Instructions to use kaan84/whisper-sinhala-proto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaan84/whisper-sinhala-proto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kaan84/whisper-sinhala-proto", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kaan84/whisper-sinhala-proto") model = AutoModelForSpeechSeq2Seq.from_pretrained("kaan84/whisper-sinhala-proto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Sinhala Prototype
whisper-sinhala-proto is an experimental Sinhala speech-recognition model based on OpenAI Whisper Small. The checkpoint is intended for early testing, benchmarking, and comparison during Sinhala ASR development.
Repository status
This model appears to share the same base architecture, parameter count, task, and training-dataset tag as kaan84/whisper-small-sinhala-proto. If both repositories contain equivalent weights, consider keeping one as the canonical prototype and marking the other as archived or superseded to reduce user confusion.
Model details
| Item | Value |
|---|---|
| Task | Automatic speech recognition |
| Language | Sinhala (si) |
| Base model | openai/whisper-small |
| Model class | AutoModelForSpeechSeq2Seq |
| Parameters | Approximately 241.7M |
| Training dataset tag | large-sinhala-asr-dataset |
| Status | Experimental prototype |
Usage
from transformers import pipeline
transcriber = pipeline(
"automatic-speech-recognition",
model="kaan84/whisper-sinhala-proto",
)
result = transcriber(
"sample.wav",
generate_kwargs={"language": "si", "task": "transcribe"},
)
print(result["text"])
Recommended use
- Sinhala ASR experiments
- Baseline and checkpoint comparison
- Error analysis across accents and recording conditions
- Further fine-tuning on appropriately licensed Sinhala speech data
This prototype is not documented or evaluated sufficiently for high-impact production use.
Training and evaluation information needed
To complete this model card, add:
- Dataset source, license, duration, speakers, and split sizes
- Audio resampling and transcript-normalization rules
- Training hyperparameters and hardware
- Validation and test WER/CER
- Comparison with the base
openai/whisper-smallmodel - Known failure cases and subgroup analysis
Limitations
The model may hallucinate, omit, or incorrectly transcribe speech. Performance can vary with audio quality, noise, code-switching, accent, vocabulary, and speaker characteristics. Human review is necessary when accuracy matters.
License
Apache License 2.0. Confirm compatibility with the base-model license and every dataset or audio source used during fine-tuning.
Related repositories
- Release candidate:
kaan84/whisper-small-sinhala-v1 - Related prototype:
kaan84/whisper-small-sinhala-proto
- Downloads last month
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Model tree for kaan84/whisper-sinhala-proto
Base model
openai/whisper-small