metadata
library_name: peft
language:
- si
license: apache-2.0
base_model: openai/whisper-small
tags:
- base_model:adapter:openai/whisper-small
- lora
- transformers
datasets:
- SPEAK-ASR/openslr-sinhala-asr-preprocessed-1
- SPEAK-ASR/openslr-sinhala-asr-preprocessed-2
- SPEAK-ASR/openslr-sinhala-asr-preprocessed-3
- SPEAK-ASR/youtube-sinhala-asr-preprocessed
model-index:
- name: SPEAK-ASR/whisper-small-si-openslr_plus_youtube_data
results: []
SPEAK-ASR/whisper-small-si-openslr_plus_youtube_data
This model is a fine-tuned version of openai/whisper-small on the Whisper Small - Sinhala ASR Fine-Tuned dataset. It achieves the following results on the evaluation set:
- Loss: 0.1465
Model description
- WandB ID: hardy-hill-51
- Used full OpenSLR dataset + Our Youtube dataset
- On eval didn't use Youtube dataset test split
- Used LoRA adapters
- Previous name was
SPEAK-ASR/whisper-small-si-openslr_plus_youtube_data
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3137 | 0.4254 | 1000 | 0.2489 |
| 0.2126 | 0.8507 | 2000 | 0.1943 |
| 0.1506 | 1.2761 | 3000 | 0.1737 |
| 0.1835 | 1.7014 | 4000 | 0.1607 |
| 0.1879 | 2.1268 | 5000 | 0.1538 |
| 0.1485 | 2.5521 | 6000 | 0.1494 |
| 0.1473 | 2.9775 | 7000 | 0.1465 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2