Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Shona
whisper
audio
multi-stage-finetuning
Eval Results (legacy)
Instructions to use Code-Dev/whisper-small-shona-v3-a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Code-Dev/whisper-small-shona-v3-a with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Code-Dev/whisper-small-shona-v3-a")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Code-Dev/whisper-small-shona-v3-a") model = AutoModelForSpeechSeq2Seq.from_pretrained("Code-Dev/whisper-small-shona-v3-a", device_map="auto") - Notebooks
- Google Colab
- Kaggle
πΎ Verdi AI OS: Whisper Small Shona ASR (Stage 1c Completed)
This repository hosts a completely standalone, fully compiled model optimized natively for the Shona language (sn-ZW). It forms the core Speech-to-Text foundation for Verdi AI OSβan agricultural operating system built for logistics metrics automation and voice marketplace tracking.
π Training Progression Metrics
- Stage 1a (FLEURS Baseline): Dropped baseline WER to 37.15%
- Stage 1b (Bible Chunk 1): Dropped WER to 29.39%
- Stage 1c (Bible Chunk 2 - 6,000 Rows): Finished at 26.42% WER with an un-normalized training loss of 0.079
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Evaluation results
- Test WER on google/fleurs + manassehzw/shona-bible-bdsc-aligned (Chunk 2)self-reported26.420