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
| language: | |
| - sn | |
| tags: | |
| - automatic-speech-recognition | |
| - whisper | |
| - audio | |
| - multi-stage-finetuning | |
| model-index: | |
| - name: Whisper Small Shona - Verdi AI OS Stage 1c | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: google/fleurs + manassehzw/shona-bible-bdsc-aligned | |
| type: google/fleurs | |
| args: sn_zw | |
| metrics: | |
| - name: Test WER | |
| type: wer | |
| value: 25.52 | |
| pipeline_tag: automatic-speech-recognition | |
| library_name: transformers | |
| # 🌾 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`). | |
| ## 📊 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. | |