EchoLLaMA-1B

EchoLLaMA: 3D-to-Speech with Multimodal AI

Overview

EchoLLaMA is a multimodal AI system that transforms 3D visual data into natural spoken descriptions while enabling interactive dialogue through speech input. This repository contains the implementation of the LLaMA-3.2-1B-Instruct model fine-tuned with Direct Preference Optimization (DPO) for generating rich textual descriptions of 3D scenes.

Model Architecture

The EchoLLaMA pipeline integrates four specialized models:

1. Image Analysis:

   • DETR (DEtection TRansformer) for object detection
   • MiDaS for monocular depth estimation
   • Moondream for holistic image captioning

2. Text Generation:

   • LLaMA-3.2-1B-Instruct fine-tuned with DPO

3. Speech Synthesis:

   • Orpheus-3B-0.1-ft TTS model fine-tuned on the Elise English speech dataset

4. Speech Recognition:

   • SpeechRecognition package for transcribing user speech input

Key Features

• 3D Object Detection Matrix: Constructs a grid-based representation of detected objects with spatial coordinates
• Depth-Aware Scene Understanding: Incorporates relative depth values to capture 3D relationships
• Natural Language Generation: Produces coherent and contextually rich descriptions
• High-Quality Speech Synthesis: Converts textual descriptions into natural-sounding speech

Training Details

LLaMA Model

The LLaMA-3.2-1B-Instruct model was fine-tuned using:

• Technique: Direct Preference Optimization (DPO) with LoRA
• Dataset: 2000 samples from COCO 2017 processed with DETR, and Moondream
• Chosen Responses: Generated by DeepSeek-V3-0324
• Rejected Responses: Generated by pre-fine-tuned LLaMA-3.2-1B-Instruct
• Training Parameters:
  • LoRA Rank: 8
  • β (DPO): 0.1
  • Learning Rate: 2×10⁻⁵ with cosine decay
  • Batch Size: 16 (with 2×8 accumulation)
  • Sequence Length: 8192
• Hardware: 2×T4 GPU
• Training Time: 1 hour 40 minutes

Orpheus Model

The Orpheus-3B-0.1-ft TTS model was fine-tuned using:

• Technique: Low-Rank Adaptation (LoRA)
• Dataset: Elise English speech dataset
• Training Parameters:
  • LoRA Rank (r): 64
  • LoRA Alpha (α): 64
  • LoRA Dropout: 0
  • Learning Rate: 2×10⁻⁴
• Hardware: 2×T4 GPU
• Training Time: 47 minutes

Usage

Installation

# Clone the repository
git clone https://github.com/The-Aqua-Labs/EchoLLaMA-Pipeline.git
cd EchoLLaMA-Pipeline

And run the Jupyter Notebook file.

Pipeline Flow

1. Image is processed with DETR for object detection and MiDaS for depth estimation
2. Moondream generates a caption describing the image content
3. The object detection matrix and caption are combined into a prompt
4. LLaMA-3.2-1B-Instruct generates a detailed textual description
5. Orpheus-3B-0.1-ft converts the text into speech

Dataset

The training dataset contains 1999 samples, each consisting of:

• An image-derived prompt with object detection matrix and caption
• A chosen response from DeepSeek-V3-0324
• A rejected response from LLaMA-3.2-1B-Instruct

You can access the dataset at AquaLabs/Spatial-DPO-Dataset

Model Weights

• LLaMA-3.2-1B-Instruct (fine-tuned): AquaLabs/EchoLLaMA-1B
• Orpheus-3B-0.1-ft (fine-tuned): AquaLabs/Orpheus-3B-0.1-ft-Elise

Contributors

• Ahmet Erdem Pamuk - GitHub | Hugging Face
• Emir Kaan Özdemir - GitHub | Hugging Face
• Şuayp Talha Kocabay - GitHub | Hugging Face

License

This project is licensed under the Apache-2.0 License.

Details are provided in the paper.