KasuleTrevor/cdli-slam-asr-luganda-atypical-projector-only-e2s107

CDLI SLAM-ASR Luganda Atypical Speech Projector-Only Checkpoint (Epoch 2 Step 107)

Projector-only atypical-speech adaptation checkpoint for SLAM-ASR on the CDLI Luganda atypical speech dataset. The encoder and Sunflower-14B decoder remain frozen; only the linear projector is updated from the ASR-adapted starting checkpoint.

What this repository contains

This Hub repository stores a partial SLAM-ASR checkpoint for use with the SLAM-LLM codebase. It is not a standalone transformers checkpoint.

• Checkpoint type: projector_only
• Architecture: Whisper encoder (Sunbird/asr-whisper-large-v3-salt) + linear projector + Sunflower-14B decoder; encoder frozen; LLM frozen; no PEFT adapters.
• Base encoder: Sunbird/asr-whisper-large-v3-salt
• Base LLM: Sunbird/Sunflower-14B
• Exported files: model.pt

Training / evaluation context

• Dataset: cdli/ugandan_luganda_nonstandard_speech_v1.0
• Evaluation split: test
• Training speakers: 36
• Validation speakers: 5
• Speaker overlap: No speaker overlap between train and validation/test

Reported metrics

• Normalized WER (JiWER scorer): 59.32%
• Normalized CER (JiWER scorer): 23.07%
• Atypical overall normalized WER: 59.81%
• Atypical overall normalized CER: 23.14%
• Atypical averaged utterance WER: 54.39%
• Atypical averaged utterance CER: 19.19%

Decode settings used for the reported metrics

Test decode used MAX_NEW_TOKENS=200, NUM_BEAMS=4, REPETITION_PENALTY=2.0, NO_REPEAT_NGRAM_SIZE=2, USE_ENCODER_PEFT=false.

Additional results notes

Notebook-style subgroup breakdown on the test split: Mild 48.88% WER, Moderate 52.14%, Severe 63.25%. By disorder: Dysarthria 49.97%, Stuttering 53.65%, Articulation Disorders 54.75%, Voice disorder 68.33%. Average hyp/ref word ratio on test was 92.16%.

Loading notes

Load through SLAM-LLM; this repository stores a partial SLAM-ASR checkpoint, not a standalone Transformers model.

Typical decode flow in this project uses:

• examples/asr_luganda/scripts/decode_luganda_sunflower.sh
• USE_ENCODER_PEFT=true for encoder-LoRA checkpoints
• matching LoRA target modules at decode time

Caveats

• This repository stores SLAM-ASR training artifacts intended for research use.
• The checkpoint must be used with the matching SLAM-LLM model code and base components.
• Results can be sensitive to decode settings and evaluation protocol.