ We provide a Colab notebook to test the pre-trained model: [![Open In Colab](htt
 
   - [LJSpeech][ljspeech]
   - [VCTK][vctk]
+  - [LibriTTS][libritts_tts]
 
 ### Supported Models
 
@@ -373,12 +373,13 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
 [commonvoice]: egs/commonvoice/ASR
 [csj]: egs/csj/ASR
 [libricss]: egs/libricss/SURT
+[libritts_asr]: egs/libritts/ASR
 [libriheavy]: egs/libriheavy/ASR
 [mgb2]: egs/mgb2/ASR
-[peoplespeech]: egs/peoplespeech/ASR
 [spgispeech]: egs/spgispeech/ASR
 [voxpopuli]: egs/voxpopuli/ASR
 [xbmu-amdo31]: egs/xbmu-amdo31/ASR
 
 [vctk]: egs/vctk/TTS
 [ljspeech]: egs/ljspeech/TTS
+[libritts_tts]: egs/libritts/TTS

docker/torch2.4.1-cuda11.8.dockerfile

@@ -0,0 +1,73 @@
+FROM pytorch/pytorch:2.4.1-cuda11.8-cudnn9-devel
+# python 3.10
+
+ENV LC_ALL C.UTF-8
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+# python 3.10
+ARG K2_VERSION="1.24.4.dev20240905+cuda11.8.torch2.4.1"
+ARG KALDIFEAT_VERSION="1.25.4.dev20240905+cuda11.8.torch2.4.1"
+ARG TORCHAUDIO_VERSION="2.4.1+cu118"
+
+LABEL authors="Fangjun Kuang <csukuangfj@gmail.com>"
+LABEL k2_version=${K2_VERSION}
+LABEL kaldifeat_version=${KALDIFEAT_VERSION}
+LABEL github_repo="https://github.com/k2-fsa/icefall"
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        curl \
+        vim \
+    	libssl-dev \
+        autoconf \
+        automake \
+        bzip2 \
+        ca-certificates \
+        ffmpeg \
+        g++ \
+        gfortran \
+        git \
+        libtool \
+        make \
+        patch \
+        sox \
+        subversion \
+        unzip \
+        valgrind \
+        wget \
+        zlib1g-dev \
+        && rm -rf /var/lib/apt/lists/*
+
+# Install dependencies
+RUN pip install --no-cache-dir \
+      torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/torchaudio/ \
+      k2==${K2_VERSION} -f https://k2-fsa.github.io/k2/cuda.html \
+      git+https://github.com/lhotse-speech/lhotse \
+      kaldifeat==${KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cuda.html \
+      kaldi_native_io \
+      kaldialign \
+      kaldifst \
+      kaldilm \
+      sentencepiece>=0.1.96 \
+      tensorboard \
+      typeguard \
+      dill \
+      onnx \
+      onnxruntime \
+      onnxmltools \
+      onnxoptimizer \
+      onnxsim \
+      multi_quantization \
+      typeguard \
+      numpy \
+      pytest \
+      graphviz
+
+RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
+    cd /workspace/icefall && \
+    pip install --no-cache-dir -r requirements.txt
+
+ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
+
+WORKDIR /workspace/icefall

docker/torch2.4.1-cuda12.1.dockerfile

@@ -0,0 +1,73 @@
+FROM pytorch/pytorch:2.4.1-cuda12.1-cudnn9-devel
+# python 3.10
+
+ENV LC_ALL C.UTF-8
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+# python 3.10
+ARG K2_VERSION="1.24.4.dev20240905+cuda12.1.torch2.4.1"
+ARG KALDIFEAT_VERSION="1.25.4.dev20240905+cuda12.1.torch2.4.1"
+ARG TORCHAUDIO_VERSION="2.4.1+cu121"
+
+LABEL authors="Fangjun Kuang <csukuangfj@gmail.com>"
+LABEL k2_version=${K2_VERSION}
+LABEL kaldifeat_version=${KALDIFEAT_VERSION}
+LABEL github_repo="https://github.com/k2-fsa/icefall"
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        curl \
+        vim \
+    	libssl-dev \
+        autoconf \
+        automake \
+        bzip2 \
+        ca-certificates \
+        ffmpeg \
+        g++ \
+        gfortran \
+        git \
+        libtool \
+        make \
+        patch \
+        sox \
+        subversion \
+        unzip \
+        valgrind \
+        wget \
+        zlib1g-dev \
+        && rm -rf /var/lib/apt/lists/*
+
+# Install dependencies
+RUN pip install --no-cache-dir \
+      torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/torchaudio/ \
+      k2==${K2_VERSION} -f https://k2-fsa.github.io/k2/cuda.html \
+      git+https://github.com/lhotse-speech/lhotse \
+      kaldifeat==${KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cuda.html \
+      kaldi_native_io \
+      kaldialign \
+      kaldifst \
+      kaldilm \
+      sentencepiece>=0.1.96 \
+      tensorboard \
+      typeguard \
+      dill \
+      onnx \
+      onnxruntime \
+      onnxmltools \
+      onnxoptimizer \
+      onnxsim \
+      multi_quantization \
+      typeguard \
+      numpy \
+      pytest \
+      graphviz
+
+RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
+    cd /workspace/icefall && \
+    pip install --no-cache-dir -r requirements.txt
+
+ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
+
+WORKDIR /workspace/icefall

docker/torch2.4.1-cuda12.4.dockerfile

@@ -0,0 +1,73 @@
+FROM pytorch/pytorch:2.4.1-cuda12.4-cudnn9-devel
+# python 3.10
+
+ENV LC_ALL C.UTF-8
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+# python 3.10
+ARG K2_VERSION="1.24.4.dev20240905+cuda12.4.torch2.4.1"
+ARG KALDIFEAT_VERSION="1.25.4.dev20240905+cuda12.4.torch2.4.1"
+ARG TORCHAUDIO_VERSION="2.4.1+cu124"
+
+LABEL authors="Fangjun Kuang <csukuangfj@gmail.com>"
+LABEL k2_version=${K2_VERSION}
+LABEL kaldifeat_version=${KALDIFEAT_VERSION}
+LABEL github_repo="https://github.com/k2-fsa/icefall"
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        curl \
+        vim \
+    	libssl-dev \
+        autoconf \
+        automake \
+        bzip2 \
+        ca-certificates \
+        ffmpeg \
+        g++ \
+        gfortran \
+        git \
+        libtool \
+        make \
+        patch \
+        sox \
+        subversion \
+        unzip \
+        valgrind \
+        wget \
+        zlib1g-dev \
+        && rm -rf /var/lib/apt/lists/*
+
+# Install dependencies
+RUN pip install --no-cache-dir \
+      torchaudio==${TORCHAUDIO_VERSION} -f https://download.pytorch.org/whl/torchaudio/ \
+      k2==${K2_VERSION} -f https://k2-fsa.github.io/k2/cuda.html \
+      git+https://github.com/lhotse-speech/lhotse \
+      kaldifeat==${KALDIFEAT_VERSION} -f https://csukuangfj.github.io/kaldifeat/cuda.html \
+      kaldi_native_io \
+      kaldialign \
+      kaldifst \
+      kaldilm \
+      sentencepiece>=0.1.96 \
+      tensorboard \
+      typeguard \
+      dill \
+      onnx \
+      onnxruntime \
+      onnxmltools \
+      onnxoptimizer \
+      onnxsim \
+      multi_quantization \
+      typeguard \
+      numpy \
+      pytest \
+      graphviz
+
+RUN git clone https://github.com/k2-fsa/icefall /workspace/icefall && \
+    cd /workspace/icefall && \
+    pip install --no-cache-dir -r requirements.txt
+
+ENV PYTHONPATH /workspace/icefall:$PYTHONPATH
+
+WORKDIR /workspace/icefall

docs/source/docker/intro.rst

@@ -34,6 +34,12 @@ which will give you something like below:
 
 .. code-block:: bash
 
+  "torch2.4.1-cuda12.4"
+  "torch2.4.1-cuda12.1"
+  "torch2.4.1-cuda11.8"
+  "torch2.4.0-cuda12.4"
+  "torch2.4.0-cuda12.1"
+  "torch2.4.0-cuda11.8"
   "torch2.3.1-cuda12.1"
   "torch2.3.1-cuda11.8"
   "torch2.2.2-cuda12.1"

egs/alimeeting/ASR/prepare.sh

@@ -87,7 +87,7 @@ fi
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
   log "Stage 3: Prepare musan manifest"
   # We assume that you have downloaded the musan corpus
-  # to data/musan
+  # to $dl_dir/musan
   if [ ! -f data/manifests/.musan_manifests.done ]; then
     log "It may take 6 minutes"
     mkdir -p data/manifests

egs/alimeeting/ASR_v2/local/prepare_alimeeting_gss.sh

@@ -58,7 +58,7 @@ if [ $stage -le 4 ]; then
   # for train, we use smaller context and larger batches to speed-up processing
   for JOB in $(seq $nj); do
     gss enhance cuts $EXP_DIR/cuts_train.jsonl.gz \
-      $EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.JOB.jsonl.gz $EXP_DIR/enhanced \
+      $EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.$JOB.jsonl.gz $EXP_DIR/enhanced \
       --bss-iterations 10 \
       --context-duration 5.0 \
       --use-garbage-class \
@@ -77,7 +77,7 @@ if [ $stage -le 5 ]; then
   for part in eval test; do
     for JOB in $(seq $nj); do
       gss enhance cuts $EXP_DIR/cuts_${part}.jsonl.gz \
-      $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.JOB.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.$JOB.jsonl.gz \
       $EXP_DIR/enhanced \
       --bss-iterations 10 \
       --context-duration 15.0 \

egs/alimeeting/ASR_v2/prepare.sh

@@ -65,7 +65,7 @@ fi
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
   log "Stage 2: Prepare musan manifest"
   # We assume that you have downloaded the musan corpus
-  # to data/musan
+  # to $dl_dir/musan
   mkdir -p data/manifests
   lhotse prepare musan $dl_dir/musan data/manifests
 fi

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/asr_datamodule.py

@@ -82,7 +82,7 @@ class AlimeetingAsrDataModule:
         group.add_argument(
             "--manifest-dir",
             type=Path,
-            default=Path("data/manifests"),
+            default=Path("data/fbank"),
             help="Path to directory with train/valid/test cuts.",
         )
         group.add_argument(
@@ -327,9 +327,11 @@ class AlimeetingAsrDataModule:
     def test_dataloaders(self, cuts: CutSet) -> DataLoader:
         logging.debug("About to create test dataset")
         test = K2SpeechRecognitionDataset(
-            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            input_strategy=(
-            if self.args.on_the_fly_feats
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
-            else PrecomputedFeatures(),
+                if self.args.on_the_fly_feats
+                else PrecomputedFeatures()
+            ),
             return_cuts=True,
         )
         sampler = DynamicBucketingSampler(

egs/ami/ASR/local/prepare_ami_gss.sh

@@ -58,7 +58,7 @@ if [ $stage -le 4 ]; then
   # for train, we use smaller context and larger batches to speed-up processing
   for JOB in $(seq $nj); do
     gss enhance cuts $EXP_DIR/cuts_train.jsonl.gz \
-      $EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.JOB.jsonl.gz $EXP_DIR/enhanced \
+      $EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.$JOB.jsonl.gz $EXP_DIR/enhanced \
       --bss-iterations 10 \
       --context-duration 5.0 \
       --use-garbage-class \
@@ -77,7 +77,7 @@ if [ $stage -le 5 ]; then
   for part in dev test; do
     for JOB in $(seq $nj); do
       gss enhance cuts $EXP_DIR/cuts_${part}.jsonl.gz \
-      $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.JOB.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.$JOB.jsonl.gz \
       $EXP_DIR/enhanced \
       --bss-iterations 10 \
       --context-duration 15.0 \

egs/audioset/AT/RESULTS.md

@@ -35,16 +35,40 @@ python zipformer/train.py \
     --master-port 13455
 ```
 
+We recommend that you train the model with weighted sampler, as the model converges
+faster with better performance:
+
+| Model | mAP |
+| ------ | ------- |
+| Zipformer-AT, train with weighted sampler | 46.6 |
+
 The evaluation command is:
 
 ```bash
-python zipformer/evaluate.py \
+export CUDA_VISIBLE_DEVICES="4,5,6,7"
-    --epoch 32 \
+subset=full
-    --avg 8 \
+weighted_sampler=1
-    --exp-dir zipformer/exp_at_as_full \
+bucket_sampler=0
-    --max-duration 500
+lr_epochs=15
+
+python zipformer/train.py \
+    --world-size 4 \
+    --audioset-subset $subset \
+    --num-epochs 120 \
+    --start-epoch 1 \
+    --use-fp16 1 \
+    --num-events 527 \
+    --lr-epochs $lr_epochs \
+    --exp-dir zipformer/exp_AS_${subset}_weighted_sampler${weighted_sampler} \
+    --weighted-sampler $weighted_sampler \
+    --bucketing-sampler $bucket_sampler \
+    --max-duration 1000 \
+    --enable-musan True \
+    --master-port 13452
 ```
 
+The command for evaluation is the same. The pre-trained model can be downloaded from https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-M-weighted-sampler
+
 
 #### small-scaled model, number of model parameters: 22125218, i.e., 22.13 M
 
@@ -92,4 +116,4 @@ python zipformer/evaluate.py \
     --encoder-unmasked-dim 192,192,192,192,192,192 \
     --exp-dir zipformer/exp_small_at_as_full \
     --max-duration 500
-```
+```

egs/audioset/AT/local/compute_weight.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
+#
+# 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 file generates the manifest and computes the fbank features for AudioSet
+dataset. The generated manifests and features are stored in data/fbank.
+"""
+
+import argparse
+
+import lhotse
+from lhotse import load_manifest
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--input-manifest", type=str, default="data/fbank/cuts_audioset_full.jsonl.gz"
+    )
+
+    parser.add_argument(
+        "--output",
+        type=str,
+        required=True,
+    )
+    return parser
+
+
+def main():
+    # Reference: https://github.com/YuanGongND/ast/blob/master/egs/audioset/gen_weight_file.py
+    parser = get_parser()
+    args = parser.parse_args()
+
+    cuts = load_manifest(args.input_manifest)
+
+    print(f"A total of {len(cuts)} cuts.")
+
+    label_count = [0] * 527  # a total of 527 classes
+    for c in cuts:
+        audio_event = c.supervisions[0].audio_event
+        labels = list(map(int, audio_event.split(";")))
+        for label in labels:
+            label_count[label] += 1
+
+    with open(args.output, "w") as f:
+        for c in cuts:
+            audio_event = c.supervisions[0].audio_event
+            labels = list(map(int, audio_event.split(";")))
+            weight = 0
+            for label in labels:
+                weight += 1000 / (label_count[label] + 0.01)
+            f.write(f"{c.id} {weight}\n")
+
+
+if __name__ == "__main__":
+    main()

egs/audioset/AT/prepare.sh

@@ -10,6 +10,7 @@ stage=-1
 stop_stage=4
 
 dl_dir=$PWD/download
+fbank_dir=data/fbank
 
 # we assume that you have your downloaded the AudioSet and placed
 # it under $dl_dir/audioset, the folder structure should look like
@@ -49,7 +50,6 @@ fi
 
 if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
   log "Stage 0: Construct the audioset manifest and compute the fbank features for balanced set"
-  fbank_dir=data/fbank
   if [! -e $fbank_dir/.balanced.done]; then
     python local/generate_audioset_manifest.py \
       --dataset-dir $dl_dir/audioset \
@@ -102,3 +102,14 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
     touch data/fbank/.musan.done
   fi
 fi
+
+# The following stages are required to do weighted-sampling training
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Prepare for weighted-sampling training"
+  if [ ! -e $fbank_dir/cuts_audioset_full.jsonl.gz ]; then
+    lhotse combine $fbank_dir/cuts_audioset_balanced.jsonl.gz $fbank_dir/cuts_audioset_unbalanced.jsonl.gz $fbank_dir/cuts_audioset_full.jsonl.gz
+  fi
+  python ./local/compute_weight.py \
+    --input-manifest $fbank_dir/cuts_audioset_full.jsonl.gz \
+    --output $fbank_dir/sampling_weights_full.txt
+fi

egs/audioset/AT/zipformer/at_datamodule.py

@@ -31,6 +31,7 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     PrecomputedFeatures,
     SimpleCutSampler,
     SpecAugment,
+    WeightedSimpleCutSampler,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
     AudioSamples,
@@ -99,6 +100,20 @@ class AudioSetATDatamodule:
             help="Maximum pooled recordings duration (seconds) in a "
             "single batch. You can reduce it if it causes CUDA OOM.",
         )
+        group.add_argument(
+            "--weighted-sampler",
+            type=str2bool,
+            default=False,
+            help="When enabled, samples are drawn from by their weights. "
+            "It cannot be used together with bucketing sampler",
+        )
+        group.add_argument(
+            "--num-samples",
+            type=int,
+            default=200000,
+            help="The number of samples to be drawn in each epoch. Only be used"
+            "for weighed sampler",
+        )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
@@ -295,6 +310,9 @@ class AudioSetATDatamodule:
             )
 
         if self.args.bucketing_sampler:
+            assert (
+                not self.args.weighted_sampler
+            ), "weighted sampling is not supported in bucket sampler"
             logging.info("Using DynamicBucketingSampler.")
             train_sampler = DynamicBucketingSampler(
                 cuts_train,
@@ -304,13 +322,26 @@ class AudioSetATDatamodule:
                 drop_last=self.args.drop_last,
             )
         else:
-            logging.info("Using SimpleCutSampler.")
+            if self.args.weighted_sampler:
-            train_sampler = SimpleCutSampler(
+                # assert self.args.audioset_subset == "full", "Only use weighted sampling for full audioset"
-                cuts_train,
+                logging.info("Using weighted SimpleCutSampler")
-                max_duration=self.args.max_duration,
+                weights = self.audioset_sampling_weights()
-                shuffle=self.args.shuffle,
+                train_sampler = WeightedSimpleCutSampler(
-                drop_last=self.args.drop_last,
+                    cuts_train,
-            )
+                    weights,
+                    num_samples=self.args.num_samples,
+                    max_duration=self.args.max_duration,
+                    shuffle=False,  # do not support shuffle
+                    drop_last=self.args.drop_last,
+                )
+            else:
+                logging.info("Using SimpleCutSampler.")
+                train_sampler = SimpleCutSampler(
+                    cuts_train,
+                    max_duration=self.args.max_duration,
+                    shuffle=self.args.shuffle,
+                    drop_last=self.args.drop_last,
+                )
         logging.info("About to create train dataloader")
 
         if sampler_state_dict is not None:
@@ -373,11 +404,9 @@ class AudioSetATDatamodule:
     def test_dataloaders(self, cuts: CutSet) -> DataLoader:
         logging.debug("About to create test dataset")
         test = AudioTaggingDataset(
-            input_strategy=(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
-                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            if self.args.on_the_fly_feats
-                if self.args.on_the_fly_feats
+            else eval(self.args.input_strategy)(),
-                else eval(self.args.input_strategy)()
-            ),
             return_cuts=self.args.return_cuts,
         )
         sampler = DynamicBucketingSampler(
@@ -397,21 +426,30 @@ class AudioSetATDatamodule:
     @lru_cache()
     def audioset_train_cuts(self) -> CutSet:
         logging.info("About to get the audioset training cuts.")
-        balanced_cuts = load_manifest_lazy(
+        if not self.args.weighted_sampler:
-            self.args.manifest_dir / "cuts_audioset_balanced.jsonl.gz"
+            balanced_cuts = load_manifest_lazy(
-        )
+                self.args.manifest_dir / "cuts_audioset_balanced.jsonl.gz"
-        if self.args.audioset_subset == "full":
-            unbalanced_cuts = load_manifest_lazy(
-                self.args.manifest_dir / "cuts_audioset_unbalanced.jsonl.gz"
-            )
-            cuts = CutSet.mux(
-                balanced_cuts,
-                unbalanced_cuts,
-                weights=[20000, 2000000],
-                stop_early=True,
             )
+            if self.args.audioset_subset == "full":
+                unbalanced_cuts = load_manifest_lazy(
+                    self.args.manifest_dir / "cuts_audioset_unbalanced.jsonl.gz"
+                )
+                cuts = CutSet.mux(
+                    balanced_cuts,
+                    unbalanced_cuts,
+                    weights=[20000, 2000000],
+                    stop_early=True,
+                )
+            else:
+                cuts = balanced_cuts
         else:
-            cuts = balanced_cuts
+            # assert self.args.audioset_subset == "full", "Only do weighted sampling for full AudioSet"
+            cuts = load_manifest(
+                self.args.manifest_dir
+                / f"cuts_audioset_{self.args.audioset_subset}.jsonl.gz"
+            )
+            logging.info(f"Get {len(cuts)} cuts in total.")
+
         return cuts
 
     @lru_cache()
@@ -420,3 +458,22 @@ class AudioSetATDatamodule:
         return load_manifest_lazy(
             self.args.manifest_dir / "cuts_audioset_eval.jsonl.gz"
         )
+
+    @lru_cache()
+    def audioset_sampling_weights(self):
+        logging.info(
+            f"About to get the sampling weight for {self.args.audioset_subset} in AudioSet"
+        )
+        weights = []
+        with open(
+            self.args.manifest_dir / f"sample_weights_{self.args.audioset_subset}.txt",
+            "r",
+        ) as f:
+            while True:
+                line = f.readline()
+                if not line:
+                    break
+                weight = float(line.split()[1])
+                weights.append(weight)
+        logging.info(f"Get the sampling weight for {len(weights)} cuts")
+        return weights

egs/audioset/AT/zipformer/train.py

@@ -789,12 +789,14 @@ def train_one_epoch(
             rank=0,
         )
 
+    num_samples = 0
     for batch_idx, batch in enumerate(train_dl):
         if batch_idx % 10 == 0:
             set_batch_count(model, get_adjusted_batch_count(params))
 
         params.batch_idx_train += 1
         batch_size = batch["inputs"].size(0)
+        num_samples += batch_size
 
         try:
             with torch.cuda.amp.autocast(enabled=params.use_fp16):
@@ -919,6 +921,12 @@ def train_one_epoch(
                     tb_writer, "train/valid_", params.batch_idx_train
                 )
 
+        if num_samples > params.num_samples:
+            logging.info(
+                f"Number of training samples exceeds {params.num_samples} in this epoch, move on to next epoch"
+            )
+            break
+
     loss_value = tot_loss["loss"] / tot_loss["frames"]
     params.train_loss = loss_value
     if params.train_loss < params.best_train_loss:
@@ -1032,7 +1040,8 @@ def run(rank, world_size, args):
 
         return True
 
-    train_cuts = train_cuts.filter(remove_short_and_long_utt)
+    if not params.weighted_sampler:
+        train_cuts = train_cuts.filter(remove_short_and_long_utt)
 
     if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
         # We only load the sampler's state dict when it loads a checkpoint

egs/baker_zh/TTS/.gitignore

@@ -0,0 +1,6 @@
+path.sh
+*.onnx
+*.wav
+generator_v1
+generator_v2
+generator_v3

egs/baker_zh/TTS/README.md

@@ -0,0 +1,146 @@
+# Introduction
+
+It is for the dataset from
+https://en.data-baker.com/datasets/freeDatasets/
+
+The dataset contains 10000 Chinese sentences of a native Chinese female speaker,
+which is about 12 hours.
+
+
+**Note**: The dataset is for non-commercial use only.
+
+
+# matcha
+
+[./matcha](./matcha) contains the code for training [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS)
+
+Checkpoints and training logs can be found [here](https://huggingface.co/csukuangfj/icefall-tts-baker-matcha-zh-2024-12-27).
+The pull-request for this recipe can be found at <https://github.com/k2-fsa/icefall/pull/1849>
+
+The training command is given below:
+```bash
+python3 ./matcha/train.py \
+  --exp-dir ./matcha/exp-1/ \
+  --num-workers 4 \
+  --world-size 1 \
+  --num-epochs 2000 \
+  --max-duration 1200 \
+  --bucketing-sampler 1 \
+  --start-epoch 1
+```
+
+To inference, use:
+
+```bash
+# Download Hifigan vocoder. We use Hifigan v2 below. You can select from v1, v2, or v3
+
+wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
+
+python3 ./matcha/infer.py \
+  --epoch 2000 \
+  --exp-dir ./matcha/exp-1 \
+  --vocoder ./generator_v2 \
+  --tokens ./data/tokens.txt \
+  --cmvn ./data/fbank/cmvn.json \
+  --input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
+  --output-wav ./generated.wav
+```
+
+```bash
+soxi ./generated.wav
+```
+
+prints:
+```
+Input File     : './generated.wav'
+Channels       : 1
+Sample Rate    : 22050
+Precision      : 16-bit
+Duration       : 00:00:17.31 = 381696 samples ~ 1298.29 CDDA sectors
+File Size      : 763k
+Bit Rate       : 353k
+Sample Encoding: 16-bit Signed Integer PCM
+```
+
+https://github.com/user-attachments/assets/88d4e88f-ebc4-4f32-b216-16d46b966024
+
+
+To export the checkpoint to onnx:
+```bash
+python3 ./matcha/export_onnx.py \
+  --exp-dir ./matcha/exp-1 \
+  --epoch 2000 \
+  --tokens ./data/tokens.txt \
+  --cmvn ./data/fbank/cmvn.json
+```
+
+The above command generates the following files:
+```
+-rw-r--r-- 1 kuangfangjun root 72M Dec 27 18:53 model-steps-2.onnx
+-rw-r--r-- 1 kuangfangjun root 73M Dec 27 18:54 model-steps-3.onnx
+-rw-r--r-- 1 kuangfangjun root 73M Dec 27 18:54 model-steps-4.onnx
+-rw-r--r-- 1 kuangfangjun root 74M Dec 27 18:55 model-steps-5.onnx
+-rw-r--r-- 1 kuangfangjun root 74M Dec 27 18:57 model-steps-6.onnx
+```
+
+where the 2 in `model-steps-2.onnx` means it uses 2 steps for the ODE solver.
+
+**HINT**: If you get the following error while running `export_onnx.py`:
+
+```
+torch.onnx.errors.UnsupportedOperatorError: Exporting the operator
+'aten::scaled_dot_product_attention' to ONNX opset version 14 is not supported.
+```
+
+please use `torch>=2.2.0`.
+
+To export the Hifigan vocoder to onnx, please use:
+
+```bash
+wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
+wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
+wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v3
+
+python3 ./matcha/export_onnx_hifigan.py
+```
+
+The above command generates 3 files:
+
+  - hifigan_v1.onnx
+  - hifigan_v2.onnx
+  - hifigan_v3.onnx
+
+**HINT**: You can download pre-exported hifigan ONNX models from
+<https://github.com/k2-fsa/sherpa-onnx/releases/tag/vocoder-models>
+
+To use the generated onnx files to generate speech from text, please run:
+
+```bash
+
+# First, generate ./lexicon.txt
+python3 ./matcha/generate_lexicon.py
+
+python3 ./matcha/onnx_pretrained.py \
+  --acoustic-model ./model-steps-4.onnx \
+  --vocoder ./hifigan_v2.onnx \
+  --tokens ./data/tokens.txt \
+  --lexicon ./lexicon.txt \
+  --input-text "在一个阳光明媚的夏天，小马、小羊和小狗它们一块儿在广阔的草地上，嬉戏玩耍，这时小猴来了，还带着它心爱的足球活蹦乱跳地跑前、跑后教小马、小羊、小狗踢足球。" \
+  --output-wav ./1.wav
+```
+
+```bash
+soxi ./1.wav
+
+Input File     : './1.wav'
+Channels       : 1
+Sample Rate    : 22050
+Precision      : 16-bit
+Duration       : 00:00:16.37 = 360960 samples ~ 1227.76 CDDA sectors
+File Size      : 722k
+Bit Rate       : 353k
+Sample Encoding: 16-bit Signed Integer PCM
+```
+
+https://github.com/user-attachments/assets/578d04bb-fee8-47e5-9984-a868dcce610e
+

egs/baker_zh/TTS/local/audio.py

@@ -0,0 +1 @@
+../matcha/audio.py

egs/baker_zh/TTS/local/compute_fbank_baker_zh.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+# Copyright    2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       Zengwei Yao)
+#
+# 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 file computes fbank features of the baker-zh dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import argparse
+import logging
+import os
+from pathlib import Path
+
+import torch
+from fbank import MatchaFbank, MatchaFbankConfig
+from lhotse import CutSet, LilcomChunkyWriter, load_manifest
+from lhotse.audio import RecordingSet
+from lhotse.supervision import SupervisionSet
+
+from icefall.utils import get_executor
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--num-jobs",
+        type=int,
+        default=4,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        """,
+    )
+    return parser
+
+
+def compute_fbank_baker_zh(num_jobs: int):
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+
+    if num_jobs < 1:
+        num_jobs = os.cpu_count()
+
+    logging.info(f"num_jobs: {num_jobs}")
+    logging.info(f"src_dir: {src_dir}")
+    logging.info(f"output_dir: {output_dir}")
+    config = MatchaFbankConfig(
+        n_fft=1024,
+        n_mels=80,
+        sampling_rate=22050,
+        hop_length=256,
+        win_length=1024,
+        f_min=0,
+        f_max=8000,
+    )
+
+    prefix = "baker_zh"
+    suffix = "jsonl.gz"
+
+    extractor = MatchaFbank(config)
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        cuts_filename = f"{prefix}_cuts.{suffix}"
+        logging.info(f"Processing {cuts_filename}")
+        cut_set = load_manifest(src_dir / cuts_filename).resample(22050)
+
+        cut_set = cut_set.compute_and_store_features(
+            extractor=extractor,
+            storage_path=f"{output_dir}/{prefix}_feats",
+            num_jobs=num_jobs if ex is None else 80,
+            executor=ex,
+            storage_type=LilcomChunkyWriter,
+        )
+
+        cut_set.to_file(output_dir / cuts_filename)
+
+
+if __name__ == "__main__":
+    # 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
+    # even when we are not invoking the main (e.g. when spawning subprocesses).
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_parser().parse_args()
+    compute_fbank_baker_zh(args.num_jobs)

egs/baker_zh/TTS/local/compute_fbank_statistics.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/local/compute_fbank_statistics.py

egs/baker_zh/TTS/local/convert_text_to_tokens.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+
+import argparse
+import re
+from typing import List
+
+import jieba
+from lhotse import load_manifest
+from pypinyin import Style, lazy_pinyin, load_phrases_dict
+
+load_phrases_dict(
+    {
+        "行长": [["hang2"], ["zhang3"]],
+        "银行行长": [["yin2"], ["hang2"], ["hang2"], ["zhang3"]],
+    }
+)
+
+whiter_space_re = re.compile(r"\s+")
+
+punctuations_re = [
+    (re.compile(x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("，", ","),
+        ("。", "."),
+        ("！", "!"),
+        ("？", "?"),
+        ("“", '"'),
+        ("”", '"'),
+        ("‘", "'"),
+        ("’", "'"),
+        ("：", ":"),
+        ("、", ","),
+        ("Ｂ", "逼"),
+        ("Ｐ", "批"),
+    ]
+]
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--in-file",
+        type=str,
+        required=True,
+        help="Input cutset.",
+    )
+
+    parser.add_argument(
+        "--out-file",
+        type=str,
+        required=True,
+        help="Output cutset.",
+    )
+
+    return parser
+
+
+def normalize_white_spaces(text):
+    return whiter_space_re.sub(" ", text)
+
+
+def normalize_punctuations(text):
+    for regex, replacement in punctuations_re:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def split_text(text: str) -> List[str]:
+    """
+    Example input:  '你好呀，You are 一个好人。   去银行存钱？How about    you?'
+    Example output: ['你好', '呀', ',', 'you are', '一个', '好人', '.', '去', '银行', '存钱', '?', 'how about you', '?']
+    """
+    text = text.lower()
+    text = normalize_white_spaces(text)
+    text = normalize_punctuations(text)
+    ans = []
+
+    for seg in jieba.cut(text):
+        if seg in ",.!?:\"'":
+            ans.append(seg)
+        elif seg == " " and len(ans) > 0:
+            if ord("a") <= ord(ans[-1][-1]) <= ord("z"):
+                ans[-1] += seg
+        elif ord("a") <= ord(seg[0]) <= ord("z"):
+            if len(ans) == 0:
+                ans.append(seg)
+                continue
+
+            if ans[-1][-1] == " ":
+                ans[-1] += seg
+                continue
+
+            ans.append(seg)
+        else:
+            ans.append(seg)
+
+    ans = [s.strip() for s in ans]
+    return ans
+
+
+def main():
+    args = get_parser().parse_args()
+    cuts = load_manifest(args.in_file)
+    for c in cuts:
+        assert len(c.supervisions) == 1, (len(c.supervisions), c.supervisions)
+        text = c.supervisions[0].normalized_text
+
+        text_list = split_text(text)
+        tokens = lazy_pinyin(text_list, style=Style.TONE3, tone_sandhi=True)
+
+        c.tokens = tokens
+
+    cuts.to_file(args.out_file)
+
+    print(f"saved to {args.out_file}")
+
+
+if __name__ == "__main__":
+    main()

egs/baker_zh/TTS/local/fbank.py

@@ -0,0 +1 @@
+../matcha/fbank.py

egs/baker_zh/TTS/local/generate_tokens.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+
+"""
+This file generates the file tokens.txt.
+
+Usage:
+
+python3 ./local/generate_tokens.py > data/tokens.txt
+"""
+
+
+import argparse
+from typing import List
+
+import jieba
+from pypinyin import Style, lazy_pinyin, pinyin_dict
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        required=True,
+        help="Path to to save tokens.txt.",
+    )
+
+    return parser
+
+
+def generate_token_list() -> List[str]:
+    token_set = set()
+
+    word_dict = pinyin_dict.pinyin_dict
+    i = 0
+    for key in word_dict:
+        if not (0x4E00 <= key <= 0x9FFF):
+            continue
+
+        w = chr(key)
+        t = lazy_pinyin(w, style=Style.TONE3, tone_sandhi=True)[0]
+        token_set.add(t)
+
+    no_digit = set()
+    for t in token_set:
+        if t[-1] not in "1234":
+            no_digit.add(t)
+        else:
+            no_digit.add(t[:-1])
+
+    no_digit.add("dei")
+    no_digit.add("tou")
+    no_digit.add("dia")
+
+    for t in no_digit:
+        token_set.add(t)
+        for i in range(1, 5):
+            token_set.add(f"{t}{i}")
+
+    ans = list(token_set)
+    ans.sort()
+
+    punctuations = list(",.!?:\"'")
+    ans = punctuations + ans
+
+    # use ID 0 for blank
+    # Use ID 1 of _ for padding
+    ans.insert(0, " ")
+    ans.insert(1, "_")  #
+
+    return ans
+
+
+def main():
+    args = get_parser().parse_args()
+    token_list = generate_token_list()
+    with open(args.tokens, "w", encoding="utf-8") as f:
+        for indx, token in enumerate(token_list):
+            f.write(f"{token} {indx}\n")
+
+
+if __name__ == "__main__":
+    main()

egs/baker_zh/TTS/local/validate_manifest.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# Copyright    2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       Zengwei Yao)
+#
+# 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 checks the following assumptions of the generated manifest:
+
+- Single supervision per cut
+
+We will add more checks later if needed.
+
+Usage example:
+
+    python3 ./local/validate_manifest.py \
+            ./data/spectrogram/baker_zh_cuts_all.jsonl.gz
+
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from lhotse import CutSet, load_manifest_lazy
+from lhotse.dataset.speech_synthesis import validate_for_tts
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "manifest",
+        type=Path,
+        help="Path to the manifest file",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    manifest = args.manifest
+    logging.info(f"Validating {manifest}")
+
+    assert manifest.is_file(), f"{manifest} does not exist"
+    cut_set = load_manifest_lazy(manifest)
+    assert isinstance(cut_set, CutSet), type(cut_set)
+
+    validate_for_tts(cut_set)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/baker_zh/TTS/matcha/audio.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/audio.py

egs/baker_zh/TTS/matcha/export_onnx.py

@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+This script exports a Matcha-TTS model to ONNX.
+Note that the model outputs fbank. You need to use a vocoder to convert
+it to audio. See also ./export_onnx_hifigan.py
+
+python3 ./matcha/export_onnx.py \
+  --exp-dir ./matcha/exp-1 \
+  --epoch 2000 \
+  --tokens ./data/tokens.txt \
+  --cmvn ./data/fbank/cmvn.json
+
+"""
+
+import argparse
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict
+
+import onnx
+import torch
+from tokenizer import Tokenizer
+from train import get_model, get_params
+
+from icefall.checkpoint import load_checkpoint
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=2000,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=Path,
+        default="matcha/exp-new-3",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=Path,
+        default="data/tokens.txt",
+    )
+
+    parser.add_argument(
+        "--cmvn",
+        type=str,
+        default="data/fbank/cmvn.json",
+        help="""Path to vocabulary.""",
+    )
+
+    return parser
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, Any]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+
+    onnx.save(model, filename)
+
+
+class ModelWrapper(torch.nn.Module):
+    def __init__(self, model, num_steps: int = 5):
+        super().__init__()
+        self.model = model
+        self.num_steps = num_steps
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lengths: torch.Tensor,
+        noise_scale: torch.Tensor,
+        length_scale: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args: :
+          x: (batch_size, num_tokens), torch.int64
+          x_lengths: (batch_size,), torch.int64
+          noise_scale: (1,), torch.float32
+          length_scale (1,), torch.float32
+        Returns:
+          audio: (batch_size, num_samples)
+
+        """
+        mel = self.model.synthesise(
+            x=x,
+            x_lengths=x_lengths,
+            n_timesteps=self.num_steps,
+            temperature=noise_scale,
+            length_scale=length_scale,
+        )["mel"]
+        # mel: (batch_size, feat_dim, num_frames)
+
+        return mel
+
+
+@torch.inference_mode()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    params = get_params()
+
+    params.update(vars(args))
+
+    tokenizer = Tokenizer(params.tokens)
+    params.pad_id = tokenizer.pad_id
+    params.vocab_size = tokenizer.vocab_size
+    params.model_args.n_vocab = params.vocab_size
+
+    with open(params.cmvn) as f:
+        stats = json.load(f)
+        params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.data_args.data_statistics.mel_std = stats["fbank_std"]
+
+        params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.model_args.data_statistics.mel_std = stats["fbank_std"]
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+    load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+
+    for num_steps in [2, 3, 4, 5, 6]:
+        logging.info(f"num_steps: {num_steps}")
+        wrapper = ModelWrapper(model, num_steps=num_steps)
+        wrapper.eval()
+
+        # Use a large value so the rotary position embedding in the text
+        # encoder has a large initial length
+        x = torch.ones(1, 1000, dtype=torch.int64)
+        x_lengths = torch.tensor([x.shape[1]], dtype=torch.int64)
+        noise_scale = torch.tensor([1.0])
+        length_scale = torch.tensor([1.0])
+
+        opset_version = 14
+        filename = f"model-steps-{num_steps}.onnx"
+        torch.onnx.export(
+            wrapper,
+            (x, x_lengths, noise_scale, length_scale),
+            filename,
+            opset_version=opset_version,
+            input_names=["x", "x_length", "noise_scale", "length_scale"],
+            output_names=["mel"],
+            dynamic_axes={
+                "x": {0: "N", 1: "L"},
+                "x_length": {0: "N"},
+                "mel": {0: "N", 2: "L"},
+            },
+        )
+
+        meta_data = {
+            "model_type": "matcha-tts",
+            "language": "Chinese",
+            "has_espeak": 0,
+            "n_speakers": 1,
+            "jieba": 1,
+            "sample_rate": 22050,
+            "version": 1,
+            "pad_id": params.pad_id,
+            "model_author": "icefall",
+            "maintainer": "k2-fsa",
+            "dataset": "baker-zh",
+            "use_eos_bos": 0,
+            "dataset_url": "https://www.data-baker.com/open_source.html",
+            "dataset_comment": "The dataset is for non-commercial use only.",
+            "num_ode_steps": num_steps,
+        }
+        add_meta_data(filename=filename, meta_data=meta_data)
+        print(meta_data)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/baker_zh/TTS/matcha/export_onnx_hifigan.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/export_onnx_hifigan.py

egs/baker_zh/TTS/matcha/fbank.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/fbank.py

egs/baker_zh/TTS/matcha/generate_lexicon.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+
+import jieba
+from pypinyin import Style, lazy_pinyin, load_phrases_dict, phrases_dict, pinyin_dict
+from tokenizer import Tokenizer
+
+load_phrases_dict(
+    {
+        "行长": [["hang2"], ["zhang3"]],
+        "银行行长": [["yin2"], ["hang2"], ["hang2"], ["zhang3"]],
+    }
+)
+
+
+def main():
+    filename = "lexicon.txt"
+    tokens = "./data/tokens.txt"
+    tokenizer = Tokenizer(tokens)
+
+    word_dict = pinyin_dict.pinyin_dict
+    phrases = phrases_dict.phrases_dict
+
+    i = 0
+    with open(filename, "w", encoding="utf-8") as f:
+        for key in word_dict:
+            if not (0x4E00 <= key <= 0x9FFF):
+                continue
+
+            w = chr(key)
+            tokens = lazy_pinyin(w, style=Style.TONE3, tone_sandhi=True)[0]
+
+            f.write(f"{w} {tokens}\n")
+
+        for key in phrases:
+            tokens = lazy_pinyin(key, style=Style.TONE3, tone_sandhi=True)
+            tokens = " ".join(tokens)
+
+            f.write(f"{key} {tokens}\n")
+
+
+if __name__ == "__main__":
+    main()

egs/baker_zh/TTS/matcha/hifigan

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/hifigan

egs/baker_zh/TTS/matcha/infer.py

@@ -0,0 +1,342 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+"""
+python3 ./matcha/infer.py \
+  --epoch 2000 \
+  --exp-dir ./matcha/exp-1 \
+  --vocoder ./generator_v2 \
+  --tokens ./data/tokens.txt \
+  --cmvn ./data/fbank/cmvn.json \
+  --input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
+  --output-wav ./generated.wav
+"""
+
+import argparse
+import datetime as dt
+import json
+import logging
+from pathlib import Path
+
+import soundfile as sf
+import torch
+import torch.nn as nn
+from hifigan.config import v1, v2, v3
+from hifigan.denoiser import Denoiser
+from hifigan.models import Generator as HiFiGAN
+from local.convert_text_to_tokens import split_text
+from pypinyin import Style, lazy_pinyin
+from tokenizer import Tokenizer
+from train import get_model, get_params
+from tts_datamodule import BakerZhTtsDataModule
+
+from icefall.checkpoint import load_checkpoint
+from icefall.utils import AttributeDict, setup_logger
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=4000,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=Path,
+        default="matcha/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--vocoder",
+        type=Path,
+        default="./generator_v1",
+        help="Path to the vocoder",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=Path,
+        default="data/tokens.txt",
+    )
+
+    parser.add_argument(
+        "--cmvn",
+        type=str,
+        default="data/fbank/cmvn.json",
+        help="""Path to vocabulary.""",
+    )
+
+    # The following arguments are used for inference on single text
+    parser.add_argument(
+        "--input-text",
+        type=str,
+        required=False,
+        help="The text to generate speech for",
+    )
+
+    parser.add_argument(
+        "--output-wav",
+        type=str,
+        required=False,
+        help="The filename of the wave to save the generated speech",
+    )
+
+    parser.add_argument(
+        "--sampling-rate",
+        type=int,
+        default=22050,
+        help="The sampling rate of the generated speech (default: 22050 for baker_zh)",
+    )
+
+    return parser
+
+
+def load_vocoder(checkpoint_path: Path) -> nn.Module:
+    checkpoint_path = str(checkpoint_path)
+    if checkpoint_path.endswith("v1"):
+        h = AttributeDict(v1)
+    elif checkpoint_path.endswith("v2"):
+        h = AttributeDict(v2)
+    elif checkpoint_path.endswith("v3"):
+        h = AttributeDict(v3)
+    else:
+        raise ValueError(f"supports only v1, v2, and v3, given {checkpoint_path}")
+
+    hifigan = HiFiGAN(h).to("cpu")
+    hifigan.load_state_dict(
+        torch.load(checkpoint_path, map_location="cpu")["generator"]
+    )
+    _ = hifigan.eval()
+    hifigan.remove_weight_norm()
+    return hifigan
+
+
+def to_waveform(
+    mel: torch.Tensor, vocoder: nn.Module, denoiser: nn.Module
+) -> torch.Tensor:
+    audio = vocoder(mel).clamp(-1, 1)
+    audio = denoiser(audio.squeeze(0), strength=0.00025).cpu().squeeze()
+    return audio.squeeze()
+
+
+def process_text(text: str, tokenizer: Tokenizer, device: str = "cpu") -> dict:
+    text = split_text(text)
+    tokens = lazy_pinyin(text, style=Style.TONE3, tone_sandhi=True)
+
+    x = tokenizer.texts_to_token_ids([tokens])
+    x = torch.tensor(x, dtype=torch.long, device=device)
+    x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device)
+    return {"x_orig": text, "x": x, "x_lengths": x_lengths}
+
+
+def synthesize(
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    n_timesteps: int,
+    text: str,
+    length_scale: float,
+    temperature: float,
+    device: str = "cpu",
+    spks=None,
+) -> dict:
+    text_processed = process_text(text=text, tokenizer=tokenizer, device=device)
+    start_t = dt.datetime.now()
+    output = model.synthesise(
+        text_processed["x"],
+        text_processed["x_lengths"],
+        n_timesteps=n_timesteps,
+        temperature=temperature,
+        spks=spks,
+        length_scale=length_scale,
+    )
+    # merge everything to one dict
+    output.update({"start_t": start_t, **text_processed})
+    return output
+
+
+def infer_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    vocoder: nn.Module,
+    denoiser: nn.Module,
+    tokenizer: Tokenizer,
+) -> None:
+    """Decode dataset.
+    The ground-truth and generated audio pairs will be saved to `params.save_wav_dir`.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      tokenizer:
+        Used to convert text to phonemes.
+    """
+
+    device = next(model.parameters()).device
+    num_cuts = 0
+    log_interval = 5
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    for batch_idx, batch in enumerate(dl):
+        batch_size = len(batch["tokens"])
+
+        texts = [c.supervisions[0].normalized_text for c in batch["cut"]]
+
+        audio = batch["audio"]
+        audio_lens = batch["audio_lens"].tolist()
+        cut_ids = [cut.id for cut in batch["cut"]]
+
+        for i in range(batch_size):
+            output = synthesize(
+                model=model,
+                tokenizer=tokenizer,
+                n_timesteps=params.n_timesteps,
+                text=texts[i],
+                length_scale=params.length_scale,
+                temperature=params.temperature,
+                device=device,
+            )
+            output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
+
+            sf.write(
+                file=params.save_wav_dir / f"{cut_ids[i]}_pred.wav",
+                data=output["waveform"],
+                samplerate=params.data_args.sampling_rate,
+                subtype="PCM_16",
+            )
+            sf.write(
+                file=params.save_wav_dir / f"{cut_ids[i]}_gt.wav",
+                data=audio[i].numpy(),
+                samplerate=params.data_args.sampling_rate,
+                subtype="PCM_16",
+            )
+
+        num_cuts += batch_size
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+
+
+@torch.inference_mode()
+def main():
+    parser = get_parser()
+    BakerZhTtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    params.suffix = f"epoch-{params.epoch}"
+
+    params.res_dir = params.exp_dir / "infer" / params.suffix
+    params.save_wav_dir = params.res_dir / "wav"
+    params.save_wav_dir.mkdir(parents=True, exist_ok=True)
+
+    setup_logger(f"{params.res_dir}/log-infer-{params.suffix}")
+    logging.info("Infer started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    logging.info(f"Device: {device}")
+
+    tokenizer = Tokenizer(params.tokens)
+    params.vocab_size = tokenizer.vocab_size
+    params.model_args.n_vocab = params.vocab_size
+
+    with open(params.cmvn) as f:
+        stats = json.load(f)
+        params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.data_args.data_statistics.mel_std = stats["fbank_std"]
+
+        params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.model_args.data_statistics.mel_std = stats["fbank_std"]
+
+    # Number of ODE Solver steps
+    params.n_timesteps = 2
+
+    # Changes to the speaking rate
+    params.length_scale = 1.0
+
+    # Sampling temperature
+    params.temperature = 0.667
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+    model.to(device)
+    model.eval()
+
+    # we need cut ids to organize tts results.
+    args.return_cuts = True
+    baker_zh = BakerZhTtsDataModule(args)
+
+    test_cuts = baker_zh.test_cuts()
+    test_dl = baker_zh.test_dataloaders(test_cuts)
+
+    if not Path(params.vocoder).is_file():
+        raise ValueError(f"{params.vocoder} does not exist")
+
+    vocoder = load_vocoder(params.vocoder)
+    vocoder.to(device)
+
+    denoiser = Denoiser(vocoder, mode="zeros")
+    denoiser.to(device)
+
+    if params.input_text is not None and params.output_wav is not None:
+        logging.info("Synthesizing a single text")
+        output = synthesize(
+            model=model,
+            tokenizer=tokenizer,
+            n_timesteps=params.n_timesteps,
+            text=params.input_text,
+            length_scale=params.length_scale,
+            temperature=params.temperature,
+            device=device,
+        )
+        output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
+
+        sf.write(
+            file=params.output_wav,
+            data=output["waveform"],
+            samplerate=params.sampling_rate,
+            subtype="PCM_16",
+        )
+    else:
+        logging.info("Decoding the test set")
+        infer_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            vocoder=vocoder,
+            denoiser=denoiser,
+            tokenizer=tokenizer,
+        )
+
+
+if __name__ == "__main__":
+    main()

egs/baker_zh/TTS/matcha/model.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/model.py

egs/baker_zh/TTS/matcha/models

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/models

egs/baker_zh/TTS/matcha/monotonic_align

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/monotonic_align

egs/baker_zh/TTS/matcha/onnx_pretrained.py

@@ -0,0 +1,316 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+python3 ./matcha/onnx_pretrained.py \
+  --acoustic-model ./model-steps-4.onnx \
+  --vocoder ./hifigan_v2.onnx \
+  --tokens ./data/tokens.txt \
+  --lexicon ./lexicon.txt \
+  --input-text "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。" \
+  --output-wav ./b.wav
+"""
+
+import argparse
+import datetime as dt
+import logging
+import re
+from typing import Dict, List
+
+import jieba
+import onnxruntime as ort
+import soundfile as sf
+import torch
+from infer import load_vocoder
+from utils import intersperse
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--acoustic-model",
+        type=str,
+        required=True,
+        help="Path to the acoustic model",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        required=True,
+        help="Path to the tokens.txt",
+    )
+
+    parser.add_argument(
+        "--lexicon",
+        type=str,
+        required=True,
+        help="Path to the lexicon.txt",
+    )
+
+    parser.add_argument(
+        "--vocoder",
+        type=str,
+        required=True,
+        help="Path to the vocoder",
+    )
+
+    parser.add_argument(
+        "--input-text",
+        type=str,
+        required=True,
+        help="The text to generate speech for",
+    )
+
+    parser.add_argument(
+        "--output-wav",
+        type=str,
+        required=True,
+        help="The filename of the wave to save the generated speech",
+    )
+
+    return parser
+
+
+class OnnxHifiGANModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.session_opts = session_opts
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+
+        for i in self.model.get_inputs():
+            print(i)
+
+        print("-----")
+
+        for i in self.model.get_outputs():
+            print(i)
+
+    def __call__(self, x: torch.tensor):
+        assert x.ndim == 3, x.shape
+        assert x.shape[0] == 1, x.shape
+
+        audio = self.model.run(
+            [self.model.get_outputs()[0].name],
+            {
+                self.model.get_inputs()[0].name: x.numpy(),
+            },
+        )[0]
+        # audio: (batch_size, num_samples)
+
+        return torch.from_numpy(audio)
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 2
+
+        self.session_opts = session_opts
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+
+        logging.info(f"{self.model.get_modelmeta().custom_metadata_map}")
+        metadata = self.model.get_modelmeta().custom_metadata_map
+        self.sample_rate = int(metadata["sample_rate"])
+
+        for i in self.model.get_inputs():
+            print(i)
+
+        print("-----")
+
+        for i in self.model.get_outputs():
+            print(i)
+
+    def __call__(self, x: torch.tensor):
+        assert x.ndim == 2, x.shape
+        assert x.shape[0] == 1, x.shape
+
+        x_lengths = torch.tensor([x.shape[1]], dtype=torch.int64)
+        print("x_lengths", x_lengths)
+        print("x", x.shape)
+
+        noise_scale = torch.tensor([1.0], dtype=torch.float32)
+        length_scale = torch.tensor([1.0], dtype=torch.float32)
+
+        mel = self.model.run(
+            [self.model.get_outputs()[0].name],
+            {
+                self.model.get_inputs()[0].name: x.numpy(),
+                self.model.get_inputs()[1].name: x_lengths.numpy(),
+                self.model.get_inputs()[2].name: noise_scale.numpy(),
+                self.model.get_inputs()[3].name: length_scale.numpy(),
+            },
+        )[0]
+        # mel: (batch_size, feat_dim, num_frames)
+
+        return torch.from_numpy(mel)
+
+
+def read_tokens(filename: str) -> Dict[str, int]:
+    token2id = dict()
+    with open(filename, encoding="utf-8") as f:
+        for line in f.readlines():
+            info = line.rstrip().split()
+            if len(info) == 1:
+                # case of space
+                token = " "
+                idx = int(info[0])
+            else:
+                token, idx = info[0], int(info[1])
+            assert token not in token2id, token
+            token2id[token] = idx
+    return token2id
+
+
+def read_lexicon(filename: str) -> Dict[str, List[str]]:
+    word2token = dict()
+    with open(filename, encoding="utf-8") as f:
+        for line in f.readlines():
+            info = line.rstrip().split()
+            w = info[0]
+            tokens = info[1:]
+            word2token[w] = tokens
+    return word2token
+
+
+def convert_word_to_tokens(word2tokens: Dict[str, List[str]], word: str) -> List[str]:
+    if word in word2tokens:
+        return word2tokens[word]
+
+    if len(word) == 1:
+        return []
+
+    ans = []
+    for w in word:
+        t = convert_word_to_tokens(word2tokens, w)
+        ans.extend(t)
+    return ans
+
+
+def normalize_text(text):
+    whiter_space_re = re.compile(r"\s+")
+
+    punctuations_re = [
+        (re.compile(x[0], re.IGNORECASE), x[1])
+        for x in [
+            ("，", ","),
+            ("。", "."),
+            ("！", "!"),
+            ("？", "?"),
+            ("“", '"'),
+            ("”", '"'),
+            ("‘", "'"),
+            ("’", "'"),
+            ("：", ":"),
+            ("、", ","),
+        ]
+    ]
+
+    for regex, replacement in punctuations_re:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+@torch.no_grad()
+def main():
+    params = get_parser().parse_args()
+    logging.info(vars(params))
+    token2id = read_tokens(params.tokens)
+    word2tokens = read_lexicon(params.lexicon)
+
+    text = normalize_text(params.input_text)
+    seg = jieba.cut(text)
+    tokens = []
+    for s in seg:
+        if s in token2id:
+            tokens.append(s)
+            continue
+
+        t = convert_word_to_tokens(word2tokens, s)
+        if t:
+            tokens.extend(t)
+
+    model = OnnxModel(params.acoustic_model)
+    vocoder = OnnxHifiGANModel(params.vocoder)
+
+    x = []
+    for t in tokens:
+        if t in token2id:
+            x.append(token2id[t])
+
+    x = intersperse(x, item=token2id["_"])
+
+    x = torch.tensor(x, dtype=torch.int64).unsqueeze(0)
+
+    start_t = dt.datetime.now()
+    mel = model(x)
+    end_t = dt.datetime.now()
+
+    start_t2 = dt.datetime.now()
+    audio = vocoder(mel)
+    end_t2 = dt.datetime.now()
+
+    print("audio", audio.shape)  # (1, 1, num_samples)
+    audio = audio.squeeze()
+
+    sample_rate = model.sample_rate
+
+    t = (end_t - start_t).total_seconds()
+    t2 = (end_t2 - start_t2).total_seconds()
+    rtf_am = t * sample_rate / audio.shape[-1]
+    rtf_vocoder = t2 * sample_rate / audio.shape[-1]
+    print("RTF for acoustic model ", rtf_am)
+    print("RTF for vocoder", rtf_vocoder)
+
+    # skip denoiser
+    sf.write(params.output_wav, audio, sample_rate, "PCM_16")
+    logging.info(f"Saved to {params.output_wav}")
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()
+
+"""
+
+|HifiGAN   |RTF  |#Parameters (M)|
+|----------|-----|---------------|
+|v1        |0.818|  13.926       |
+|v2        |0.101|   0.925       |
+|v3        |0.118|   1.462       |
+
+|Num steps|Acoustic Model RTF|
+|---------|------------------|
+| 2       |    0.039         |
+| 3       |    0.047         |
+| 4       |    0.071         |
+| 5       |    0.076         |
+| 6       |    0.103         |
+
+"""

egs/baker_zh/TTS/matcha/tokenizer.py

@@ -0,0 +1,119 @@
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import logging
+from typing import Dict, List
+
+import tacotron_cleaner.cleaners
+
+try:
+    from piper_phonemize import phonemize_espeak
+except Exception as ex:
+    raise RuntimeError(
+        f"{ex}\nPlease run\n"
+        "pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html"
+    )
+
+from utils import intersperse
+
+
+# This tokenizer supports both English and Chinese.
+# We assume you have used
+# ../local/convert_text_to_tokens.py
+# to process your text
+class Tokenizer(object):
+    def __init__(self, tokens: str):
+        """
+        Args:
+            tokens: the file that maps tokens to ids
+        """
+        # Parse token file
+        self.token2id: Dict[str, int] = {}
+        with open(tokens, "r", encoding="utf-8") as f:
+            for line in f.readlines():
+                info = line.rstrip().split()
+                if len(info) == 1:
+                    # case of space
+                    token = " "
+                    id = int(info[0])
+                else:
+                    token, id = info[0], int(info[1])
+                assert token not in self.token2id, token
+                self.token2id[token] = id
+
+        # Refer to https://github.com/rhasspy/piper/blob/master/TRAINING.md
+        self.pad_id = self.token2id["_"]  # padding
+        self.space_id = self.token2id[" "]  # word separator (whitespace)
+
+        self.vocab_size = len(self.token2id)
+
+    def texts_to_token_ids(
+        self,
+        sentence_list: List[List[str]],
+        intersperse_blank: bool = True,
+        lang: str = "en-us",
+    ) -> List[List[int]]:
+        """
+        Args:
+          sentence_list:
+            A list of sentences.
+          intersperse_blank:
+            Whether to intersperse blanks in the token sequence.
+          lang:
+            Language argument passed to phonemize_espeak().
+
+        Returns:
+          Return a list of token id list [utterance][token_id]
+        """
+        token_ids_list = []
+
+        for sentence in sentence_list:
+            tokens_list = []
+            for word in sentence:
+                if word in self.token2id:
+                    tokens_list.append(word)
+                    continue
+
+                tmp_tokens_list = phonemize_espeak(word, lang)
+                for t in tmp_tokens_list:
+                    tokens_list.extend(t)
+
+            token_ids = []
+            for t in tokens_list:
+                if t not in self.token2id:
+                    logging.warning(f"Skip OOV {t} {sentence}")
+                    continue
+
+                if t == " " and len(token_ids) > 0 and token_ids[-1] == self.space_id:
+                    continue
+
+                token_ids.append(self.token2id[t])
+
+            if intersperse_blank:
+                token_ids = intersperse(token_ids, self.pad_id)
+
+            token_ids_list.append(token_ids)
+
+        return token_ids_list
+
+
+def test_tokenizer():
+    import jieba
+    from pypinyin import Style, lazy_pinyin
+
+    tokenizer = Tokenizer("data/tokens.txt")
+    text1 = "今天is Monday, tomorrow is 星期二"
+    text2 = "你好吗? 我很好, how about you?"
+
+    text1 = list(jieba.cut(text1))
+    text2 = list(jieba.cut(text2))
+    tokens1 = lazy_pinyin(text1, style=Style.TONE3, tone_sandhi=True)
+    tokens2 = lazy_pinyin(text2, style=Style.TONE3, tone_sandhi=True)
+    print(tokens1)
+    print(tokens2)
+
+    ids = tokenizer.texts_to_token_ids([tokens1, tokens2])
+    print(ids)
+
+
+if __name__ == "__main__":
+    test_tokenizer()

egs/baker_zh/TTS/matcha/train.py

@@ -0,0 +1,717 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+
+import argparse
+import json
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, Optional, Union
+
+import k2
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from lhotse.utils import fix_random_seed
+from model import fix_len_compatibility
+from models.matcha_tts import MatchaTTS
+from tokenizer import Tokenizer
+from torch.cuda.amp import GradScaler, autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.utils.tensorboard import SummaryWriter
+from tts_datamodule import BakerZhTtsDataModule
+from utils import MetricsTracker
+
+from icefall.checkpoint import load_checkpoint, save_checkpoint
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.utils import AttributeDict, setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12335,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=1000,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=1,
+        help="""Resume training from this epoch. It should be positive.
+        If larger than 1, it will load checkpoint from
+        exp-dir/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=Path,
+        default="matcha/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
+    parser.add_argument(
+        "--cmvn",
+        type=str,
+        default="data/fbank/cmvn.json",
+        help="""Path to vocabulary.""",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    parser.add_argument(
+        "--save-every-n",
+        type=int,
+        default=10,
+        help="""Save checkpoint after processing this number of epochs"
+        periodically. We save checkpoint to exp-dir/ whenever
+        params.cur_epoch % save_every_n == 0. The checkpoint filename
+        has the form: f'exp-dir/epoch-{params.cur_epoch}.pt'.
+        Since it will take around 1000 epochs, we suggest using a large
+        save_every_n to save disk space.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to use half precision training.",
+    )
+
+    return parser
+
+
+def get_data_statistics():
+    return AttributeDict(
+        {
+            "mel_mean": 0,
+            "mel_std": 1,
+        }
+    )
+
+
+def _get_data_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "name": "baker-zh",
+            "train_filelist_path": "./filelists/ljs_audio_text_train_filelist.txt",
+            "valid_filelist_path": "./filelists/ljs_audio_text_val_filelist.txt",
+            #  "batch_size": 64,
+            #  "num_workers": 1,
+            #  "pin_memory": False,
+            "cleaners": ["english_cleaners2"],
+            "add_blank": True,
+            "n_spks": 1,
+            "n_fft": 1024,
+            "n_feats": 80,
+            "sampling_rate": 22050,
+            "hop_length": 256,
+            "win_length": 1024,
+            "f_min": 0,
+            "f_max": 8000,
+            "seed": 1234,
+            "load_durations": False,
+            "data_statistics": get_data_statistics(),
+        }
+    )
+    return params
+
+
+def _get_model_params() -> AttributeDict:
+    n_feats = 80
+    filter_channels_dp = 256
+    encoder_params_p_dropout = 0.1
+    params = AttributeDict(
+        {
+            "n_spks": 1,  # for baker-zh.
+            "spk_emb_dim": 64,
+            "n_feats": n_feats,
+            "out_size": None,  # or use 172
+            "prior_loss": True,
+            "use_precomputed_durations": False,
+            "data_statistics": get_data_statistics(),
+            "encoder": AttributeDict(
+                {
+                    "encoder_type": "RoPE Encoder",  # not used
+                    "encoder_params": AttributeDict(
+                        {
+                            "n_feats": n_feats,
+                            "n_channels": 192,
+                            "filter_channels": 768,
+                            "filter_channels_dp": filter_channels_dp,
+                            "n_heads": 2,
+                            "n_layers": 6,
+                            "kernel_size": 3,
+                            "p_dropout": encoder_params_p_dropout,
+                            "spk_emb_dim": 64,
+                            "n_spks": 1,
+                            "prenet": True,
+                        }
+                    ),
+                    "duration_predictor_params": AttributeDict(
+                        {
+                            "filter_channels_dp": filter_channels_dp,
+                            "kernel_size": 3,
+                            "p_dropout": encoder_params_p_dropout,
+                        }
+                    ),
+                }
+            ),
+            "decoder": AttributeDict(
+                {
+                    "channels": [256, 256],
+                    "dropout": 0.05,
+                    "attention_head_dim": 64,
+                    "n_blocks": 1,
+                    "num_mid_blocks": 2,
+                    "num_heads": 2,
+                    "act_fn": "snakebeta",
+                }
+            ),
+            "cfm": AttributeDict(
+                {
+                    "name": "CFM",
+                    "solver": "euler",
+                    "sigma_min": 1e-4,
+                }
+            ),
+            "optimizer": AttributeDict(
+                {
+                    "lr": 1e-4,
+                    "weight_decay": 0.0,
+                }
+            ),
+        }
+    )
+
+    return params
+
+
+def get_params():
+    params = AttributeDict(
+        {
+            "model_args": _get_model_params(),
+            "data_args": _get_data_params(),
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": -1,  # 0
+            "log_interval": 10,
+            "valid_interval": 1500,
+            "env_info": get_env_info(),
+        }
+    )
+    return params
+
+
+def get_model(params):
+    m = MatchaTTS(**params.model_args)
+    return m
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict, model: nn.Module
+) -> Optional[Dict[str, Any]]:
+    """Load checkpoint from file.
+
+    If params.start_epoch is larger than 1, it will load the checkpoint from
+    `params.start_epoch - 1`.
+
+    Apart from loading state dict for `model` and `optimizer` it also updates
+    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+    Returns:
+      Return a dict containing previously saved training info.
+    """
+    if params.start_epoch > 1:
+        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    else:
+        return None
+
+    assert filename.is_file(), f"{filename} does not exist!"
+
+    saved_params = load_checkpoint(filename, model=model)
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    return saved_params
+
+
+def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device, params):
+    """Parse batch data"""
+    mel_mean = params.data_args.data_statistics.mel_mean
+    mel_std_inv = 1 / params.data_args.data_statistics.mel_std
+    for i in range(batch["features"].shape[0]):
+        n = batch["features_lens"][i]
+        batch["features"][i : i + 1, :n, :] = (
+            batch["features"][i : i + 1, :n, :] - mel_mean
+        ) * mel_std_inv
+        batch["features"][i : i + 1, n:, :] = 0
+
+    audio = batch["audio"].to(device)
+    features = batch["features"].to(device)
+    audio_lens = batch["audio_lens"].to(device)
+    features_lens = batch["features_lens"].to(device)
+    tokens = batch["tokens"]
+
+    tokens = tokenizer.texts_to_token_ids(tokens, intersperse_blank=True)
+    tokens = k2.RaggedTensor(tokens)
+    row_splits = tokens.shape.row_splits(1)
+    tokens_lens = row_splits[1:] - row_splits[:-1]
+    tokens = tokens.to(device)
+    tokens_lens = tokens_lens.to(device)
+    # a tensor of shape (B, T)
+    tokens = tokens.pad(mode="constant", padding_value=tokenizer.pad_id)
+
+    max_feature_length = fix_len_compatibility(features.shape[1])
+    if max_feature_length > features.shape[1]:
+        pad = max_feature_length - features.shape[1]
+        features = torch.nn.functional.pad(features, (0, 0, 0, pad))
+
+        #  features_lens[features_lens.argmax()] += pad
+
+    return audio, audio_lens, features, features_lens.long(), tokens, tokens_lens.long()
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: Tokenizer,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+    rank: int = 0,
+) -> MetricsTracker:
+    """Run the validation process."""
+    model.eval()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses
+
+    # used to summary the stats over iterations
+    tot_loss = MetricsTracker()
+
+    with torch.no_grad():
+        for batch_idx, batch in enumerate(valid_dl):
+            (
+                audio,
+                audio_lens,
+                features,
+                features_lens,
+                tokens,
+                tokens_lens,
+            ) = prepare_input(batch, tokenizer, device, params)
+
+            losses = get_losses(
+                {
+                    "x": tokens,
+                    "x_lengths": tokens_lens,
+                    "y": features.permute(0, 2, 1),
+                    "y_lengths": features_lens,
+                    "spks": None,  # should change it for multi-speakers
+                    "durations": None,
+                }
+            )
+
+            batch_size = len(batch["tokens"])
+
+            loss_info = MetricsTracker()
+            loss_info["samples"] = batch_size
+
+            s = 0
+
+            for key, value in losses.items():
+                v = value.detach().item()
+                loss_info[key] = v * batch_size
+                s += v * batch_size
+
+            loss_info["tot_loss"] = s
+
+            # summary stats
+            tot_loss = tot_loss + loss_info
+
+    if world_size > 1:
+        tot_loss.reduce(device)
+
+    loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: Tokenizer,
+    optimizer: Optimizer,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    scaler: GradScaler,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+    rank: int = 0,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      optimizer:
+        The optimizer.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      scaler:
+        The scaler used for mix precision training.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+    """
+    model.train()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses
+
+    # used to track the stats over iterations in one epoch
+    tot_loss = MetricsTracker()
+
+    saved_bad_model = False
+
+    def save_bad_model(suffix: str = ""):
+        save_checkpoint(
+            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
+            model=model,
+            params=params,
+            optimizer=optimizer,
+            scaler=scaler,
+            rank=0,
+        )
+
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+        # audio: (N, T), float32
+        # features: (N, T, C), float32
+        # audio_lens, (N,), int32
+        # features_lens, (N,), int32
+        # tokens: List[List[str]], len(tokens) == N
+
+        batch_size = len(batch["tokens"])
+
+        (
+            audio,
+            audio_lens,
+            features,
+            features_lens,
+            tokens,
+            tokens_lens,
+        ) = prepare_input(batch, tokenizer, device, params)
+        try:
+            with autocast(enabled=params.use_fp16):
+                losses = get_losses(
+                    {
+                        "x": tokens,
+                        "x_lengths": tokens_lens,
+                        "y": features.permute(0, 2, 1),
+                        "y_lengths": features_lens,
+                        "spks": None,  # should change it for multi-speakers
+                        "durations": None,
+                    }
+                )
+
+                loss = sum(losses.values())
+
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                optimizer.zero_grad()
+
+                loss_info = MetricsTracker()
+                loss_info["samples"] = batch_size
+
+                s = 0
+
+                for key, value in losses.items():
+                    v = value.detach().item()
+                    loss_info[key] = v * batch_size
+                    s += v * batch_size
+
+                loss_info["tot_loss"] = s
+
+                tot_loss = tot_loss + loss_info
+        except:  # noqa
+            save_bad_model()
+            raise
+
+        if params.batch_idx_train % 100 == 0 and params.use_fp16:
+            # If the grad scale was less than 1, try increasing it.
+            # The _growth_interval of the grad scaler is configurable,
+            # but we can't configure it to have different
+            # behavior depending on the current grad scale.
+            cur_grad_scale = scaler._scale.item()
+
+            if cur_grad_scale < 8.0 or (
+                cur_grad_scale < 32.0 and params.batch_idx_train % 400 == 0
+            ):
+                scaler.update(cur_grad_scale * 2.0)
+            if cur_grad_scale < 0.01:
+                if not saved_bad_model:
+                    save_bad_model(suffix="-first-warning")
+                    saved_bad_model = True
+                logging.warning(f"Grad scale is small: {cur_grad_scale}")
+            if cur_grad_scale < 1.0e-05:
+                save_bad_model()
+                raise RuntimeError(
+                    f"grad_scale is too small, exiting: {cur_grad_scale}"
+                )
+
+        if params.batch_idx_train % params.log_interval == 0:
+            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
+
+            logging.info(
+                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
+                f"global_batch_idx: {params.batch_idx_train}, "
+                f"batch size: {batch_size}, "
+                f"loss[{loss_info}], tot_loss[{tot_loss}], "
+                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
+            )
+
+            if tb_writer is not None:
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                if params.use_fp16:
+                    tb_writer.add_scalar(
+                        "train/grad_scale", cur_grad_scale, params.batch_idx_train
+                    )
+
+        if params.batch_idx_train % params.valid_interval == 1:
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                tokenizer=tokenizer,
+                valid_dl=valid_dl,
+                world_size=world_size,
+                rank=rank,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            logging.info(
+                "Maximum memory allocated so far is "
+                f"{torch.cuda.max_memory_allocated()//1000000}MB"
+            )
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+
+    loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def run(rank, world_size, args):
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(params.seed)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    logging.info("Training started")
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+    logging.info(f"Device: {device}")
+
+    tokenizer = Tokenizer(params.tokens)
+    params.pad_id = tokenizer.pad_id
+    params.vocab_size = tokenizer.vocab_size
+    params.model_args.n_vocab = params.vocab_size
+
+    with open(params.cmvn) as f:
+        stats = json.load(f)
+        params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.data_args.data_statistics.mel_std = stats["fbank_std"]
+
+        params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.model_args.data_statistics.mel_std = stats["fbank_std"]
+
+    logging.info(params)
+    print(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of parameters: {num_param}")
+
+    assert params.start_epoch > 0, params.start_epoch
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model.to(device)
+
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
+
+    optimizer = torch.optim.Adam(model.parameters(), **params.model_args.optimizer)
+
+    logging.info("About to create datamodule")
+
+    baker_zh = BakerZhTtsDataModule(args)
+
+    train_cuts = baker_zh.train_cuts()
+    train_dl = baker_zh.train_dataloaders(train_cuts)
+
+    valid_cuts = baker_zh.valid_cuts()
+    valid_dl = baker_zh.valid_dataloaders(valid_cuts)
+
+    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    if checkpoints and "grad_scaler" in checkpoints:
+        logging.info("Loading grad scaler state dict")
+        scaler.load_state_dict(checkpoints["grad_scaler"])
+
+    for epoch in range(params.start_epoch, params.num_epochs + 1):
+        logging.info(f"Start epoch {epoch}")
+        fix_random_seed(params.seed + epoch - 1)
+        if "sampler" in train_dl:
+            train_dl.sampler.set_epoch(epoch - 1)
+
+        params.cur_epoch = epoch
+
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            tokenizer=tokenizer,
+            optimizer=optimizer,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            scaler=scaler,
+            tb_writer=tb_writer,
+            world_size=world_size,
+            rank=rank,
+        )
+
+        if epoch % params.save_every_n == 0 or epoch == params.num_epochs:
+            filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+            save_checkpoint(
+                filename=filename,
+                params=params,
+                model=model,
+                optimizer=optimizer,
+                scaler=scaler,
+                rank=rank,
+            )
+            if rank == 0:
+                if params.best_train_epoch == params.cur_epoch:
+                    best_train_filename = params.exp_dir / "best-train-loss.pt"
+                    copyfile(src=filename, dst=best_train_filename)
+
+                if params.best_valid_epoch == params.cur_epoch:
+                    best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+                    copyfile(src=filename, dst=best_valid_filename)
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def main():
+    parser = get_parser()
+    BakerZhTtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    main()

egs/baker_zh/TTS/matcha/tts_datamodule.py

@@ -0,0 +1,340 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022-2023  Xiaomi Corporation     (Authors: Mingshuang Luo,
+#                                                            Zengwei Yao)
+#
+# 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.
+
+
+import argparse
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from fbank import MatchaFbank, MatchaFbankConfig
+from lhotse import CutSet, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpeechSynthesisDataset,
+)
+from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
+    AudioSamples,
+    OnTheFlyFeatures,
+)
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class BakerZhTtsDataModule:
+    """
+    DataModule for tts experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - on-the-fly feature extraction
+
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="TTS data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/fbank"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=30,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help="When enabled, use on-the-fly cut mixing and feature "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=True,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, each batch will have the "
+            "field: batch['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        logging.info("About to create train dataset")
+        train = SpeechSynthesisDataset(
+            return_text=False,
+            return_tokens=True,
+            feature_input_strategy=eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = MatchaFbankConfig(
+                n_fft=1024,
+                n_mels=80,
+                sampling_rate=sampling_rate,
+                hop_length=256,
+                win_length=1024,
+                f_min=0,
+                f_max=8000,
+            )
+            train = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                buffer_size=self.args.num_buckets * 2000,
+                shuffle_buffer_size=self.args.num_buckets * 5000,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=True,
+            pin_memory=True,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = MatchaFbankConfig(
+                n_fft=1024,
+                n_mels=80,
+                sampling_rate=sampling_rate,
+                hop_length=256,
+                win_length=1024,
+                f_min=0,
+                f_max=8000,
+            )
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            num_buckets=self.args.num_buckets,
+            shuffle=False,
+        )
+        logging.info("About to create valid dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=True,
+            pin_memory=True,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.info("About to create test dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = MatchaFbankConfig(
+                n_fft=1024,
+                n_mels=80,
+                sampling_rate=sampling_rate,
+                hop_length=256,
+                win_length=1024,
+                f_min=0,
+                f_max=8000,
+            )
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        test_sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            num_buckets=self.args.num_buckets,
+            shuffle=False,
+        )
+        logging.info("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=test_sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "baker_zh_cuts_train.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get validation cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "baker_zh_cuts_valid.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "baker_zh_cuts_test.jsonl.gz"
+        )

egs/baker_zh/TTS/matcha/utils.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/matcha/utils.py

egs/baker_zh/TTS/prepare.sh

@@ -0,0 +1,151 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+set -eou pipefail
+
+stage=-1
+stop_stage=100
+
+dl_dir=$PWD/download
+mkdir -p $dl_dir
+
+. shared/parse_options.sh || exit 1
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
+  log "Stage -1: build monotonic_align lib (used by ./matcha)"
+  for recipe in matcha; do
+    if [ ! -d $recipe/monotonic_align/build ]; then
+      cd $recipe/monotonic_align
+      python3 setup.py build_ext --inplace
+      cd ../../
+    else
+      log "monotonic_align lib for $recipe already built"
+    fi
+  done
+fi
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download data"
+
+  # The directory $dl_dir/BANSYP contains the following 3 directories
+
+  # ls -lh $dl_dir/BZNSYP/
+  # total 0
+  # drwxr-xr-x 10002 kuangfangjun root 0 Jan  4  2019 PhoneLabeling
+  # drwxr-xr-x     3 kuangfangjun root 0 Jan 31  2019 ProsodyLabeling
+  # drwxr-xr-x 10003 kuangfangjun root 0 Aug 26 17:45 Wave
+
+  # If you have trouble accessing huggingface.co, please use
+  #
+  # cd $dl_dir
+  # wget https://huggingface.co/openspeech/BZNSYP/resolve/main/BZNSYP.tar.bz2
+  # tar xf BZNSYP.tar.bz2
+  # cd ..
+
+  # If you have pre-downloaded it to /path/to/BZNSYP, you can create a symlink
+  #
+  #   ln -sfv /path/to/BZNSYP $dl_dir/BZNSYP
+  #
+  if [ ! -d $dl_dir/BZNSYP/Wave ]; then
+    lhotse download baker-zh $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare baker-zh manifest"
+  # We assume that you have downloaded the baker corpus
+  # to $dl_dir/BZNSYP
+  mkdir -p data/manifests
+  if [ ! -e data/manifests/.baker-zh.done ]; then
+    lhotse prepare baker-zh $dl_dir/BZNSYP data/manifests
+    touch data/manifests/.baker-zh.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Generate tokens.txt"
+  if [ ! -e data/tokens.txt ]; then
+    python3 ./local/generate_tokens.py --tokens data/tokens.txt
+  fi
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Generate raw cutset"
+  if [ ! -e data/manifests/baker_zh_cuts_raw.jsonl.gz ]; then
+    lhotse cut simple \
+      -r ./data/manifests/baker_zh_recordings_all.jsonl.gz \
+      -s ./data/manifests/baker_zh_supervisions_all.jsonl.gz \
+      ./data/manifests/baker_zh_cuts_raw.jsonl.gz
+  fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Convert text to tokens"
+  if [ ! -e data/manifests/baker_zh_cuts.jsonl.gz ]; then
+    python3 ./local/convert_text_to_tokens.py \
+      --in-file ./data/manifests/baker_zh_cuts_raw.jsonl.gz \
+      --out-file ./data/manifests/baker_zh_cuts.jsonl.gz
+  fi
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Generate fbank (used by ./matcha)"
+  mkdir -p data/fbank
+  if [ ! -e data/fbank/.baker-zh.done ]; then
+    ./local/compute_fbank_baker_zh.py
+    touch data/fbank/.baker-zh.done
+  fi
+
+  if [ ! -e data/fbank/.baker-zh-validated.done ]; then
+    log "Validating data/fbank for baker-zh (used by ./matcha)"
+    python3 ./local/validate_manifest.py \
+      data/fbank/baker_zh_cuts.jsonl.gz
+    touch data/fbank/.baker-zh-validated.done
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Split the baker-zh cuts into train, valid and test sets (used by ./matcha)"
+  if [ ! -e data/fbank/.baker_zh_split.done ]; then
+    lhotse subset --last 600 \
+      data/fbank/baker_zh_cuts.jsonl.gz \
+      data/fbank/baker_zh_cuts_validtest.jsonl.gz
+    lhotse subset --first 100 \
+      data/fbank/baker_zh_cuts_validtest.jsonl.gz \
+      data/fbank/baker_zh_cuts_valid.jsonl.gz
+    lhotse subset --last 500 \
+      data/fbank/baker_zh_cuts_validtest.jsonl.gz \
+      data/fbank/baker_zh_cuts_test.jsonl.gz
+
+    rm data/fbank/baker_zh_cuts_validtest.jsonl.gz
+
+    n=$(( $(gunzip -c data/fbank/baker_zh_cuts.jsonl.gz | wc -l) - 600 ))
+
+    lhotse subset --first $n  \
+      data/fbank/baker_zh_cuts.jsonl.gz \
+      data/fbank/baker_zh_cuts_train.jsonl.gz
+
+    touch data/fbank/.baker_zh_split.done
+  fi
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 6: Compute fbank mean and std (used by ./matcha)"
+  if [ ! -f ./data/fbank/cmvn.json ]; then
+    ./local/compute_fbank_statistics.py ./data/fbank/baker_zh_cuts_train.jsonl.gz ./data/fbank/cmvn.json
+  fi
+fi

egs/baker_zh/TTS/shared

@@ -0,0 +1 @@
+../../../icefall/shared

egs/commonvoice/ASR/prepare.sh

@@ -339,7 +339,7 @@ if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
         # 2. chmod +x ./jq
         # 3. cp jq /usr/bin
         gunzip -c ${file} \
-          | jq '.text' | sed 's/"//g' > $lang_dir/transcript_words.txt
+          | jq '.supervisions[].text' | sed 's/"//g' > $lang_dir/transcript_words.txt
 
         # Ensure space only appears once
         sed -i 's/\t/ /g' $lang_dir/transcript_words.txt

egs/gigaspeech/ASR/prepare.sh

@@ -161,14 +161,14 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   log "Stage 5: Split XL subset into pieces (may take 30 minutes)"
   split_dir=data/fbank/XL_split
   if [ ! -f $split_dir/.split_completed ]; then
-    lhotse split-lazy ./data/fbank/cuts_XL_raw.jsonl.gz $split_dir $num_per_split
+    lhotse split-lazy ./data/fbank/gigaspeech_cuts_XL_raw.jsonl.gz $split_dir $num_per_split
     touch $split_dir/.split_completed
   fi
 fi
 
 if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
   log "Stage 6: Compute features for XL"
-  num_splits=$(find data/fbank/XL_split -name "cuts_XL_raw.*.jsonl.gz" | wc -l)
+  num_splits=$(find data/fbank/XL_split -name "gigaspeech_cuts_XL_raw.*.jsonl.gz" | wc -l)
   python3 ./local/compute_fbank_gigaspeech_splits.py \
     --num-workers 20 \
     --batch-duration 600 \
@@ -177,9 +177,9 @@ fi
 
 if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
   log "Stage 7: Combine features for XL (may take 3 hours)"
-  if [ ! -f data/fbank/cuts_XL.jsonl.gz ]; then
+  if [ ! -f data/fbank/gigaspeech_cuts_XL.jsonl.gz ]; then
-    pieces=$(find data/fbank/XL_split -name "cuts_XL.*.jsonl.gz")
+    pieces=$(find data/fbank/XL_split -name "gigaspeech_cuts_XL.*.jsonl.gz")
-    lhotse combine $pieces data/fbank/cuts_XL.jsonl.gz
+    lhotse combine $pieces data/fbank/gigaspeech_cuts_XL.jsonl.gz
   fi
 fi
 

egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -260,7 +260,7 @@ def decode_one_batch(
       Return the decoding result. See above description for the format of
       the returned dict.
     """
-    device = model.device
+    device = next(model.parameters()).device
     feature = batch["inputs"]
     assert feature.ndim == 3
 

egs/ksponspeech/ASR/local/compute_fbank_musan.py

@@ -1,158 +0,0 @@
-#!/usr/bin/env python3
-# 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 file computes fbank features of the musan dataset.
-It looks for manifests in the directory `src_dir` (default is data/manifests).
-
-The generated fbank features are saved in data/fbank.
-"""
-import argparse
-import logging
-import os
-from pathlib import Path
-
-import torch
-from lhotse import (
-    CutSet,
-    Fbank,
-    FbankConfig,
-    LilcomChunkyWriter,
-    MonoCut,
-    WhisperFbank,
-    WhisperFbankConfig,
-    combine,
-)
-from lhotse.recipes.utils import read_manifests_if_cached
-
-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.
-# Do this outside of main() in case it needs to take effect
-# even when we are not invoking the main (e.g. when spawning subprocesses).
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
-
-def is_cut_long(c: MonoCut) -> bool:
-    return c.duration > 5
-
-
-def compute_fbank_musan(
-    src_dir: str = "data/manifests",
-    num_mel_bins: int = 80,
-    whisper_fbank: bool = False,
-    output_dir: str = "data/fbank",
-):
-    src_dir = Path(src_dir)
-    output_dir = Path(output_dir)
-    num_jobs = min(15, os.cpu_count())
-
-    dataset_parts = (
-        "music",
-        "speech",
-        "noise",
-    )
-    prefix = "musan"
-    suffix = "jsonl.gz"
-    manifests = read_manifests_if_cached(
-        dataset_parts=dataset_parts,
-        output_dir=src_dir,
-        prefix=prefix,
-        suffix=suffix,
-    )
-    assert manifests is not None
-
-    assert len(manifests) == len(dataset_parts), (
-        len(manifests),
-        len(dataset_parts),
-        list(manifests.keys()),
-        dataset_parts,
-    )
-
-    musan_cuts_path = output_dir / "musan_cuts.jsonl.gz"
-
-    if musan_cuts_path.is_file():
-        logging.info(f"{musan_cuts_path} already exists - skipping")
-        return
-
-    logging.info("Extracting features for Musan")
-
-    if whisper_fbank:
-        extractor = WhisperFbank(
-            WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
-        )
-    else:
-        extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
-
-    with get_executor() as ex:  # Initialize the executor only once.
-        # create chunks of Musan with duration 5 - 10 seconds
-        musan_cuts = (
-            CutSet.from_manifests(
-                recordings=combine(part["recordings"] for part in manifests.values())
-            )
-            .cut_into_windows(10.0)
-            .filter(is_cut_long)
-            .compute_and_store_features(
-                extractor=extractor,
-                storage_path=f"{output_dir}/musan_feats",
-                num_jobs=num_jobs if ex is None else 80,
-                executor=ex,
-                storage_type=LilcomChunkyWriter,
-            )
-        )
-        musan_cuts.to_file(musan_cuts_path)
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--src-dir",
-        type=str,
-        default="data/manifests",
-        help="Source manifests directory.",
-    )
-    parser.add_argument(
-        "--num-mel-bins",
-        type=int,
-        default=80,
-        help="""The number of mel bins for Fbank""",
-    )
-    parser.add_argument(
-        "--whisper-fbank",
-        type=str2bool,
-        default=False,
-        help="Use WhisperFbank instead of Fbank. Default: False.",
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        default="data/fbank",
-        help="Output directory. Default: data/fbank.",
-    )
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    args = get_args()
-    compute_fbank_musan(
-        src_dir=args.src_dir,
-        num_mel_bins=args.num_mel_bins,
-        whisper_fbank=args.whisper_fbank,
-        output_dir=args.output_dir,
-    )

egs/ksponspeech/ASR/local/compute_fbank_musan.py

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

egs/ksponspeech/ASR/local/filter_cuts.py

@@ -1,157 +0,0 @@
-#!/usr/bin/env python3
-
-# 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 removes short and long utterances from a cutset.
-
-Caution:
-  You may need to tune the thresholds for your own dataset.
-
-Usage example:
-
-  python3 ./local/filter_cuts.py \
-    --bpe-model data/lang_bpe_5000/bpe.model \
-    --in-cuts data/fbank/speechtools_cuts_test.jsonl.gz \
-    --out-cuts data/fbank-filtered/speechtools_cuts_test.jsonl.gz
-"""
-
-import argparse
-import logging
-from pathlib import Path
-
-import sentencepiece as spm
-from lhotse import CutSet, load_manifest_lazy
-from lhotse.cut import Cut
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--bpe-model",
-        type=Path,
-        help="Path to the bpe.model",
-    )
-
-    parser.add_argument(
-        "--in-cuts",
-        type=Path,
-        help="Path to the input cutset",
-    )
-
-    parser.add_argument(
-        "--out-cuts",
-        type=Path,
-        help="Path to the output cutset",
-    )
-
-    return parser.parse_args()
-
-
-def filter_cuts(cut_set: CutSet, sp: spm.SentencePieceProcessor):
-    total = 0  # number of total utterances before removal
-    removed = 0  # number of removed utterances
-
-    def remove_short_and_long_utterances(c: Cut):
-        """Return False to exclude the input cut"""
-        nonlocal removed, total
-        # Keep only utterances with duration between 1 second and 20 seconds
-        #
-        # Caution: There is a reason to select 20.0 here. Please see
-        # ./display_manifest_statistics.py
-        #
-        # You should use ./display_manifest_statistics.py to get
-        # an utterance duration distribution for your dataset to select
-        # the threshold
-        total += 1
-        if c.duration < 1.0 or c.duration > 20.0:
-            logging.warning(
-                f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
-            )
-            removed += 1
-            return False
-
-        # In pruned RNN-T, we require that T >= S
-        # where T is the number of feature frames after subsampling
-        # and S is the number of tokens in the utterance
-
-        # In ./pruned_transducer_stateless2/conformer.py, the
-        # conv module uses the following expression
-        # for subsampling
-        if c.num_frames is None:
-            num_frames = c.duration * 100  # approximate
-        else:
-            num_frames = c.num_frames
-
-        T = ((num_frames - 1) // 2 - 1) // 2
-        # Note: for ./lstm_transducer_stateless/lstm.py, the formula is
-        #  T = ((num_frames - 3) // 2 - 1) // 2
-
-        # Note: for ./pruned_transducer_stateless7/zipformer.py, the formula is
-        # T = ((num_frames - 7) // 2 + 1) // 2
-
-        tokens = sp.encode(c.supervisions[0].text, out_type=str)
-
-        if T < len(tokens):
-            logging.warning(
-                f"Exclude cut with ID {c.id} from training. "
-                f"Number of frames (before subsampling): {c.num_frames}. "
-                f"Number of frames (after subsampling): {T}. "
-                f"Text: {c.supervisions[0].text}. "
-                f"Tokens: {tokens}. "
-                f"Number of tokens: {len(tokens)}"
-            )
-            removed += 1
-            return False
-
-        return True
-
-    # We use to_eager() here so that we can print out the value of total
-    # and removed below.
-    ans = cut_set.filter(remove_short_and_long_utterances).to_eager()
-    ratio = removed / total * 100
-    logging.info(
-        f"Removed {removed} cuts from {total} cuts. {ratio:.3f}% data is removed."
-    )
-    return ans
-
-
-def main():
-    args = get_args()
-    logging.info(vars(args))
-
-    if args.out_cuts.is_file():
-        logging.info(f"{args.out_cuts} already exists - skipping")
-        return
-
-    assert args.in_cuts.is_file(), f"{args.in_cuts} does not exist"
-    assert args.bpe_model.is_file(), f"{args.bpe_model} does not exist"
-
-    sp = spm.SentencePieceProcessor()
-    sp.load(str(args.bpe_model))
-
-    cut_set = load_manifest_lazy(args.in_cuts)
-    assert isinstance(cut_set, CutSet)
-
-    cut_set = filter_cuts(cut_set, sp)
-    logging.info(f"Saving to {args.out_cuts}")
-    args.out_cuts.parent.mkdir(parents=True, exist_ok=True)
-    cut_set.to_file(args.out_cuts)
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-
-    main()

egs/ksponspeech/ASR/local/filter_cuts.py

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

egs/ksponspeech/ASR/local/train_bpe_model.py

@@ -1,115 +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.
-
-
-# You can install sentencepiece via:
-#
-#  pip install sentencepiece
-#
-# Due to an issue reported in
-# https://github.com/google/sentencepiece/pull/642#issuecomment-857972030
-#
-# Please install a version >=0.1.96
-
-import argparse
-import shutil
-from pathlib import Path
-from typing import Dict
-
-import sentencepiece as spm
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--lang-dir",
-        type=str,
-        help="""Input and output directory.
-        The generated bpe.model is saved to this directory.
-        """,
-    )
-
-    parser.add_argument(
-        "--transcript",
-        type=str,
-        help="Training transcript.",
-    )
-
-    parser.add_argument(
-        "--vocab-size",
-        type=int,
-        help="Vocabulary size for BPE training",
-    )
-
-    return parser.parse_args()
-
-
-def generate_tokens(lang_dir: Path):
-    """
-    Generate the tokens.txt from a bpe model.
-    """
-    sp = spm.SentencePieceProcessor()
-    sp.load(str(lang_dir / "bpe.model"))
-    token2id: Dict[str, int] = {sp.id_to_piece(i): i for i in range(sp.vocab_size())}
-    with open(lang_dir / "tokens.txt", "w", encoding="utf-8") as f:
-        for sym, i in token2id.items():
-            f.write(f"{sym} {i}\n")
-
-
-def main():
-    args = get_args()
-    vocab_size = args.vocab_size
-    lang_dir = Path(args.lang_dir)
-
-    model_type = "unigram"
-
-    model_prefix = f"{lang_dir}/{model_type}_{vocab_size}"
-    train_text = args.transcript
-    character_coverage = 1.0
-    input_sentence_size = 100000000
-
-    user_defined_symbols = ["<blk>", "<sos/eos>"]
-    unk_id = len(user_defined_symbols)
-    # Note: unk_id is fixed to 2.
-    # If you change it, you should also change other
-    # places that are using it.
-
-    model_file = Path(model_prefix + ".model")
-    if not model_file.is_file():
-        spm.SentencePieceTrainer.train(
-            input=train_text,
-            vocab_size=vocab_size,
-            model_type=model_type,
-            model_prefix=model_prefix,
-            input_sentence_size=input_sentence_size,
-            character_coverage=character_coverage,
-            user_defined_symbols=user_defined_symbols,
-            unk_id=unk_id,
-            bos_id=-1,
-            eos_id=-1,
-        )
-    else:
-        print(f"{model_file} exists - skipping")
-        return
-
-    shutil.copyfile(model_file, f"{lang_dir}/bpe.model")
-
-    generate_tokens(lang_dir)
-
-
-if __name__ == "__main__":
-    main()

egs/ksponspeech/ASR/local/train_bpe_model.py

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

egs/ksponspeech/ASR/local/validate_manifest.py

@@ -1,101 +0,0 @@
-#!/usr/bin/env python3
-# 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 checks the following assumptions of the generated manifest:
-
-- Single supervision per cut
-- Supervision time bounds are within cut time bounds
-
-We will add more checks later if needed.
-
-Usage example:
-
-    python3 ./local/validate_manifest.py \
-            ./data/fbank/speechtools_cuts_train.jsonl.gz
-
-"""
-
-import argparse
-import logging
-from pathlib import Path
-
-from lhotse import CutSet, load_manifest_lazy
-from lhotse.cut import Cut
-from lhotse.dataset.speech_recognition import validate_for_asr
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "manifest",
-        type=Path,
-        help="Path to the manifest file",
-    )
-
-    return parser.parse_args()
-
-
-def validate_one_supervision_per_cut(c: Cut):
-    if len(c.supervisions) != 1:
-        raise ValueError(f"{c.id} has {len(c.supervisions)} supervisions")
-
-
-def validate_supervision_and_cut_time_bounds(c: Cut):
-    tol = 2e-3  # same tolerance as in 'validate_for_asr()'
-    s = c.supervisions[0]
-
-    # Supervision start time is relative to Cut ...
-    # https://lhotse.readthedocs.io/en/v0.10_e/cuts.html
-    if s.start < -tol:
-        raise ValueError(
-            f"{c.id}: Supervision start time {s.start} must not be negative."
-        )
-    if s.start > tol:
-        raise ValueError(
-            f"{c.id}: Supervision start time {s.start} is not at the beginning of the Cut. Please apply `lhotse cut trim-to-supervisions`."
-        )
-    if c.start + s.end > c.end + tol:
-        raise ValueError(
-            f"{c.id}: Supervision end time {c.start+s.end} is larger "
-            f"than cut end time {c.end}"
-        )
-
-
-def main():
-    args = get_args()
-
-    manifest = args.manifest
-    logging.info(f"Validating {manifest}")
-
-    assert manifest.is_file(), f"{manifest} does not exist"
-    cut_set = load_manifest_lazy(manifest)
-    assert isinstance(cut_set, CutSet)
-
-    for c in cut_set:
-        validate_one_supervision_per_cut(c)
-        validate_supervision_and_cut_time_bounds(c)
-
-    # Validation from K2 training
-    # - checks supervision start is 0
-    # - checks supervision.duration is not longer than cut.duration
-    # - there is tolerance 2ms
-    validate_for_asr(cut_set)
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-
-    main()

egs/ksponspeech/ASR/local/validate_manifest.py

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

egs/ksponspeech/ASR/zipformer/README.md

@@ -1 +0,0 @@
-This recipe implements Zipformer model.

egs/libricss/SURT/dprnn_zipformer/pretrained.py

@@ -0,0 +1,303 @@
+#!/usr/bin/env python3
+"""
+Usage:
+
+1. Download pre-trained models from
+https://huggingface.co/desh2608/icefall-surt-libricss-dprnn-zipformer
+
+2.
+
+./dprnn_zipformer/pretrained.py \
+  --checkpoint /path/to/pretrained.pt \
+  --tokens /path/to/data/lang_bpe_500/tokens.txt \
+  /path/to/foo.wav
+"""
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from beam_search import (
+    beam_search,
+    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_surt_model
+
+from icefall.utils import num_tokens
+
+
+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(
+        "--tokens",
+        type=str,
+        required=True,
+        help="""Path to tokens.txt.""",
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame. Used only when
+        --method is greedy_search.
+        """,
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert (
+            sample_rate == expected_sample_rate
+        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        # We use only the first channel
+        ans.append(wave[0].contiguous())
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+
+    params.update(vars(args))
+
+    token_table = k2.SymbolTable.from_file(params.tokens)
+
+    params.blank_id = token_table["<blk>"]
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1
+
+    logging.info(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_surt_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+    opts.mel_opts.high_freq = -400
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    B, T, F = features.shape
+    processed = model.mask_encoder(features)  # B,T,F*num_channels
+    masks = processed.view(B, T, F, params.num_channels).unbind(dim=-1)
+    x_masked = [features * m for m in masks]
+
+    # Recognition
+    # Concatenate the inputs along the batch axis
+    h = torch.cat(x_masked, dim=0)
+    h_lens = feature_lengths.repeat(params.num_channels)
+    encoder_out, encoder_out_lens = model.encoder(x=h, x_lens=h_lens)
+
+    if model.joint_encoder_layer is not None:
+        encoder_out = model.joint_encoder_layer(encoder_out)
+
+    def _group_channels(hyps: List[str]) -> List[List[str]]:
+        """
+        Currently we have a batch of size M*B, where M is the number of
+        channels and B is the batch size. We need to group the hypotheses
+        into B groups, each of which contains M hypotheses.
+
+        Example:
+            hyps = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2']
+            _group_channels(hyps) = [['a1', 'a2'], ['b1', 'b2'], ['c1', 'c2']]
+        """
+        assert len(hyps) == B * params.num_channels
+        out_hyps = []
+        for i in range(B):
+            out_hyps.append(hyps[i::B])
+        return out_hyps
+
+    hyps = []
+    msg = f"Using {params.method}"
+    logging.info(msg)
+
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
+    if params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+        )
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+                hyps.append(token_ids_to_words(hyp))
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        s += f"{filename}:\n{hyp}\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/libricss/SURT/dprnn_zipformer/train.py

@@ -62,9 +62,7 @@ from asr_datamodule import LibriCssAsrDataModule
 from decoder import Decoder
 from dprnn import DPRNN
 from einops.layers.torch import Rearrange
-from graph_pit.loss.optimized import optimized_graph_pit_mse_loss as gpit_mse
 from joiner import Joiner
-from lhotse.cut import Cut
 from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import LOG_EPSILON, fix_random_seed
 from model import SURT

egs/libriheavy/ASR/local/prepare_manifest.py

@@ -20,6 +20,8 @@ import json
 import sys
 from pathlib import Path
 
+from icefall.utils import str2bool
+
 
 def simple_cleanup(text: str) -> str:
     table = str.maketrans("’‘，。；？！（）：-《》、“”【】", "'',.;?!(): <>/\"\"[]")
@@ -29,17 +31,21 @@ def simple_cleanup(text: str) -> str:
 
 # Assign text of the supervisions and remove unnecessary entries.
 def main():
-    assert len(sys.argv) == 3, "Usage: ./local/prepare_manifest.py INPUT OUTPUT_DIR"
+    assert (
+        len(sys.argv) == 4
+    ), "Usage: ./local/prepare_manifest.py INPUT OUTPUT_DIR KEEP_CUSTOM_FIELDS"
     fname = Path(sys.argv[1]).name
     oname = Path(sys.argv[2]) / fname
+    keep_custom_fields = str2bool(sys.argv[3])
     with gzip.open(sys.argv[1], "r") as fin, gzip.open(oname, "w") as fout:
         for line in fin:
             cut = json.loads(line)
             cut["supervisions"][0]["text"] = simple_cleanup(
                 cut["supervisions"][0]["custom"]["texts"][0]
             )
-            del cut["supervisions"][0]["custom"]
+            if not keep_custom_fields:
-            del cut["custom"]
+                del cut["supervisions"][0]["custom"]
+                del cut["custom"]
             fout.write((json.dumps(cut) + "\n").encode())
 
 

egs/libriheavy/ASR/prepare.sh

@@ -29,6 +29,11 @@ export CUDA_VISIBLE_DEVICES=""
 #     - speech
 dl_dir=$PWD/download
 
+# If you want to do PromptASR experiments, please set it to True
+# as this will keep the texts and pre_text information required for
+# the training of PromptASR.
+keep_custom_fields=False
+
 . shared/parse_options.sh || exit 1
 
 # vocab size for sentence piece models.
@@ -134,7 +139,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
   for subset in small medium large dev test_clean test_other; do
     if [ ! -e $manifests_dir/libriheavy_cuts_${subset}.jsonl.gz ]; then
       log "Prepare manifest for subset : ${subset}"
-      ./local/prepare_manifest.py $dl_dir/libriheavy/libriheavy_cuts_${subset}.jsonl.gz $manifests_dir
+      ./local/prepare_manifest.py $dl_dir/libriheavy/libriheavy_cuts_${subset}.jsonl.gz $manifests_dir $keep_custom_fields
     fi
   done
 fi

egs/librispeech/ASR/README.md

@@ -50,7 +50,7 @@ We place an additional Conv1d layer right after the input embedding layer.
 | `conformer-ctc2`             | Reworked Conformer | Use auxiliary attention head |
 | `conformer-ctc3`             | Reworked Conformer | Streaming version + delay penalty |
 | `zipformer-ctc`              | Zipformer          | Use auxiliary attention head |
-| `zipformer`                  | Upgraded Zipformer | Use auxiliary transducer head / attention-decoder head | The latest recipe |
+| `zipformer`                  | Upgraded Zipformer | Use auxiliary transducer head / attention-decoder head (the latest recipe) |
 
 # MMI
 
@@ -58,3 +58,9 @@ We place an additional Conv1d layer right after the input embedding layer.
 |------------------------------|-----------|---------------------------------------------------|
 | `conformer-mmi`              | Conformer |                                                   |
 | `zipformer-mmi`              | Zipformer | CTC warmup + use HP as decoding graph for decoding |
+
+# CR-CTC
+
+|                              | Encoder            | Comment                      |
+|------------------------------|--------------------|------------------------------|
+| `zipformer`                  | Upgraded Zipformer | Could also be an auxiliary loss to improve transducer or CTC/AED (the latest recipe) |

egs/librispeech/ASR/RESULTS.md

@@ -1,5 +1,318 @@
 ## Results
 
+### zipformer (zipformer + pruned-transducer w/ CR-CTC)
+
+See <https://github.com/k2-fsa/icefall/pull/1766> for more details.
+
+[zipformer](./zipformer)
+
+#### Non-streaming
+
+##### large-scale model, number of model parameters: 148824074, i.e., 148.8 M
+
+You can find a pretrained model, training logs, decoding logs, and decoding results at:
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-large-transducer-with-CR-CTC-20241019>
+
+You can use <https://github.com/k2-fsa/sherpa> to deploy it.
+
+| decoding method                      | test-clean | test-other | comment             |
+|--------------------------------------|------------|------------|---------------------|
+| greedy_search                        | 1.9        | 3.96       | --epoch 50 --avg 26 |
+| modified_beam_search                 | 1.88       | 3.95       | --epoch 50 --avg 26 |
+
+The training command using 2 80G-A100 GPUs is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1"
+# for non-streaming model training:
+./zipformer/train.py \
+  --world-size 2 \
+  --num-epochs 50 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp-large-cr-ctc-rnnt \
+  --use-cr-ctc 1 \
+  --use-ctc 1 \
+  --use-transducer 1 \
+  --use-attention-decoder 0 \
+  --num-encoder-layers 2,2,4,5,4,2 \
+  --feedforward-dim 512,768,1536,2048,1536,768 \
+  --encoder-dim 192,256,512,768,512,256 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --ctc-loss-scale 0.1 \
+  --enable-spec-aug 0 \
+  --cr-loss-scale 0.02 \
+  --time-mask-ratio 2.5 \
+  --full-libri 1 \
+  --max-duration 1400 \
+  --master-port 12345
+```
+
+The decoding command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+for m in greedy_search modified_beam_search; do
+  ./zipformer/decode.py \
+    --epoch 50 \
+    --avg 26 \
+    --exp-dir zipformer/exp-large-cr-ctc-rnnt \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 1 \
+    --use-attention-decoder 0 \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 512,768,1536,2048,1536,768 \
+    --encoder-dim 192,256,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --max-duration 300 \
+    --decoding-method $m
+done
+```
+
+### zipformer (zipformer + CR-CTC-AED)
+
+See <https://github.com/k2-fsa/icefall/pull/1766> for more details.
+
+[zipformer](./zipformer)
+
+#### Non-streaming
+
+##### large-scale model, number of model parameters: 174319650, i.e., 174.3 M
+
+You can find a pretrained model, training logs, decoding logs, and decoding results at:
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-large-cr-ctc-aed-20241020>
+
+You can use <https://github.com/k2-fsa/sherpa> to deploy it.
+
+| decoding method                      | test-clean | test-other | comment             |
+|--------------------------------------|------------|------------|---------------------|
+| attention-decoder-rescoring-no-ngram | 1.96       | 4.08       | --epoch 50 --avg 20 |
+
+The training command using 2 80G-A100 GPUs is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1"
+# for non-streaming model training:
+./zipformer/train.py \
+  --world-size 2 \
+  --num-epochs 50 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp-large-cr-ctc-aed \
+  --use-cr-ctc 1 \
+  --use-ctc 1 \
+  --use-transducer 0 \
+  --use-attention-decoder 1 \
+  --num-encoder-layers 2,2,4,5,4,2 \
+  --feedforward-dim 512,768,1536,2048,1536,768 \
+  --encoder-dim 192,256,512,768,512,256 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --ctc-loss-scale 0.1 \
+  --attention-decoder-loss-scale 0.9 \
+  --enable-spec-aug 0 \
+  --cr-loss-scale 0.02 \
+  --time-mask-ratio 2.5 \
+  --full-libri 1 \
+  --max-duration 1200 \
+  --master-port 12345
+```
+
+The decoding command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+./zipformer/ctc_decode.py \
+  --epoch 50 \
+  --avg 20 \
+  --exp-dir zipformer/exp-large-cr-ctc-aed/ \
+  --use-cr-ctc 1 \
+  --use-ctc 1 \
+  --use-transducer 0 \
+  --use-attention-decoder 1 \
+  --num-encoder-layers 2,2,4,5,4,2 \
+  --feedforward-dim 512,768,1536,2048,1536,768 \
+  --encoder-dim 192,256,512,768,512,256 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --max-duration 200 \
+  --decoding-method attention-decoder-rescoring-no-ngram
+done
+```
+
+### zipformer (zipformer + CR-CTC)
+
+See <https://github.com/k2-fsa/icefall/pull/1766> for more details.
+
+[zipformer](./zipformer)
+
+#### Non-streaming
+
+##### small-scale model, number of model parameters: 22118279, i.e., 22.1 M
+
+You can find a pretrained model, training logs, decoding logs, and decoding results at:
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-small-cr-ctc-20241018>
+
+You can use <https://github.com/k2-fsa/sherpa> to deploy it.
+
+| decoding method                      | test-clean | test-other | comment             |
+|--------------------------------------|------------|------------|---------------------|
+| ctc-greedy-decoding                  | 2.57       | 5.95       | --epoch 50 --avg 25 |
+| ctc-prefix-beam-search               | 2.52       | 5.85       | --epoch 50 --avg 25 |
+
+The training command using 2 32G-V100 GPUs is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1"
+# for non-streaming model training:
+./zipformer/train.py \
+  --world-size 2 \
+  --num-epochs 50 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp-small/ \
+  --use-cr-ctc 1 \
+  --use-ctc 1 \
+  --use-transducer 0 \
+  --use-attention-decoder 0 \
+  --num-encoder-layers 2,2,2,2,2,2 \
+  --feedforward-dim 512,768,768,768,768,768 \
+  --encoder-dim 192,256,256,256,256,256 \
+  --encoder-unmasked-dim 192,192,192,192,192,192 \
+  --base-lr 0.04 \
+  --enable-spec-aug 0 \
+  --cr-loss-scale 0.2 \
+  --time-mask-ratio 2.5 \
+  --full-libri 1 \
+  --max-duration 850 \
+  --master-port 12345
+```
+
+The decoding command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+for m in ctc-greedy-search ctc-prefix-beam-search; do
+  ./zipformer/ctc_decode.py \
+    --epoch 50 \
+    --avg 25 \
+    --exp-dir zipformer/exp-small \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 0 \
+    --use-attention-decoder 0 \
+    --num-encoder-layers 2,2,2,2,2,2 \
+    --feedforward-dim 512,768,768,768,768,768 \
+    --encoder-dim 192,256,256,256,256,256 \
+    --encoder-unmasked-dim 192,192,192,192,192,192 \
+    --max-duration 600 \
+    --decoding-method $m
+done
+```
+
+##### medium-scale model, number of model parameters: 64250603, i.e., 64.3 M
+
+You can find a pretrained model, training logs, decoding logs, and decoding results at:
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-medium-cr-ctc-20241018>
+
+You can use <https://github.com/k2-fsa/sherpa> to deploy it.
+
+| decoding method                      | test-clean | test-other | comment             |
+|--------------------------------------|------------|------------|---------------------|
+| ctc-greedy-decoding                  | 2.12       | 4.62       | --epoch 50 --avg 24 |
+| ctc-prefix-beam-search               | 2.1        | 4.61       | --epoch 50 --avg 24 |
+
+The training command using 4 32G-V100 GPUs is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+# For non-streaming model training:
+./zipformer/train.py \
+  --world-size 4 \
+  --num-epochs 50 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp \
+  --use-cr-ctc 1 \
+  --use-ctc 1 \
+  --use-transducer 0 \
+  --use-attention-decoder 0 \
+  --enable-spec-aug 0 \
+  --cr-loss-scale 0.2 \
+  --time-mask-ratio 2.5 \
+  --full-libri 1 \
+  --max-duration 700 \
+  --master-port 12345
+```
+
+The decoding command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+for m in ctc-greedy-search ctc-prefix-beam-search; do
+  ./zipformer/ctc_decode.py \
+    --epoch 50 \
+    --avg 24 \
+    --exp-dir zipformer/exp \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 0 \
+    --use-attention-decoder 0 \
+    --max-duration 600 \
+    --decoding-method $m
+done
+```
+
+##### large-scale model, number of model parameters: 147010094, i.e., 147.0 M
+
+You can find a pretrained model, training logs, decoding logs, and decoding results at:
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-large-cr-ctc-20241018>
+
+You can use <https://github.com/k2-fsa/sherpa> to deploy it.
+
+| decoding method                      | test-clean | test-other | comment             |
+|--------------------------------------|------------|------------|---------------------|
+| ctc-greedy-decoding                  | 2.03       | 4.37       | --epoch 50 --avg 26 |
+| ctc-prefix-beam-search               | 2.02       | 4.35       | --epoch 50 --avg 26 |
+
+The training command using 2 80G-A100 GPUs is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1"
+# For non-streaming model training:
+./zipformer/train.py \
+  --world-size 2 \
+  --num-epochs 50 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp-large \
+  --use-cr-ctc 1 \
+  --use-ctc 1 \
+  --use-transducer 0 \
+  --use-attention-decoder 0 \
+  --num-encoder-layers 2,2,4,5,4,2 \
+  --feedforward-dim 512,768,1536,2048,1536,768 \
+  --encoder-dim 192,256,512,768,512,256 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --enable-spec-aug 0 \
+  --cr-loss-scale 0.2 \
+  --time-mask-ratio 2.5 \
+  --full-libri 1 \
+  --max-duration 1400 \
+  --master-port 12345
+```
+
+The decoding command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+for m in ctc-greedy-search ctc-prefix-beam-search; do
+  ./zipformer/ctc_decode.py \
+    --epoch 50 \
+    --avg 26 \
+    --exp-dir zipformer/exp-large \
+    --use-cr-ctc 1 \
+    --use-ctc 1 \
+    --use-transducer 0 \
+    --use-attention-decoder 0 \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 512,768,1536,2048,1536,768 \
+    --encoder-dim 192,256,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --max-duration 600 \
+    --decoding-method $m
+done
+```
+
 ### zipformer (zipformer + CTC/AED)
 
 See <https://github.com/k2-fsa/icefall/pull/1389> for more details.
@@ -307,6 +620,23 @@ done
 
 To decode with external language models, please refer to the documentation [here](https://k2-fsa.github.io/icefall/decoding-with-langugage-models/index.html).
 
+We also support training Zipformer with AMP+bf16 format (requires bf16 support). See [here](https://github.com/k2-fsa/icefall/pull/1700) for more details and pre-trained models. **The same command can be used for decoding and exporting the model.**
+
+The amp+bf16 training command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+./zipformer/train.py \
+  --world-size 4 \
+  --num-epochs 50 \
+  --start-epoch 1 \
+  --use-fp16 0 \
+  --use-bf16 1 \
+  --exp-dir zipformer/exp_amp_bf16 \
+  --causal 0 \
+  --full-libri 1 \
+  --max-duration 1000
+```
+
 ##### small-scaled model, number of model parameters: 23285615, i.e., 23.3 M
 
 The tensorboard log can be found at

egs/librispeech/ASR/conformer_ctc/conformer.py

@@ -32,7 +32,7 @@ class Conformer(Transformer):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         num_decoder_layers (int): number of decoder layers
         dropout (float): dropout rate
@@ -902,7 +902,7 @@ class Swish(torch.nn.Module):
     """Construct an Swish object."""
 
     def forward(self, x: Tensor) -> Tensor:
-        """Return Swich activation function."""
+        """Return Swish activation function."""
         return x * torch.sigmoid(x)
 
 

egs/librispeech/ASR/conformer_ctc2/conformer.py

@@ -42,7 +42,7 @@ class Conformer(Transformer):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension, also the output dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         num_decoder_layers (int): number of decoder layers
         dropout (float): dropout rate

egs/librispeech/ASR/conformer_mmi/conformer.py

@@ -33,7 +33,7 @@ class Conformer(Transformer):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         num_decoder_layers (int): number of decoder layers
         dropout (float): dropout rate

egs/librispeech/ASR/pruned2_knowledge/conformer.py

@@ -42,7 +42,7 @@ class Conformer(EncoderInterface):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension, also the output dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         dropout (float): dropout rate
         layer_dropout (float): layer-dropout rate.

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -42,7 +42,7 @@ class Conformer(EncoderInterface):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension, also the output dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         dropout (float): dropout rate
         layer_dropout (float): layer-dropout rate.

egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py

@@ -42,7 +42,7 @@ class Conformer(EncoderInterface):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension, also the output dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         dropout (float): dropout rate
         layer_dropout (float): layer-dropout rate.

egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py

@@ -42,7 +42,7 @@ class Conformer(EncoderInterface):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension, also the output dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         dropout (float): dropout rate
         layer_dropout (float): layer-dropout rate.

egs/librispeech/ASR/streaming_conformer_ctc/conformer.py

@@ -69,7 +69,7 @@ class Conformer(Transformer):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         num_decoder_layers (int): number of decoder layers
         dropout (float): dropout rate

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -35,7 +35,7 @@ class Conformer(Transformer):
         subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
         d_model (int): attention dimension
         nhead (int): number of head
-        dim_feedforward (int): feedforward dimention
+        dim_feedforward (int): feedforward dimension
         num_encoder_layers (int): number of encoder layers
         dropout (float): dropout rate
         cnn_module_kernel (int): Kernel size of convolution module

egs/librispeech/ASR/zipformer/attention_decoder.py

@@ -236,7 +236,7 @@ class TransformerDecoder(nn.Module):
         causal_mask = subsequent_mask(x.shape[0], device=x.device)  # (seq_len, seq_len)
         attn_mask = torch.logical_or(
             padding_mask.unsqueeze(1),  # (batch, 1, seq_len)
-            torch.logical_not(causal_mask).unsqueeze(0)  # (1, seq_len, seq_len)
+            torch.logical_not(causal_mask).unsqueeze(0),  # (1, seq_len, seq_len)
         )  # (batch, seq_len, seq_len)
 
         if memory is not None:
@@ -367,7 +367,9 @@ class MultiHeadAttention(nn.Module):
         self.num_heads = num_heads
         self.head_dim = attention_dim // num_heads
         assert self.head_dim * num_heads == attention_dim, (
-            self.head_dim, num_heads, attention_dim
+            self.head_dim,
+            num_heads,
+            attention_dim,
         )
         self.dropout = dropout
         self.name = None  # will be overwritten in training code; for diagnostics.
@@ -437,15 +439,19 @@ class MultiHeadAttention(nn.Module):
         if key_padding_mask is not None:
             assert key_padding_mask.shape == (batch, src_len), key_padding_mask.shape
             attn_weights = attn_weights.masked_fill(
-                key_padding_mask.unsqueeze(1).unsqueeze(2), float("-inf"),
+                key_padding_mask.unsqueeze(1).unsqueeze(2),
+                float("-inf"),
             )
 
         if attn_mask is not None:
-            assert (
+            assert attn_mask.shape == (batch, 1, src_len) or attn_mask.shape == (
-                attn_mask.shape == (batch, 1, src_len)
+                batch,
-                or attn_mask.shape == (batch, tgt_len, src_len)
+                tgt_len,
+                src_len,
             ), attn_mask.shape
-            attn_weights = attn_weights.masked_fill(attn_mask.unsqueeze(1), float("-inf"))
+            attn_weights = attn_weights.masked_fill(
+                attn_mask.unsqueeze(1), float("-inf")
+            )
 
         attn_weights = attn_weights.view(batch * num_heads, tgt_len, src_len)
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
@@ -456,7 +462,11 @@ class MultiHeadAttention(nn.Module):
 
         # (batch * head, tgt_len, head_dim)
         attn_output = torch.bmm(attn_weights, v)
-        assert attn_output.shape == (batch * num_heads, tgt_len, head_dim), attn_output.shape
+        assert attn_output.shape == (
+            batch * num_heads,
+            tgt_len,
+            head_dim,
+        ), attn_output.shape
 
         attn_output = attn_output.transpose(0, 1).contiguous()
         attn_output = attn_output.view(tgt_len, batch, num_heads * head_dim)

egs/librispeech/ASR/zipformer/ctc_decode.py

@@ -111,6 +111,7 @@ Usage:
 import argparse
 import logging
 import math
+import os
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
@@ -120,6 +121,7 @@ import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
+from lhotse import set_caching_enabled
 from train import add_model_arguments, get_model, get_params
 
 from icefall.checkpoint import (
@@ -128,8 +130,12 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.context_graph import ContextGraph, ContextState
 from icefall.decode import (
     ctc_greedy_search,
+    ctc_prefix_beam_search,
+    ctc_prefix_beam_search_attention_decoder_rescoring,
+    ctc_prefix_beam_search_shallow_fussion,
     get_lattice,
     nbest_decoding,
     nbest_oracle,
@@ -140,6 +146,8 @@ from icefall.decode import (
     rescore_with_whole_lattice,
 )
 from icefall.lexicon import Lexicon
+from icefall.lm_wrapper import LmScorer
+from icefall.ngram_lm import NgramLm, NgramLmStateCost
 from icefall.utils import (
     AttributeDict,
     get_texts,
@@ -254,6 +262,12 @@ def get_parser():
           lattice, rescore them with the attention decoder.
         - (9) attention-decoder-rescoring-with-ngram. Extract n paths from the LM
           rescored lattice, rescore them with the attention decoder.
+        - (10) ctc-prefix-beam-search. Extract n paths with the given beam, the best
+          path of the n paths is the decoding result.
+        - (11) ctc-prefix-beam-search-attention-decoder-rescoring. Extract n paths with
+          the given beam, rescore them with the attention decoder.
+        - (12) ctc-prefix-beam-search-shallow-fussion. Use NNLM shallow fussion during
+          beam search, LODR and hotwords are also supported in this decoding method.
         """,
     )
 
@@ -279,6 +293,23 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--nnlm-type",
+        type=str,
+        default="rnn",
+        help="Type of NN lm",
+        choices=["rnn", "transformer"],
+    )
+
+    parser.add_argument(
+        "--nnlm-scale",
+        type=float,
+        default=0,
+        help="""The scale of the neural network LM, 0 means don't use nnlm shallow fussion.
+        Used only when `--use-shallow-fusion` is set to True.
+        """,
+    )
+
     parser.add_argument(
         "--hlg-scale",
         type=float,
@@ -296,6 +327,54 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--backoff-id",
+        type=int,
+        default=500,
+        help="ID of the backoff symbol in the ngram LM",
+    )
+
+    parser.add_argument(
+        "--lodr-ngram",
+        type=str,
+        help="The path to the lodr ngram",
+    )
+
+    parser.add_argument(
+        "--lodr-lm-scale",
+        type=float,
+        default=0,
+        help="The scale of lodr ngram, should be less than 0. 0 means don't use lodr.",
+    )
+
+    parser.add_argument(
+        "--context-score",
+        type=float,
+        default=0,
+        help="""
+        The bonus score of each token for the context biasing words/phrases.
+        0 means don't use contextual biasing.
+        Used only when --decoding-method is ctc-prefix-beam-search-shallow-fussion.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-file",
+        type=str,
+        default="",
+        help="""
+        The path of the context biasing lists, one word/phrase each line
+        Used only when --decoding-method is ctc-prefix-beam-search-shallow-fussion.
+        """,
+    )
+
+    parser.add_argument(
+        "--skip-scoring",
+        type=str2bool,
+        default=False,
+        help="""Skip scoring, but still save the ASR output (for eval sets).""",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -306,11 +385,12 @@ def get_decoding_params() -> AttributeDict:
     params = AttributeDict(
         {
             "frame_shift_ms": 10,
-            "search_beam": 20,
+            "search_beam": 20,  # for k2 fsa composition
-            "output_beam": 8,
+            "output_beam": 8,  # for k2 fsa composition
             "min_active_states": 30,
             "max_active_states": 10000,
             "use_double_scores": True,
+            "beam": 4,  # for prefix-beam-search
         }
     )
     return params
@@ -325,6 +405,9 @@ def decode_one_batch(
     batch: dict,
     word_table: k2.SymbolTable,
     G: Optional[k2.Fsa] = None,
+    NNLM: Optional[LmScorer] = None,
+    LODR_lm: Optional[NgramLm] = None,
+    context_graph: Optional[ContextGraph] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
     following format:
@@ -369,10 +452,7 @@ def decode_one_batch(
       Return the decoding result. See above description for the format of
       the returned dict. Note: If it decodes to nothing, then return None.
     """
-    if HLG is not None:
+    device = params.device
-        device = HLG.device
-    else:
-        device = H.device
     feature = batch["inputs"]
     assert feature.ndim == 3
     feature = feature.to(device)
@@ -403,6 +483,51 @@ def decode_one_batch(
         key = "ctc-greedy-search"
         return {key: hyps}
 
+    if params.decoding_method == "ctc-prefix-beam-search":
+        token_ids = ctc_prefix_beam_search(
+            ctc_output=ctc_output, encoder_out_lens=encoder_out_lens
+        )
+        # hyps is a list of str, e.g., ['xxx yyy zzz', ...]
+        hyps = bpe_model.decode(token_ids)
+
+        # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
+        hyps = [s.split() for s in hyps]
+        key = "prefix-beam-search"
+        return {key: hyps}
+
+    if params.decoding_method == "ctc-prefix-beam-search-attention-decoder-rescoring":
+        best_path_dict = ctc_prefix_beam_search_attention_decoder_rescoring(
+            ctc_output=ctc_output,
+            attention_decoder=model.attention_decoder,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        ans = dict()
+        for a_scale_str, token_ids in best_path_dict.items():
+            # hyps is a list of str, e.g., ['xxx yyy zzz', ...]
+            hyps = bpe_model.decode(token_ids)
+            # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
+            hyps = [s.split() for s in hyps]
+            ans[a_scale_str] = hyps
+        return ans
+
+    if params.decoding_method == "ctc-prefix-beam-search-shallow-fussion":
+        token_ids = ctc_prefix_beam_search_shallow_fussion(
+            ctc_output=ctc_output,
+            encoder_out_lens=encoder_out_lens,
+            NNLM=NNLM,
+            LODR_lm=LODR_lm,
+            LODR_lm_scale=params.lodr_lm_scale,
+            context_graph=context_graph,
+        )
+        # hyps is a list of str, e.g., ['xxx yyy zzz', ...]
+        hyps = bpe_model.decode(token_ids)
+
+        # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
+        hyps = [s.split() for s in hyps]
+        key = "prefix-beam-search-shallow-fussion"
+        return {key: hyps}
+
     supervision_segments = torch.stack(
         (
             supervisions["sequence_idx"],
@@ -455,7 +580,7 @@ def decode_one_batch(
         # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
         hyps = [s.split() for s in hyps]
         key = "ctc-decoding"
-        return {key: hyps}
+        return {key: hyps}  # note: returns words
 
     if params.decoding_method == "attention-decoder-rescoring-no-ngram":
         best_path_dict = rescore_with_attention_decoder_no_ngram(
@@ -492,7 +617,7 @@ def decode_one_batch(
         )
         hyps = get_texts(best_path)
         hyps = [[word_table[i] for i in ids] for ids in hyps]
-        key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}"  # noqa
+        key = f"oracle_{params.num_paths}_nbest-scale-{params.nbest_scale}"  # noqa
         return {key: hyps}
 
     if params.decoding_method in ["1best", "nbest"]:
@@ -500,7 +625,7 @@ def decode_one_batch(
             best_path = one_best_decoding(
                 lattice=lattice, use_double_scores=params.use_double_scores
             )
-            key = "no_rescore"
+            key = "no-rescore"
         else:
             best_path = nbest_decoding(
                 lattice=lattice,
@@ -508,11 +633,11 @@ def decode_one_batch(
                 use_double_scores=params.use_double_scores,
                 nbest_scale=params.nbest_scale,
             )
-            key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}"  # noqa
+            key = f"no-rescore_nbest-scale-{params.nbest_scale}-{params.num_paths}"  # noqa
 
         hyps = get_texts(best_path)
         hyps = [[word_table[i] for i in ids] for ids in hyps]
-        return {key: hyps}
+        return {key: hyps}  # note: returns BPE tokens
 
     assert params.decoding_method in [
         "nbest-rescoring",
@@ -576,6 +701,9 @@ def decode_dataset(
     bpe_model: Optional[spm.SentencePieceProcessor],
     word_table: k2.SymbolTable,
     G: Optional[k2.Fsa] = None,
+    NNLM: Optional[LmScorer] = None,
+    LODR_lm: Optional[NgramLm] = None,
+    context_graph: Optional[ContextGraph] = None,
 ) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
     """Decode dataset.
 
@@ -626,6 +754,9 @@ def decode_dataset(
             batch=batch,
             word_table=word_table,
             G=G,
+            NNLM=NNLM,
+            LODR_lm=LODR_lm,
+            context_graph=context_graph,
         )
 
         for name, hyps in hyps_dict.items():
@@ -646,13 +777,32 @@ def decode_dataset(
     return results
 
 
-def save_results(
+def save_asr_output(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+):
+    """
+    Save text produced by ASR.
+    """
+    for key, results in results_dict.items():
+
+        recogs_filename = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
+
+        results = sorted(results)
+        store_transcripts(filename=recogs_filename, texts=results)
+
+        logging.info(f"The transcripts are stored in {recogs_filename}")
+
+
+def save_wer_results(
     params: AttributeDict,
     test_set_name: str,
     results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
 ):
     if params.decoding_method in (
-        "attention-decoder-rescoring-with-ngram", "whole-lattice-rescoring"
+        "attention-decoder-rescoring-with-ngram",
+        "whole-lattice-rescoring",
     ):
         # Set it to False since there are too many logs.
         enable_log = False
@@ -661,32 +811,30 @@ def save_results(
 
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-        results = sorted(results)
-        store_transcripts(filename=recog_path, texts=results)
-        if enable_log:
-            logging.info(f"The transcripts are stored in {recog_path}")
-
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
         errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-        with open(errs_filename, "w") as f:
+        with open(errs_filename, "w", encoding="utf8") as fd:
-            wer = write_error_stats(f, f"{test_set_name}-{key}", results)
+            wer = write_error_stats(
+                fd, f"{test_set_name}_{key}", results, enable_log=enable_log
+            )
             test_set_wers[key] = wer
-        if enable_log:
+
-            logging.info("Wrote detailed error stats to {}".format(errs_filename))
+        logging.info(f"Wrote detailed error stats to {errs_filename}")
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-    with open(errs_info, "w") as f:
-        print("settings\tWER", file=f)
-        for key, val in test_set_wers:
-            print("{}\t{}".format(key, val), file=f)
 
-    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    wer_filename = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-    note = "\tbest for {}".format(test_set_name)
+
+    with open(wer_filename, "w", encoding="utf8") as fd:
+        print("settings\tWER", file=fd)
+        for key, val in test_set_wers:
+            print(f"{key}\t{val}", file=fd)
+
+    s = f"\nFor {test_set_name}, WER of different settings are:\n"
+    note = f"\tbest for {test_set_name}"
     for key, val in test_set_wers:
-        s += "{}\t{}{}\n".format(key, val, note)
+        s += f"{key}\t{val}{note}\n"
         note = ""
     logging.info(s)
 
@@ -695,6 +843,7 @@ def save_results(
 def main():
     parser = get_parser()
     LibriSpeechAsrDataModule.add_arguments(parser)
+    LmScorer.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
     args.lang_dir = Path(args.lang_dir)
@@ -705,9 +854,15 @@ def main():
     params.update(get_decoding_params())
     params.update(vars(args))
 
+    # enable AudioCache
+    set_caching_enabled(True)  # lhotse
+
     assert params.decoding_method in (
-        "ctc-greedy-search",
         "ctc-decoding",
+        "ctc-greedy-search",
+        "ctc-prefix-beam-search",
+        "ctc-prefix-beam-search-attention-decoder-rescoring",
+        "ctc-prefix-beam-search-shallow-fussion",
         "1best",
         "nbest",
         "nbest-rescoring",
@@ -719,9 +874,9 @@ def main():
     params.res_dir = params.exp_dir / params.decoding_method
 
     if params.iter > 0:
-        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+        params.suffix = f"iter-{params.iter}_avg-{params.avg}"
     else:
-        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+        params.suffix = f"epoch-{params.epoch}_avg-{params.avg}"
 
     if params.causal:
         assert (
@@ -730,11 +885,21 @@ def main():
         assert (
             "," not in params.left_context_frames
         ), "left_context_frames should be one value in decoding."
-        params.suffix += f"-chunk-{params.chunk_size}"
+        params.suffix += f"_chunk-{params.chunk_size}"
-        params.suffix += f"-left-context-{params.left_context_frames}"
+        params.suffix += f"_left-context-{params.left_context_frames}"
+
+    if "prefix-beam-search" in params.decoding_method:
+        params.suffix += f"_beam-{params.beam}"
+        if params.decoding_method == "ctc-prefix-beam-search-shallow-fussion":
+            if params.nnlm_scale != 0:
+                params.suffix += f"_nnlm-scale-{params.nnlm_scale}"
+            if params.lodr_lm_scale != 0:
+                params.suffix += f"_lodr-scale-{params.lodr_lm_scale}"
+            if params.context_score != 0:
+                params.suffix += f"_context_score-{params.context_score}"
 
     if params.use_averaged_model:
-        params.suffix += "-use-averaged-model"
+        params.suffix += "_use-averaged-model"
 
     setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
     logging.info("Decoding started")
@@ -742,6 +907,7 @@ def main():
     device = torch.device("cpu")
     if torch.cuda.is_available():
         device = torch.device("cuda", 0)
+    params.device = device
 
     logging.info(f"Device: {device}")
     logging.info(params)
@@ -757,14 +923,24 @@ def main():
     params.sos_id = 1
 
     if params.decoding_method in [
-        "ctc-greedy-search", "ctc-decoding", "attention-decoder-rescoring-no-ngram"
+        "ctc-decoding",
+        "ctc-greedy-search",
+        "ctc-prefix-beam-search",
+        "ctc-prefix-beam-search-attention-decoder-rescoring",
+        "ctc-prefix-beam-search-shallow-fussion",
+        "attention-decoder-rescoring-no-ngram",
     ]:
         HLG = None
-        H = k2.ctc_topo(
+        H = None
-            max_token=max_token_id,
+        if params.decoding_method in [
-            modified=False,
+            "ctc-decoding",
-            device=device,
+            "attention-decoder-rescoring-no-ngram",
-        )
+        ]:
+            H = k2.ctc_topo(
+                max_token=max_token_id,
+                modified=False,
+                device=device,
+            )
         bpe_model = spm.SentencePieceProcessor()
         bpe_model.load(str(params.lang_dir / "bpe.model"))
     else:
@@ -815,7 +991,8 @@ def main():
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method in [
-            "whole-lattice-rescoring", "attention-decoder-rescoring-with-ngram"
+            "whole-lattice-rescoring",
+            "attention-decoder-rescoring-with-ngram",
         ]:
             # Add epsilon self-loops to G as we will compose
             # it with the whole lattice later
@@ -829,6 +1006,51 @@ def main():
     else:
         G = None
 
+    # only load the neural network LM if required
+    NNLM = None
+    if (
+        params.decoding_method == "ctc-prefix-beam-search-shallow-fussion"
+        and params.nnlm_scale != 0
+    ):
+        NNLM = LmScorer(
+            lm_type=params.nnlm_type,
+            params=params,
+            device=device,
+            lm_scale=params.nnlm_scale,
+        )
+        NNLM.to(device)
+        NNLM.eval()
+
+    LODR_lm = None
+    if (
+        params.decoding_method == "ctc-prefix-beam-search-shallow-fussion"
+        and params.lodr_lm_scale != 0
+    ):
+        assert os.path.exists(
+            params.lodr_ngram
+        ), f"LODR ngram does not exists, given path : {params.lodr_ngram}"
+        logging.info(f"Loading LODR (token level lm): {params.lodr_ngram}")
+        LODR_lm = NgramLm(
+            params.lodr_ngram,
+            backoff_id=params.backoff_id,
+            is_binary=False,
+        )
+        logging.info(f"num states: {LODR_lm.lm.num_states}")
+
+    context_graph = None
+    if (
+        params.decoding_method == "ctc-prefix-beam-search-shallow-fussion"
+        and params.context_score != 0
+    ):
+        assert os.path.exists(
+            params.context_file
+        ), f"context_file does not exists, given path : {params.context_file}"
+        contexts = []
+        for line in open(params.context_file).readlines():
+            contexts.append(bpe_model.encode(line.strip()))
+        context_graph = ContextGraph(params.context_score)
+        context_graph.build(contexts)
+
     logging.info("About to create model")
     model = get_model(params)
 
@@ -938,14 +1160,24 @@ def main():
             bpe_model=bpe_model,
             word_table=lexicon.word_table,
             G=G,
+            NNLM=NNLM,
+            LODR_lm=LODR_lm,
+            context_graph=context_graph,
         )
 
-        save_results(
+        save_asr_output(
             params=params,
             test_set_name=test_set,
             results_dict=results_dict,
         )
 
+        if not params.skip_scoring:
+            save_wer_results(
+                params=params,
+                test_set_name=test_set,
+                results_dict=results_dict,
+            )
+
     logging.info("Done!")
 
 

egs/librispeech/ASR/zipformer/decode.py

@@ -121,6 +121,7 @@ from beam_search import (
     modified_beam_search_lm_shallow_fusion,
     modified_beam_search_LODR,
 )
+from lhotse import set_caching_enabled
 from train import add_model_arguments, get_model, get_params
 
 from icefall import ContextGraph, LmScorer, NgramLm
@@ -369,6 +370,14 @@ def get_parser():
         modified_beam_search_LODR.
         """,
     )
+
+    parser.add_argument(
+        "--skip-scoring",
+        type=str2bool,
+        default=False,
+        help="""Skip scoring, but still save the ASR output (for eval sets).""",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -590,21 +599,23 @@ def decode_one_batch(
                 )
             hyps.append(sp.decode(hyp).split())
 
+    # prefix = ( "greedy_search" | "fast_beam_search_nbest" | "modified_beam_search" )
+    prefix = f"{params.decoding_method}"
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
     elif "fast_beam_search" in params.decoding_method:
-        key = f"beam_{params.beam}_"
+        prefix += f"_beam-{params.beam}"
-        key += f"max_contexts_{params.max_contexts}_"
+        prefix += f"_max-contexts-{params.max_contexts}"
-        key += f"max_states_{params.max_states}"
+        prefix += f"_max-states-{params.max_states}"
         if "nbest" in params.decoding_method:
-            key += f"_num_paths_{params.num_paths}_"
+            prefix += f"_num-paths-{params.num_paths}"
-            key += f"nbest_scale_{params.nbest_scale}"
+            prefix += f"_nbest-scale-{params.nbest_scale}"
             if "LG" in params.decoding_method:
-                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+                prefix += f"_ngram-lm-scale-{params.ngram_lm_scale}"
 
-        return {key: hyps}
+        return {prefix: hyps}
     elif "modified_beam_search" in params.decoding_method:
-        prefix = f"beam_size_{params.beam_size}"
+        prefix += f"_beam-size-{params.beam_size}"
         if params.decoding_method in (
             "modified_beam_search_lm_rescore",
             "modified_beam_search_lm_rescore_LODR",
@@ -617,10 +628,11 @@ def decode_one_batch(
             return ans
         else:
             if params.has_contexts:
-                prefix += f"-context-score-{params.context_score}"
+                prefix += f"_context-score-{params.context_score}"
             return {prefix: hyps}
     else:
-        return {f"beam_size_{params.beam_size}": hyps}
+        prefix += f"_beam-size-{params.beam_size}"
+        return {prefix: hyps}
 
 
 def decode_dataset(
@@ -707,46 +719,58 @@ def decode_dataset(
     return results
 
 
-def save_results(
+def save_asr_output(
     params: AttributeDict,
     test_set_name: str,
     results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
 ):
+    """
+    Save text produced by ASR.
+    """
+    for key, results in results_dict.items():
+
+        recogs_filename = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
+
+        results = sorted(results)
+        store_transcripts(filename=recogs_filename, texts=results)
+
+        logging.info(f"The transcripts are stored in {recogs_filename}")
+
+
+def save_wer_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str], Tuple]]],
+):
+    """
+    Save WER and per-utterance word alignments.
+    """
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
-        results = sorted(results)
-        store_transcripts(filename=recog_path, texts=results)
-        logging.info(f"The transcripts are stored in {recog_path}")
-
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        with open(errs_filename, "w", encoding="utf8") as fd:
-        )
-        with open(errs_filename, "w") as f:
             wer = write_error_stats(
-                f, f"{test_set_name}-{key}", results, enable_log=True
+                fd, f"{test_set_name}-{key}", results, enable_log=True
             )
             test_set_wers[key] = wer
 
-        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+        logging.info(f"Wrote detailed error stats to {errs_filename}")
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
-    with open(errs_info, "w") as f:
-        print("settings\tWER", file=f)
-        for key, val in test_set_wers:
-            print("{}\t{}".format(key, val), file=f)
 
-    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    wer_filename = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-    note = "\tbest for {}".format(test_set_name)
+
+    with open(wer_filename, "w", encoding="utf8") as fd:
+        print("settings\tWER", file=fd)
+        for key, val in test_set_wers:
+            print(f"{key}\t{val}", file=fd)
+
+    s = f"\nFor {test_set_name}, WER of different settings are:\n"
+    note = f"\tbest for {test_set_name}"
     for key, val in test_set_wers:
-        s += "{}\t{}{}\n".format(key, val, note)
+        s += f"{key}\t{val}{note}\n"
         note = ""
     logging.info(s)
 
@@ -762,6 +786,9 @@ def main():
     params = get_params()
     params.update(vars(args))
 
+    # enable AudioCache
+    set_caching_enabled(True)  # lhotse
+
     assert params.decoding_method in (
         "greedy_search",
         "beam_search",
@@ -783,9 +810,9 @@ def main():
         params.has_contexts = False
 
     if params.iter > 0:
-        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+        params.suffix = f"iter-{params.iter}_avg-{params.avg}"
     else:
-        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+        params.suffix = f"epoch-{params.epoch}_avg-{params.avg}"
 
     if params.causal:
         assert (
@@ -794,20 +821,20 @@ def main():
         assert (
             "," not in params.left_context_frames
         ), "left_context_frames should be one value in decoding."
-        params.suffix += f"-chunk-{params.chunk_size}"
+        params.suffix += f"_chunk-{params.chunk_size}"
-        params.suffix += f"-left-context-{params.left_context_frames}"
+        params.suffix += f"_left-context-{params.left_context_frames}"
 
     if "fast_beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"_beam-{params.beam}"
-        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"_max-contexts-{params.max_contexts}"
-        params.suffix += f"-max-states-{params.max_states}"
+        params.suffix += f"_max-states-{params.max_states}"
         if "nbest" in params.decoding_method:
-            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"_nbest-scale-{params.nbest_scale}"
-            params.suffix += f"-num-paths-{params.num_paths}"
+            params.suffix += f"_num-paths-{params.num_paths}"
             if "LG" in params.decoding_method:
-                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
+                params.suffix += f"_ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        params.suffix += f"__{params.decoding_method}__beam-size-{params.beam_size}"
         if params.decoding_method in (
             "modified_beam_search",
             "modified_beam_search_LODR",
@@ -815,19 +842,19 @@ def main():
             if params.has_contexts:
                 params.suffix += f"-context-score-{params.context_score}"
     else:
-        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"_context-{params.context_size}"
-        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+        params.suffix += f"_max-sym-per-frame-{params.max_sym_per_frame}"
 
     if params.use_shallow_fusion:
-        params.suffix += f"-{params.lm_type}-lm-scale-{params.lm_scale}"
+        params.suffix += f"_{params.lm_type}-lm-scale-{params.lm_scale}"
 
         if "LODR" in params.decoding_method:
             params.suffix += (
-                f"-LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
+                f"_LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
             )
 
     if params.use_averaged_model:
-        params.suffix += "-use-averaged-model"
+        params.suffix += "_use-averaged-model"
 
     setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
     logging.info("Decoding started")
@@ -1038,12 +1065,19 @@ def main():
             ngram_lm_scale=ngram_lm_scale,
         )
 
-        save_results(
+        save_asr_output(
             params=params,
             test_set_name=test_set,
             results_dict=results_dict,
         )
 
+        if not params.skip_scoring:
+            save_wer_results(
+                params=params,
+                test_set_name=test_set,
+                results_dict=results_dict,
+            )
+
     logging.info("Done!")
 
 

egs/librispeech/ASR/zipformer/export-onnx-streaming.py

@@ -74,7 +74,6 @@ import onnx
 import torch
 import torch.nn as nn
 from decoder import Decoder
-from onnxconverter_common import float16
 from onnxruntime.quantization import QuantType, quantize_dynamic
 from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_model, get_params
@@ -488,6 +487,7 @@ def export_encoder_model_onnx(
 
     add_meta_data(filename=encoder_filename, meta_data=meta_data)
 
+
 def export_decoder_model_onnx(
     decoder_model: OnnxDecoder,
     decoder_filename: str,
@@ -755,29 +755,31 @@ def main():
     )
     logging.info(f"Exported joiner to {joiner_filename}")
 
-    if(params.fp16) :
+    if params.fp16:
+        from onnxconverter_common import float16
+
         logging.info("Generate fp16 models")
 
         encoder = onnx.load(encoder_filename)
         encoder_fp16 = float16.convert_float_to_float16(encoder, keep_io_types=True)
         encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
-        onnx.save(encoder_fp16,encoder_filename_fp16)
+        onnx.save(encoder_fp16, encoder_filename_fp16)
 
         decoder = onnx.load(decoder_filename)
         decoder_fp16 = float16.convert_float_to_float16(decoder, keep_io_types=True)
         decoder_filename_fp16 = params.exp_dir / f"decoder-{suffix}.fp16.onnx"
-        onnx.save(decoder_fp16,decoder_filename_fp16)
+        onnx.save(decoder_fp16, decoder_filename_fp16)
 
         joiner = onnx.load(joiner_filename)
         joiner_fp16 = float16.convert_float_to_float16(joiner, keep_io_types=True)
         joiner_filename_fp16 = params.exp_dir / f"joiner-{suffix}.fp16.onnx"
-        onnx.save(joiner_fp16,joiner_filename_fp16)
+        onnx.save(joiner_fp16, joiner_filename_fp16)
 
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
 
     logging.info("Generate int8 quantization models")
-    
+
     encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
     quantize_dynamic(
         model_input=encoder_filename,