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---
language:
- ta
license: apache-2.0
widgets:
- label: "Example 1"
audio: "https://yourdomain.com/path/to/example1.wav"
- label: "Example 2"
audio: "https://yourdomain.com/path/to/example2.wav"
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: carl-whisper-small-finetuned-tamil
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: ta
split: None
args: ta
metrics:
- type: wer
value: 21.830330026321118
name: Wer
---
<!-- 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. -->
# carl-whisper-small-finetuned-tamil
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4660
- Wer Ortho: 62.9625
- Wer: 21.8303
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 10
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.3504 | 0.37 | 100 | 0.4660 | 62.9625 | 21.8303 |
### Framework versions
- Transformers 4.38.2
- Pytorch 1.11.0+cu102
- Datasets 2.18.0
- Tokenizers 0.15.2 |