nemotron-3.5-asr-streaming-hausa

Nemotron 3.5 ASR Streaming — Hausa (ha-NG) Fine-Tune, Round 2

Continued fine-tune of EYEDOL/nemotron-3.5-asr-streaming-hausa (itself a fine-tune of nvidia/nemotron-3.5-asr-streaming-0.6b) for 15 additional epochs on Hausa speech data.

Training data

• 41.1 hours of Hausa speech from [EYEDOL/naija-voices-hausa-split_2-8 + EYEDOL/naija-voices-hausa-split_2-9](https://huggingface.co/datasets/EYEDOL/naija-voices-hausa-split_2-8 + EYEDOL/naija-voices-hausa-split_2-9).
• Held out 56 speakers (speaker-disjoint, audited for zero train/test overlap — see Section 3b) for evaluation.
• Transcripts normalized: Hausa hooked consonants (ɗ ɓ ƙ ƴ and uppercase variants) mapped to plain Latin equivalents (d b k y) before training, same as round 1.

Evaluation

Metrics measured on the held-out speaker-disjoint test set using true cache-aware streaming inference (chunked audio, carried encoder/decoder state — not an offline full-utterance shortcut), att_context_size=[56,0] (80ms chunk, 0ms lookahead).

No-space CER (CER computed after stripping spaces from both reference and hypothesis) is reported alongside WER to separate genuine character-level recognition errors from word-boundary/segmentation differences.

Full evaluation reports (model/manifest paths, hashes, elapsed time, RTFx) are written to eval_round1/report.json and eval_round2/report.json and included in training_log.txt above.

Usage

import nemo.collections.asr as nemo_asr

model = nemo_asr.models.ASRModel.from_pretrained("EYEDOL/nemotron-3.5-asr-streaming-hausa")
# Streaming inference: see speech_to_text_cache_aware_streaming_infer.py in NeMo's repo.