| import gradio as gr | |
| from transformers import pipeline | |
| trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish") | |
| clasificador = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis") | |
| def audio_to_text(audio): | |
| text = trans(audio)["text"] | |
| return text | |
| def text_to_feel(text): | |
| return clasificador(text)[0]["label"] | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("Demo de blocks") | |
| with gr.Tabs(): | |
| with gr.TabItem("trans"): | |
| with gr.Row(): | |
| audio = gr.Audio(source="microphone", type="filepath") | |
| transcription = gr.Textbox() | |
| b1 = gr.Button("Transcribe pf") | |
| with gr.TabItem("sent"): | |
| with gr.Row(): | |
| texto = gr.Textbox() | |
| label = gr.Label() | |
| b2 = gr.Button("Sent porfa") | |
| b1.click(audio_to_text, inputs=audio, outputs=transcription) | |
| b2.click(text_to_feel, inputs=texto, outputs=label) | |
| demo.launch() |