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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 estΓ‘ 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) |