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import gradio as gr
import whisper
from PIL import Image
import os
MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
from diffusers import StableDiffusionPipeline
whisper_model = whisper.load_model("small")
device="cpu"
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=MY_SECRET_TOKEN)
pipe.to(device)
def get_transcribe(audio):
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
_, probs = whisper_model.detect_language(mel)
options = whisper.DecodingOptions(task="translate", fp16 = False)
result = whisper.decode(whisper_model, mel, options)
print(result)
print(result.text)
return result.text
def get_images(audio):
prompt = get_transcribe(audio)
#image = pipe(prompt, init_image=init_image)["sample"][0]
images_list = pipe([prompt] * 2)
images = []
safe_image = Image.open(r"unsafe.png")
for i, image in enumerate(images_list["sample"]):
if(images_list["nsfw_content_detected"][i]):
images.append(safe_image)
else:
images.append(image)
return prompt, images
#inputs
audio = gr.Audio(label="Input Audio of an image description", show_label=True, source="microphone", type="filepath")
#outputs
translated_prompt = gr.Textbox(label="Translated audio", lines=6)
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
title="Whisper to Stable Diffusion"
description="""
<p style='text-align: center;'>
This demo is running on CPU. Build by Sylvain <a href='https://twitter.com/fffiloni' target='_blank'>@fffiloni</a> • <img id='visitor-badge' alt='visitor badge' src='https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.sd-img-variations' style='display: inline-block' />
Record an audio description of an image, stop recording, then hit the Submit button to get 2 images from Stable Diffusion.
Your audio will be translated to English, then sent as a prompt to stable diffusion.
Try it in French ! ;)
</p>
"""
gr.Interface(fn=get_images, inputs=audio, outputs=[translated_prompt, gallery]).queue(max_size=1000).launch(enable_queue=True) |