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
Whisper Small ta - Lingalingeswaran
This model is a fine-tuned version of 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