w

app.py

@@ -1,57 +1,44 @@
 import gradio as gr
 from pytube import YouTube
 import subprocess
-from huggingsound import SpeechRecognitionModel
 import torch
+from huggingsound import SpeechRecognitionModel
+import librosa
+import soundfile as sf
 from transformers import pipeline
 
-def process_video(video_url):
-    response = {
-        'status': 'Success',
-        'message': '',
-        'data': ''
-    }
-
-    try:
-        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'], check=True)
-    except Exception as e:
-        response['status'] = 'Error'
-        response['message'] = f'Failed to download and convert video: {str(e)}'
-        return response
-
-    try:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english", device=device)
-        transcription = model.transcribe(['ytaudio.wav'])[0]['transcription']
-    except Exception as e:
-        response['status'] = 'Error'
-        response['message'] = f'Failed during speech recognition: {str(e)}'
-        return response
-
-    try:
-        summarization = pipeline('summarization')
-        summarized_text = summarization(transcription, max_length=130, min_length=30, do_sample=False)
-        response['data'] = summarized_text[0]['summary_text']
-    except Exception as e:
-        response['status'] = 'Error'
-        response['message'] = f'Failed during summarization: {str(e)}'
-        return response
-
-    return response
+def summarize_video(youtube_link):
+    # Download YouTube video's audio
+    yt = YouTube(youtube_link)
+    yt.streams.filter(only_audio=True, file_extension='mp4').first().download(filename='ytaudio.mp4')
+    
+    # Convert to WAV format
+    subprocess.run(['ffmpeg', '-i', 'ytaudio.mp4', '-acodec', 'pcm_s16le', '-ar', '16000', 'ytaudio.wav'], check=True)
+    
+    # Initialize speech recognition model
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english", device=device)
+    
+    # Process audio file and transcribe
+    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'])
+        full_transcript += ' '.join([item['transcription'] for item in transcription])
+    
+    # Summarize the transcript
+    summarizer = pipeline('summarization')
+    summarized_text = summarizer(full_transcript, max_length=130, min_length=30, do_sample=False)
+    return summarized_text[0]['summary_text']
 
-iface = gr.Interface(
-    fn=process_video,
-    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter YouTube Video URL Here..."),
-    outputs=[
-        gr.outputs.Textbox(label="Status"),
-        gr.outputs.Textbox(label="Message"),
-        gr.outputs.Textbox(label="Summary")
-    ],
-    title="YouTube Video Summarizer",
-    description="This tool extracts audio from a YouTube video, transcribes it, and provides a summary.",
-    enable_queue=True  # Enable request queuing
-)
+# Set up the Gradio interface
+iface = gr.Interface(fn=summarize_video,
+                     inputs=gr.inputs.Textbox(lines=2, placeholder="Enter YouTube Video Link Here..."),
+                     outputs="text",
+                     title="YouTube Video Text Summarizer",
+                     description="This tool summarizes the text extracted from a given YouTube video. Please enter the video link below.")
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()