cd /opt/kaldi/egs/sevil/ conda activate base python ./demo/main.py There are 2 directories that are required to preprocess the data for training/inference: cd /opt/kaldi/egs/sevil /prepare contains the code to download the data from s3 and preprocess it. There is users.txt file which is the id of users that are extracted from the s3. ./data/local. After the preprocess is done we need to have corpus.txt file, which is the collection of all possible inputs of ASR. Also we need to update ./data/local/dict/lexicon.txt file with all the word tokens in corpus.txt.

Work that is merged PR: pymc4/#306 Issues: pymc4/#187, pymc4/#171 The main goal of the work during the summer was to provide the support for various samplers and sampling of discrete variables. We have written and pushed and an inteface that could easily expanded for various number of sampling algorithms. First, we have provided the base class for all the pymc4 sampling algorithms: class _BaseSampler(metaclass=abc.ABCMeta): def _sample(self, ...):

First login and move to the directory: ssh ssh rrkarim@52.152.171.105 pass: Sevil@@@@2664 sudo su docker exec -it 39c6a98a79a1 bash cd egs/sevil/demo There is a directory ./dir, to run the demo: Move your audio file .ogg or .wav to ./dir