File size: 2,228 Bytes
ce85da2 713e80d 0e5871d 713e80d ce85da2 a92da65 845a206 3fb5e78 a92da65 3fb5e78 3e1fc67 0e5871d 3fb5e78 52deb65 0e5871d 23ac7a2 ce85da2 23ac7a2 68af898 26b284c 0c89c5e 23ac7a2 ce85da2 5ba7dcf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
from reader import get_article
import gradio as gr
from transformers import pipeline
info = get_article()
#Model_1 = "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"
#Model_2 ="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"
#model_name2id = {"Model A": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD", "Model B": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"}
def classify_sentiment(audio):
pipe = pipeline("audio-classification", model="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD")
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")]
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.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=[["basta_neutral.wav"], ["detras_disgust.wav"], ["mortal_sadness.wav"], ["respiracion_happiness.wav"], ["robo_fear.wav"]],
#title = "🔊 Audio Sentiment Classifier",
#description = "Demostración de Gradio para la clasificación de sentimientos de audios usando Wav2Vec2",
#theme="huggingface").launch()
# generate and launch interface
interface = gr.Interface(fn=classify_sentiment, inputs=input_audio, outputs=label, examples=[["basta_neutral.wav"], ["detras_disgust.wav"], ["mortal_sadness.wav"], ["respiracion_happiness.wav"], ["robo_fear.wav"]], article=info['article'], css=info['css'], theme='huggingface', title=info['title'], allow_flagging='never', description=info['description'])
interface.launch() |