├── requirements.txt ├── .gitattributes ├── data └── samples │ ├── linyi │ └── 【愤怒】你这问题太弱智了，我都不知道该从哪开始骂你。.WAV │ └── paimeng │ └── 疑问—哇，这个，还有这个…只是和史莱姆打了一场，就有这么多结论吗？.wav ├── text ├── chat_template.txt └── questions.txt ├── tools └── compress_model.py ├── main ├── chat.py └── start_qa_dialogue.py ├── README.md ├── .gitignore └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | mlx 2 | mlx-lm 3 | simpleaudio -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /data/samples/linyi/【愤怒】你这问题太弱智了，我都不知道该从哪开始骂你。.WAV: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyiLYi/bilibot/HEAD/data/samples/linyi/【愤怒】你这问题太弱智了，我都不知道该从哪开始骂你。.WAV -------------------------------------------------------------------------------- /data/samples/paimeng/疑问—哇，这个，还有这个…只是和史莱姆打了一场，就有这么多结论吗？.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linyiLYi/bilibot/HEAD/data/samples/paimeng/疑问—哇，这个，还有这个…只是和史莱姆打了一场，就有这么多结论吗？.wav -------------------------------------------------------------------------------- /text/chat_template.txt: -------------------------------------------------------------------------------- 1 | <|im_start|>system 2 | You are a helpful assistant<|im_end|> 3 | <|im_start|>user 4 | 你是一位B站老用户，请使用暴躁的语言风格，对以下问题给出简短、机智的回答： 5 | {usr_msg}<|im_end|> 6 | <|im_start|>assistant 7 | -------------------------------------------------------------------------------- /tools/compress_model.py: -------------------------------------------------------------------------------- 1 | from mlx_lm import convert 2 | 3 | # Compress Qwen1.5-32B-Chat. 4 | convert( 5 | "models/Qwen1.5-32B-Chat", 6 | mlx_path="models/Qwen1.5-32B-Chat-4Bit", 7 | quantize=True 8 | ) 9 | 10 | # Compress fine-tuned Qwen1.5-32B-Chat. 11 | # convert( 12 | # "models/Qwen1.5-32B-Chat-FT", 13 | # mlx_path="models/Qwen1.5-32B-Chat-FT-4Bit", 14 | # quantize=True 15 | # ) -------------------------------------------------------------------------------- /text/questions.txt: -------------------------------------------------------------------------------- 1 | 天下没有不散的筵席，那么我们相聚的意义又是什么？ 2 | 咖啡严格来说是不是也可以叫豆浆？ 3 | 石油也是油，为啥没人用它来炒菜？ 4 | 执行死刑时可以委托律师替我去吗？ 5 | 目前我国同性群体离婚率为零，这是否说明同性间结婚比异性更有优势？ 6 | 大家都说我嘴很甜，他们是怎么尝出来的？ 7 | 吃了降压药之后可以摸高压电吗？ 8 | 我爸再婚，我是不是就有了个新娘？ 9 | 为什么我爸妈结婚的时候没邀请我参加婚礼？ 10 | 蓝牙耳机坏了，去医院挂号挂牙科还是耳科？ 11 | 赤道那么暖和，为什么不给东北修一条？ 12 | 为什么长寿的都是老年人？ 13 | 跳多高才能跳过广告？ 14 | 我女朋友的老公应该叫我什么？ 15 | 午餐肉可以在晚餐吃吗？ 16 | 我想配个六千元左右的电脑，大概要多少钱？ 17 | 香菇掉粪坑里了还能叫香菇吗？ 18 | 变形金刚买保险是买车险还是人险？ 19 | 生蚝煮熟了之后叫什么？ 20 | 老鼠生病了，可以吃老鼠药治好吗？ 21 | 去自首的路上被抓了还算自首吗？ 22 | 监狱里罪犯那么多，警察为什么不直接到监狱抓人？ 23 | 地球上的望远镜能看到那么多星球，为什么看不到地球？ -------------------------------------------------------------------------------- /main/chat.py: -------------------------------------------------------------------------------- 1 | import json 2 | import time 3 | 4 | from mlx_lm import load, generate 5 | 6 | # with open('../models/Qwen1.5-32B-Chat/tokenizer_config.json', 'r') as file: 7 | # tokenizer_config = json.load(file) 8 | 9 | with open('../models/Qwen1.5-32B-Chat-FT-4Bit/tokenizer_config.json', 'r') as file: 10 | tokenizer_config = json.load(file) 11 | 12 | # model, tokenizer = load( 13 | # "mlx_model/Qwen1.5-32B-Chat/", 14 | # tokenizer_config=tokenizer_config 15 | # ) 16 | 17 | model, tokenizer = load( 18 | "../models/Qwen1.5-32B-Chat-FT-4Bit/", 19 | tokenizer_config=tokenizer_config 20 | ) 21 | 22 | sys_msg = 'You are a helpful assistant' 23 | 24 | # with open('../text/chat_template.txt', 'r') as template_file: 25 | # template = template_file.read() 26 | 27 | with open('../text/chat_template.txt', 'r') as template_file: 28 | template = template_file.read() 29 | 30 | while True: 31 | usr_msg = input("用户: ") # Get user message from terminal 32 | if usr_msg.lower() == 'quit()': # Allows the user to exit the loop 33 | break 34 | 35 | prompt = template.replace("{usr_msg}", usr_msg) 36 | 37 | time_ckpt = time.time() 38 | response = generate( 39 | model, 40 | tokenizer, 41 | prompt=prompt, 42 | temp=0.3, 43 | max_tokens=500, 44 | verbose=False 45 | ) 46 | 47 | print("%s: %s (Time %d ms)\n" % ("回答", response, (time.time() - time_ckpt) * 1000)) -------------------------------------------------------------------------------- /main/start_qa_dialogue.py: -------------------------------------------------------------------------------- 1 | import io 2 | import re 3 | import json 4 | import time 5 | import wave 6 | import requests 7 | import simpleaudio as sa 8 | 9 | from mlx_lm import load, generate 10 | 11 | def generate_speech(text, port): 12 | time_ckpt = time.time() 13 | data={ 14 | "text": text, 15 | "text_language": "zh" 16 | } 17 | response = requests.post("http://127.0.0.1:{}".format(port), json=data) 18 | if response.status_code == 400: 19 | raise Exception(f"GPT-SoVITS ERROR: {response.message}") 20 | audio_data = io.BytesIO(response.content) 21 | with wave.open(audio_data, 'rb') as wave_read: 22 | audio_frames = wave_read.readframes(wave_read.getnframes()) 23 | audio_wave_obj = sa.WaveObject(audio_frames, wave_read.getnchannels(), wave_read.getsampwidth(), wave_read.getframerate()) 24 | play_obj = audio_wave_obj.play() 25 | play_obj.wait_done() 26 | print("Audio Generation Time: %d ms\n" % ((time.time() - time_ckpt) * 1000)) 27 | 28 | def split_text(text): 29 | sentence_endings = ['！', '。', '？'] 30 | for punctuation in sentence_endings: 31 | text = text.replace(punctuation, punctuation + '\n') 32 | pattern = r'\[.*?\]' 33 | text = re.sub(pattern, '', text) 34 | return text 35 | 36 | if __name__ == "__main__": 37 | with open('../models/Qwen1.5-32B-Chat-FT-4Bit/tokenizer_config.json', 'r') as file: 38 | tokenizer_config = json.load(file) 39 | 40 | model, tokenizer = load( 41 | "../models/Qwen1.5-32B-Chat-FT-4Bit/", 42 | tokenizer_config=tokenizer_config 43 | ) 44 | 45 | questions = [] 46 | with open('../text/questions.txt', 'r', encoding='utf-8') as file: 47 | for line in file: 48 | line = line.strip() 49 | if line: 50 | questions.append(line) 51 | 52 | sys_msg = 'You are a helpful assistant' 53 | 54 | with open('../text/chat_template.txt', 'r') as template_file: 55 | template = template_file.read() 56 | 57 | for question in questions: 58 | prompt = template.replace("{usr_msg}", question) 59 | print("%s: %s" % ("问题", question)) 60 | question = split_text(question) 61 | generate_speech(question, 9880) 62 | 63 | time_ckpt = time.time() 64 | response = generate( 65 | model, 66 | tokenizer, 67 | prompt=prompt, 68 | temp=0.3, 69 | max_tokens=500, 70 | verbose=False 71 | ) 72 | 73 | print("%s: %s (Time %d ms)\n" % ("哔友", response, (time.time() - time_ckpt) * 1000)) 74 | response = split_text(response) 75 | generate_speech(response, 9881) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 哔哩哔哩聊天机器人 2 | 3 | 由[哔哩哔哩](https://bilibili.com)用户评论微调训练而成的本地聊天机器人。支持文字聊天，也可以通过 questions.txt 生成针对给定问题的语音对话。 4 | 5 | 本项目文字生成使用的基础模型为 [Qwen1.5-32B-Chat](https://huggingface.co/Qwen/Qwen1.5-32B-Chat)，借助苹果 [mlx-lm LORA 示例项目](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md) 对基础模型进行微调训练。语音生成部分基于开源项目 [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)，问题语音来自 B 站用户[白菜工厂1145号员工](https://space.bilibili.com/518098961)训练的派蒙语音模型。 6 | 7 | ### 文件结构 8 | 9 | 项目主要脚本存放在 `main/` 文件夹下，模型存放于 `models/` 文件夹。提示词模板、问题列表存放在 `text/` 文件夹下。`tools/compress_model.py` 可以对完整模型进行量化压缩，大大加快模型内容生成速度。 10 | 11 | ## 运行指南 12 | 13 | 本项目基于 Python 编程语言，程序运行使用的 Python 版本为 3.10，建议使用 [Anaconda](https://www.anaconda.com) 配置 Python 环境。以下配置过程已在 macOS 系统测试通过。 14 | 15 | 16 | ### 配置环境 17 | 18 | ``` 19 | conda create -n bilibot python=3.10 20 | conda activate bilibot 21 | cd bilibot 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | ### 模型微调训练与推理测试 26 | 27 | 使用控制台指令，借助 [mlx-lm](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md) 对 Qwen1.5-32B-Chat 进行微调： 28 | 29 | ``` 30 | python -m mlx_lm.lora --model models/Qwen1.5-32B-Chat --data data/ --train --iters 1000 --batch-size 16 --lora-layers 12 31 | ``` 32 | 33 | 将微调后的 `adapters` 文件与基础模型合并： 34 | 35 | ``` 36 | python -m mlx_lm.fuse --model models/Qwen1.5-32B-Chat --save-path models/Qwen1.5-32B-Chat-FT --adapter-path models/Qwen1.5-32B-Chat-Adapters 37 | ``` 38 | 39 | 对合并后的模型进行量化加速： 40 | python tools/compress_model.py 41 | 42 | 对微调训练后的模型进行对话测试： 43 | python chat.py 44 | 45 | ### 语音生成 46 | 本项目借助开源项目 [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) 进行语音生成。 47 | 48 | 首先参考 [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) 的官方指南配置环境并运行语音生成程序。 49 | 50 | ``` 51 | conda create -n GPTSOVITS python=3.9 52 | conda activate GPTSOVITS 53 | cd GPT-SoVITS 54 | pip install -r requirements.txt 55 | python webui.py 56 | ``` 57 | 58 | 运行 api 程序，分别使用端口 9880 与 9881 提供派蒙与林亦的语音生成服务，以下请使用 GPT-SoVITS 代码库完成： 59 | ``` 60 | python api.py -s SoVITS_weights/paimeng2_e110_s159940.pth -g GPT_weights/paimeng2-e10.ckpt -dr samples/Paimon/疑问—哇，这个，还有这个…只是和史莱姆打了一场，就有这么多结论吗？.wav -dt "哇，这个，还有这个…只是和史莱姆打了一场，就有这么多结论吗？" -dl "zh" -a 127.0.0.1 -p 9880 61 | python api.py -s SoVITS_weights/linyi_e25_s1150.pth -g GPT_weights/linyi-e50.ckpt -dr "samples/linyi/【愤怒】你这问题太弱智了，我都不知道该从哪开始骂你。.WAV" -dt "你这问题太弱智了，我都不知道该从哪开始骂你。" -dl "zh" -a 127.0.0.1 -p 9881 62 | ``` 63 | 64 | 运行问答生成程序： 65 | ``` 66 | python start_qa_dialogue.py 67 | ``` 68 | 69 | ## 参考 70 | 71 | 1. 机器学习框架 MLX，来自苹果机器学习研究组：https://github.com/ml-explore/mlx 72 | 2. 阿里通义千问 Qwen1.5：https://qwenlm.github.io/zh/blog/qwen1.5/ 73 | 3. 开源文本转语音项目 GPT-SoVITS，作者[花儿不哭](https://space.bilibili.com/5760446)：https://github.com/RVC-Boss/GPT-SoVITS 74 | 4. 派蒙语音模型，作者[白菜工厂1145号员工](https://space.bilibili.com/518098961)：[【GPT-SoVITS】30小时超大数据集测试，堆时长真的有用吗？](https://www.bilibili.com/video/BV1Yu4m1N79m) -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # 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