This repository provides the recipe for the paper USC: An Open-Source Uzbek Speech Corpus.
The Uzbek speech corpus (USC) has been developed in academic collaboration between ISSAI and Image and Speech Processing Laboratory, Department of Computer Systems, TUIT.
Authors:
- Muhammadjon Musaev;
- Saida Mussakhojayeva;
- Ilyos Khujayorov;
- Yerbolat Khassanov;
- Mannon Ochilov;
- Huseyin Atakan Varol;
Our code builds upon ESPnet, and requires prior installation of the framework. Please follow the installation guide and put the repository inside espnet/egs/ directory.
After succesfull installation of ESPnet & Kaldi, go to Uzbek_ASR/asr1 directory and create links to the dependencies:
ln -s ../../../tools/kaldi/egs/wsj/s5/steps steps
ln -s ../../../tools/kaldi/egs/wsj/s5/utils utils
The directory for running the experiments (Uzbek_ASR/<exp-name) can be created by running the following script:
./setup_experiment.sh <exp-name>
Download USC dataset and untar in the directory of your choice. Specify the path to the data in ./run.sh script:
dataset_dir=/path-to/ISSAI_USC
To train the models, run the script ./run.sh inside Uzbek_ASR/<exp-name>/ folder.
You can find the link to the latest pre-trained Conformer model. Untar it in Uzbek_ASR/<exp-name>/.
To decode a single audio, specify paths to the following files inside recog_wav.sh script:
lang_model= path to rnnlm.model.best
cmvn= path to cmvn.ark, for example data/train/cmvn.ark
recog_model= path to e2e model, in case of conformer: model.last10.avg.best
Then, run the following script:
./recog_wav.sh <path-to-audio-file>