🗣️ StutteredSpeechASR Research Demo

A Gradio-based research demonstration showcasing StutteredSpeechASR, a Whisper model fine-tuned specifically for stuttered speech recognition (Mandarin). Compare its performance against baseline Whisper models to see the improvement on stuttered speech patterns.

🎯 Features

• StutteredSpeechASR Research: Showcases fine-tuned Whisper model specifically designed for stuttered speech
• Comparative Analysis: Side-by-side comparison with baseline Whisper models
• Audio Input Flexibility: Record via microphone or upload audio files
• Specialized for Stuttered Speech: Better handling of repetitions, prolongations, and blocks
• Clean Interface: Organized model cards with clear transcription results
• Lightweight Deployment: All inference via Hugging Face APIs - no GPU required

🤖 Models Included

Model	Type	Description
🗣️ StutteredSpeechASR	Fine-tuned Research Model	Whisper fine-tuned specifically for stuttered speech (Mandarin)
🎙️ Whisper Large V3	Baseline Model	OpenAI's Whisper Large V3 model via HF Inference API
🔊 Whisper Large V3 Turbo	Baseline Model	OpenAI's Whisper Large V3 Turbo (faster) via HF Inference API

📋 Requirements

• Python 3.9+
• Hugging Face API key
• Docker (optional, for containerized deployment)

🔑 Environment Setup

Create a .env file in the project root with your Hugging Face credentials:

HF_ENDPOINT=https://your-endpoint-url.aws.endpoints.huggingface.cloud
HF_API_KEY=hf_your_api_key_here

Variable	Description
HF_ENDPOINT	Your dedicated Hugging Face Inference Endpoint URL for StutteredSpeechASR
HF_API_KEY	Your Hugging Face API token (get one at huggingface.co/settings/tokens)

🚀 Quick Start

Option 1: Run with Docker (Recommended)

1. Create your .env file with HuggingFace credentials (see above)

2. Build and run with Docker Compose

   docker compose up --build
   

3. Open your browser and navigate to http://localhost:7860

Option 2: Run Locally

1. Clone the repository

   git clone <your-repo-url>
   cd asr_demo
   

2. Create a virtual environment (recommended)

   python -m venv venv
   
   # Windows
   venv\Scripts\activate
   
   # Linux/macOS
   source venv/bin/activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Create your .env file with HuggingFace credentials (see Environment Setup above)

5. Run the application

   python app.py
   

6. Open your browser and navigate to http://localhost:7860

🧪 Research Notes

• Target Language: The StutteredSpeechASR model is specifically trained for Mandarin Chinese
• Use Cases: Research demonstration, stuttered speech analysis, comparative ASR evaluation
• Best Results: Use clear audio recordings for optimal model performance
• Baseline Comparison: The Whisper models may struggle with stuttered speech patterns that StutteredSpeechASR handles well

📚 References

• Gradio Documentation
• Hugging Face Inference API
• Hugging Face Inference Endpoints
• AImpower StutteredSpeechASR
• OpenAI Whisper