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| from transformers import pipeline | |
| from pytube import YouTube | |
| import gradio as gr | |
| import requests | |
| pipe = pipeline(model="Silemo/whisper-it") # change to "your-username/the-name-you-picked" | |
| def download_audio(audio_url, filename): | |
| # URL of the image to be downloaded is defined as audio_url | |
| r = requests.get(audio_url) # create HTTP response object | |
| # send a HTTP request to the server and save | |
| # the HTTP response in a response object called r | |
| with open(filename,'wb') as f: | |
| # Saving received content as a mp3 file in | |
| # binary format | |
| # write the contents of the response (r.content) | |
| # to a new file in binary mode. | |
| f.write(r.content) | |
| def transcribe(audio): | |
| text = pipe(audio)["text"] | |
| return text | |
| def transcribe_video(url): | |
| yt = YouTube(url) | |
| stream = yt.streams.get_audio_only() | |
| # Saves the audio in the /audio folder | |
| audio = stream.download() | |
| text = transcribe(audio) | |
| return text | |
| audio1_url = "https://github.com/Silemo/sml-lab2-2023-manfredi-meneghin/raw/main/task1/audio/offer.mp3" | |
| audio1_filename = "offer.mp3" | |
| download_audio(audio1_url, audio1_filename) | |
| audio2_url = "https://github.com/Silemo/sml-lab2-2023-manfredi-meneghin/raw/main/task1/audio/fantozzi.mp3" | |
| audio2_filename = "fantozzi.mp3" | |
| download_audio(audio2_url, audio2_filename) | |
| # Multiple interfaces using tabs -> https://github.com/gradio-app/gradio/issues/450 | |
| io1 = gr.Interface( | |
| fn = transcribe, | |
| inputs = gr.Audio(sources=["microphone", "upload"], type="filepath"), | |
| outputs = "text", | |
| examples=[ | |
| [audio1_filename], | |
| [audio2_filename], | |
| ], | |
| title = "Whisper Small - Italian - Microphone or Audio file", | |
| description = "Realtime demo for Italian speech recognition using a fine-tuned Whisper small model. It uses the computer microphone or an audio file as audio input", | |
| ) | |
| io2 = gr.Interface( | |
| fn = transcribe_video, | |
| inputs = gr.Textbox(label = "YouTube URL", placeholder = "https://youtu.be/9DImRZERJNs?si=1Lme7o_KH2oCxU7y"), | |
| outputs = "text", | |
| examples=[ | |
| # Per me è la cipolla | |
| ["https://youtu.be/QbwZlURClSA?si=DKMtIiKE-nO2mfcV"], | |
| # Breaking Italy - Lollobrigida ferma il treno | |
| ["https://youtu.be/9MPBN0tnA_E?si=8-hqkJS05LNkWprX&t=2"], | |
| # Mussolini discorso | |
| ["https://youtu.be/UmnxcjRk37Q?si=uxt8oqnMDJ3vFzIB&t=77"], | |
| ], | |
| title = "Whisper Small - Italian - YouTube link", | |
| description = "Realtime demo for Italian speech recognition using a fine-tuned Whisper small model. It uses a YouTube link as audio input", | |
| ) | |
| gr.TabbedInterface( | |
| [io1, io2], {"Microphone or audio file", "YouTube"} | |
| ).launch() |