CDLI Parakeet TDT 1.1B English Fine-Tune (Uganda + Kenya)
This repository contains a NeMo ASR model fine-tuned from
nvidia/parakeet-tdt-1.1b on a merged atypical-English training mix from
Uganda and Kenya.
The setup mirrors the mixed-country 0.6B notebook, but swaps in the 1.1B
checkpoint and keeps the same evaluation pattern: Uganda-only validation for
selection, then separate held-out reporting for Uganda and Kenya.
Model Details
- Base model:
nvidia/parakeet-tdt-1.1b
- Fine-tuning framework: NVIDIA NeMo
- Language: English
- Acoustic model family: FastConformer-TDT / RNNT-BPE
- Output text: lower-case English transcription with standard ASR normalization
Datasets
- Uganda:
cdli/ugandan_english_nonstandard_speech_v1.0
- Kenya:
cdli/kenyan_english_nonstandard_speech_v1.0
- License:
cc-by-sa-4.0
- Audio sampling rate:
16 kHz
Split sizes used in this run:
- Uganda train:
5175
- Uganda validation:
638
- Uganda test:
1016
- Kenya train:
4374
- Kenya validation:
542
- Kenya test:
928
- Merged train total:
9549 rows, 56.46 hours
- Validation selection set: Uganda only,
638 rows, 4.33 hours
Training Configuration
- Work root:
/jupyter_kernel/parakeet_cdli_en_ug_ke
- Train mix: Uganda + Kenya
- Primary validation country: Uganda
- Max manifest audio length:
40.0 s
- Max training audio length:
40.0 s
- Min audio length:
0.2 s
- Train batch size:
8
- Eval batch size:
8
- Gradient accumulation steps:
8
- Effective train batch size:
64
- Learning rate:
5e-5
- Weight decay:
1e-3
- Warmup steps:
100
- Scheduler:
CosineAnnealing
- Max steps:
20000
- Validation interval:
200 steps
- Early stopping patience:
12
- Precision:
bf16-mixed when supported, otherwise mixed precision fallback
Evaluation
Evaluation was run separately on the held-out Uganda and Kenya test splits using
both raw transcript comparison and normalized transcript comparison.
Uganda Test Set
- Raw WER:
33.14%
- Raw CER:
15.53%
- Normalized WER:
21.59%
- Normalized CER:
12.92%
- Average normalized utterance WER (capped at
1.0): 20.86%
- Average normalized utterance CER (capped at
1.0): 12.88%
Kenya Test Set
- Raw WER:
33.62%
- Raw CER:
11.92%
- Normalized WER:
12.45%
- Normalized CER:
7.59%
- Average normalized utterance WER (capped at
1.0): 12.48%
- Average normalized utterance CER (capped at
1.0): 7.63%
Uganda and Kenya Comparison
The same shared 1.1B model performs much better on the Kenyan test split than
on the Ugandan test split.
- Kenya vs Uganda normalized WER:
12.45% vs 21.59%
- Kenya vs Uganda normalized CER:
7.59% vs 12.92%
- Absolute normalized WER gap:
9.14 points in favor of Kenya
Relative to the mixed-country 0.6B run
(KasuleTrevor/cdli-parakeet-en-ug-ke), the 1.1B model is uneven:
- Uganda normalized WER:
21.59% vs 21.33% for 0.6B
- Kenya normalized WER:
12.45% vs 14.56% for 0.6B
So the larger model did not improve Uganda, but it improved Kenya materially by
2.11 absolute normalized WER points.
Interpretation
This run is not a clean across-the-board win over the 0.6B model. The main
benefit is stronger Kenyan generalization, while Uganda remains roughly flat to
slightly worse.
That matters for model selection:
- If Uganda remains the primary deployment target, the
0.6B mixed-country run
is still competitive.
- If the goal is broader East African English transfer, the
1.1B run is more
interesting because of the Kenya gain.
Usage
1from nemo.collections.asr.models import ASRModel
2
3model = ASRModel.from_pretrained("KasuleTrevor/cdli-parakeet-en-ug-ke-1.1b")
4predictions = model.transcribe(["path/to/audio.wav"])
5print(predictions[0].text if hasattr(predictions[0], "text") else predictions[0])
Files
EN-PARAKEET-TDT-UG-KE-1.1b.nemo: exported NeMo checkpointresults/EN-PARAKEET-TDT-UG-KE-1.1b/: uploaded result tables and breakdown CSVstrain_mix_summary_ug_ke.jsoncountry_summary_ug_ke.csvtest_predictions_scored_uganda.csvtest_predictions_scored_kenya.csvseverity_breakdown_combined_ug_ke.csvdisorder_breakdown_combined_ug_ke.csvetiology_breakdown_combined_ug_ke.csv
Notes
- Validation for early stopping and checkpoint selection was Uganda-only, even
though training used both Uganda and Kenya.
- Source datasets are gated. Review the dataset terms before requesting access.
- Result artifacts for both countries were uploaded to the Hub under
results/EN-PARAKEET-TDT-UG-KE-1.1b/.
