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import os
import re
import torch
import ffmpeg
import yt_dlp
import torchaudio
import gradio as gr
import shutil
from torch.utils.data import Dataset, DataLoader
from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound, CouldNotRetrieveTranscript, VideoUnavailable
from youtube_transcript_api.formatters import TextFormatter
from transformers import (
pipeline,
WhisperProcessor,
WhisperForConditionalGeneration,
)
# === UTILITIES FROM CODE 1 ===
def get_video_id(url):
match = re.search(r'(?:v=|\/)([0-9A-Za-z_-]{11})', url)
return match.group(1) if match else None
def try_download_transcript_file(video_id, lang="en"):
try:
transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=[lang])
formatted = TextFormatter().format_transcript(transcript)
path = f"{video_id}_transcript.txt"
with open(path, "w", encoding="utf-8") as f:
f.write(formatted)
return path
except Exception:
return None
def try_download_audio_file(url, sabr_only=True):
try:
ydl_opts = {
'format': 'bestaudio[asr>0]/bestaudio/best' if sabr_only else 'bestaudio/best',
'outtmpl': 'fallback_audio.%(ext)s',
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'mp3',
}],
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
return "fallback_audio.mp3"
except Exception:
return None
def try_download_video_file(url, sabr_only=True):
try:
ydl_opts = {
'format': 'bestvideo+bestaudio/best' if sabr_only else 'best',
'outtmpl': 'fallback_video.%(ext)s',
'merge_output_format': 'mp4',
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
return "fallback_video.mp4"
except Exception:
return None
# === AUDIO / NLP UTILS ===
def extract_audio_from_video(video_path, audio_path="audio.wav"):
ffmpeg.input(video_path).output(audio_path, ac=1, ar=16000).run(overwrite_output=True)
return audio_path
def split_audio(input_path, chunk_length_sec=30, target_sr=16000):
waveform, sr = torchaudio.load(input_path)
if sr != target_sr:
resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sr)
waveform = resampler(waveform)
if waveform.shape[0] > 1:
waveform = waveform.mean(dim=0, keepdim=True)
chunk_samples = target_sr * chunk_length_sec
chunks = [waveform[:, i:i+chunk_samples] for i in range(0, waveform.shape[1], chunk_samples)]
return chunks, target_sr
class AudioChunksDataset(Dataset):
def __init__(self, chunks):
self.chunks = chunks
def __len__(self):
return len(self.chunks)
def __getitem__(self, idx):
return self.chunks[idx].squeeze(0)
def collate_audio_batch(batch):
max_len = max([b.shape[0] for b in batch])
padded_batch = [torch.nn.functional.pad(b, (0, max_len - b.shape[0])) for b in batch]
return torch.stack(padded_batch)
def transcribe_chunks_dataset(chunks, sr, model_name="openai/whisper-small", batch_size=4):
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name).to(device)
model.eval()
dataset = AudioChunksDataset(chunks)
dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_audio_batch)
full_transcript = []
for batch_waveforms in dataloader:
wave_list = [waveform.numpy() for waveform in batch_waveforms]
input_features = processor(wave_list, sampling_rate=sr, return_tensors="pt", padding="max_length").input_features.to(device)
with torch.no_grad():
predicted_ids = model.generate(input_features, language="en")
transcriptions = processor.batch_decode(predicted_ids, skip_special_tokens=True)
full_transcript.extend(transcriptions)
return " ".join(full_transcript)
def summarize_with_bart(text, max_tokens=1024):
summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=0 if torch.cuda.is_available() else -1)
sentences = text.split(". ")
chunks = []
current_chunk = ""
for sentence in sentences:
if len(current_chunk + sentence) <= max_tokens:
current_chunk += sentence + ". "
else:
chunks.append(current_chunk.strip())
current_chunk = sentence + ". "
if current_chunk:
chunks.append(current_chunk.strip())
summary = ""
for chunk in chunks:
out = summarizer(chunk, max_length=150, min_length=30, do_sample=False)
summary += out[0]['summary_text'] + " "
return summary.strip()
def generate_questions_with_pipeline(text, num_questions=5):
question_generator = pipeline("text2text-generation", model="valhalla/t5-base-qg-hl", device=0 if torch.cuda.is_available() else -1)
sentences = text.split(". ")
questions = []
for sentence in sentences[:num_questions * 2]:
if not sentence.strip():
continue
input_text = f"generate question: {sentence.strip()}"
out = question_generator(input_text, max_length=50, do_sample=True, temperature=0.9)
question = out[0]["generated_text"].strip()
if question:
questions.append(question)
return questions[:num_questions]
# === MAIN PROCESSING FUNCTION ===
def process_input_gradio(url_input, file_input, cookies_file):
try:
cookies_path = None
if cookies_file is not None:
cookies_path = "cookies.txt"
shutil.copyfile(cookies_file.name, cookies_path)
if file_input is not None:
audio_path = extract_audio_from_video(file_input.name)
chunks, sr = split_audio(audio_path, chunk_length_sec=15)
transcript = transcribe_chunks_dataset(chunks, sr)
elif url_input:
video_id = get_video_id(url_input)
transcript_path = try_download_transcript_file(video_id)
if transcript_path:
with open(transcript_path, "r", encoding="utf-8") as f:
transcript = f.read()
else:
audio_file = try_download_audio_file(url_input)
if audio_file and os.path.exists(audio_file):
audio_path = extract_audio_from_video(audio_file)
chunks, sr = split_audio(audio_path, chunk_length_sec=15)
transcript = transcribe_chunks_dataset(chunks, sr)
else:
video_file = try_download_video_file(url_input)
if video_file and os.path.exists(video_file):
audio_path = extract_audio_from_video(video_file)
chunks, sr = split_audio(audio_path, chunk_length_sec=15)
transcript = transcribe_chunks_dataset(chunks, sr)
else:
return "⚠️ Could not download transcript, audio, or video for this URL. Try uploading manually.", ""
else:
return "Please provide a URL or upload a video file.", ""
summary = summarize_with_bart(transcript)
questions = generate_questions_with_pipeline(summary)
return summary, "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)])
except Exception as e:
return f"Error: {str(e)}", ""
# === GRADIO UI ===
iface = gr.Interface(
fn=process_input_gradio,
inputs=[
gr.Textbox(label="YouTube or Direct Video URL", placeholder="https://..."),
gr.File(label="Or Upload a Video File", file_types=[".mp4", ".mkv", ".webm"]),
gr.File(label="Optional cookies.txt for YouTube", file_types=[".txt"]),
],
outputs=[
gr.Textbox(label="Summary", lines=10),
gr.Textbox(label="Generated Questions", lines=10),
],
title="Lecture Summary & Question Generator",
description="Provide a YouTube/Direct video URL or upload a video file. If the video is restricted, upload cookies.txt or the video file directly."
)
iface.launch()
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