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import gradio as gr
from transformers import pipeline
def classify_sentiment(audio, model):
pipe = pipeline("audio-classification", model=model)
pred = pipe(audio)
return {dic["label"]: dic["score"] for dic in pred}
input_audio = [gr.inputs.Audio(source="microphone", type="filepath", label="Record/ Drop audio"), gr.inputs.Dropdown(["DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v11", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"], label="Model Name")]
label = gr.outputs.Label(num_top_classes=5)
################### Gradio Web APP ################################
title = "Audio Sentiment Classifier"
description = """
<p>
<center>
This application classifies the sentiment of the audio input provided by the user.
</center>
</p>
<center>
<img src="https://huggingface.co./spaces/hackathon-pln-es/Audio-Sentiment-Classifier/tree/main/sentiment.PNG" alt="logo" width="550"/>
</center>
"""
gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath"),
"state"
],
outputs=[
"textbox",
"state"
],
title=title,
description=description,
article=article,
live=True).launch()
gr.Interface(
fn = classify_sentiment,
inputs = input_audio,
outputs = label,
#examples=[["test1.wav", "DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v11"], ["test2.wav", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"]],
theme="grass").launch()
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