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app.py

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+import gradio as gr
+import torch
+
+from diffusers import DiffusionPipeline
+from transformers import (
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
+processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+
+diffuser_pipeline = DiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    custom_pipeline="speech_to_image_diffusion",
+    speech_model=model,
+    speech_processor=processor,
+    revision="fp16",
+    torch_dtype=torch.float16,
+)
+
+diffuser_pipeline.enable_attention_slicing()
+diffuser_pipeline = diffuser_pipeline.to(device)
+
+#————————————————————————————————————————————
+# GRADIO SETUP
+
+audio_input = gr.Audio(source="microphone")
+image_output = gr.Image()
+
+def speech_to_text(audio_sample):
+  text = audio_sample["text"].lower()
+  print(text)
+  speech_data = audio_sample["audio"]["array"]
+  output = diffuser_pipeline(speech_data)
+  
+  return output.images[0]
+
+demo = gr.Interface(fn=speech_to_text, inputs=audio_input, outputs=image_output)
+demo.launch()