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from transformers import pipeline
import gradio as gr
pipe = pipeline(model="jbatista79/whisper-small-eu") # change to "your-username/the-name-you-picked"
def transcribe(audio):
text = pipe(audio)["text"]
return text
'''
iface = gr.Interface(
fn=transcribe,
# inputs=gr.Audio(source="microphone", type="filepath"),
inputs=gr.Audio(sources=["microphone"], type="filepath"),
outputs="text",
title="Whisper Small Basque - Euskara (EU)",
description="Realtime proof-of-concept demo for Basque speech recognition using a fine-tuned Whisper small model. Created by Josué R. Batista - 2024-01-13",
)
iface.launch(share=True)
'''
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 Basque - Euskara (EU)")
gr.Markdown("Realtime proof-of-concept demo for Basque speech recognition using a fine-tuned Whisper small model. Created by Josué R. Batista - 2024-01-13")
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()
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