Griot Nano 1 Multilingual KenLM

This is the multilingual 3-gram KenLM language model for Qlerqly/griot-nano-1. It covers Akan, Dagbani, Ewe, Ghanaian English, and Ga using the same normalized transcript domain as the Griot Nano 1 acoustic model.

Recommended decoding configuration

For best contextual accuracy, we recommend KenLM-assisted CTC beam search with Griot Nano 1. It improves the decoder's preference for plausible character and word sequences, particularly around ambiguous acoustics and word boundaries.

KenLM is a compact n-gram model rather than a second neural network. This keeps memory and compute overhead low relative to neural language-model rescoring. Greedy CTC remains the lowest-latency option.

Suggested starting settings:

  • Beam width: 25
  • LM weight (alpha): 0.5
  • Word insertion bonus (beta): 1.0

These are starting points rather than universal optima; tune them on a held-out set for your target language and domain.

Results Vs Greedy and Beam search decode

Language Samples Greedy CER KenLM CER Greedy WER KenLM WER Relative WER reduction
Akan (ak) 21,538 19.30% 19.60% 41.67% 36.43% 12.58%
Dagbani (dag) 5,347 7.05% 5.64% 23.17% 14.56% 37.16%
Ewe (ee) 6,939 10.01% 9.22% 30.97% 24.60% 20.57%
English (en) 56,592 12.76% 12.45% 25.38% 20.93% 17.56%
Ga (ga) 9,707 8.65% 8.40% 16.02% 13.12% 18.12%
Combined 100,123 13.58% 13.33% 28.73% 23.91% 16.76%

Download and use

uvx --from huggingface-hub hf download Qlerqly/griot-nano-1-kenlm \
  --local-dir griot-nano-1-kenlm

uv run --with-requirements requirements-kenlm.txt python inference.py \
  --model-dir . --audio recording.wav \
  --kenlm ../griot-nano-1-kenlm/multilingual.arpa --beam-width 25

The acoustic-model repositories provide requirements-kenlm.txt and the KenLM-enabled inference script.

Model details

  • Format: ARPA
  • Order: 3-gram
  • Size: 214.2 MiB
  • SHA-256: c017eaaa575a0bb0dfc30ab74953a95f9e11939ca7d0a034eed3c446ded328f6
  • Quantized: no
N-gram order Entries
1 524,748
2 2,215,703
3 4,933,206

Training data and license

This language model was derived from normalized transcripts associated with the Griot Nano 1 training sources. See ATTRIBUTIONS.md for the complete source, creator, and license record.

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