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import os
import gradio as gr
demo = gr.Blocks()
EXAMPLES = ["cantina.wav"]
def speech_to_text(x):
return [("yada yada", "speaker 0"), ("blah blah blah", "speaker 1")]
def summarize(y, c):
return "> " + len(c)*"stuff"
def sentiment(x, y):
if y == 0:
return [("yada yada", "happy")]
if y == 1:
return [("blah blah blah", "sad")]
with demo:
with gr.Row():
with gr.Column():
audio = gr.Audio(label="Audio file", type='filepath')
with gr.Row():
btn = gr.Button("Transcribe")
with gr.Row():
examples = gr.components.Dataset(components=[audio], samples=[EXAMPLES], type="index")
with gr.Column():
gr.Markdown("**Diarized Output:**")
diarized = gr.HighlightedText(label="Diarized Output")
gr.Markdown("Choose speaker(s) for summarization:")
check = gr.CheckboxGroup(["Speaker 0", "Speaker 1"], show_label=False)
gr.Textbox("**Summary:**")
summary = gr.Markdown()
gr.Markdown("Choose speaker for sentiment analysis:")
radio = gr.Radio(["Speaker 0", "Speaker 1"], show_label=False, type="index")
analyzed = gr.HighlightedText(label="Customer Sentiment")
btn.click(speech_to_text, audio, diarized)
check.change(summarize, [diarized, check], summary)
radio.change(sentiment, [diarized, radio], analyzed)
def load_example(example_id):
processed_examples = audio.preprocess_example(EXAMPLES[example_id])
return processed_examples
examples.click(load_example, inputs=[examples], outputs=[audio], _preprocess=False)
demo.launch() |