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contain equivalent weights, consider keeping one as the canonical prototype and marking the other as archived or superseded to reduce user confusion.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-small model
  • 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.

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