chatmusician/README.md

chatmusician

基于 chatmusician 构建中国传统音乐数据集训练，模型，微调

Develop

运行 chatmusician

git clone https://github.com/hf-lin/chatmusician

pip install -r requirements.txt 

apt-get update
apt-get install abcmidi

cd chatmusician/
python model/infer/chatmusician_web_demo.py -c "m-a-p/chatmusician" --server_port 8888


cd chatmusician/
python model/infer/predict.py --base_model {merged_model_path} --with_prompt --interactive

• Python 3.8
• Pytorch 2.0
• CUDA 11.4
• Deepspeed 0.10

训练微调

整理收集,

# 把音频转为乐谱
python convert_abc.py

# 将文本数据转换为模型可以处理的 token IDs
cd chatmusician
python model/train/data_preprocess.py \
    -t m-a-p/chatmusician-Base \
    -i /path/to/your/dataset \
    -o datasets \
    --tokenize_fn sft 

训练：

# 微调
./model/train/scripts/train.sh datasets m-a-p/chatmusician-Base

# 合并 Peft 模型
cd chatmusician/
python model/train/merge.py --ori_model_dir m-a-p/chatmusician-Base --model_dir /path/to/lora/checkpoint --output_dir /path/to/output


# 测试
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("m-a-p/chatmusician-Base")

def preprocess_data(data):
    tokenized_data = []
    for sample in data:
        input_text = sample["input"]
        output_text = sample["output"]
        input_ids = tokenizer.encode(input_text, return_tensors="pt")
        output_ids = tokenizer.encode(output_text, return_tensors="pt")
        tokenized_data.append({"input_ids": input_ids, "output_ids": output_ids})
    return tokenized_data

## 假设 data 是你的中国传统音乐数据集
preprocessed_data = preprocess_data(data)