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app.py

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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(fp16 = False)
+    result = whisper.decode(whisper_model, mel, options)
+    
+    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 images
+#inputs
+audio = gr.Audio(label="Input Audio", show_label=False, source="microphone", type="filepath")
+#outputs
+gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
+
+gr.Interface(fn=get_images, inputs=audio, outputs=gallery).queue(max_size=10).launch(enable_queue=True)