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---
language:
- ka
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
model-index:
- name: whisper-small-tamil
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
config: ta_in
split: test
type: google/fleurs
metrics:
- name: Wer
type: wer
value: 23.1257
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-tamil
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs dataset for Kannada.
It achieves the following results on the evaluation set:
- Loss: 0.2507
- Wer: 23.1257
## Model description
More information needed
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0792 | 2.27 | 500 | 0.2674 | 24.7048 |
| 0.0067 | 12.19 | 1000 | 0.1930 | 23.7758 |
| 0.0011 | 18.29 | 1500 | 0.2161 | 23.3225 |
| 0.0002 | 24.39 | 2000 | 0.2294 | 23.1332 |
| 0.0001 | 30.48 | 2500 | 0.2406 | 23.1652 |
| 0.0001 | 36.58 | 3000 | 0.2461 | 23.1531 |
| 0.0001 | 42.68 | 3500 | 0.2493 | 23.1108 |
| 0.0001 | 48.78 | 4000 | 0.2507 | 23.1257 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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