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import gradio as gr
import subprocess
import tempfile
import os
import shutil
from logging_config import logger
from transcription_tool import TranscriptTool # Assuming TranscriptionTool is in `transcription_tool.py`
# smolagent transcription tool
transcript_tool = TranscriptTool()
def transcribe_url(url):
"""
Transcribes audio or video from a given URL.
Downloads the media from the URL, converts it to WAV format,
and then uses the TranscriptTool to perform the transcription in English
Args:
url (str): The URL of the audio or video file.
Returns:
str: The transcription of the audio/video in english, or an error message if
download or transcription fails.
"""
local_file_path = None
try:
if not url:
return "Error: Please provide a URL." # Removed the second empty string as the function only returns one value
logger.info(f"Attempting to download audio from URL: {url}")
temp_download_dir = "./temp_downloads"
os.makedirs(temp_download_dir, exist_ok=True)
# Use yt-dlp to download the best audio format and convert to wav
# -f bestaudio: selects the best audio format
# -x: extracts audio
# --audio-format wav: converts to wav format
# -o: specifies output template
output_template = os.path.join(temp_download_dir, "downloaded_audio.%(ext)s")
# --- Start Added Code ---
# Ensure any previous download is removed before starting a new one
logger.info(f"Checking for existing files matching 'downloaded_audio.*' in {temp_download_dir}")
for filename in os.listdir(temp_download_dir):
if filename.startswith("downloaded_audio."):
file_to_delete = os.path.join(temp_download_dir, filename)
try:
os.remove(file_to_delete)
logger.info(f"Removed existing file: {file_to_delete}")
except OSError as e:
logger.error(f"Error removing file {file_to_delete}: {e.strerror}")
# --- End Added Code ---
cmd = ["yt-dlp", "-f", "bestaudio", "-x", "--audio-format", "wav", "-o", output_template, url]
process = subprocess.run(cmd, check=True, capture_output=True, text=True)
logger.info("yt-dlp stdout:\n" + process.stdout)
logger.info("yt-dlp stderr:\n" + process.stderr)
# Find the downloaded file (yt-dlp might add .wav extension)
downloaded_files = [f for f in os.listdir(temp_download_dir) if f.startswith("downloaded_audio.")]
if not downloaded_files:
raise RuntimeError("yt-dlp failed to download or convert audio.")
local_file_path = os.path.join(temp_download_dir, downloaded_files[0])
logger.info(f"Downloaded audio to temporary file: {local_file_path}")
print(f"Downloaded audio to temporary file: {local_file_path}")
# Perform transcription
transcription_result = transcript_tool.forward(local_file_path)
return transcription_result
except subprocess.CalledProcessError as e:
error_message = f"yt-dlp error: {e.stderr}"
logger.error(error_message)
return f"An error occurred during download: {error_message}"
except Exception as e:
error_message = f"An unexpected error occurred: {str(e)}"
logger.error(error_message)
return error_message
with gr.Blocks() as app:
gr.Markdown("# <center>gradio-transcript-mcp: Transcribe Audio/Video from URL</center>")
gr.Markdown(
"""
This application functions as an MCP server that transcribes audio or video from a URL using OpenAI's Whisper model.
It downloads the media, converts it to WAV, and performs the transcription.
### Connecting to the Hosted Server
To connect your MCP client that supports SSE to this hosted server, add a configuration entry similar to this:
```json
{
"mcpServers": {
"gradio-transcript": {
"url": "https://bismay-gradio-transcript-mcp.hf.space/gradio_api/mcp/sse"
}
}
}
```
For more details on setup and MCP usage, see the [README.md](README.md).
"""
)
url_input = gr.Textbox(label="Enter Audio/Video URL", placeholder="e.g., https://www.youtube.com/watch?v=dQw4w9WgXcQ")
transcribe_button = gr.Button("Transcribe")
gr.Markdown("Provide a URL to transcribe audio or YT video.")
transcription_output = gr.Textbox(label="Transcription", lines=10)
transcribe_button.click(
fn=transcribe_url,
inputs=[url_input],
outputs=[transcription_output]
)
if __name__ == "__main__":
app.launch(mcp_server=True)
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