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README.md
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@@ -78,3 +78,34 @@ The following hyperparameters were used during training:
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- Pytorch 2.5.1+cu121
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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- Pytorch 2.5.1+cu121
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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## Example Usage
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Here is an example of how to use the model for Sinhala speech recognition with Gradio:
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```python
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import gradio as gr
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from transformers import pipeline
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# Initialize the pipeline with the specified model
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pipe = pipeline(model="Lingalingeswaran/whisper-small-sinhala")
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def transcribe(audio):
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# Transcribe the audio file to text
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text = pipe(audio)["text"]
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return text
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# Create the Gradio interface
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iface = gr.Interface(
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fn=transcribe,
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inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
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outputs="text",
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title="Whisper Small Sinhala",
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description="Realtime demo for Sinhala speech recognition using a fine-tuned Whisper small model.",
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)
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# Launch the interface
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if __name__ == "__main__":
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iface.launch()
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