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| import torch | |
| import gradio as gr | |
| import pytube as pt | |
| from transformers import pipeline | |
| from diffusers import StableDiffusionPipeline | |
| MODEL_NAME = "whispy/whisper_italian" | |
| YOUR_TOKEN="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" | |
| # whisper model fine-tuned for italian | |
| speech_ppl = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| chunk_length_s=30, | |
| device="cpu" | |
| ) | |
| # model summarizing text | |
| summarizer_ppl = pipeline( | |
| "summarization", | |
| model="it5/it5-efficient-small-el32-news-summarization" | |
| ) | |
| # model translating text from Italian to English | |
| translator_ppl = pipeline( | |
| "translation", | |
| model="Helsinki-NLP/opus-mt-it-en" | |
| ) | |
| # model producing an image from text | |
| image_ppl = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) | |
| #def transcribe(microphone, file_upload): | |
| def transcribe(microphone): | |
| warn_output = "" | |
| # if (microphone is not None) and (file_upload is not None): | |
| if (microphone is not None): | |
| warn_output = ( | |
| "WARNING: You've uploaded an audio file and used the microphone. " | |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| ) | |
| # elif (microphone is None) and (file_upload is None): | |
| elif (microphone is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| # file = microphone if microphone is not None else file_upload | |
| file = microphone | |
| text = speech_ppl(file)["text"] | |
| print("Text: ", text) | |
| translate = translator_ppl(text) | |
| print("Translate: ", translate) | |
| translate = translate[0]["translation_text"] | |
| print("Translate 2: ", translate) | |
| print("Building image .....") | |
| #image = image_ppl(translate).images[0] | |
| #image = image_ppl(translate, num_inference_steps=15)["sample"] | |
| prompt = "a photograph of an astronaut riding a horse" | |
| image = image_ppl(prompt, num_inference_steps=15) | |
| print("Image output: ", image) | |
| print("Image: ", image.images) | |
| #image.save("text-to-image.png") | |
| return warn_output + text, translate, image | |
| def _return_yt_html_embed(yt_url): | |
| video_id = yt_url.split("?v=")[-1] | |
| HTML_str = ( | |
| f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' | |
| " </center>" | |
| ) | |
| return HTML_str | |
| def yt_transcribe(yt_url): | |
| yt = pt.YouTube(yt_url) | |
| html_embed_str = _return_yt_html_embed(yt_url) | |
| stream = yt.streams.filter(only_audio=True)[0] | |
| stream.download(filename="audio.mp3") | |
| text = pipe("audio.mp3")["text"] | |
| summary = summarizer(text) | |
| summary = summary[0]["summary_text"] | |
| translate = translator(summary) | |
| translate = translate[0]["translation_text"] | |
| return html_embed_str, text, summary, translate | |
| #demo = gr.Blocks() | |
| mf_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True), | |
| #gr.inputs.Audio(source="upload", type="filepath", optional=True), | |
| ], | |
| outputs=[gr.Textbox(label="Transcribed text"), | |
| gr.Textbox(label="Summarized text"), | |
| gr.Image(type="pil", label="Output image")], | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Demo: Transcribe Audio to Image", | |
| description=( | |
| "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned" | |
| f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" | |
| " of arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| ''' | |
| yt_transcribe = gr.Interface( | |
| fn=yt_transcribe, | |
| inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")], | |
| outputs=["html", "text", "text", "text"], | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="Whisper Demo: Transcribe YouTube", | |
| description=( | |
| "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:" | |
| f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of" | |
| " arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| ''' | |
| ''' | |
| with demo: | |
| #gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) | |
| gr.TabbedInterface(mf_transcribe, "Transcribe Audio to Image") | |
| demo.launch(enable_queue=True) | |
| ''' | |
| mf_transcribe.launch(enable_queue=True) | |