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omniASR_LLM_1B (Omnilingual ASR) on Lingala, Shona,
and Luganda for the WAXAL Hackathon (Zindi).step_N/ directory is a full fairseq2 training checkpoint (model +
optimizer + trainer + data-reader state) -- resumable for continued
training, not just inference. For inference, only step_N/model/pp_00/tp_00/sdp_00.pt
(a plain PyTorch state_dict) is needed.step_124000 (WER ~7.6% on a small in-loop validation sample;
step_122500 scored marginally better at the time but wasn't checkpointed).omniASR_tokenizer_v1's vocabulary has no entries for uppercase letters or
for most punctuation (. , ! ? ; : etc. all map to <unk>; confirmed via
direct tokenizer round-trip tests). This isn't a training shortfall, it's
what the tokenizer can represent. If you need written-form output, run the
transcription through a separate punctuation-restoration tool.inference/inference.py in the GitHub repo linked above -- it
downloads a checkpoint from this repo and transcribes audio standalone
(Colab-friendly, no training-pod setup needed). Pass nbest=5, compression_window=100, compression_threshold=4.0 for beam search (the
inference pipeline's own default of nbest=1 gives noticeably worse
results -- less room to recover from an uncertain first token).omniASR_LLM_1B (full pretrained checkpoint, not a fresh decoder)lang_embeddings_p=0.001 (near-zero, per the
paper's Table 22 ablation, matching WAXAL Phase 2's zero-metadata
inference constraint)