app.py

from transformers import pipeline
import base64 
import gradio as gr

model_id = "openai/whisper-medium"  # update with your model id
#model_id ="openai/whisper-tiny"
pipe = pipeline("automatic-speech-recognition", model=model_id)

def transcribe_speech(filepath):
    output = pipe(
        filepath,
        max_new_tokens=256,
        generate_kwargs={
            "task": "transcribe",
            "language": "spanish",
        },  # update with the language you've fine-tuned on
        chunk_length_s=30,
        batch_size=8,
    )
    return output["text"]




with open("Iso_Logotipo_Ceibal.png", "rb") as image_file:
    encoded_image = base64.b64encode(image_file.read()).decode()

demo = gr.Blocks()

mic_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs="textbox",
)

file_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs="textbox",
)


with demo:
    gr.Markdown(
    """
    <center>
    <h1>
    Uso de AI para transcribir audio a texto.
    </h1>
    <img src='data:image/jpg;base64,{}' width=200px>
    <h3>
    Con este espacio podrás transcribir audio a texto. 
    </h3>
    </center>
    """.format(encoded_image))

    gr.TabbedInterface(
        [mic_transcribe, file_transcribe],
        ["Transcribir desde el micrófono.", "Transcribir desde un Archivo de Audio."],
    )

demo.launch()