|
import gradio as gr |
|
from transformers import pipeline |
|
|
|
pipe = pipeline(model="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd") |
|
|
|
def classify_sentiment(audio): |
|
sentiment_classifier = pipe(audio) |
|
return sentiment_classifier |
|
|
|
|
|
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) |
|
|
|
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() |
|
|