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import gradio as gr
from transformers import pipeline
transcripcion = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
clasificador = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
def audio_a_texto(audio):
texto = transcripcion(audio)["text"]
return texto, texto
def texto_a_sentimiento(texto):
sentimiento = clasificador(texto)[0]["label"]
return sentimiento
demo = gr.Blocks()
with demo:
gr.Markdown("## Transcribe audio2text and sentimental classification - Spanish")
with gr.Tabs():
with gr.TabItem("Transcribe"):
with gr.Row():
audio_input = gr.Audio(sources=["microphone"], type="filepath")
texto_output = gr.Textbox(label="Audio to text")
with gr.Row():
b1 = gr.Button("Transcribe 🎙️✍🏻")
with gr.TabItem("Sentimental Classification"):
with gr.Row():
texto_input = gr.Textbox(label="Text to sentimental")
sentimiento_output = gr.Label()
with gr.Row():
b2 = gr.Button("Sentimental Classification 🤖")
# Keep the 2 text box with the same text.
b1.click(audio_a_texto, inputs=audio_input, outputs=[texto_output,texto_input])
b2.click(texto_a_sentimiento, inputs=texto_input, outputs=sentimiento_output)
demo.launch()
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