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kaan84/whisper-small-sinhala-proto
whisper-small-sinhala-proto is a automatic speech recognition model from kaan84. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
This repository contains an experimental fine-tuned checkpoint of OpenAI Whisper Small for Sinhala automatic speech recognition. It was trained with the large-sinhala-asr-dataset and is published as a prototype for ev…
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From the Hugging Face model README
This repository contains an experimental fine-tuned checkpoint of OpenAI Whisper Small for Sinhala automatic speech recognition. It was trained with the large-sinhala-asr-dataset and is published as a prototype for evaluation and continued development.
| Item | Value |
|---|---|
| Task | Automatic speech recognition |
| Language | Sinhala (si) |
| Base model | openai/whisper-small |
| Architecture | Whisper / AutoModelForSpeechSeq2Seq |
| Parameters | Approximately 241.7M |
| Framework | Transformers |
| Status | Experimental prototype |
Use this checkpoint for research, baseline comparison, testing, and further fine-tuning. It should not be treated as a validated production model until dataset documentation and held-out evaluation results are added.
import torch
from transformers import pipeline
model_id = "kaan84/whisper-small-sinhala-proto"
device = 0 if torch.cuda.is_available() else -1
transcriber = pipeline(
task="automatic-speech-recognition",
model=model_id,
device=device,
)
result = transcriber(
"sample.wav",
generate_kwargs={"language": "si", "task": "transcribe"},
)
print(result["text"])
The model metadata identifies large-sinhala-asr-dataset as the training dataset. For reproducibility, document its repository link, source, license, total duration, speaker distribution, split strategy, audio sampling rate, transcript normalization, and quality-control process.
The checkpoint was generated using the Transformers Trainer workflow. Add the exact learning rate, batch size, gradient-accumulation steps, epoch or step count, warm-up schedule, precision, hardware, seed, and checkpoint-selection rule.
This prototype was fine-tuned using 3,000 Sinhala speech samples and prepared with an 800-sample test subset.
| Metric | Result |
|---|---|
| Training steps | 500 |
| Epochs completed | 1.33 |
| Final training loss | 0.6327 |
| Training runtime | 984.74 seconds |
A qualitative test example produced a sample-level WER of 50.00% and CER of 11.11%. These values are based on one notebook example and must not be interpreted as corpus-level performance.
Full evaluation across the 800-sample test subset was not completed in the available experiment. This model should therefore be treated as a research prototype rather than a production-ready ASR system.
Apache License 2.0. Users must also comply with the licenses and terms of the base model and training data.
This repository should remain clearly labeled as a prototype. If kaan84/whisper-small-sinhala-v1 is the selected release model, direct general users to that repository and retain this checkpoint for comparison and reproducibility.