| import gradio as gr | |
| from transformers import pipeline | |
| def classify_sentiment(audio, model): | |
| pipe = pipeline("audio-classification", model=model) | |
| 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() | |