| 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() |
