Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Shona
whisper
audio
multi-stage-finetuning
Eval Results (legacy)
Instructions to use Code-Dev/whisper-small-shona-v3 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 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")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Code-Dev/whisper-small-shona-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("Code-Dev/whisper-small-shona-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
πΎ Verdi AI OS: Whisper Small Shona ASR (Stage 1c Mastered Core)
This repository hosts a completely standalone, fully compiled model optimized natively for the Shona language (sn-ZW).
π Training Progression Metrics
- Stage 1a (FLEURS Baseline): Baseline WER dropped to 37.15%
- Stage 1b (Bible Chunk 1): Dropped WER to 29.39%
- Stage 1c (Bible Chunk 2 & 3 Increments): Achieved new floor of 25.52% WER.
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Evaluation results
- Test WER on google/fleurs + manassehzw/shona-bible-bdsc-alignedself-reported25.520