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import gradio as gr |
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import whisper |
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from PIL import Image |
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import os |
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MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') |
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from diffusers import StableDiffusionPipeline |
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whisper_model = whisper.load_model("small") |
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device="cpu" |
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pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=MY_SECRET_TOKEN) |
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pipe.to(device) |
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def get_transcribe(audio): |
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audio = whisper.load_audio(audio) |
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audio = whisper.pad_or_trim(audio) |
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mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device) |
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_, probs = whisper_model.detect_language(mel) |
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options = whisper.DecodingOptions(fp16 = False) |
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result = whisper.decode(whisper_model, mel, options) |
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print(result.text) |
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return result.text |
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def get_images(audio): |
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prompt = get_transcribe(audio) |
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images_list = pipe([prompt] * 2) |
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images = [] |
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safe_image = Image.open(r"unsafe.png") |
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for i, image in enumerate(images_list["sample"]): |
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if(images_list["nsfw_content_detected"][i]): |
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images.append(safe_image) |
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else: |
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images.append(image) |
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return images |
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audio = gr.Audio(label="Input Audio", show_label=False, source="microphone", type="filepath") |
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gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") |
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gr.Interface(fn=get_images, inputs=audio, outputs=gallery).queue(max_size=10).launch(enable_queue=True) |
