| from transformers import pipeline |
| from huggingface_hub import login |
| from diffusers import StableDiffusionPipeline |
| import gradio as gr |
| import torch |
| import transformers |
| |
| hf_token = "your_huggingface_token_here" |
| login(hf_token) |
|
|
| |
| speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-base") |
|
|
| |
| text_to_image = StableDiffusionPipeline.from_pretrained( |
| "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16 |
| ).to("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| |
| def transcribe_audio(audio_file): |
| try: |
| result = speech_to_text(audio_file) |
| transcription = result["text"] |
| return transcription |
| except Exception as e: |
| return f"Error in transcription: {str(e)}" |
|
|
| |
| def generate_image_from_text(text): |
| try: |
| image = text_to_image(text).images[0] |
| return image |
| except Exception as e: |
| return f"Error in image generation: {str(e)}" |
|
|
| |
| def process_audio_and_generate_image(audio_file): |
| transcription = transcribe_audio(audio_file) |
| if "Error" in transcription: |
| return None, transcription |
|
|
| image = generate_image_from_text(transcription) |
| if isinstance(image, str) and "Error" in image: |
| return None, image |
|
|
| return image, transcription |
|
|
| |
| iface = gr.Interface( |
| fn=process_audio_and_generate_image, |
| inputs=gr.Audio(type="filepath", label="Upload audio file (WAV/MP3)"), |
| outputs=[ |
| gr.Image(label="Generated Image"), |
| gr.Textbox(label="Transcription") |
| ], |
| title="Speech-to-Text and Image Generation", |
| description="Upload an audio file to transcribe speech to text, and then generate an image based on the transcription.", |
| ) |
|
|
| |
| iface.launch(share=True) |
|
|
