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| import gradio as gr | |
| from transformers import pipeline | |
| trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish") | |
| classifier = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis") | |
| def audio_to_text(audio): | |
| text = trans(audio)["text"] | |
| return text | |
| def text_to_sentiment(text): | |
| return classifier(text)[0]["label"] | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("This is the second demo with Blocks") | |
| with gr.Tabs(): | |
| with gr.TabItem("Transcribe audio in Spanish"): | |
| with gr.Row(): | |
| audio = gr.Audio(source="microphone", type="filepath") | |
| transcription = gr.Textbox() | |
| b1 = gr.Button("Transcribe") | |
| with gr.TabItem("Sentiment Analysis in Spanish"): | |
| with gr.Row(): | |
| texto = gr.Textbox() | |
| label = gr.Label() | |
| b2 = gr.Button("Sentiment") | |
| b1.click(audio_to_text, inputs=audio, outputs=transcription) | |
| b2.click(text_to_sentiment, inputs=texto, outputs=label) | |
| demo.launch() |