small fixes

egs/audioset/AT/zipformer/export-onnx.py

@@ -6,56 +6,28 @@
 """
 This script exports a transducer model from PyTorch to ONNX.
 
-We use the pre-trained model from
+Usage of this script:
-https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
-as an example to show how to use this file.
 
-1. Download the pre-trained model
+  repo_url=https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12
-
-cd egs/librispeech/ASR
-
-repo_url=https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12#/
-GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
   repo=$(basename $repo_url)
-
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-pushd $repo
+  pushd $repo/exp
-git lfs pull --include "exp/pretrained.pt"
+  git lfs pull --include pretrained.pt
-
-cd exp
   ln -s pretrained.pt epoch-99.pt
   popd
 
-2. Export the model to ONNX
+  python3 zipformer/export-onnx.py \
-
+      --exp-dir $repo/exp \
-./zipformer/export-onnx.py \
-  --use-averaged-model 0 \
       --epoch 99 \
       --avg 1 \
-  --exp-dir $repo/exp \
+      --use-averaged-model 0
-  --num-encoder-layers "2,2,3,4,3,2" \
-  --downsampling-factor "1,2,4,8,4,2" \
-  --feedforward-dim "512,768,1024,1536,1024,768" \
-  --num-heads "4,4,4,8,4,4" \
-  --encoder-dim "192,256,384,512,384,256" \
-  --query-head-dim 32 \
-  --value-head-dim 12 \
-  --pos-head-dim 4 \
-  --pos-dim 48 \
-  --encoder-unmasked-dim "192,192,256,256,256,192" \
-  --cnn-module-kernel "31,31,15,15,15,31" \
-  --decoder-dim 512 \
-  --joiner-dim 512 \
-  --causal False \
-  --chunk-size "16,32,64,-1" \
-  --left-context-frames "64,128,256,-1"
 
-It will generate the following 3 files inside $repo/exp:
+  pushd $repo/exp
+  mv model-epoch-99-avg-1.onnx model.onnx
+  mv model-epoch-99-avg-1.int8.onnx model.int8.onnx
+  popd
 
-  - encoder-epoch-99-avg-1.onnx
+See ./onnx_pretrained.py
-  - decoder-epoch-99-avg-1.onnx
-  - joiner-epoch-99-avg-1.onnx
-
-See ./onnx_pretrained.py and ./onnx_check.py for how to
 use the exported ONNX models.
 """
 
@@ -261,6 +233,16 @@ def export_audio_tagging_model_onnx(
     add_meta_data(filename=filename, meta_data=meta_data)
 
 
+def optimize_model(filename):
+    # see https://github.com/microsoft/onnxruntime/issues/1899#issuecomment-534806537
+    from onnx import optimizer
+
+    passes = ["extract_constant_to_initializer", "eliminate_unused_initializer"]
+    onnx_model = onnx.load(filename)
+    optimized_model = optimizer.optimize(onnx_model, passes)
+    onnx.save(optimized_model, filename)
+
+
 @torch.no_grad()
 def main():
     args = get_parser().parse_args()
@@ -389,6 +371,7 @@ def main():
         model_filename,
         opset_version=opset_version,
     )
+    optimized_model(model_filename)
     logging.info(f"Exported audio tagging model to {model_filename}")
 
     # Generate int8 quantization models
@@ -403,6 +386,7 @@ def main():
         op_types_to_quantize=["MatMul"],
         weight_type=QuantType.QInt8,
     )
+    optimized_model(model_filename_int8)
 
 
 if __name__ == "__main__":

egs/audioset/AT/zipformer/export.py

@@ -42,6 +42,7 @@ load it by `torch.jit.load("jit_script.pt")`.
 Check ./jit_pretrained.py for its usage.
 
 Check https://github.com/k2-fsa/sherpa
+and https://github.com/k2-fsa/sherpa-onnx
 for how to use the exported models outside of icefall.
 
 (2) Export `model.state_dict()`
@@ -55,13 +56,13 @@ for how to use the exported models outside of icefall.
 It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
 load it by `icefall.checkpoint.load_checkpoint()`.
 
-To use the generated file with `zipformer/decode.py`,
+To use the generated file with `zipformer/evaluate.py`,
 you can do:
 
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
 
-    cd /path/to/egs/librispeech/ASR
+    cd /path/to/egs/audioset/AT
     ./zipformer/evaluate.py \
         --exp-dir ./zipformer/exp \
         --use-averaged-model False \

egs/audioset/AT/zipformer/jit_pretrained.py

@@ -28,10 +28,20 @@ You can use the following command to get the exported models:
 
 Usage of this script:
 
-./zipformer/jit_pretrained.py \
+  repo_url=https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12
-  --nn-model-filename ./zipformer/exp/cpu_jit.pt \
+  repo=$(basename $repo_url)
-  /path/to/foo.wav \
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-  /path/to/bar.wav
+  pushd $repo/exp
+  git lfs pull --include pretrained.pt
+  popd
+
+  python3 zipformer/jit_pretrained.py \
+      --nn-model-filename $repo/exp/jit_script.pt \
+      --label-dict $repo/data/class_labels_indices.csv \
+      $repo/test_wavs/1.wav \
+      $repo/test_wavs/2.wav \
+      $repo/test_wavs/3.wav \
+      $repo/test_wavs/4.wav
 """
 
 import argparse
@@ -168,7 +178,8 @@ def main():
         topk_prob, topk_index = logit.sigmoid().topk(5)
         topk_labels = [label_dict[index.item()] for index in topk_index]
         logging.info(
-            f"{filename}: Top 5 predicted labels are {topk_labels} with probability of {topk_prob.tolist()}"
+            f"{filename}: Top 5 predicted labels are {topk_labels} with "
+            f"probability of {topk_prob.tolist()}"
         )
 
     logging.info("Done")

egs/audioset/AT/zipformer/onnx_pretrained.py

@@ -17,48 +17,25 @@
 # limitations under the License.
 """
 This script loads ONNX models and uses them to decode waves.
-You can use the following command to get the exported models:
 
-We use the pre-trained model from
+Usage of this script:
-https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12#/
-as an example to show how to use this file.
 
-1. Download the pre-trained model
+  repo_url=https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12
-
-cd egs/librispeech/ASR
-
-repo_url=https://huggingface.co/marcoyang/icefall-audio-tagging-audioset-zipformer-2024-03-12#/
-GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
   repo=$(basename $repo_url)
-
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-pushd $repo
+  pushd $repo/exp
-git lfs pull --include "exp/pretrained.pt"
+  git lfs pull --include "*.onnx"
-
-cd exp
-ln -s pretrained.pt epoch-99.pt
   popd
 
-2. Export the model to ONNX
+  for m in model.onnx model.int8.onnx; do
-
+    python3 zipformer/onnx_pretrained.py \
-./zipformer/export-onnx.py \
+        --model-filename $repo/exp/model.onnx \
-  --use-averaged-model 0 \
+        --label-dict $repo/data/class_labels_indices.csv \
-  --epoch 99 \
+        $repo/test_wavs/1.wav \
-  --avg 1 \
+        $repo/test_wavs/2.wav \
-  --exp-dir $repo/exp \
+        $repo/test_wavs/3.wav \
-  --causal False
+        $repo/test_wavs/4.wav
-
+  done
-It will generate the following 3 files inside $repo/exp:
-
-  - model-epoch-99-avg-1.onnx
-
-3. Run this file
-
-./zipformer/onnx_pretrained.py \
-  --model-filename $repo/exp/model-epoch-99-avg-1.onnx \
-  --tokens $repo/data/lang_bpe_500/tokens.txt \
-  $repo/test_wavs/1089-134686-0001.wav \
-  $repo/test_wavs/1221-135766-0001.wav \
-  $repo/test_wavs/1221-135766-0002.wav
 """
 
 import argparse

egs/audioset/AT/zipformer/pretrained.py

@@ -33,7 +33,10 @@ Usage of this script:
   python3 zipformer/pretrained.py \
     --checkpoint $repo/exp/pretrained.pt \
     --label-dict $repo/data/class_labels_indices.csv \
-    $repo/test_wavs/1.wav
+    $repo/test_wavs/1.wav \
+    $repo/test_wavs/2.wav \
+    $repo/test_wavs/3.wav \
+    $repo/test_wavs/4.wav
 """