Support exporting to ONNX for the wenetspeech recipe (#615)

* Support exporting to ONNX for the wenetspeech recipe

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

@@ -0,0 +1,124 @@
+#!/usr/bin/env bash
+
+set -e
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/wenetspeech/ASR
+
+repo_url=https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+git clone $repo_url
+repo=$(basename $repo_url)
+
+
+log "Display test files"
+tree $repo/
+soxi $repo/test_wavs/*.wav
+ls -lh $repo/test_wavs/*.wav
+
+pushd $repo/exp
+ln -s pretrained_epoch_10_avg_2.pt pretrained.pt
+ln -s pretrained_epoch_10_avg_2.pt epoch-99.pt
+popd
+
+log "Test exporting to ONNX format"
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir $repo/exp \
+  --lang-dir $repo/data/lang_char \
+  --epoch 99 \
+  --avg 1 \
+  --onnx 1
+
+log "Export to torchscript model"
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir $repo/exp \
+  --lang-dir $repo/data/lang_char \
+  --epoch 99 \
+  --avg 1 \
+  --jit 1
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir $repo/exp \
+  --lang-dir $repo/data/lang_char \
+  --epoch 99 \
+  --avg 1 \
+  --jit-trace 1
+
+ls -lh $repo/exp/*.onnx
+ls -lh $repo/exp/*.pt
+
+log "Decode with ONNX models"
+
+./pruned_transducer_stateless2/onnx_check.py \
+  --jit-filename $repo/exp/cpu_jit.pt \
+  --onnx-encoder-filename $repo/exp/encoder.onnx \
+  --onnx-decoder-filename $repo/exp/decoder.onnx \
+  --onnx-joiner-filename $repo/exp/joiner.onnx \
+  --onnx-joiner-encoder-proj-filename $repo/exp/joiner_encoder_proj.onnx \
+  --onnx-joiner-decoder-proj-filename $repo/exp/joiner_decoder_proj.onnx
+
+./pruned_transducer_stateless2/onnx_pretrained.py \
+  --tokens $repo/data/lang_char/tokens.txt \
+  --encoder-model-filename $repo/exp/encoder.onnx \
+  --decoder-model-filename $repo/exp/decoder.onnx \
+  --joiner-model-filename $repo/exp/joiner.onnx \
+  --joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
+  --joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.onnx \
+  $repo/test_wavs/DEV_T0000000000.wav \
+  $repo/test_wavs/DEV_T0000000001.wav \
+  $repo/test_wavs/DEV_T0000000002.wav
+
+log "Decode with models exported by torch.jit.trace()"
+
+./pruned_transducer_stateless2/jit_pretrained.py \
+  --tokens $repo/data/lang_char/tokens.txt \
+  --encoder-model-filename $repo/exp/encoder_jit_trace.pt \
+  --decoder-model-filename $repo/exp/decoder_jit_trace.pt \
+  --joiner-model-filename $repo/exp/joiner_jit_trace.pt \
+  $repo/test_wavs/DEV_T0000000000.wav \
+  $repo/test_wavs/DEV_T0000000001.wav \
+  $repo/test_wavs/DEV_T0000000002.wav
+
+./pruned_transducer_stateless2/jit_pretrained.py \
+  --tokens $repo/data/lang_char/tokens.txt \
+  --encoder-model-filename $repo/exp/encoder_jit_script.pt \
+  --decoder-model-filename $repo/exp/decoder_jit_script.pt \
+  --joiner-model-filename $repo/exp/joiner_jit_script.pt \
+  $repo/test_wavs/DEV_T0000000000.wav \
+  $repo/test_wavs/DEV_T0000000001.wav \
+  $repo/test_wavs/DEV_T0000000002.wav
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless2/pretrained.py \
+    --checkpoint $repo/exp/epoch-99.pt \
+    --lang-dir $repo/data/lang_char \
+    --decoding-method greedy_search \
+    --max-sym-per-frame $sym \
+  $repo/test_wavs/DEV_T0000000000.wav \
+  $repo/test_wavs/DEV_T0000000001.wav \
+  $repo/test_wavs/DEV_T0000000002.wav
+done
+
+for method in modified_beam_search beam_search fast_beam_search; do
+  log "$method"
+
+  ./pruned_transducer_stateless2/pretrained.py \
+    --decoding-method $method \
+    --beam-size 4 \
+    --checkpoint $repo/exp/epoch-99.pt \
+    --lang-dir $repo/data/lang_char \
+  $repo/test_wavs/DEV_T0000000000.wav \
+  $repo/test_wavs/DEV_T0000000001.wav \
+  $repo/test_wavs/DEV_T0000000002.wav
+done

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

@@ -0,0 +1,80 @@
+# Copyright      2021  Fangjun Kuang (csukuangfj@gmail.com)
+
+# See ../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: run-wenetspeech-pruned-transducer-stateless2
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+jobs:
+  run_librispeech_pruned_transducer_stateless3_2022_05_13:
+    if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'wenetspeech'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-18.04]
+        python-version: [3.8]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
+
+      - name: Install Python dependencies
+        run: |
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
+          pip uninstall -y protobuf
+          pip install --no-binary protobuf protobuf
+
+      - name: Cache kaldifeat
+        id: my-cache
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}-2022-09-25
+
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/install-kaldifeat.sh
+
+      - name: Inference with pre-trained model
+        shell: bash
+        env:
+          GITHUB_EVENT_NAME: ${{ github.event_name }}
+          GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
+        run: |
+          sudo apt-get -qq install git-lfs tree sox
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
+          .github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh

egs/librispeech/ASR/pruned_transducer_stateless3/export.py

@@ -73,6 +73,10 @@ Check `onnx_check.py` for how to use them.
 
 Please see ./onnx_pretrained.py for usage of the generated files
 
+Check
+https://github.com/k2-fsa/sherpa-onnx
+for how to use the exported models outside of icefall.
+
 (4) Export `model.state_dict()`
 
 ./pruned_transducer_stateless3/export.py \

egs/librispeech/ASR/pruned_transducer_stateless3/onnx_pretrained.py

@@ -31,6 +31,8 @@ Usage of this script:
   --encoder-model-filename ./pruned_transducer_stateless3/exp/encoder.onnx \
   --decoder-model-filename ./pruned_transducer_stateless3/exp/decoder.onnx \
   --joiner-model-filename ./pruned_transducer_stateless3/exp/joiner.onnx \
+  --joiner-encoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_encoder_proj.onnx \
+  --joiner-decoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_decoder_proj.onnx \
   --bpe-model ./data/lang_bpe_500/bpe.model \
   /path/to/foo.wav \
   /path/to/bar.wav

egs/wenetspeech/ASR/pruned_transducer_stateless2/export.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
@@ -18,6 +19,64 @@
 # to a single one using model averaging.
 """
 Usage:
+
+(1) Export to torchscript model using torch.jit.script()
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --lang-dir data/lang_char \
+  --epoch 10 \
+  --avg 2 \
+  --jit 1
+
+It will generate a file `cpu_jit.pt` in the given `exp_dir`. You can later
+load it by `torch.jit.load("cpu_jit.pt")`.
+
+Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
+are on CPU. You can use `to("cuda")` to move them to a CUDA device.
+
+Please refer to
+https://k2-fsa.github.io/sherpa/python/offline_asr/conformer/index.html
+for how to use `cpu_jit.pt` for speech recognition.
+
+It will also generate 3 other files: `encoder_jit_script.pt`,
+`decoder_jit_script.pt`, and `joiner_jit_script.pt`. Check ./jit_pretrained.py
+for how to use them.
+
+(2) Export to torchscript model using torch.jit.trace()
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --lang-dir data/lang_char \
+  --epoch 10 \
+  --avg 2 \
+  --jit-trace 1
+
+It will generate the following files:
+    - encoder_jit_trace.pt
+    - decoder_jit_trace.pt
+    - joiner_jit_trace.pt
+
+Check ./jit_pretrained.py for usage.
+
+(3) Export to ONNX format
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --lang-dir data/lang_char \
+  --epoch 10 \
+  --avg 2 \
+  --onnx 1
+
+Refer to ./onnx_check.py and ./onnx_pretrained.py
+for usage.
+
+Check
+https://github.com/k2-fsa/sherpa-onnx
+for how to use the exported models outside of icefall.
+
+(4) Export `model.state_dict()`
+
 ./pruned_transducer_stateless2/export.py \
   --exp-dir ./pruned_transducer_stateless2/exp \
   --lang-dir data/lang_char \
@@ -35,10 +94,13 @@ you can do:
     cd /path/to/egs/wenetspeech/ASR
     ./pruned_transducer_stateless2/decode.py \
         --exp-dir ./pruned_transducer_stateless2/exp \
-        --epoch 10 \
+        --epoch 9999 \
-        --avg 2 \
+        --avg 1 \
         --max-duration 100 \
         --lang-dir data/lang_char
+
+You can find pretrained models at
+https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2/tree/main/exp
 """
 
 import argparse
@@ -46,6 +108,8 @@ import logging
 from pathlib import Path
 
 import torch
+import torch.nn as nn
+from scaling_converter import convert_scaled_to_non_scaled
 from train import get_params, get_transducer_model
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
@@ -96,6 +160,44 @@ def get_parser():
         type=str2bool,
         default=False,
         help="""True to save a model after applying torch.jit.script.
+        It will generate 4 files:
+         - encoder_jit_script.pt
+         - decoder_jit_script.pt
+         - joiner_jit_script.pt
+         - cpu_jit.pt (which combines the above 3 files)
+
+        Check ./jit_pretrained.py for how to use xxx_jit_script.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--jit-trace",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.trace.
+        It will generate 3 files:
+         - encoder_jit_trace.pt
+         - decoder_jit_trace.pt
+         - joiner_jit_trace.pt
+
+        Check ./jit_pretrained.py for how to use them.
+        """,
+    )
+
+    parser.add_argument(
+        "--onnx",
+        type=str2bool,
+        default=False,
+        help="""If True, --jit is ignored and it exports the model
+        to onnx format. It will generate the following files:
+
+            - encoder.onnx
+            - decoder.onnx
+            - joiner.onnx
+            - joiner_encoder_proj.onnx
+            - joiner_decoder_proj.onnx
+
+        Refer to ./onnx_check.py and ./onnx_pretrained.py for how to use them.
         """,
     )
 
@@ -110,6 +212,332 @@ def get_parser():
     return parser
 
 
+def export_encoder_model_jit_script(
+    encoder_model: nn.Module,
+    encoder_filename: str,
+) -> None:
+    """Export the given encoder model with torch.jit.script()
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported model.
+    """
+    script_model = torch.jit.script(encoder_model)
+    script_model.save(encoder_filename)
+    logging.info(f"Saved to {encoder_filename}")
+
+
+def export_decoder_model_jit_script(
+    decoder_model: nn.Module,
+    decoder_filename: str,
+) -> None:
+    """Export the given decoder model with torch.jit.script()
+
+    Args:
+      decoder_model:
+        The input decoder model
+      decoder_filename:
+        The filename to save the exported model.
+    """
+    script_model = torch.jit.script(decoder_model)
+    script_model.save(decoder_filename)
+    logging.info(f"Saved to {decoder_filename}")
+
+
+def export_joiner_model_jit_script(
+    joiner_model: nn.Module,
+    joiner_filename: str,
+) -> None:
+    """Export the given joiner model with torch.jit.trace()
+
+    Args:
+      joiner_model:
+        The input joiner model
+      joiner_filename:
+        The filename to save the exported model.
+    """
+    script_model = torch.jit.script(joiner_model)
+    script_model.save(joiner_filename)
+    logging.info(f"Saved to {joiner_filename}")
+
+
+def export_encoder_model_jit_trace(
+    encoder_model: nn.Module,
+    encoder_filename: str,
+) -> None:
+    """Export the given encoder model with torch.jit.trace()
+
+    Note: The warmup argument is fixed to 1.
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported model.
+    """
+    x = torch.zeros(1, 100, 80, dtype=torch.float32)
+    x_lens = torch.tensor([100], dtype=torch.int64)
+
+    traced_model = torch.jit.trace(encoder_model, (x, x_lens))
+    traced_model.save(encoder_filename)
+    logging.info(f"Saved to {encoder_filename}")
+
+
+def export_decoder_model_jit_trace(
+    decoder_model: nn.Module,
+    decoder_filename: str,
+) -> None:
+    """Export the given decoder model with torch.jit.trace()
+
+    Note: The argument need_pad is fixed to False.
+
+    Args:
+      decoder_model:
+        The input decoder model
+      decoder_filename:
+        The filename to save the exported model.
+    """
+    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
+    need_pad = torch.tensor([False])
+
+    traced_model = torch.jit.trace(decoder_model, (y, need_pad))
+    traced_model.save(decoder_filename)
+    logging.info(f"Saved to {decoder_filename}")
+
+
+def export_joiner_model_jit_trace(
+    joiner_model: nn.Module,
+    joiner_filename: str,
+) -> None:
+    """Export the given joiner model with torch.jit.trace()
+
+    Note: The argument project_input is fixed to True. A user should not
+    project the encoder_out/decoder_out by himself/herself. The exported joiner
+    will do that for the user.
+
+    Args:
+      joiner_model:
+        The input joiner model
+      joiner_filename:
+        The filename to save the exported model.
+
+    """
+    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
+    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
+    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
+    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
+
+    traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out))
+    traced_model.save(joiner_filename)
+    logging.info(f"Saved to {joiner_filename}")
+
+
+def export_encoder_model_onnx(
+    encoder_model: nn.Module,
+    encoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the given encoder model to ONNX format.
+    The exported model has two inputs:
+
+        - x, a tensor of shape (N, T, C); dtype is torch.float32
+        - x_lens, a tensor of shape (N,); dtype is torch.int64
+
+    and it has two outputs:
+
+        - encoder_out, a tensor of shape (N, T, C)
+        - encoder_out_lens, a tensor of shape (N,)
+
+    Note: The warmup argument is fixed to 1.
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    x = torch.zeros(1, 100, 80, dtype=torch.float32)
+    x_lens = torch.tensor([100], dtype=torch.int64)
+
+    #  encoder_model = torch.jit.script(encoder_model)
+    # It throws the following error for the above statement
+    #
+    # RuntimeError: Exporting the operator __is_ to ONNX opset version
+    # 11 is not supported. Please feel free to request support or
+    # submit a pull request on PyTorch GitHub.
+    #
+    # I cannot find which statement causes the above error.
+    # torch.onnx.export() will use torch.jit.trace() internally, which
+    # works well for the current reworked model
+    warmup = 1.0
+    torch.onnx.export(
+        encoder_model,
+        (x, x_lens, warmup),
+        encoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["x", "x_lens", "warmup"],
+        output_names=["encoder_out", "encoder_out_lens"],
+        dynamic_axes={
+            "x": {0: "N", 1: "T"},
+            "x_lens": {0: "N"},
+            "encoder_out": {0: "N", 1: "T"},
+            "encoder_out_lens": {0: "N"},
+        },
+    )
+    logging.info(f"Saved to {encoder_filename}")
+
+
+def export_decoder_model_onnx(
+    decoder_model: nn.Module,
+    decoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the decoder model to ONNX format.
+
+    The exported model has one input:
+
+        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
+
+    and has one output:
+
+        - decoder_out: a torch.float32 tensor of shape (N, 1, C)
+
+    Note: The argument need_pad is fixed to False.
+
+    Args:
+      decoder_model:
+        The decoder model to be exported.
+      decoder_filename:
+        Filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
+    need_pad = False  # Always False, so we can use torch.jit.trace() here
+    # Note(fangjun): torch.jit.trace() is more efficient than torch.jit.script()
+    # in this case
+    torch.onnx.export(
+        decoder_model,
+        (y, need_pad),
+        decoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["y", "need_pad"],
+        output_names=["decoder_out"],
+        dynamic_axes={
+            "y": {0: "N"},
+            "decoder_out": {0: "N"},
+        },
+    )
+    logging.info(f"Saved to {decoder_filename}")
+
+
+def export_joiner_model_onnx(
+    joiner_model: nn.Module,
+    joiner_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the joiner model to ONNX format.
+    The exported joiner model has two inputs:
+
+        - projected_encoder_out: a tensor of shape (N, joiner_dim)
+        - projected_decoder_out: a tensor of shape (N, joiner_dim)
+
+    and produces one output:
+
+        - logit: a tensor of shape (N, vocab_size)
+
+    The exported encoder_proj model has one input:
+
+        - encoder_out: a tensor of shape (N, encoder_out_dim)
+
+    and produces one output:
+
+        - projected_encoder_out: a tensor of shape (N, joiner_dim)
+
+    The exported decoder_proj model has one input:
+
+        - decoder_out: a tensor of shape (N, decoder_out_dim)
+
+    and produces one output:
+
+        - projected_decoder_out: a tensor of shape (N, joiner_dim)
+    """
+    encoder_proj_filename = str(joiner_filename).replace(
+        ".onnx", "_encoder_proj.onnx"
+    )
+
+    decoder_proj_filename = str(joiner_filename).replace(
+        ".onnx", "_decoder_proj.onnx"
+    )
+
+    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
+    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
+    joiner_dim = joiner_model.decoder_proj.weight.shape[0]
+
+    projected_encoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
+    projected_decoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
+
+    project_input = False
+    # Note: It uses torch.jit.trace() internally
+    torch.onnx.export(
+        joiner_model,
+        (projected_encoder_out, projected_decoder_out, project_input),
+        joiner_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=[
+            "projected_encoder_out",
+            "projected_decoder_out",
+            "project_input",
+        ],
+        output_names=["logit"],
+        dynamic_axes={
+            "projected_encoder_out": {0: "N"},
+            "projected_decoder_out": {0: "N"},
+            "logit": {0: "N"},
+        },
+    )
+    logging.info(f"Saved to {joiner_filename}")
+
+    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
+    torch.onnx.export(
+        joiner_model.encoder_proj,
+        encoder_out,
+        encoder_proj_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["encoder_out"],
+        output_names=["projected_encoder_out"],
+        dynamic_axes={
+            "encoder_out": {0: "N"},
+            "projected_encoder_out": {0: "N"},
+        },
+    )
+    logging.info(f"Saved to {encoder_proj_filename}")
+
+    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
+    torch.onnx.export(
+        joiner_model.decoder_proj,
+        decoder_out,
+        decoder_proj_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["decoder_out"],
+        output_names=["projected_decoder_out"],
+        dynamic_axes={
+            "decoder_out": {0: "N"},
+            "projected_decoder_out": {0: "N"},
+        },
+    )
+    logging.info(f"Saved to {decoder_proj_filename}")
+
+
 def main():
     args = get_parser().parse_args()
     args.exp_dir = Path(args.exp_dir)
@@ -147,22 +575,66 @@ def main():
         model.to(device)
         model.load_state_dict(average_checkpoints(filenames, device=device))
 
-    model.eval()
-
     model.to("cpu")
     model.eval()
 
-    if params.jit:
+    if params.onnx is True:
+        convert_scaled_to_non_scaled(model, inplace=True)
+        opset_version = 11
+        logging.info("Exporting to onnx format")
+        encoder_filename = params.exp_dir / "encoder.onnx"
+        export_encoder_model_onnx(
+            model.encoder,
+            encoder_filename,
+            opset_version=opset_version,
+        )
+
+        decoder_filename = params.exp_dir / "decoder.onnx"
+        export_decoder_model_onnx(
+            model.decoder,
+            decoder_filename,
+            opset_version=opset_version,
+        )
+
+        joiner_filename = params.exp_dir / "joiner.onnx"
+        export_joiner_model_onnx(
+            model.joiner,
+            joiner_filename,
+            opset_version=opset_version,
+        )
+    elif params.jit:
+        convert_scaled_to_non_scaled(model, inplace=True)
+        logging.info("Using torch.jit.script")
         # We won't use the forward() method of the model in C++, so just ignore
         # it here.
         # Otherwise, one of its arguments is a ragged tensor and is not
         # torch scriptabe.
         model.__class__.forward = torch.jit.ignore(model.__class__.forward)
-        logging.info("Using torch.jit.script")
         model = torch.jit.script(model)
         filename = params.exp_dir / "cpu_jit.pt"
         model.save(str(filename))
         logging.info(f"Saved to {filename}")
+
+        # Also export encoder/decoder/joiner separately
+        encoder_filename = params.exp_dir / "encoder_jit_script.pt"
+        export_encoder_model_jit_script(model.encoder, encoder_filename)
+
+        decoder_filename = params.exp_dir / "decoder_jit_script.pt"
+        export_decoder_model_jit_script(model.decoder, decoder_filename)
+
+        joiner_filename = params.exp_dir / "joiner_jit_script.pt"
+        export_joiner_model_jit_script(model.joiner, joiner_filename)
+    elif params.jit_trace is True:
+        convert_scaled_to_non_scaled(model, inplace=True)
+        logging.info("Using torch.jit.trace()")
+        encoder_filename = params.exp_dir / "encoder_jit_trace.pt"
+        export_encoder_model_jit_trace(model.encoder, encoder_filename)
+
+        decoder_filename = params.exp_dir / "decoder_jit_trace.pt"
+        export_decoder_model_jit_trace(model.decoder, decoder_filename)
+
+        joiner_filename = params.exp_dir / "joiner_jit_trace.pt"
+        export_joiner_model_jit_trace(model.joiner, joiner_filename)
     else:
         logging.info("Not using torch.jit.script")
         # Save it using a format so that it can be loaded

egs/wenetspeech/ASR/pruned_transducer_stateless2/jit_pretrained.py

@@ -0,0 +1,339 @@
+#!/usr/bin/env python3
+# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script loads torchscript models, either exported by `torch.jit.trace()`
+or by `torch.jit.script()`, and uses them to decode waves.
+You can use the following command to get the exported models:
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --tokens data/lang_char/tokens.txt \
+  --epoch 10 \
+  --avg 2 \
+  --jit-trace 1
+
+or
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --tokens data/lang_char/tokens.txt \
+  --epoch 10 \
+  --avg 2 \
+  --jit 1
+
+Usage of this script:
+
+./pruned_transducer_stateless2/jit_pretrained.py \
+  --encoder-model-filename ./pruned_transducer_stateless2/exp/encoder_jit_trace.pt \
+  --decoder-model-filename ./pruned_transducer_stateless2/exp/decoder_jit_trace.pt \
+  --joiner-model-filename ./pruned_transducer_stateless2/exp/joiner_jit_trace.pt \
+  --tokens data/lang_char/tokens.txt \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+or
+
+./pruned_transducer_stateless2/jit_pretrained.py \
+  --encoder-model-filename ./pruned_transducer_stateless2/exp/encoder_jit_script.pt \
+  --decoder-model-filename ./pruned_transducer_stateless2/exp/decoder_jit_script.pt \
+  --joiner-model-filename ./pruned_transducer_stateless2/exp/joiner_jit_script.pt \
+  --tokens data/lang_char/tokens.txt \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+You can find pretrained models at
+https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2/tree/main/exp
+"""
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from torch.nn.utils.rnn import pad_sequence
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--encoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the encoder torchscript model. ",
+    )
+
+    parser.add_argument(
+        "--decoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the decoder torchscript model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner torchscript model. ",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="""Path to tokens.txt""",
+    )
+
+    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(
+        "--context-size",
+        type=int,
+        default=2,
+        help="Context size of the decoder model",
+    )
+
+    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}. "
+            f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def greedy_search(
+    decoder: torch.jit.ScriptModule,
+    joiner: torch.jit.ScriptModule,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    context_size: int,
+) -> List[List[int]]:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
+    Args:
+      decoder:
+        The decoder model.
+      joiner:
+        The joiner model.
+      encoder_out:
+        A 3-D tensor of shape (N, T, C)
+      encoder_out_lens:
+        A 1-D tensor of shape (N,).
+      context_size:
+        The context size of the decoder model.
+    Returns:
+      Return the decoded results for each utterance.
+    """
+    assert encoder_out.ndim == 3
+    assert encoder_out.size(0) >= 1, encoder_out.size(0)
+
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
+
+    device = encoder_out.device
+    blank_id = 0  # hard-code to 0
+
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    hyps = [[blank_id] * context_size for _ in range(N)]
+
+    decoder_input = torch.tensor(
+        hyps,
+        device=device,
+        dtype=torch.int64,
+    )  # (N, context_size)
+
+    decoder_out = decoder(
+        decoder_input,
+        need_pad=torch.tensor([False]),
+    ).squeeze(1)
+
+    offset = 0
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = packed_encoder_out.data[start:end]
+        current_encoder_out = current_encoder_out
+        # current_encoder_out's shape: (batch_size, encoder_out_dim)
+        offset = end
+
+        decoder_out = decoder_out[:batch_size]
+
+        logits = joiner(
+            current_encoder_out,
+            decoder_out,
+        )
+        # logits'shape (batch_size, vocab_size)
+
+        assert logits.ndim == 2, logits.shape
+        y = logits.argmax(dim=1).tolist()
+        emitted = False
+        for i, v in enumerate(y):
+            if v != blank_id:
+                hyps[i].append(v)
+                emitted = True
+        if emitted:
+            # update decoder output
+            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
+            decoder_input = torch.tensor(
+                decoder_input,
+                device=device,
+                dtype=torch.int64,
+            )
+            decoder_out = decoder(
+                decoder_input,
+                need_pad=torch.tensor([False]),
+            )
+            decoder_out = decoder_out.squeeze(1)
+
+    sorted_ans = [h[context_size:] for h in hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
+
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    logging.info(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    encoder = torch.jit.load(args.encoder_model_filename)
+    decoder = torch.jit.load(args.decoder_model_filename)
+    joiner = torch.jit.load(args.joiner_model_filename)
+
+    encoder.eval()
+    decoder.eval()
+    joiner.eval()
+
+    encoder.to(device)
+    decoder.to(device)
+    joiner.to(device)
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = args.sample_rate
+    opts.mel_opts.num_bins = 80
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {args.sound_files}")
+    waves = read_sound_files(
+        filenames=args.sound_files,
+        expected_sample_rate=args.sample_rate,
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(
+        features,
+        batch_first=True,
+        padding_value=math.log(1e-10),
+    )
+
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    encoder_out, encoder_out_lens = encoder(
+        x=features,
+        x_lens=feature_lengths,
+    )
+
+    hyps = greedy_search(
+        decoder=decoder,
+        joiner=joiner,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        context_size=args.context_size,
+    )
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+    s = "\n"
+    for filename, hyp in zip(args.sound_files, hyps):
+        words = "".join([symbol_table[i] for i in hyp])
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/wenetspeech/ASR/pruned_transducer_stateless2/onnx_check.py

@@ -0,0 +1,307 @@
+#!/usr/bin/env python3
+#
+# Copyright 2022 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script checks that exported onnx models produce the same output
+with the given torchscript model for the same input.
+
+Usage:
+
+./pruned_transducer_stateless2/onnx_check.py \
+  --jit-filename ./t/cpu_jit.pt \
+  --onnx-encoder-filename ./t/encoder.onnx \
+  --onnx-decoder-filename ./t/decoder.onnx \
+  --onnx-joiner-filename ./t/joiner.onnx \
+  --onnx-joiner-encoder-proj-filename ./t/joiner_encoder_proj.onnx \
+  --onnx-joiner-decoder-proj-filename ./t/joiner_decoder_proj.onnx
+
+You can generate cpu_jit.pt, encoder.onnx, decoder.onnx, and other
+xxx.onnx files using ./export.py
+
+We provide pretrained models at:
+https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2/tree/main/exp
+"""
+
+import argparse
+import logging
+
+import onnxruntime as ort
+import torch
+
+ort.set_default_logger_severity(3)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--jit-filename",
+        required=True,
+        type=str,
+        help="Path to the torchscript model exported by torch.jit.script",
+    )
+
+    parser.add_argument(
+        "--onnx-encoder-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx encoder model",
+    )
+
+    parser.add_argument(
+        "--onnx-decoder-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx decoder model",
+    )
+
+    parser.add_argument(
+        "--onnx-joiner-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx joiner model",
+    )
+
+    parser.add_argument(
+        "--onnx-joiner-encoder-proj-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx joiner encoder projection model",
+    )
+
+    parser.add_argument(
+        "--onnx-joiner-decoder-proj-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx joiner decoder projection model",
+    )
+
+    return parser
+
+
+def test_encoder(
+    model: torch.jit.ScriptModule,
+    encoder_session: ort.InferenceSession,
+):
+    inputs = encoder_session.get_inputs()
+    outputs = encoder_session.get_outputs()
+    input_names = [n.name for n in inputs]
+    output_names = [n.name for n in outputs]
+
+    assert inputs[0].shape == ["N", "T", 80]
+    assert inputs[1].shape == ["N"]
+
+    for N in [1, 5]:
+        for T in [12, 25]:
+            print("N, T", N, T)
+            x = torch.rand(N, T, 80, dtype=torch.float32)
+            x_lens = torch.randint(low=10, high=T + 1, size=(N,))
+            x_lens[0] = T
+
+            encoder_inputs = {
+                input_names[0]: x.numpy(),
+                input_names[1]: x_lens.numpy(),
+            }
+            encoder_out, encoder_out_lens = encoder_session.run(
+                output_names,
+                encoder_inputs,
+            )
+
+            torch_encoder_out, torch_encoder_out_lens = model.encoder(x, x_lens)
+
+            encoder_out = torch.from_numpy(encoder_out)
+            assert torch.allclose(encoder_out, torch_encoder_out, atol=1e-05), (
+                (encoder_out - torch_encoder_out).abs().max(),
+                encoder_out.shape,
+                torch_encoder_out.shape,
+            )
+
+
+def test_decoder(
+    model: torch.jit.ScriptModule,
+    decoder_session: ort.InferenceSession,
+):
+    inputs = decoder_session.get_inputs()
+    outputs = decoder_session.get_outputs()
+    input_names = [n.name for n in inputs]
+    output_names = [n.name for n in outputs]
+
+    assert inputs[0].shape == ["N", 2]
+    for N in [1, 5, 10]:
+        y = torch.randint(low=1, high=500, size=(10, 2))
+
+        decoder_inputs = {input_names[0]: y.numpy()}
+        decoder_out = decoder_session.run(
+            output_names,
+            decoder_inputs,
+        )[0]
+        decoder_out = torch.from_numpy(decoder_out)
+
+        torch_decoder_out = model.decoder(y, need_pad=False)
+        assert torch.allclose(decoder_out, torch_decoder_out, atol=1e-5), (
+            (decoder_out - torch_decoder_out).abs().max()
+        )
+
+
+def test_joiner(
+    model: torch.jit.ScriptModule,
+    joiner_session: ort.InferenceSession,
+    joiner_encoder_proj_session: ort.InferenceSession,
+    joiner_decoder_proj_session: ort.InferenceSession,
+):
+    joiner_inputs = joiner_session.get_inputs()
+    joiner_outputs = joiner_session.get_outputs()
+    joiner_input_names = [n.name for n in joiner_inputs]
+    joiner_output_names = [n.name for n in joiner_outputs]
+
+    assert joiner_inputs[0].shape == ["N", 512]
+    assert joiner_inputs[1].shape == ["N", 512]
+
+    joiner_encoder_proj_inputs = joiner_encoder_proj_session.get_inputs()
+    encoder_proj_input_name = joiner_encoder_proj_inputs[0].name
+
+    assert joiner_encoder_proj_inputs[0].shape == ["N", 512]
+
+    joiner_encoder_proj_outputs = joiner_encoder_proj_session.get_outputs()
+    encoder_proj_output_name = joiner_encoder_proj_outputs[0].name
+
+    joiner_decoder_proj_inputs = joiner_decoder_proj_session.get_inputs()
+    decoder_proj_input_name = joiner_decoder_proj_inputs[0].name
+
+    assert joiner_decoder_proj_inputs[0].shape == ["N", 512]
+
+    joiner_decoder_proj_outputs = joiner_decoder_proj_session.get_outputs()
+    decoder_proj_output_name = joiner_decoder_proj_outputs[0].name
+
+    for N in [1, 5, 10]:
+        encoder_out = torch.rand(N, 512)
+        decoder_out = torch.rand(N, 512)
+
+        projected_encoder_out = torch.rand(N, 512)
+        projected_decoder_out = torch.rand(N, 512)
+
+        joiner_inputs = {
+            joiner_input_names[0]: projected_encoder_out.numpy(),
+            joiner_input_names[1]: projected_decoder_out.numpy(),
+        }
+        joiner_out = joiner_session.run(joiner_output_names, joiner_inputs)[0]
+        joiner_out = torch.from_numpy(joiner_out)
+
+        torch_joiner_out = model.joiner(
+            projected_encoder_out,
+            projected_decoder_out,
+            project_input=False,
+        )
+        assert torch.allclose(joiner_out, torch_joiner_out, atol=1e-5), (
+            (joiner_out - torch_joiner_out).abs().max()
+        )
+
+        # Now test encoder_proj
+        joiner_encoder_proj_inputs = {
+            encoder_proj_input_name: encoder_out.numpy()
+        }
+        joiner_encoder_proj_out = joiner_encoder_proj_session.run(
+            [encoder_proj_output_name], joiner_encoder_proj_inputs
+        )[0]
+        joiner_encoder_proj_out = torch.from_numpy(joiner_encoder_proj_out)
+
+        torch_joiner_encoder_proj_out = model.joiner.encoder_proj(encoder_out)
+        assert torch.allclose(
+            joiner_encoder_proj_out, torch_joiner_encoder_proj_out, atol=1e-5
+        ), (
+            (joiner_encoder_proj_out - torch_joiner_encoder_proj_out)
+            .abs()
+            .max()
+        )
+
+        # Now test decoder_proj
+        joiner_decoder_proj_inputs = {
+            decoder_proj_input_name: decoder_out.numpy()
+        }
+        joiner_decoder_proj_out = joiner_decoder_proj_session.run(
+            [decoder_proj_output_name], joiner_decoder_proj_inputs
+        )[0]
+        joiner_decoder_proj_out = torch.from_numpy(joiner_decoder_proj_out)
+
+        torch_joiner_decoder_proj_out = model.joiner.decoder_proj(decoder_out)
+        assert torch.allclose(
+            joiner_decoder_proj_out, torch_joiner_decoder_proj_out, atol=1e-5
+        ), (
+            (joiner_decoder_proj_out - torch_joiner_decoder_proj_out)
+            .abs()
+            .max()
+        )
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    logging.info(vars(args))
+
+    model = torch.jit.load(args.jit_filename)
+
+    options = ort.SessionOptions()
+    options.inter_op_num_threads = 1
+    options.intra_op_num_threads = 1
+
+    logging.info("Test encoder")
+    encoder_session = ort.InferenceSession(
+        args.onnx_encoder_filename,
+        sess_options=options,
+    )
+    test_encoder(model, encoder_session)
+
+    logging.info("Test decoder")
+    decoder_session = ort.InferenceSession(
+        args.onnx_decoder_filename,
+        sess_options=options,
+    )
+    test_decoder(model, decoder_session)
+
+    logging.info("Test joiner")
+    joiner_session = ort.InferenceSession(
+        args.onnx_joiner_filename,
+        sess_options=options,
+    )
+    joiner_encoder_proj_session = ort.InferenceSession(
+        args.onnx_joiner_encoder_proj_filename,
+        sess_options=options,
+    )
+    joiner_decoder_proj_session = ort.InferenceSession(
+        args.onnx_joiner_decoder_proj_filename,
+        sess_options=options,
+    )
+    test_joiner(
+        model,
+        joiner_session,
+        joiner_encoder_proj_session,
+        joiner_decoder_proj_session,
+    )
+    logging.info("Finished checking ONNX models")
+
+
+if __name__ == "__main__":
+    torch.manual_seed(20220727)
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/wenetspeech/ASR/pruned_transducer_stateless2/onnx_pretrained.py

@@ -0,0 +1,391 @@
+#!/usr/bin/env python3
+# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script loads ONNX models and uses them to decode waves.
+You can use the following command to get the exported models:
+
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless3/exp \
+  --lang-dir data/lang_char \
+  --epoch 20 \
+  --avg 10 \
+  --onnx 1
+
+Usage of this script:
+
+./pruned_transducer_stateless3/onnx_pretrained.py \
+  --encoder-model-filename ./pruned_transducer_stateless3/exp/encoder.onnx \
+  --decoder-model-filename ./pruned_transducer_stateless3/exp/decoder.onnx \
+  --joiner-model-filename ./pruned_transducer_stateless3/exp/joiner.onnx \
+  --joiner-encoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_encoder_proj.onnx \
+  --joiner-decoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_decoder_proj.onnx \
+  --tokens data/lang_char/tokens.txt \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+We provide pretrained models at:
+https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2/tree/main/exp
+"""
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import numpy as np
+import onnxruntime as ort
+import torch
+import torchaudio
+from torch.nn.utils.rnn import pad_sequence
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--encoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the encoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--decoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the decoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner onnx model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-encoder-proj-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner encoder_proj onnx model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-decoder-proj-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner decoder_proj onnx model. ",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="""Path to tokens.txt""",
+    )
+
+    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(
+        "--context-size",
+        type=int,
+        default=2,
+        help="Context size of the decoder model",
+    )
+
+    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}. "
+            f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def greedy_search(
+    decoder: ort.InferenceSession,
+    joiner: ort.InferenceSession,
+    joiner_encoder_proj: ort.InferenceSession,
+    joiner_decoder_proj: ort.InferenceSession,
+    encoder_out: np.ndarray,
+    encoder_out_lens: np.ndarray,
+    context_size: int,
+) -> List[List[int]]:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
+    Args:
+      decoder:
+        The decoder model.
+      joiner:
+        The joiner model.
+      joiner_encoder_proj:
+        The joiner encoder projection model.
+      joiner_decoder_proj:
+        The joiner decoder projection model.
+      encoder_out:
+        A 3-D tensor of shape (N, T, C)
+      encoder_out_lens:
+        A 1-D tensor of shape (N,).
+      context_size:
+        The context size of the decoder model.
+    Returns:
+      Return the decoded results for each utterance.
+    """
+    encoder_out = torch.from_numpy(encoder_out)
+    encoder_out_lens = torch.from_numpy(encoder_out_lens)
+    assert encoder_out.ndim == 3
+    assert encoder_out.size(0) >= 1, encoder_out.size(0)
+
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
+
+    projected_encoder_out = joiner_encoder_proj.run(
+        [joiner_encoder_proj.get_outputs()[0].name],
+        {
+            joiner_encoder_proj.get_inputs()[
+                0
+            ].name: packed_encoder_out.data.numpy()
+        },
+    )[0]
+
+    blank_id = 0  # hard-code to 0
+
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    hyps = [[blank_id] * context_size for _ in range(N)]
+
+    decoder_input_nodes = decoder.get_inputs()
+    decoder_output_nodes = decoder.get_outputs()
+
+    joiner_input_nodes = joiner.get_inputs()
+    joiner_output_nodes = joiner.get_outputs()
+
+    decoder_input = torch.tensor(
+        hyps,
+        dtype=torch.int64,
+    )  # (N, context_size)
+
+    decoder_out = decoder.run(
+        [decoder_output_nodes[0].name],
+        {
+            decoder_input_nodes[0].name: decoder_input.numpy(),
+        },
+    )[0].squeeze(1)
+    projected_decoder_out = joiner_decoder_proj.run(
+        [joiner_decoder_proj.get_outputs()[0].name],
+        {joiner_decoder_proj.get_inputs()[0].name: decoder_out},
+    )[0]
+
+    projected_decoder_out = torch.from_numpy(projected_decoder_out)
+
+    offset = 0
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = projected_encoder_out[start:end]
+        # current_encoder_out's shape: (batch_size, encoder_out_dim)
+        offset = end
+
+        projected_decoder_out = projected_decoder_out[:batch_size]
+
+        logits = joiner.run(
+            [joiner_output_nodes[0].name],
+            {
+                joiner_input_nodes[0].name: current_encoder_out,
+                joiner_input_nodes[1].name: projected_decoder_out.numpy(),
+            },
+        )[0]
+        logits = torch.from_numpy(logits).squeeze(1).squeeze(1)
+        # logits'shape (batch_size, vocab_size)
+
+        assert logits.ndim == 2, logits.shape
+        y = logits.argmax(dim=1).tolist()
+        emitted = False
+        for i, v in enumerate(y):
+            if v != blank_id:
+                hyps[i].append(v)
+                emitted = True
+        if emitted:
+            # update decoder output
+            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
+            decoder_input = torch.tensor(
+                decoder_input,
+                dtype=torch.int64,
+            )
+            decoder_out = decoder.run(
+                [decoder_output_nodes[0].name],
+                {
+                    decoder_input_nodes[0].name: decoder_input.numpy(),
+                },
+            )[0].squeeze(1)
+            projected_decoder_out = joiner_decoder_proj.run(
+                [joiner_decoder_proj.get_outputs()[0].name],
+                {joiner_decoder_proj.get_inputs()[0].name: decoder_out},
+            )[0]
+            projected_decoder_out = torch.from_numpy(projected_decoder_out)
+
+    sorted_ans = [h[context_size:] for h in hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
+
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    logging.info(vars(args))
+
+    session_opts = ort.SessionOptions()
+    session_opts.inter_op_num_threads = 1
+    session_opts.intra_op_num_threads = 1
+
+    encoder = ort.InferenceSession(
+        args.encoder_model_filename,
+        sess_options=session_opts,
+    )
+
+    decoder = ort.InferenceSession(
+        args.decoder_model_filename,
+        sess_options=session_opts,
+    )
+
+    joiner = ort.InferenceSession(
+        args.joiner_model_filename,
+        sess_options=session_opts,
+    )
+
+    joiner_encoder_proj = ort.InferenceSession(
+        args.joiner_encoder_proj_model_filename,
+        sess_options=session_opts,
+    )
+
+    joiner_decoder_proj = ort.InferenceSession(
+        args.joiner_decoder_proj_model_filename,
+        sess_options=session_opts,
+    )
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = args.sample_rate
+    opts.mel_opts.num_bins = 80
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {args.sound_files}")
+    waves = read_sound_files(
+        filenames=args.sound_files,
+        expected_sample_rate=args.sample_rate,
+    )
+
+    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, dtype=torch.int64)
+
+    encoder_input_nodes = encoder.get_inputs()
+    encoder_out_nodes = encoder.get_outputs()
+    encoder_out, encoder_out_lens = encoder.run(
+        [encoder_out_nodes[0].name, encoder_out_nodes[1].name],
+        {
+            encoder_input_nodes[0].name: features.numpy(),
+            encoder_input_nodes[1].name: feature_lengths.numpy(),
+        },
+    )
+
+    hyps = greedy_search(
+        decoder=decoder,
+        joiner=joiner,
+        joiner_encoder_proj=joiner_encoder_proj,
+        joiner_decoder_proj=joiner_decoder_proj,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        context_size=args.context_size,
+    )
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+    s = "\n"
+    for filename, hyp in zip(args.sound_files, hyps):
+        words = "".join([symbol_table[i] for i in hyp])
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/wenetspeech/ASR/pruned_transducer_stateless2/pretrained.py

@@ -21,7 +21,7 @@ Usage:
 ./pruned_transducer_stateless2/pretrained.py \
         --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
         --lang-dir ./data/lang_char \
-        --method greedy_search \
+        --decoding-method greedy_search \
         --max-sym-per-frame 1 \
         /path/to/foo.wav \
         /path/to/bar.wav
@@ -29,7 +29,7 @@ Usage:
 ./pruned_transducer_stateless2/pretrained.py \
         --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
         --lang-dir ./data/lang_char \
-        --method modified_beam_search \
+        --decoding-method modified_beam_search \
         --beam-size 4 \
         /path/to/foo.wav \
         /path/to/bar.wav
@@ -37,7 +37,7 @@ Usage:
 ./pruned_transducer_stateless2/pretrained.py \
         --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
         --lang-dir ./data/lang_char \
-        --method fast_beam_search \
+        --decoding-method fast_beam_search \
         --beam 4 \
         --max-contexts 4 \
         --max-states 8 \
@@ -116,7 +116,7 @@ def get_parser():
     parser.add_argument(
         "--sample-rate",
         type=int,
-        default=48000,
+        default=16000,
         help="The sample rate of the input sound file",
     )
 
@@ -124,7 +124,8 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --method is beam_search and modified_beam_search ",
+        help="""Used only when --decoding-method is beam_search
+        and modified_beam_search """,
     )
 
     parser.add_argument(
@@ -166,7 +167,7 @@ def get_parser():
         type=int,
         default=1,
         help="""Maximum number of symbols per frame. Used only when
-        --method is greedy_search.
+        --decoding-method is greedy_search.
         """,
     )
 

egs/wenetspeech/ASR/pruned_transducer_stateless2/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless3/scaling_converter.py