Welcome to the future of conversational AI! SpeechGPT is a revolutionary large language model that effortlessly bridges the gap between text and spoken dialogue using its intrinsic cross-modal abilities. With SpeechGPT, you can interact in multiple ways—be it through speaking or typing, as it comprehends and generates multi-modal content based on human instructions. In this guide, we will walk you through the basics of setting up and utilizing SpeechGPT effectively.

Table of Contents

• Installation
• Talk with SpeechGPT
• Train SpeechGPT
• Finetune SpeechGPT
• Troubleshooting

Installation

To begin your journey with SpeechGPT, follow these simple steps to install it on your local machine:

git clone https://github.com/0nutation/SpeechGPT
cd SpeechGPT
conda create --name SpeechGPT python=3.8
conda activate SpeechGPT
pip install -r requirements.txt

Talk with SpeechGPT

Once you have SpeechGPT installed, it’s time to engage in conversation! Download the relevant models to begin:

• SpeechGPT-7B-cm
• SpeechGPT-7B-com

Complete the following to allow SpeechGPT to understand your commands:

s2u_dir=utils/speech2unit
cd $s2u_dir
wget https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3.pt
wget https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin

Now you’re ready for some fun! Interact using the CLI:

python3 speechgpt/src/infer/cli_infer.py --model-name-or-path path/to/SpeechGPT-7B-cm --lora-weights path/to/SpeechGPT-7B-com --s2u-dir $s2u_dir

Train SpeechGPT

If you’d like to train SpeechGPT yourself, the process involves three distinct stages:

Stage 1: Modality-adaptation Pre-training

Think of this stage as teaching a toddler how to say their first words. Initially, they repeat what they hear, associating sounds with meanings. In the case of SpeechGPT, we leverage the mHuBERT for discretizing datasets, which helps the model learn to separate and recognize distinct sounds.

bash scripts/ma_pretrain.sh $NNODE $NODE_RANK $MASTER_ADDR $MASTER_PORT

Stage 2: Cross-modal Instruction Finetuning

Once the toddler has learned some words, we teach them how to form sentences. This stage has the model fine-tuning its understanding of how to provide responses based on multi-modal instructions from users.

bash scripts/cm_sft.sh $NNODE $NODE_RANK $MASTER_ADDR $MASTER_PORT

Stage 3: Chain-of-modality Instruction Finetuning

Finally, as the toddler grows, we help them learn to have meaningful conversations. This step further polishes the model’s capacity to connect varied inputs and respond effectively.

bash scripts/com_sft.sh $NNODE $NODE_RANK $MASTER_ADDR $MASTER_PORT

Finetune SpeechGPT

Fine-tuning is important when adapting a model to specific tasks or data. To fine-tune SpeechGPT:

1. Ensure your data matches the format required by the SpeechInstruct Cross-modal Instruction set.
2. Download SpeechGPT-7B-cm locally.
3. Modify the required parameters in the fine-tuning script and execute it.

Troubleshooting

While working with SpeechGPT, you might encounter a few bumps along the way. Here are some common problems and solutions:

• Inaccuracy in speech recognition: Ensure the audio quality is good and clear. Poor audio can lead to misunderstandings.
• Task recognition errors: Make sure you’re structuring your commands properly—this helps the model in understanding your needs.
• Performance issues: Consider adjusting parameters or reviewing your data if you notice inconsistencies.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.