GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
SinhaSpeech
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
SinhaSpeech
Model Tree
openai/whisper-small
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import pipeline asr = pipeline( "automatic-speech-recognition", model="SinhaSpeech/whisper-small-sinhala-run12-best", chunk_length_s=30,)print(asr("sample.wav", generate_kwargs={"language": "sinhala", "task": "transcribe"})["text"])chunk_length_s=30, or window the audio yourself for longer recordings.faster-whisper) conversion of this model for live captioning.| Base model | openai/whisper-small |
| Method | Full fine-tune (every weight updated) |
| Data | stratified_v6 — SinhaSpeech/sinhala-asr-data-v6 |
| Split sizes | 123,205 train / 15,763 validation / 15,860 test, speaker-disjoint |
| Learning rate | 8.56e-5, cosine decay, 500 warmup steps |
| Batch size | 64 effective (64 per device × 1 accumulation step) |
| Epochs / steps | 6 epochs, 11,556 steps |
| Precision / hardware | bf16 on AMD Instinct MI300X (ROCm) |
| Augmentation | SpecAugment + noise/stretch/pitch, prob=0.2 each |
| Hyperparameter search | Optuna TPE sweep (20 trials); this is the best trial |
| Train runtime | ~5.75 h (final train loss 0.0238, average train loss ~0.089) |
| Final checkpoint | step 11,556 (epoch 6) |
| Split | WER | CER |
|---|---|---|
| Validation (epoch 6) | 18.70% | 6.07% |
| Test (15,860 rows) | 15.52% | 4.36% |
Train loss falls from ~2.41 to 0.024. Eval loss falls steadily from 0.169 to 0.076 by epoch 5, then ticks up slightly to 0.079 at epoch 6 while WER still improves marginally. This is a mild divergence — not clear overfitting.
| Epoch | Val WER | Val CER | Val loss |
|---|---|---|---|
| 1 | 31.18% | 9.92% | 0.1688 |
| 2 | 26.12% | 8.21% | 0.1087 |
| 3 | 22.52% | 7.02% | 0.1051 |
| 4 | 20.22% | 6.48% | 0.0858 |
| 5 | 18.75% | 6.06% | 0.0756 |
| 6 | 18.70% | 6.07% | 0.0787 |
The cosine schedule peaks at ~8.56e-5 after the 500-step warmup, then decays smoothly to near zero.
| Run | Setup | Test WER | Test CER |
|---|---|---|---|
| run10 | v6 data, linear lr=3e-5, 5 ep | 17.17% | 4.71% |
| run11 | v6 data, linear lr=3e-5, 6 ep | 16.38% | 4.43% |
| run12 (this) | v6 data, cosine lr=8.56e-5, 6 ep | 15.52% | 4.36% |
The switch from linear to cosine schedule and the higher learning rate (found by the Optuna sweep) are what produced the improvement. At the same epoch count, run12 beats run11 by 0.86 points WER and 0.07 points CER.
The repo root holds the final model (model.safetensors, tokenizer and config files) and the training-curve images in images/.
bibtex
@misc{jayasinghe2026whispersinhala, author = {Yohan Jayasinghe}, title = {whisper-small-sinhala-run12: Whisper-small fine-tuned for Sinhala ASR}, year = {2026}, url = {https://huggingface.co/SinhaSpeech/whisper-small-sinhala-run12-best}}Built on Whisper (Radford et al., 2022). Training data includes OpenSLR-52 (Sinhala).
from transformers import pipeline asr = pipeline( "automatic-speech-recognition", model="SinhaSpeech/whisper-small-sinhala-run12-best", chunk_length_s=30,)print(asr("sample.wav", generate_kwargs={"language": "sinhala", "task": "transcribe"})["text"])@misc{jayasinghe2026whispersinhala, author = {Yohan Jayasinghe}, title = {whisper-small-sinhala-run12: Whisper-small fine-tuned for Sinhala ASR}, year = {2026}, url = {https://huggingface.co/SinhaSpeech/whisper-small-sinhala-run12-best}}