This model is a fine-tuned version of openai/whisper-small on the Lingalingeswaran/asr-sinhala-dataset_json_v1 dataset. It achieves the following results on the evaluation set:

Loss: 0.2086
Wer: 46.4577

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: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH 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
training_steps: 3000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1852	1.7606	1000	0.1875	50.9772
0.0602	3.5211	2000	0.1886	47.5774
0.0238	5.2817	3000	0.2086	46.4577

Framework versions

Transformers 4.48.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Example Usage

Here is an example of how to use the model for Sinhala speech recognition with Gradio:

python

import gradio as gr
from transformers import pipeline

# Initialize the pipeline with the specified model
pipe = pipeline(model="Lingalingeswaran/whisper-small-sinhala_v3")

def transcribe(audio):
    # Transcribe the audio file to text
    text = pipe(audio)["text"]
    return text

# Create the Gradio interface

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
    outputs="text",
    title="Whisper Small Sinhala",
    description="Realtime demo for Sinhala speech recognition using a fine-tuned Whisper small model.",
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()