metadata

title: Accent Analyzer Agent
emoji: 🏢
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
  - streamlit
pinned: false
short_description: Various english accent detection
license: mit

Accent Analyzer

This is a Streamlit-based web application that analyzes the English accent in spoken videos. Users can provide a public video URL (MP4), receive a transcription of the speech, and ask follow-up questions based on the transcript using Gemma3.

What It Does

Accepts a public MP4 video URL
Extracts audio and transcribes it using OpenAI Whisper Medium
Detects accent using a Jzuluaga/accent-id-commonaccent_xlsr-en-english model
Lets users ask follow-up questions about the transcript using Gemma3
Deploys easily on Hugging Face Spaces with CPU

Tech Stack

Streamlit — UI
OpenAI Whisper (medium): For speech-to-text transcription.
Jzuluaga/accent-id-commonaccent_xlsr-en-english: For English accent classification.
Gemma3 via Ollama: For generating answers to follow-up questions using context from the transcript.
Docker — containerized for deployment
Hugging Face Spaces — for hosting with CPU

Project Structure

accent-analyzer/
├── Dockerfile                  # Container setup
├── requirements.txt            # Python dependencies
├── streamlit_app.py            # Main UI app
└── src/
    ├── custome_interface.py    # SpeechBrain custom interface
    ├── tools/
    │   └── accent_tool.py      # Audio analysis tool
    └── app/
        └── main_agent.py       # Analysis + LLaMA agents

Running Locally (GPU Required)

Clone the repo:

git clone https://github.com/your-username/accent-analyzer
cd accent-analyzer

Build the Docker image:

docker build -t accent-analyzer .

Run the container:

docker run --gpus all -p 8501:8501 accent-analyzer

Visit: http://localhost:8501

Requirements

requirements.txt should include at least:

streamlit>=1.25.0
requests==2.31.0
pydub==0.25.1
torch==1.11.0
torchaudio==0.11.0
speechbrain==0.5.12
transformers==4.29.2
asyncio==3.4.3
ffmpeg-python==0.2.0
openai-whisper==20230314
numpy==1.22.4
langchain>=0.1.0
langchain-community>=0.0.30
torchvision==0.12.0
langgraph>=0.0.20

Notes

Gemma3 is accessed via Ollama inside Docker — ensure it pulls on build.
custome_interface.py is required by the accent model — it’s automatically downloaded in Dockerfile.
Video URLs must be direct links to .mp4 files.

Example Prompt

Analyze this video: https://www.learningcontainer.com/wp-content/uploads/2020/05/sample-mp4-file.mp4

Then follow up with:

Where is the speaker probably from?
What is the tone or emotion?
Summarize the video?

Acknowledgments

This project uses the following models, frameworks, and tools:

OpenAI Whisper: Automatic speech recognition model.
SpeechBrain: Toolkit used for building and fine-tuning speech processing models.
Accent-ID CommonAccent: Fine-tuned wav2vec2 model hosted on Hugging Face for English accent classification.
CustomEncoderWav2vec2Classifier: Custom interface used to load and run the accent model.
Gemma3 via Ollama: Large language model used for natural language follow-up based on transcripts.
Streamlit: Python framework for building web applications.
Hugging Face Spaces: Platform used for deploying this application on GPU infrastructure.

Author

Developed by Aswathi T S

License

This project is licensed under the MIT License.