Add app and requirements files

app.py

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+from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+import torch
+
+model_id = "stabilityai/stable-diffusion-2"
+
+# Use the Euler scheduler here instead
+scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+def text_to_image(prompt):
+  image = pipe(prompt).images[0]
+  return image
+
+from transformers import pipeline
+import gradio as gr
+
+# Indicamos el tipo de tarea para la que se está creando el pipeline (ASR)
+model = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
+
+def transcribe_audio(mic=None, file=None):
+    if mic is not None:
+        audio = mic
+    elif file is not None:
+        audio = file
+    else:
+        return "You must either provide a mic recording or a file"
+    transcription = model(audio)["text"]
+    image = text_to_image(transcription)
+    return [transcription, image]
+
+gr.Interface(
+    fn=transcribe_audio,
+    inputs=[
+        gr.Audio(sources=["microphone"], type="filepath", label="Speak here..."),
+        gr.Audio(sources=["upload"], type="filepath", label="Upload file here..."),
+    ],
+    outputs=[gr.Textbox(label="Transcription"), gr.Image(label="Generated Image")],
+).launch(debug=True)

requirements.txt

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+transformers
+torch
+diffusers