app

app.py

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+import gradio as gr
+from pytube import YouTube
+import subprocess
+from huggingsound import SpeechRecognitionModel
+import torch
+import librosa
+import soundfile as sf
+from transformers import pipeline
+
+def process_video(video_url):
+    yt = YouTube(video_url)
+    audio_file = yt.streams.filter(only_audio=True, file_extension='mp4').first().download(filename='ytaudio.mp4')
+    subprocess.run(['ffmpeg', '-i', 'ytaudio.mp4', '-acodec', 'pcm_s16le', '-ar', '16000', 'ytaudio.wav'])
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english", device=device)
+    
+    input_file = 'ytaudio.wav'
+    stream = librosa.stream(input_file, block_length=30, frame_length=16000, hop_length=16000)
+    
+    full_transcript = ''
+    for i, speech in enumerate(stream):
+        sf.write(f'{i}.wav', speech, 16000)
+        transcription = model.transcribe([f'{i}.wav'])[0]['transcription']
+        full_transcript += transcription + ' '
+
+    summarization = pipeline('summarization')
+    summarized_text = summarization(full_transcript, max_length=130, min_length=30, do_sample=False)
+    return summarized_text[0]['summary_text']
+
+iface = gr.Interface(fn=process_video, inputs="text", outputs="text", title="YouTube Video Summarizer")
+iface.launch()