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import gradio as gr
import requests
# from transcribe import transcribe
from sentiment_analysis import sentiment_analyser
from summary import summarizer
from topic import topic_gen
def transcribe2():
response = requests.post("https://dwarkesh-whisper-speaker-recognition.hf.space/run/predict", json={
"data": [
{"name":"audio.wav","data":"data:audio/wav;base64,UklGRiQAAABXQVZFZm10IBAAAAABAAEARKwAAIhYAQACABAAZGF0YQAAAAA="},
2,
]}).json()
data = response["data"]
def main(audio_file, number_of_speakers):
# Audio to Text Converter
# text_data = transcribe(audio_file, number_of_speakers)
# print(text_data)
text_data = ""
topic = topic_gen(text_data)
summary = summarizer(text_data)
sentiment_analysis = sentiment_analyser(text_data)
return topic, summary, sentiment_analysis
# UI Interface on the Hugging Face Page
with gr.Blocks() as demo:
with gr.Box():
with gr.Row():
with gr.Column():
audio_file = gr.Audio(label="Upload an Audio file (.wav)", source="upload", type="filepath")
number_of_speakers = gr.Number(label="Number of Speakers", value=2)
with gr.Row():
btn_clear = gr.ClearButton(value="Clear", components=[audio_file, number_of_speakers])
btn_submit = gr.Button(value="Submit")
with gr.Column():
topic = gr.Textbox(label="Title", placeholder="Title for Conversation")
summary = gr.Textbox(label="Short Summary", placeholder="Short Summary for Conversation")
sentiment_analysis = gr.Textbox(label="Sentiment Analysis", placeholder="Sentiment Analysis for Conversation")
btn_submit.click(fn=main, inputs=[audio_file, number_of_speakers], outputs=[topic, summary, sentiment_analysis])
gr.Markdown("## Examples")
gr.Examples(
examples=[
["./examples/sample4.wav", 2],
],
inputs=[audio_file, number_of_speakers],
outputs=[topic, summary, sentiment_analysis],
fn=main,
)
gr.Markdown(
"""
See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
for more details.
"""
)
demo.launch()
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