manassehzw/sna-parakeet-tdt-0.6b-v3

Shona Parakeet-TDT ASR is a Shona (sna) automatic speech recognition model fine-tuned from nvidia/parakeet-tdt-0.6b-v3 using the annotated dataset manassehzw/sna-dataset-annotated.

Overview

This is a first-pass Shona fine-tune of NVIDIA Parakeet-TDT with especially promising qualitative behavior on Shona-English code-switched speech. While the aggregate WER is not yet state of the art for this dataset, the model produced strong real-world transcriptions and preserved useful English recognition ability after Shona adaptation.

Model Details

• Curated by: Manasseh Changachirere
• Base model: nvidia/parakeet-tdt-0.6b-v3
• Training dataset: manassehzw/sna-dataset-annotated
• Run name: sna-parakeet-v1
• Uploaded artifact: sna-parakeet-v1.nemo
• Best validation WER during training: N/A
• Final validation WER: N/A
• Final test WER: N/A

Training Summary

• Training examples: 13715
• Validation examples: 762
• Test examples: 762
• Valid rows after filtering: 15239
• Learning rate: N/A
• Microbatch size: N/A
• Gradient accumulation steps: N/A
• Effective batch size: N/A
• Max epochs: N/A
• Train max duration: N/A
• Validation max duration: N/A

Qualitative Notes

• Shona-only transcription is usable, with mostly soft orthographic substitutions.
• Shona-English code-switching remained surprisingly strong after fine-tuning.
• Real-world perceived quality appears stronger than the headline WER alone suggests.

Limitations

• This is a first-pass fine-tune on a relatively small Shona corpus.
• The reported metrics come from the project’s held-out split, not from an external benchmark.
• Orthographic drift and near-phonetic substitutions are still common.
• Performance may differ on noisy audio, unseen accents, domain-shifted speech, and longer recordings.

Files

This repository contains:

• the exported NeMo model artifact sna-parakeet-v1.nemo
• training/summary.json
• training/preprocess_summary.json
• eval/validation_results.json
• eval/test_results.json
• optional eval/last_inference.json qualitative sample output

Example Usage

import nemo.collections.asr as nemo_asr

repo_id = "manassehzw/sna-parakeet-tdt-0.6b-v3"
model = nemo_asr.models.ASRModel.from_pretrained(repo_id)
texts = model.transcribe(["sample.wav"])
print(texts[0].text if hasattr(texts[0], "text") else texts[0])