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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
from bs4 import BeautifulSoup
import re
import numpy as np
# Load the transcription model
transcription_pipeline = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
def download_audio_from_url(url):
try:
if "share" in url:
print("Processing shareable link...")
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
video_tag = soup.find('video')
if video_tag and 'src' in video_tag.attrs:
video_url = video_tag['src']
print(f"Extracted video URL: {video_url}")
else:
raise ValueError("Direct video URL not found in the shareable link.")
else:
video_url = url
print(f"Downloading video from URL: {video_url}")
response = requests.get(video_url)
audio_bytes = response.content
print(f"Successfully downloaded {len(audio_bytes)} bytes of data")
return audio_bytes
except Exception as e:
print(f"Error in download_audio_from_url: {str(e)}")
raise
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")
# Convert torch.Tensor to numpy.ndarray
waveform_np = waveform.numpy().squeeze()
# Transcribe the audio
result = transcription_pipeline(waveform_np, 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_chunk, sample_rate_chunk = torchaudio.load("temp_chunk.wav")
os.remove("temp_chunk.wav")
# Convert torch.Tensor to numpy.ndarray
waveform_chunk_np = waveform_chunk.numpy().squeeze()
chunk_result = transcription_pipeline(waveform_chunk_np, 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):
try:
print(f"Attempting to download audio from URL: {url}")
audio_bytes = download_audio_from_url(url)
print(f"Successfully downloaded {len(audio_bytes)} bytes of audio data")
print("Starting audio transcription...")
transcript = transcribe_audio(audio_bytes)
print("Transcription completed successfully")
return transcript
except Exception as e:
error_message = f"An error occurred: {str(e)}"
print(error_message)
return error_message
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() |