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import gradio as gr |
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import numpy as np |
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import torch |
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from datasets import load_dataset |
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from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device) |
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greek_translation_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-el") |
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model_id = "microsoft/speecht5_tts" |
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model = SpeechT5ForTextToSpeech.from_pretrained(model_id) |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") |
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
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speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0) |
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processor = SpeechT5Processor.from_pretrained(model_id) |
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model_id_greek = "Sandiago21/speecht5_finetuned_google_fleurs_greek" |
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model_greek = SpeechT5ForTextToSpeech.from_pretrained(model_id_greek) |
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processor_greek = SpeechT5Processor.from_pretrained(model_id_greek) |
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replacements = [ |
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("á", "a"), |
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("â", "a"), |
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("ã", "a"), |
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("í", "i"), |
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("á", "a"), |
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("í", "i"), |
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("ñ", "n"), |
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("ó", "o"), |
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("ú", "u"), |
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("ü", "u"), |
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("á", "a"), |
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("ç", "c"), |
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("è", "e"), |
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("ì", "i"), |
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("í", "i"), |
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("ò", "o"), |
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("ó", "o"), |
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("ù", "u"), |
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("ú", "u"), |
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("š", "s"), |
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("ï", "i"), |
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("à", "a"), |
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("â", "a"), |
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("ç", "c"), |
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("è", "e"), |
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("ë", "e"), |
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("î", "i"), |
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("ï", "i"), |
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("ô", "o"), |
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("ù", "u"), |
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("û", "u"), |
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("ü", "u"), |
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("ου", "u"), |
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("αυ", "af"), |
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("ευ", "ef"), |
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("ει", "i"), |
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("οι", "i"), |
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("αι", "e"), |
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("ού", "u"), |
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("εί", "i"), |
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("οί", "i"), |
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("αί", "e"), |
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("Ά", "A"), |
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("Έ", "E"), |
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("Ή", "H"), |
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("Ί", "I"), |
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("Ό", "O"), |
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("Ύ", "Y"), |
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("Ώ", "O"), |
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("ΐ", "i"), |
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("Α", "A"), |
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("Β", "B"), |
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("Γ", "G"), |
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("Δ", "L"), |
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("Ε", "Ε"), |
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("Ζ", "Z"), |
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("Η", "I"), |
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("Θ", "Th"), |
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("Ι", "I"), |
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("Κ", "K"), |
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("Λ", "L"), |
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("Μ", "M"), |
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("Ν", "N"), |
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("Ξ", "Ks"), |
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("Ο", "O"), |
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("Π", "P"), |
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("Ρ", "R"), |
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("Σ", "S"), |
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("Τ", "T"), |
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("Υ", "Y"), |
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("Φ", "F"), |
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("Χ", "X"), |
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("Ω", "O"), |
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("ά", "a"), |
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("έ", "e"), |
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("ή", "i"), |
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("ί", "i"), |
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("α", "a"), |
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("β", "v"), |
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("γ", "g"), |
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("δ", "d"), |
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("ε", "e"), |
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("ζ", "z"), |
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("η", "i"), |
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("θ", "th"), |
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("ι", "i"), |
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("κ", "k"), |
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("λ", "l"), |
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("μ", "m"), |
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("ν", "n"), |
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("ξ", "ks"), |
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("ο", "o"), |
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("π", "p"), |
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("ρ", "r"), |
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("ς", "s"), |
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("σ", "s"), |
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("τ", "t"), |
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("υ", "i"), |
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("φ", "f"), |
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("χ", "h"), |
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("ψ", "ps"), |
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("ω", "o"), |
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("ϊ", "i"), |
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("ϋ", "i"), |
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("ό", "o"), |
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("ύ", "i"), |
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("ώ", "o"), |
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("í", "i"), |
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("õ", "o"), |
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("Ε", "E"), |
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("Ψ", "Ps"), |
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] |
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def cleanup_text(text): |
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for src, dst in replacements: |
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text = text.replace(src, dst) |
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return text |
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def synthesize_speech(text): |
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text = cleanup_text(text) |
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inputs = processor(text=text, return_tensors="pt") |
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speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) |
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return gr.Audio.update(value=(16000, speech.cpu().numpy())) |
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def translate_to_english(audio): |
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outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate", "language": "english"}) |
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return outputs["text"] |
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def synthesise_from_english(text): |
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text = cleanup_text(text) |
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inputs = processor(text=text, return_tensors="pt") |
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speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) |
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return speech.cpu().numpy() |
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def translate_from_english_to_greek(text): |
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return greek_translation_pipe(text)[0]["translation_text"] |
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def synthesise_from_greek(text): |
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text = cleanup_text(text) |
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inputs = processor_greek(text=text, return_tensors="pt") |
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speech = model_greek.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) |
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return speech.cpu() |
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def speech_to_speech_translation(audio): |
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translated_text = translate_to_english(audio) |
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translated_text = translate_from_english_to_greek(translated_text) |
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synthesised_speech = synthesise_from_greek(translated_text) |
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synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16) |
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return ((16000, synthesised_speech), translated_text) |
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title = "Cascaded STST" |
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description = """ |
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Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Greek. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_google_fleurs_greek](https://huggingface.co/Sandiago21/speecht5_finetuned_google_fleurs_greek) checkpoint for text-to-speech, which is based on Microsoft's |
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[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech, fine-tuned in Greek Audio dataset: |
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""" |
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demo = gr.Blocks() |
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mic_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(source="microphone", type="filepath"), |
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outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()], |
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title=title, |
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description=description, |
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) |
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file_translate = gr.Interface( |
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fn=speech_to_speech_translation, |
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inputs=gr.Audio(source="upload", type="filepath"), |
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outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()], |
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examples=[["./example.wav"]], |
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title=title, |
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description=description, |
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) |
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with demo: |
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gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"]) |
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demo.launch() |
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