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import gradio as gr
import yt_dlp as yt
import whisper
import os
import torch
import logging
# Set up logging
logging.basicConfig(filename='transcription_logs.txt', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Create a temporary download folder if it doesn't exist
temp_download_dir = os.path.join(os.getcwd(), "temp_download")
os.makedirs(temp_download_dir, exist_ok=True)
# Function to download audio from the given URL
def download_audio(url):
ydl_opts = {
'format': 'bestaudio/best',
'outtmpl': os.path.join(temp_download_dir, '%(title)s.%(ext)s'),
}
with yt.YoutubeDL(ydl_opts) as ydl:
info_dict = ydl.extract_info(url, download=True)
downloaded_file = ydl.prepare_filename(info_dict)
# Generate a new file name by replacing spaces with underscores
new_filename = os.path.join(temp_download_dir, os.path.basename(downloaded_file).replace(" ", "_"))
# Check if the new file name exists and create a unique name if necessary
base, extension = os.path.splitext(new_filename)
counter = 1
while os.path.exists(new_filename):
new_filename = f"{base}_{counter}{extension}"
counter += 1
# Rename the file
os.rename(downloaded_file, new_filename)
if os.path.exists(new_filename):
return new_filename
else:
raise Exception("Failed to download and rename audio file.")
# Function to transcribe audio to SRT format
def transcribe_to_srt(file_path):
if torch.cuda.is_available():
model = whisper.load_model("medium", device="cuda")
else:
model = whisper.load_model("medium")
result = model.transcribe(file_path)
srt_content = ""
for i, segment in enumerate(result["segments"]):
start = segment["start"]
end = segment["end"]
text = segment["text"]
srt_content += f"{i + 1}\n"
srt_content += f"{start:.3f}".replace(".", ",") + " --> " + f"{end:.3f}".replace(".", ",") + "\n"
srt_content += text + "\n\n"
return srt_content
def transcribe_video(url):
try:
logging.info(f"Transcribing video from URL: {url}")
audio_file = download_audio(url)
logging.info(f"Downloaded audio file: {audio_file}")
srt_content = transcribe_to_srt(audio_file)
logging.info("Transcription completed successfully!")
# Optionally, remove the audio file after transcription
# os.remove(audio_file)
return srt_content
except Exception as e:
logging.error(f"An error occurred: {e}")
return f"An error occurred: {e}"
iface = gr.Interface(fn=transcribe_video, inputs="text", outputs="text", live=True, title="YouTube/TikTok Video to SRT Transcription")
# Display the logs in the interface
log_viewer = gr.Textbox(text="Logs will appear here...", readonly=True, height=200)
log_handler = logging.StreamHandler(log_viewer)
log_handler.setLevel(logging.INFO)
logging.getLogger().addHandler(log_handler)
iface.launch()
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