README.md

---
library_name: transformers
language:
- ta
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small ta - Lingalingeswaran
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: ta
      split: None
      args: 'config: ta, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 43.31959037105998
pipeline_tag: automatic-speech-recognition
---

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# Whisper Small ta - Lingalingeswaran

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2150
- Wer: 43.3196

## Model description

This Whisper model has been fine-tuned specifically for the Tamil language using the Common Voice 11.0 dataset. It is designed to handle tasks such as speech-to-text transcription and language identification, making it suitable for applications where Tamil is a primary language of interest. The fine-tuning process focused on enhancing performance for Tamil, aiming to reduce the error rate in transcriptions and improve general accuracy.

## Intended uses & limitations
Intended Uses:
Speech-to-text transcription in Tamil

Limitations:
May not perform as well on languages or dialects that are not well-represented in the Common Voice dataset.
Higher Word Error Rate (WER) in noisy environments or with speakers who have heavy accents not covered in the training data.
The model is optimized for Tamil; performance in other languages may be suboptimal.

## Training and evaluation data

The training data for this model consists of voice recordings in Tamil from the Mozilla-foundation/Common Voice 11.0 dataset. The dataset is a crowd-sourced collection of transcribed speech, ensuring diversity in terms of speaker accents, age groups, and speech styles.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1753        | 0.2992 | 1000 | 0.2705          | 51.0174 |
| 0.1404        | 0.5984 | 2000 | 0.2368          | 46.9969 |
| 0.1344        | 0.8977 | 3000 | 0.2196          | 44.5325 |
| 0.0947        | 1.1969 | 4000 | 0.2150          | 43.3196 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1