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): warn_output = "" if (microphone is not None) and (file_upload 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): return "ERROR: You have to either use the microphone or upload an audio file" file = microphone if microphone is not None else file_upload 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)["sample"] print("Image: ", image) 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'
' "
" ) 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", 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"]) demo.launch(enable_queue=True)