Kaldi-ONNX is a tool for porting Kaldi Speech Recognition Toolkit neural network models to ONNX models for inference. With the converted ONNX model, you can use MACE to speedup the inference on Android, iOS, Linux or Windows devices with highly optimized NEON kernels (more heterogeneous devices will be supported in the future).
This tool supports converting both Nnet2 and Nnet3 models. Almost all components in Nnet2 and Nnet3 models are supported, and the available components are listed in converter/common.py.
Besides DNN, many speech recognition models are using RNN or TDNN networks. To make them more portable and efficient, this tool will convert these RNN and TDNN networks to DAG-like networks.
pip install -r requirements.txtThis tool only supports Kaldi's text model as an input.
If you have a binary model, Kaldi's nnet-am-copy or nnet3-copy tool can help you get a text one:
Nnet2 Model
path/to/kaldi/src/nnet2bin/nnet-am-copy --binary=false \
final.raw text.mdlNnet3 Model
path/to/kaldi/src/nnet3bin/nnet3-copy --binary=false \
--raw=true \
--prepare-for-test=true \
final.raw text.mdlDon't forget to use the --prepare-for-test=true and --raw=trueoption to optimize the model.
Before converting, you need to use nnet-am-info or nnet3-am-info to get left_context\right_context\modulus,
which is required for converting.
More details about kaldi's tools are in Kaldi's Documentation.
One command to convert:
python converter/convert.py --input=models/kaldi/model_name.mdl \
--output=models/onnx/model_name.onnx \
--transition-model=path/to/save/transition_model.trans \
--conf=path/to/save/configuration.conf \
--chunk-size=20 \
--left-context=left_context \
--right-context=right_context \
--modulus=modulus \
--subsample-factor=subsample_factor \
--nnet-type=3After converting, there is a graphic tool for you to review the onnx model: ONNX Model Viewer.
To run the ONNX models, an inference framework with Kaldi specific operators support is needed. Currently, MACE has Kaldi-ONNX support which is primarily optimized for mobile phones and IoT devices.
For more details about deployment, please refer to MACE documents.
Since MACE already supports most frequently used components in Kaldi. We can use it to validate converted Kaldi models. The validation process is giving the same inputs to Kaldi and MACE's computation and check if they will output the same results.
Generate random input
We provide a tool to generate the same random input data for Kaldi and MACE's computation.
python tools/generate_inputs.py --input-dim=40 \
--chunk-size=20 \
--left-context=left_context \
--right-context=right_contex \
--kaldi-input_file=path/to/kaldi/input/test_input.ark \
--mace-input_file=path/to/mace/input/test_input.npy
'input_dim' and 'chunk_size' are model's input dim and chunk, can be specified by case. 'kaldi_input_file' and 'mace_input_file' are paths for saving generated data files.
Compute in Kaldi
Kaldi has command line tools for computing the model's propogation.
Nnet2:
path/to/kaldi/src/nnet2bin/nnet-am-compute --chunk-size=chunk_size \
path/to/kaldi/model/file/final.mdl \
ark,t:/path/to/kaldi/input/file/test_input.ark \
ark,t:path/to/save/output/data/test_output.ark
Nnet3:
path/to/kaldi/src/nnet3-compute --frames-per-chunk=chunk_size \
path/to/kaldi/model/file/final.mdl \
ark,t:/path/to/kaldi/input/file/test_input.ark \
ark,t:path/to/save/output/data/test_output.ark
Convert output data file
After running nnet-compute, we'll get Kaldi's output data in text format .
Because MACE supports numpy data file as input or output, we need to convert Kaldi's output data file to numpy format.
The script tools/kaldi_to_mace.py will help you doing this convertion.
python tools/kaldi_to_mace.py --input=path/to/kaldi/data/file.ark \
--output=path/to/save/numpy/data/file.npyPrepare config file for MACE
Here is an example yaml config file for fisher english model.
# fisher_english.yml
library_name: fisher_english_8
target_abis: [armeabi-v7a, arm64-v8a]
model_graph_format: file
model_data_format: file
models:
fisher_english_8:
platform: onnx
model_file_path: https://cnbj1.fds.api.xiaomi.com/mace/miai-models/onnx/kaldi/nnet2/fisher_english_8_nnet_a.onnx
model_sha256_checksum: e27d8147995b0a68e1367d060dc4f41c0f434043992a52548ff961e4e1e87e6c
subgraphs:
- input_tensors:
- 0
input_shapes:
- 1,20,140
output_tensors:
- 17
output_shapes:
- 1,20,7880
backend: kaldi
input_data_formats: NONE
output_data_formats: NONE
validation_inputs_data:
- https://cnbj1.fds.api.xiaomi.com/mace/miai-models/onnx/kaldi/data/kaldi_input_20_140.npy
validation_outputs_data:
- https://cnbj1.fds.api.xiaomi.com/mace/miai-models/onnx/kaldi/data/test_fisher_english_8_20_140_out.npy
backend: kaldi
runtime: cpu
limit_opencl_kernel_time: 0
nnlib_graph_mode: 0
obfuscate: 0
Validate
Convert and validate the model in MACE:
cd path/to/mace
python tools/converter.py convert --config=path/to/fisher_english.yml
python tools/converter.py run --config=path/to/fisher_english.yml --validate
The command will give you the similarity between MACE's and Kaldi's output results.
More details about how to use MACE, please step to MACE documents.
We have converted numbers of Kaldi's Nnet2 and Nnet3 models, and put them in MACE Model Zoo.
- GitHub issues: bug reports, usage issues, feature requests
Python code should conform to PEP8 Style Guide for Python Code.
You can use pycodestyle to check the style.
pycodestyle $(find . -name "*.py")
Any kind of contribution is welcome. For bug reports, feature requests, please just open an issue without any hesitation. For code contributions, it's strongly suggested to open an issue for discussion first.
kaldi-onnx learned a lot from the following projects during the development: