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Yibing Liu
7d0458c7f7
|
8 years ago | |
|---|---|---|
| .. | ||
| README.md | 8 years ago | |
| __init__.py | 8 years ago | |
| _init_paths.py | 8 years ago | |
| ctc_decoders.cpp | 8 years ago | |
| ctc_decoders.h | 8 years ago | |
| decoder_utils.cpp | 8 years ago | |
| decoder_utils.h | 8 years ago | |
| decoders.i | 8 years ago | |
| path_trie.cpp | 8 years ago | |
| path_trie.h | 8 years ago | |
| scorer.cpp | 8 years ago | |
| scorer.h | 8 years ago | |
| setup.py | 8 years ago | |
README.md
The decoders for deployment developed in C++ are a better alternative for the prototype decoders in Pytthon, with more powerful performance in both speed and accuracy.
Installation
The build depends on several open-sourced projects, first clone or download them to current directory (i.e., deep_speech_2/deploy)
- KenLM: Faster and Smaller Language Model Queries
git clone https://github.com/kpu/kenlm.git
- OpenFst: A library for finite-state transducers
wget http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-1.6.3.tar.gz
tar -xzvf openfst-1.6.3.tar.gz
- ThreadPool: A library for C++ thread pool
git clone https://github.com/progschj/ThreadPool.git
- SWIG: A tool that provides the Python interface for the decoders, please make sure it being installed.
Then run the setup
python setup.py install --num_processes 4
cd ..
Usage
The decoders for deployment share almost the same interface with the prototye decoders in Python. After the installation succeeds, these decoders are very convenient for call in Python, and a complete example in deploy.py can be refered.
For GPU deployment
CUDA_VISIBLE_DEVICES=0 python deploy.py
For CPU deployment
python deploy.py --use_gpu=False
More help for arguments
python deploy.py --help