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import gradio as gr
def to_audioClassification():
return {
audio_classification: gr.Row(visible=True),
realtime_classification: gr.Row(visible=False),
}
def to_realtimeAudioClassification():
return {
audio_classification: gr.Row(visible=False),
realtime_classification: gr.Row(visible=True),
}
with gr.Blocks() as demo:
with gr.Row():
btn0 = gr.Button("Audio Classification", scale=1, size='lg')
btn1 = gr.Button("Realtime Audio Classification", scale=1, size='lg')
with gr.Row(visible=False) as audio_classification:
with gr.Column(min_width=700):
with gr.Accordion("Record an Audio", open=True):
inputRecord = gr.Audio(label="Audio Input", source="microphone", type="filepath")
with gr.Accordion("Upload a file", open=False):
inputUpload = gr.Audio(label="Audio Input", source="upload", type="filepath")
clearBtn = gr.ClearButton([inputRecord, inputUpload])
with gr.Column(min_width=700):
output = gr.Label(label="Audio Classification")
btn = gr.Button(value="Generate Audio")
audioOutput = gr.Audio(label="Audio Output", interactive=False)
with gr.Row(visible=False) as realtime_classification:
with gr.Column(min_width=700):
input = gr.Audio(label="Audio Input", source="microphone", type="filepath",streaming=True, every=10)
historyOutput = gr.Textbox(label="History", interactive=False)
# historyOutput = gr.Label(label="History")
with gr.Column(min_width=700):
output = gr.Label(label="Audio Classification")
btn0.click(fn=to_audioClassification, outputs=[audio_classification, realtime_classification, speech_recognition, chatbot_qa])
btn1.click(fn=to_realtimeAudioClassification, outputs=[audio_classification, realtime_classification, speech_recognition, chatbot_qa])
if __name__ == "__main__":
demo.queue()
demo.launch() |