| 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_a_text(audio): | |
| text = trans(audio)["text"] | |
| return text | |
| def texto_a_sentimiento(text): | |
| return clasificador(text)[0]["label"] | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("Demo para la clase de Platzi") | |
| audio = gr.Audio(sources="microphone", type="filepath") | |
| texto = gr.Textbox() | |
| b1 = gr.Button("Transcribe por favor") | |
| b1.click(audio_a_text, inputs=audio, outputs=texto) | |
| b2 = gr.Button("Clasifica por favor el sentimiento") | |
| label = gr.Label() | |
| b2.click(texto_a_sentimiento, inputs=texto, outputs=label) | |
| demo.launch() |