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from transformers import pipeline | |
import gradio as gr | |
pipe = pipeline(model="jbatista79/whisper-jrb-small-ta") # change to "your-username/the-name-you-picked" | |
def transcribe(audio): | |
text = pipe(audio)["text"] | |
return text | |
with gr.Blocks() as app: | |
with gr.Row(): | |
with gr.Column(): | |
gr.Image(value="lion-face-outline-cut-hi-strategia-black-strategia01-300x77.png", width=300, show_label=False, show_download_button=False, show_share_button=False) # Adjust width as needed | |
with gr.Column(): | |
gr.Markdown("## Whisper Small Tamil - தமிழ் (TA)") | |
gr.Markdown("Realtime proof-of-concept demo for Tamil speech recognition using a fine-tuned Whisper small model. Created by Josué R. Batista - 2024-01-17") | |
with gr.Row(): | |
audio_input = gr.Audio(sources=["microphone"], type="filepath") | |
output_text = gr.Textbox() | |
gr.Button("Transcribe").click(fn=transcribe, inputs=audio_input, outputs=output_text) | |
#app.launch(share=True) | |
app.launch() |