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| # Imports | |
| from pytube import YouTube | |
| from huggingsound import SpeechRecognitionModel | |
| import torch | |
| from transformers import pipeline | |
| from IPython.display import Audio | |
| from pprint import pprint | |
| import os | |
| import gradio as gr | |
| import subprocess # process in the os | |
| from subprocess import STDOUT, check_call #os process manipulation | |
| # Constants | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| MODEL = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english", device = DEVICE) | |
| summarizationPipeline = pipeline('summarization') # Hugging Face's default summarization pipeline | |
| SAMPLING_RATE = 16000 | |
| USE_ONNX = False | |
| torch.set_num_threads(1) | |
| def transcribeVideo(VIDEO_URL): | |
| # Download the '.mp4' & save it as an audio file ('.wav') for the video | |
| ytVideo = YouTube(VIDEO_URL) | |
| ytVideo.streams \ | |
| .filter(only_audio = True, file_extension = 'mp4') \ | |
| .first() \ | |
| .download(filename = 'ytaudio.mp4') \ | |
| os.system("ffmpeg -i ytaudio.mp4 -acodec pcm_s16le -ar 16000 ytaudio.wav") | |
| # Audio Chunking with Silero VAD | |
| model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', | |
| model='silero_vad', | |
| force_reload=True, | |
| onnx=USE_ONNX) | |
| (get_speech_timestamps, | |
| save_audio, | |
| read_audio, | |
| VADIterator, | |
| collect_chunks) = utils | |
| # Read '.wav' audio file | |
| audioFile = read_audio('ytaudio.wav', sampling_rate=SAMPLING_RATE) | |
| # get speech timestamps from full audio file | |
| speechTimestamps = get_speech_timestamps(audioFile, model, sampling_rate=SAMPLING_RATE) | |
| # Save the audio chunks as separate audio files | |
| index = 0 | |
| for timestamp in speechTimestamps: | |
| startTime = timestamp['start'] | |
| endTime = timestamp['end'] | |
| save_audio(f'speech-{index}.wav', audioFile[startTime:endTime], sampling_rate=SAMPLING_RATE) | |
| index += 1 | |
| # Concatenate the path of these separated audio chunks | |
| audioChunksPath = [] | |
| for i in range(len(speechTimestamps)): | |
| audioChunksPath.append(f'speech-{i}.wav') | |
| # Generate individual transcriptions & concatenate them | |
| transcriptions = MODEL.transcribe(audioChunksPath) | |
| fullTranscript = '' | |
| for transcript in transcriptions: | |
| fullTranscript += ''.join(transcript['transcription']) + ' ' | |
| return fullTranscript | |
| def summarizeTranscription(VIDEO_URL): | |
| fullTranscript = transcribeVideo(VIDEO_URL) | |
| # Generate summary from the full transcript | |
| summarizedText = summarizationPipeline(fullTranscript, max_length=300, min_length=75, do_sample=False) | |
| return summarizedText[0]['summary_text'] | |
| iface = gr.Interface(fn=summarizeTranscription, inputs=["text"], outputs=["textbox"], title='YouTube Video Summarizer').launch(inline=False) |