|
|
|
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(["hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"], label="Model Name")] |
|
label = gr.outputs.Label(num_top_classes=5) |
|
|
|
|
|
|
|
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.jpg" alt="logo" width="750"/> |
|
#<img src="https://huggingface.co/spaces/hackathon-pln-es/Audio-Sentiment-Classifier/tree/main/sentiment.jpg" style="max-width: 100%; max-height: 10%; height: 250px; object-fit: fill"> |
|
</center> |
|
""" |
|
|
|
|
|
|
|
|
|
gr.Interface( |
|
fn = classify_sentiment, |
|
inputs = input_audio, |
|
outputs = label, |
|
examples=[["Examples/basta_neutral.wav", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"], ["Examples/detras_disgust.wav", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"], ["Examples/mortal_sadness.wav", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"], ["Examples/respiracion_happiness.wav", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"], ["Examples/robo_fear.wav", "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"]], |
|
theme="grass").launch() |
|
|
|
|
