Create app.py

app.py

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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
+
+# 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
+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'/content/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)