Ransaka/SinhalaASR
Updated • 25
How to use Ransaka/whisper-tiny-sinhala-20k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Ransaka/whisper-tiny-sinhala-20k") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Ransaka/whisper-tiny-sinhala-20k")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Ransaka/whisper-tiny-sinhala-20k", device_map="auto")This model is a fine-tuned version of openai/whisper-tiny on the sinhala_asr dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4207 | 0.4 | 1000 | 0.3978 | 221.9058 |
| 0.2966 | 0.8 | 2000 | 0.3009 | 136.3423 |
| 0.226 | 1.2 | 3000 | 0.2661 | 97.6638 |
| 0.2224 | 1.6 | 4000 | 0.2510 | 92.3279 |
| 0.2034 | 2.0 | 5000 | 0.2433 | 92.9960 |
Base model
openai/whisper-tiny