README.md

metadata

title: Tiny Audio Demo
emoji: 🎤
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
python_version: '3.11'
app_file: app.py
pinned: false
license: mit
short_description: Efficient ASR with HuBERT encoder and Qwen3-8B decoder
models:
  - mazesmazes/tiny-audio
tags:
  - audio
  - automatic-speech-recognition
  - wav2vec2
  - qwen3
  - lora
suggested_hardware: cpu-upgrade
preload_from_hub:
  - mazesmazes/tiny-audio

Demo Overview

This Space demonstrates an Automatic Speech Recognition (ASR) model that combines:

• HuBERT-large encoder for audio feature extraction
• Qwen3-8B decoder for efficient text generation

Features

• 🎙️ Record from microphone or upload audio files
• ⚡ Fast inference with only ~2% trainable parameters
• 🎯 English transcription optimized for speech-to-text
• 📊 Lightweight model suitable for edge deployment

Model Architecture

The model uses a novel architecture that bridges audio and text modalities:

1. Audio Encoder: Frozen HuBERT-large encoder (317M params)
2. Projection Layer: Custom audio-to-text space mapping
3. Text Decoder: Qwen3-8B (frozen)

Usage

1. Upload an audio file (WAV, MP3, etc.) or record directly using your microphone
2. Click "Transcribe" to convert speech to text
3. The transcription will appear in the output box

Limitations

• Maximum audio length: 30 seconds
• Optimized for English language
• Best performance with clear speech and minimal background noise

Links

• 📦 Model on Hugging Face
• 💻 GitHub Repository
• 📄 Technical Details

Citation

If you use this model in your research, please cite:

@software{tiny_audio_2024,
  author = {Kroman, Alex},
  title = {Tiny Audio: Efficient ASR with HuBERT and Qwen3-8B},
  year = {2024},
  url = {https://github.com/alexkroman/tiny-audio}
}