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Add C++ deployment docs
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pkufool
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README.md
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README.md
@ -16,6 +16,7 @@ We provide three recipes at present:
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- [yesno][yesno]
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- [LibriSpeech][librispeech]
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- [Aishell][aishell]
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- [TIMIT][timit]
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### yesno
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@ -57,6 +58,31 @@ The WER for this model is:
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We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing)
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### Aishell
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We provide two models for this recipe: [conformer CTC model][Aishell_conformer_ctc]
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and [TDNN LSTM CTC model][Aishell_tdnn_lstm_ctc].
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#### Conformer CTC Model
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The best CER we currently have is:
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| | test |
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|-----|------|
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| CER | 4.26 |
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We provide a Colab notebook to run a pre-trained conformer CTC model: [](https://colab.research.google.com/drive/1WnG17io5HEZ0Gn_cnh_VzK5QYOoiiklC?usp=sharing)
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#### TDNN LSTM CTC Model
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The CER for this model is:
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| | test |
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|-----|-------|
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| CER | 10.16 |
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We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [](https://colab.research.google.com/drive/1qULaGvXq7PCu_P61oubfz9b53JzY4H3z?usp=sharing)
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### TIMIT
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@ -18,7 +18,7 @@ In this tutorial, you will learn:
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- (1) How to prepare data for training and decoding
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- (2) How to start the training, either with a single GPU or multiple GPUs
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- (3) How to do decoding after training, with 1best and attention decoder rescoring
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- (3) How to do decoding after training, with ctc-decoding, 1best and attention decoder rescoring
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- (4) How to use a pre-trained model, provided by us
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Data preparation
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@ -623,3 +623,125 @@ We do provide a colab notebook for this recipe showing how to use a pre-trained
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**Congratulations!** You have finished the aishell ASR recipe with
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conformer CTC models in ``icefall``.
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If you want to deploy your trained model in C++, please read the following section.
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Deployment with C++
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-------------------
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This section describes how to deploy the pre-trained model in C++, without
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Python dependencies.
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.. HINT::
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At present, it does NOT support streaming decoding.
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First, let us compile k2 from source:
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.. code-block:: bash
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$ cd $HOME
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$ git clone https://github.com/k2-fsa/k2
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$ cd k2
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$ git checkout v2.0-pre
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.. CAUTION::
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You have to switch to the branch ``v2.0-pre``!
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.. code-block:: bash
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$ mkdir build-release
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$ cd build-release
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$ cmake -DCMAKE_BUILD_TYPE=Release ..
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$ make -j hlg_decode
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# You will find four binaries in `./bin`, i.e. ./bin/hlg_decode,
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Now you are ready to go!
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Assume you have run:
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.. code-block:: bash
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$ cd k2/build-release
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$ ln -s /path/to/icefall-asr-aishell-conformer-ctc ./
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To view the usage of ``./bin/hlg_decode``, run:
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.. code-block::
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$ ./bin/hlg_decode
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It will show you the following message:
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.. code-block:: bash
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Please provide --nn_model
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This file implements decoding with an HLG decoding graph.
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Usage:
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./bin/hlg_decode \
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--use_gpu true \
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--nn_model <path to torch scripted pt file> \
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--hlg <path to HLG.pt> \
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--word_table <path to words.txt> \
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<path to foo.wav> \
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<path to bar.wav> \
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<more waves if any>
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To see all possible options, use
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./bin/hlg_decode --help
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Caution:
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- Only sound files (*.wav) with single channel are supported.
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- It assumes the model is conformer_ctc/transformer.py from icefall.
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If you use a different model, you have to change the code
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related to `model.forward` in this file.
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HLG decoding
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^^^^^^^^^^^^
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.. code-block:: bash
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./bin/hlg_decode \
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--use_gpu true \
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--nn_model icefall_asr_aishell_conformer_ctc/exp/cpu_jit.pt \
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--hlg icefall_asr_aishell_conformer_ctc/data/lang_char/HLG.pt \
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--word_table icefall_asr_aishell_conformer_ctc/data/lang_char/words.txt \
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icefall_asr_aishell_conformer_ctc/test_waves/BAC009S0764W0121.wav \
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icefall_asr_aishell_conformer_ctc/test_waves/BAC009S0764W0122.wav \
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icefall_asr_aishell_conformer_ctc/test_waves/BAC009S0764W0123.wav
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The output is:
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.. code-block::
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2021-11-18 14:48:20.89 [I] k2/torch/bin/hlg_decode.cu:115:int main(int, char**) Device: cpu
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2021-11-18 14:48:20.89 [I] k2/torch/bin/hlg_decode.cu:124:int main(int, char**) Load wave files
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2021-11-18 14:48:20.97 [I] k2/torch/bin/hlg_decode.cu:131:int main(int, char**) Build Fbank computer
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2021-11-18 14:48:20.98 [I] k2/torch/bin/hlg_decode.cu:142:int main(int, char**) Compute features
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2021-11-18 14:48:20.115 [I] k2/torch/bin/hlg_decode.cu:150:int main(int, char**) Load neural network model
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2021-11-18 14:48:20.693 [I] k2/torch/bin/hlg_decode.cu:165:int main(int, char**) Compute nnet_output
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2021-11-18 14:48:23.182 [I] k2/torch/bin/hlg_decode.cu:180:int main(int, char**) Load icefall_asr_aishell_conformer_ctc/data/lang_char/HLG.pt
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2021-11-18 14:48:33.489 [I] k2/torch/bin/hlg_decode.cu:185:int main(int, char**) Decoding
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2021-11-18 14:48:45.217 [I] k2/torch/bin/hlg_decode.cu:216:int main(int, char**)
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Decoding result:
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icefall_asr_aishell_conformer_ctc/test_waves/BAC009S0764W0121.wav
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甚至 出现 交易 几乎 停止 的 情况
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icefall_asr_aishell_conformer_ctc/test_waves/BAC009S0764W0122.wav
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一二 线 城市 虽然 也 处于 调整 中
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icefall_asr_aishell_conformer_ctc/test_waves/BAC009S0764W0123.wav
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但 因为 聚集 了 过多 公共 资源
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There is a Colab notebook showing you how to run a torch scripted model in C++.
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Please see |aishell asr conformer ctc torch script colab notebook|
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.. |aishell asr conformer ctc torch script colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
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:target: https://colab.research.google.com/drive/1Vh7RER7saTW01DtNbvr7CY7ovNZgmfWz?usp=sharing
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@ -28,12 +28,12 @@ from conformer import Conformer
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from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
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from icefall.checkpoint import average_checkpoints, load_checkpoint
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from icefall.decode import one_best_decoding
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from icefall.env import get_env_info
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from icefall.lexicon import Lexicon
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from icefall.utils import (
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AttributeDict,
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encode_supervisions,
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get_alignments,
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get_env_info,
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save_alignments,
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setup_logger,
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)
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