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Model Used.txt

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+superb/wav2vec2-base-superb-sid

app.py

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+import os
+import subprocess
+import sys
+
+# Ensure yt_dlp is available
+try:
+    import yt_dlp as youtube_dl
+except ImportError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "yt-dlp"])
+    import yt_dlp as youtube_dl
+
+import gradio as gr
+from transformers import pipeline
+
+def download_video(video_url, filename="downloaded_video.mp4"):
+    ydl_opts = {
+        'format': 'bestaudio/best',
+        'outtmpl': filename,
+        'noplaylist': True,
+        'quiet': True,
+        'user_agent': (
+            'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
+            'AppleWebKit/537.36 (KHTML, like Gecko) '
+            'Chrome/115.0.0.0 Safari/537.36'
+        )
+    }
+    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+        ydl.download([video_url])
+    return filename
+
+def extract_audio(video_filename, audio_filename="extracted_audio.wav"):
+    command = [
+        "ffmpeg",
+        "-y",
+        "-i", video_filename,
+        "-vn",
+        "-acodec", "pcm_s16le",
+        "-ar", "16000",
+        "-ac", "1",
+        audio_filename
+    ]
+    subprocess.run(command, check=True)
+    return audio_filename
+
+def classify_accent(audio_file, model_name="superb/wav2vec2-base-superb-sid"):
+    classifier = pipeline("audio-classification", model=model_name)
+    results = classifier(audio_file)
+    if results:
+        top = results[0]
+        return f"Speaker ID (as accent proxy): {top['label']}\nConfidence: {top['score'] * 100:.2f}%"
+    return "No result."
+
+def accent_classifier(video_url):
+    try:
+        video_file = download_video(video_url)
+        audio_file = extract_audio(video_file)
+        result = classify_accent(audio_file)
+    except Exception as e:
+        result = f"Error occurred: {e}"
+    finally:
+        for f in ["downloaded_video.mp4", "extracted_audio.wav"]:
+            if os.path.exists(f):
+                os.remove(f)
+    return result
+
+iface = gr.Interface(
+    fn=accent_classifier,
+    inputs=gr.Textbox(label="Video URL", placeholder="Paste a public YouTube or Vimeo video link here"),
+    outputs="text",
+    title="Accent Classifier",
+    description="Download a video, extract the audio, and classify the speaker (as an accent proxy) using a Hugging Face model."
+)
+
+if __name__ == "__main__":
+    iface.launch()

author.txt

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+
+---
+
+## 🧳 To package it all:
+
+If you’re on your machine:
+
+1. Create a folder (e.g. `accent_classifier`)
+2. Save all 4 files (`app.py`, `README.md`, `requirements.txt`, `packages.txt`) into it
+3. Right-click → **Send to → Compressed (zipped) folder**
+4. Upload the `.zip` to Hugging Face or unzip it into your local project
+
+---
+
+

packages.txt

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+ffmpeg

readme.md.txt

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+---
+title: Accent Classifier
+emoji: "🎙️"
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: 5.32.0
+app_file: app.py
+pinned: false
+---
+
+# 🎙️ Accent Classifier App
+
+This Gradio-powered app allows you to paste a public video URL (YouTube, Vimeo, Dailymotion), download it with `yt-dlp`, extract the audio using `ffmpeg`, and classify the speaker identity (as a proxy for accent) using the `superb/wav2vec2-base-superb-sid` model from Hugging Face.
+
+---
+
+## 🔧 Setup
+
+```bash
+pip install -r requirements.txt
+sudo apt install ffmpeg

requirements.txt

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+gradio
+transformers
+torch
+torchaudio
+yt-dlp
+tensorflow