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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() |