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| import gradio as gr | |
| import torch | |
| import whisper | |
| import requests | |
| from pytube import YouTube | |
| ### ———————————————————————————————————————— | |
| title="Transcript PDF" | |
| ### ———————————————————————————————————————— | |
| whisper_model = whisper.load_model("medium") | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| def transcribe(audio): | |
| print(""" | |
| — | |
| Sending audio to Whisper ... | |
| — | |
| """) | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device) | |
| _, probs = whisper_model.detect_language(mel) | |
| transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False) | |
| translate_options = whisper.DecodingOptions(task="translate", fp16 = False) | |
| transcription = whisper.decode(whisper_model, mel, transcript_options) | |
| translation = whisper.decode(whisper_model, mel, translate_options) | |
| print("Language Spoken: " + transcription.language) | |
| print("Transcript: " + transcription.text) | |
| print("Translated: " + translation.text) | |
| return transcription.text | |
| def transcribe_upload(audio): | |
| return transcribe(audio) | |
| def transcribe_yt(link): | |
| yt = YouTube(link) | |
| path = yt.streams.filter(only_audio=True)[0].download(filename="audio.mp3") | |
| return transcribe(path) | |
| with gr.Blocks(css = css) as demo: | |
| gr.Markdown(""" | |
| ## Multi-lingual Transcript Generator | |
| """) | |
| gr.HTML(''' | |
| <p style="margin-bottom: 10px"> | |
| Save Transcripts of videos as PDF with the help of Whisper, which is a general-purpose speech recognition model released by OpenAI that can perform multilingual speech recognition as well as speech translation and language identification. | |
| </p> | |
| ''') | |
| with gr.Column(): | |
| #gr.Markdown(""" ### Record audio """) | |
| with gr.Tab("Youtube Link"): | |
| yt_input = gr.Textbox(label = 'Youtube Link') | |
| transcribe_audio_yt = gr.Button('Transcribe') | |
| with gr.Tab("Upload Podcast as File"): | |
| audio_input_u = gr.Audio(label = 'Upload Audio',source="upload",type="filepath") | |
| transcribe_audio_u = gr.Button('Transcribe') | |
| with gr.Row(): | |
| transcript_output = gr.Textbox(label="Transcription in the language spoken", lines = 20) | |
| summary_output = gr.Textbox(label = "English Summary", lines = 10) | |
| transcribe_audio_yt.click(transcribe_yt, inputs = yt_input, outputs = [transcript_output, summary_output]) | |
| transcribe_audio_u.click(transcribe_upload, inputs = audio_input_u, outputs = [transcript_output,summary_output]) | |
| gr.HTML(''' | |
| <div class="footer"> | |
| <p>Whisper Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> | |
| </p> | |
| </div> | |
| ''') | |
| demo.queue() | |
| demo.launch() |