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| import gradio as gr | |
| from transformers import pipeline | |
| #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 = "馃攰 Audio-Sentiment-Classifier", | |
| description = "Demostraci贸n de Gradio para la clasificaci贸n de sentimientos de audios usando Wav2Vec2", | |
| theme="huggingface").launch() | |