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import gradio as gr |
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from transformers import pipeline |
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def classify_sentiment(audio, model): |
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pipe = pipeline("audio-classification", model=model) |
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pred = pipe(audio) |
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return {dic["label"]: dic["score"] for dic in pred} |
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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")] |
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label = gr.outputs.Label(num_top_classes=5) |
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gr.Interface( |
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fn = classify_sentiment, |
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inputs = input_audio, |
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outputs = label, |
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theme="grass").launch() |
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