| | --- |
| | library_name: transformers |
| | language: |
| | - ta |
| | license: apache-2.0 |
| | base_model: openai/whisper-tiny |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - mozilla-foundation/common_voice_11_0 |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: whisper-tiny-tamil-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: 58.67 |
| | --- |
| | |
| | <!-- 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-tiny-tamil-Lingalingeswaran |
| |
|
| | This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.456 |
| | - Wer: 58.67 |
| |
|
| | ## 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: Adam |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - training_steps: 4000 |
| | - mixed_precision_training: Native AMP |
| | |
| | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.48.0 |
| | - Pytorch 2.5.1+cu121 |
| | - Datasets 3.2.0 |
| | - Tokenizers 0.21.0 |
| | |
| | |
| | ### Example Usage |
| | |
| | ```python |
| | |
| | import gradio as gr |
| | from transformers import pipeline |
| | |
| | # Initialize the pipeline with the specified model |
| | pipe = pipeline(model="Lingalingeswaran/whisper-tiny-ta") |
| | |
| | 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 tiny tamil", |
| | description="Realtime demo for Tamil speech recognition using a fine-tuned Whisper tiny model.", |
| | ) |
| | |
| | # Launch the interface |
| | if __name__ == "__main__": |
| | iface.launch() |
