Create app.py

app.py

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+import gradio as gr
+import torchaudio
+import torchaudio.transforms as T
+from transformers import pipeline
+import requests
+from pydub import AudioSegment
+from pydub.silence import split_on_silence
+import io
+import os
+
+# Load the transcription model
+transcription_pipeline = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
+
+def download_audio_from_url(url):
+    response = requests.get(url)
+    audio_bytes = response.content
+    return audio_bytes
+
+def transcribe_audio(audio_bytes):
+    audio = AudioSegment.from_file(io.BytesIO(audio_bytes))
+    audio.export("temp_audio.wav", format="wav")
+    waveform, sample_rate = torchaudio.load("temp_audio.wav")
+    os.remove("temp_audio.wav")
+    
+    # Transcribe the audio
+    result = transcription_pipeline(waveform, chunk_length_s=30)
+    transcript = result['text']
+    
+    # Split transcript into paragraphs based on silence
+    chunks = split_on_silence(audio, min_silence_len=500, silence_thresh=-40)
+    paragraphs = []
+    current_paragraph = ""
+    
+    for chunk in chunks:
+        chunk.export("temp_chunk.wav", format="wav")
+        waveform, sample_rate = torchaudio.load("temp_chunk.wav")
+        os.remove("temp_chunk.wav")
+        
+        chunk_result = transcription_pipeline(waveform, chunk_length_s=30)
+        chunk_transcript = chunk_result['text']
+        
+        if chunk_transcript:
+            if current_paragraph:
+                current_paragraph += " " + chunk_transcript
+            else:
+                current_paragraph = chunk_transcript
+        else:
+            if current_paragraph:
+                paragraphs.append(current_paragraph)
+                current_paragraph = ""
+    
+    if current_paragraph:
+        paragraphs.append(current_paragraph)
+    
+    formatted_transcript = "\n\n".join(paragraphs)
+    return formatted_transcript
+
+def transcribe_video(url):
+    audio_bytes = download_audio_from_url(url)
+    transcript = transcribe_audio(audio_bytes)
+    return transcript
+
+def download_transcript(transcript):
+    return transcript, "transcript.txt"
+
+# Create the Gradio interface
+with gr.Blocks(title="Video Transcription") as demo:
+    gr.Markdown("# Video Transcription")
+    video_url = gr.Textbox(label="Video URL")
+    transcribe_button = gr.Button("Transcribe")
+    transcript_output = gr.Textbox(label="Transcript", lines=20)
+    download_button = gr.Button("Download Transcript")
+    download_link = gr.File(label="Download Transcript")
+    
+    transcribe_button.click(fn=transcribe_video, inputs=video_url, outputs=transcript_output)
+    download_button.click(fn=download_transcript, inputs=transcript_output, outputs=[download_link, download_link])
+
+demo.launch()