External scorer scripts

DeepSpeech pre-trained models include an external scorer. This document explains how to reproduce our external scorer, as well as adapt the scripts to create your own.

The scorer is composed of two sub-components, a KenLM language model and a trie data structure containing all words in the vocabulary. In order to create the scorer package, first we must create a KenLM language model (using data/lm/generate_lm.py, and then use generate_scorer_package to create the final package file including the trie data structure.

The generate_scorer_package binary is part of the native client package that is included with official releases. You can find the appropriate archive for your platform in the GitHub release downloads. The native client package is named native_client.{arch}.{config}.{plat}.tar.xz, where {arch} is the architecture the binary was built for, for example amd64 or arm64, config is the build configuration, which for building decoder packages does not matter, and {plat} is the platform the binary was built-for, for example linux or osx. If you wanted to run the generate_scorer_package binary on a Linux desktop, you would download native_client.amd64.cpu.linux.tar.xz.

Reproducing our external scorer

Our KenLM language model was generated from the LibriSpeech normalized LM training text, available here. It is created with KenLM.

You can download the LibriSpeech corpus with the following command:

cd data/lm
wget http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz

Then use the generate_lm.py script to generate lm.binary and vocab-500000.txt.

As input you can use a plain text (e.g. file.txt) or gzipped (e.g. file.txt.gz) text file with one sentence in each line.

If you are using a container created from Dockerfile.build, you can use --kenlm_bins /DeepSpeech/native_client/kenlm/build/bin/. Else you have to build KenLM first and then pass the build directory to the script.

cd data/lm
python3 generate_lm.py --input_txt librispeech-lm-norm.txt.gz --output_dir . \
  --top_k 500000 --kenlm_bins path/to/kenlm/build/bin/ \
  --arpa_order 5 --max_arpa_memory "85%" --arpa_prune "0|0|1" \
  --binary_a_bits 255 --binary_q_bits 8 --binary_type trie

Afterwards you can use generate_scorer_package to generate the scorer package using the lm.binary and vocab-500000.txt files:

cd data/lm
# Download and extract appropriate native_client package:
curl -LO http://github.com/mozilla/DeepSpeech/releases/...
tar xvf native_client.*.tar.xz
./generate_scorer_package --alphabet ../alphabet.txt --lm lm.binary --vocab vocab-500000.txt \
  --package kenlm.scorer --default_alpha 0.931289039105002 --default_beta 1.1834137581510284

The generate_scorer_package binary is part of the released native_client.tar.xz. If for some reason you need to rebuild it, please refer to how to Compile generate_scorer_package.

Building your own scorer

Building your own scorer can be useful if you’re using models in a narrow usage context, with a more limited vocabulary, for example. Building a scorer requires text data matching your intended use case, which must be formatted in a text file with one sentence per line.

The LibriSpeech LM training text used by our scorer is around 4GB uncompressed, which should give an idea of the size of a corpus needed for a reasonable language model for general speech recognition. For more constrained use cases with smaller vocabularies, you don’t need as much data, but you should still try to gather as much as you can.

With a text corpus in hand, you can then re-use generate_lm.py and generate_scorer_package to create your own scorer that is compatible with DeepSpeech clients and language bindings. Before building the language model, you must first familiarize yourself with the KenLM toolkit. Most of the options exposed by the generate_lm.py script are simply forwarded to KenLM options of the same name, so you must read the KenLM documentation in order to fully understand their behavior.

After using generate_lm.py to create a KenLM language model binary file, you can use generate_scorer_package to create a scorer package as described in the previous section. Note that we have a lm_optimizer.py script which can be used to find good default values for alpha and beta. To use it, you must first generate a package with any value set for default alpha and beta flags. For this step, it doesn’t matter what values you use, as they’ll be overridden by lm_optimizer.py later. Then, use lm_optimizer.py with this scorer file to find good alpha and beta values. Finally, use generate_scorer_package again, this time with the new values.