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speech-to-diffusion
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import gradio as gr
import os
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
from PIL import Image
from styles import css, header_html, footer_html
from examples import examples
from transformers import pipeline

ars_model = pipeline("automatic-speech-recognition")


model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda" if torch.cuda.is_available() else "cpu"

# If you are running this code locally, you need to either do a 'huggingface-cli login` or paste your User Access Token from here https://huggingface.co/settings/tokens into the use_auth_token field below.
pipe = StableDiffusionPipeline.from_pretrained(
    model_id, use_auth_token=os.environ.get('auth_token'), revision="fp16", torch_dtype=torch.float16)
pipe = pipe.to(device)


def transcribe(audio):
    text = ars_model(audio)["text"]
    return text


def infer(audio, samples, steps, scale, seed):

    prompt = transcribe(audio)
    generator = torch.Generator(device=device).manual_seed(seed)

    # If you are running locally with CPU, you can remove the `with autocast("cuda")`
    if device == "cuda":
        with autocast("cuda"):
            images_list = pipe(
                [prompt] * samples,
                num_inference_steps=steps,
                guidance_scale=scale,
                generator=generator,
            )
    else:
        images_list = pipe(
            [prompt] * samples,
            num_inference_steps=steps,
            guidance_scale=scale,
            generator=generator,
        )
    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 images


block = gr.Blocks(css=css)


with block:
    gr.HTML(header_html)
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                audio = gr.Audio(
                    label="Describe a prompt",
                    source="microphone",
                    type="filepath"
              #  ).style(
              #     border=(True, False, True, True),
              #     rounded=(True, False, False, True),
              #    container=False,
                )
                btn = gr.Button("Generate image").style(
                    margin=False,
                    rounded=(False, True, True, False),
                )

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(grid=[2], height="auto")

        advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")

        with gr.Row(elem_id="advanced-options"):
            samples = gr.Slider(label="Images", minimum=1,
                                maximum=4, value=4, step=1)
            steps = gr.Slider(label="Steps", minimum=1,
                              maximum=50, value=45, step=1)
            scale = gr.Slider(
                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
            )
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=2147483647,
                step=1,
                randomize=True,
            )

        # ex = gr.Examples(fn=infer, inputs=[
        #                 audio, samples, steps, scale, seed], outputs=gallery)
        # ex.dataset.headers = [""]

        # audio.submit(infer, inputs=[audio, samples,
        #                           steps, scale, seed], outputs=gallery)
        btn.click(infer, inputs=[audio, samples, steps,
                  scale, seed], outputs=gallery)
        advanced_button.click(
            None,
            [],
            audio,
            _js="""
            () => {
                const options = document.querySelector("body > gradio-app").querySelector("#advanced-options");
                options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none";
            }""",
        )
        gr.HTML(footer_html)

block.queue(max_size=25).launch()