Easy Kaldi

The scripts in this repository can be used as a template for training nnet3 neural networks in Kaldi, with the aim to get you going from your data to a trained model as smoothly as possible.

The code here aims to be easily readable and extensible, and makes few assumptions about the kind of data you have and where it's located on disk.

To get started, easy-kaldi should be cloned and moved into the egs dir of your local version of the latest Kaldi branch.

If you're used to typical Kaldi egs, take note that all easy-kaldi scripts in utils / local / steps exist in this repo. That is, they do not link back to the wsj example. This was done to make custom changes to the scripts, making them more readable.

Creating the input_task dir

In order to run easy-kaldi, you need to make a new input_dir directory. This is the only place you need to make changes for your own corpus.

This directory contains information about the location of your data, lexicon, language model.

Here is an example of the structure of my input_dir directory for the corpus called corpus. As you can see from the -> arrows, all of these files are softlinks. Using softlinks helps you keep your code and data separate, which becomes important if you're using cloud computing.

input_corpus/
├── lexicon_nosil.txt -> /data/corpus/lexicon/lexicon_nosil.txt
├── lexicon.txt -> /data/corpus/lexicon/lexicon.txt
├── task.arpabo -> /data/corpus/lm/corpus.arpabo
├── test_audio_path -> /data/corpus/audio/test_audio_path
├── train_audio_path -> /data/corpus/audio/train_audio_path
├── transcripts.test -> /data/corpus/audio/transcripts.test
└── transcripts.train -> /data/corpus/audio/transcripts.train

0 directories, 7 files

Most of these files are standard Kaldi format, and more detailed descriptions of them can be found on the official docs.

• lexicon_nosil.txt // Standard Kaldi // phonetic dictionary without silence phonemes
• lexicon.txt // Standard Kaldi // phonetic dictionary with silence phonemes
• task.arpabo // Standard Kaldi // language model in ARPA back-off format
• test_audio_path // Custom file! // one-line text file containing absolute path to dir of audio files (eg. WAV) for testing
• train_audio_path // Custom file! // one-line text file containing absolute path to dir of audio files (eg. WAV) for training
• transcripts.test // Custom file! // A typical Kaldi transcript file, but with only the test utterances
• transcripts.train // Custom file! // A typical Kaldi transcript file, but with only the train utterances

Running the scripts

The scripts will name files and directories dynamically. You will define the name of your input data (e.g. corpus) in the initial input_ dir, and then the rest of the generated dirs and files will be named accordingly. For instance, if you have input_corpus, then the GMM alignment stage will create data_corpus, plp_corpus and exp_corpus.

Force Align Training Data (GMM)

$ ./run_gmm.sh corpus 001

• corpus should correspond exactly to input_corpus.

• 001 is any character string, and is written to the name of the WER file: WER_nnet3_corpus_001.txt

Neural Net Acoustic Modeling (DNN)

$ ./run_nnet3.sh "corpus" $hidden-dim $num-epochs

• first argument is a character string of the corpus name (must correspond to input_corpus)

• hidden-dim is the number of nodes in your hidden layer

• num-epochs is num epochs for DNN training