first

app.py

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+from transformers import pipeline
+import base64 
+import gradio as gr
+
+model_id = "openai/whisper-small"  # 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()

requirements.txt

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+gradio
+transformers