之前5期的介绍中我们已经成功逐一实现了功能。本篇为最后一期,我们将整合所有功能,实现完整项目。
由于所有的模型一起运行,对内存要求比较大。因此我们先要调整一下系统的内存分配:
wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/v0.1.0/memory_edit_V1.6.1.deb
sudo apt install ./memory_edit_V1.6.1.deb
memory_edit.sh -c -npu 7615 -vpu 1024 -vpp 4096 bm1684x_sm7m_v1.0.dtb
sudo cp /opt/sophon/memory_edit/emmcboot.itb /boot/emmcboot.itb && sync
sudo reboot
之前每个单独的功能模块测试时,我们都使用了独立的虚拟环境。现在所有环境放在一起,难免会出现一些冲突的情况。经过测试,我实现了在同一个环境中运行所有模块。由于安装包非常多,所以使用requirement进行安装。先创建requirements.txt
,然后写入以下内容:
aiofiles==23.2.1
aiohttp==3.9.5
aiosignal==1.3.1
altair==5.3.0
annotated-types==0.7.0
anyio==4.4.0
async-timeout==4.0.3
attrs==23.2.0
audioread==3.0.1
bypy==1.8.5
certifi==2024.7.4
cffi==1.16.0
chardet==3.0.4
charset-normalizer==3.3.2
click==8.1.7
coloredlogs==15.0.1
contourpy==1.1.1
cycler==0.12.1
Cython==3.0.10
dbus-python==1.2.16
decorator==5.1.1
dfss==1.7.8
dill==0.3.8
Distance==0.1.3
distro==1.4.0
distro-info===0.23ubuntu1
dnspython==2.6.1
dtw-python==1.5.1
email-validator==2.2.0
et-xmlfile==1.1.0
exceptiongroup==1.2.2
fastapi==0.111.1
fastapi-cli==0.0.4
ffmpy==0.3.2
filelock==3.15.4
Flask==2.2.2
flatbuffers==24.3.25
fonttools==4.53.1
frozenlist==1.4.1
fsspec==2024.6.1
g2p-en==2.1.0
gradio==3.39.0
gradio-client==1.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
huggingface-hub==0.23.4
humanfriendly==10.0
idna==3.7
importlib-metadata==8.0.0
importlib-resources==6.4.0
inflect==7.3.1
itsdangerous==2.1.2
jieba==0.42.1
jinja2==3.1.4
joblib==1.4.2
jsonschema==4.23.0
jsonschema-specifications==2023.12.1
kiwisolver==1.4.5
latex2mathml==3.77.0
lazy-loader==0.4
librosa==0.10.2.post1
linkify-it-py==2.0.3
llvmlite==0.41.1
lmdb==1.5.1
Markdown==3.6
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.7.5
mdit-py-plugins==0.3.3
mdtex2html==1.2.0
mdurl==0.1.2
more-itertools==10.3.0
mpmath==1.3.0
msgpack==1.0.8
multidict==6.0.5
multiprocess==0.70.16
netifaces==0.10.4
networkx==3.1
nltk==3.8.1
numba==0.58.1
numpy==1.24.4
onnx==1.14.0
onnxruntime==1.16.3
openai-whisper==20231117
opencv-python-headless==4.10.0.84
openpyxl==3.1.5
orjson==3.10.6
packaging==24.1
panda==0.3.1
pandas==2.0.3
pillow==10.4.0
pkgutil-resolve-name==1.3.10
platformdirs==4.2.2
pooch==1.8.2
protobuf==3.20.3
psutil==5.9.1
pybind11-global==2.13.1
pycparser==2.22
pydantic==2.8.2
pydantic-core==2.20.1
pydub==0.25.1
pygments==2.18.0
PyGObject==3.36.0
pymacaroons==0.13.0
PyNaCl==1.3.0
pyparsing==3.1.2
pypinyin==0.51.0
pyserial==3.5
python-apt==2.0.0
python-dateutil==2.9.0.post0
python-multipart==0.0.9
pytz==2024.1
PyYAML==6.0.1
referencing==0.35.1
regex==2024.5.15
requests==2.32.3
requests-toolbelt==1.0.0
requests-unixsocket==0.2.0
rich==13.7.1
rpds-py==0.19.0
ruff==0.5.2
safetensors==0.4.3
scikit-learn==1.3.2
scipy==1.10.1
semantic-version==2.10.0
sentencepiece==0.1.99
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
sophon-arm==3.7.0
soundfile==0.12.1
soxr==0.3.7
ssh-import-id==5.10
starlette==0.37.2
sympy==1.13.0
threadpoolctl==3.5.0
tiktoken==0.3.3
tokenizers==0.14.1
tomlkit==0.12.0
toolz==0.12.1
torch==2.3.1
torchaudio==2.3.1
tpu-perf==1.2.31
tqdm==4.66.4
transformers==4.34.1
typeguard==4.3.0
typer==0.12.3
typing-extensions==4.12.2
tzdata==2024.1
ubuntu-advantage-tools==20.3
uc-micro-py==1.0.3
unattended-upgrades==0.1
urllib3==2.2.2
uvicorn==0.30.1
websockets==11.0.3
Werkzeug==2.2.2
whisper-timestamped==1.15.4
yacs==0.1.8
yarl==1.9.4
zipp==3.19.2
接着启动项目环境,安装所有的包:
cd /data/project/jarvis
source jarvis_venv/bin/activate
pip install -r requirements.txt
接下来,需要把每个模块所需的文件从模块对应仓库中拷到项目文件夹来。由于模型文件巨大,磁盘空间不足,因此这里我全部使用剪切来移动:
mv /data/project/LLM-TPU/models/Llama3/bmodels ./
mv /data/project/LLM-TPU/models/Llama3/token_config ./
mv /data/project/LLM-TPU/support/lib_soc ./
mv /data/project/LLM-TPU/models/Llama3/python_demo/chat.cpython-38-aarch64-linux-gnu.so ./
mv /data/project/whisper-TPU_py/bmodel ./
mv /data/project/whisper-TPU_py/bmwhisper ./
mv /data/project/EmotiVoice-TPU/config ./
mv /data/project/EmotiVoice-TPU/data ./
mv /data/project/EmotiVoice-TPU/frontend ./
mv /data/project/EmotiVoice-TPU/model_file ./
mv /data/project/EmotiVoice-TPU/models ./
mv /data/project/EmotiVoice-TPU/processed ./
mv /data/project/EmotiVoice-TPU/tone_color_conversion ./
mv /data/project/Radxa-Model-Zoo/sample/YOLOv8_det/models/BM1684X ./
mv /data/project/Radxa-Model-Zoo/sample/YOLOv8_det/python/postprocess_numpy.py ./
mv /data/project/Radxa-Model-Zoo/sample/YOLOv8_det/python/utils.py ./
mkdir results
mkdir temp
至此,完成了所有的支持文件搬运。
下面,我们需要开始编写主程序main.py
。主程序首先导入各个模块并初始化,然后在main函数中逐一调用所需的模块,从输入音频文件路径开始,读取该文件,用whisper进行语音识别,同时用request抓取webcam画面,使用yolov8进行图像识别。两个识别都完成后,合成问题信息,输入Llama3。Llama3回复后,使用TTS转化为语音,将该语音文件储存后,路径作为函数的返回值返回即可。
然后我们把这个main函数放入上一期搭好的FastAPI服务器中,就大功告成了。完整的代码如下:
from pipeline import Llama3
from bmwhisper.transcribe import cli, run_whisper
from yolov8_opencv import main as yolo
from yolov8_opencv import argsparser
from gr import tts_only
import requests
import argparse
from fastapi import FastAPI, UploadFile, File
from starlette.responses import FileResponse
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--model_path', type=str, default='./bmodels/llama3-8b_int4_1dev_512/llama3-8b_int4_1dev_512.bmodel', help='path to the bmodel file')
parser.add_argument('-t', '--tokenizer_path', type=str, default="./token_config", help='path to the tokenizer file')
parser.add_argument('-d', '--devid', type=str, default='0', help='device ID to use')
parser.add_argument('--temperature', type=float, default=1.0, help='temperature scaling factor for the likelihood distribution')
parser.add_argument('--top_p', type=float, default=1.0, help='cumulative probability of token words to consider as a set of candidates')
parser.add_argument('--repeat_penalty', type=float, default=1.0, help='penalty for repeated tokens')
parser.add_argument('--repeat_last_n', type=int, default=32, help='repeat penalty for recent n tokens')
parser.add_argument('--max_new_tokens', type=int, default=1024, help='max new token length to generate')
parser.add_argument('--generation_mode', type=str, choices=["greedy", "penalty_sample"], default="greedy", help='mode for generating next token')
parser.add_argument('--prompt_mode', type=str, choices=["prompted", "unprompted"], default="prompted", help='use prompt format or original input')
parser.add_argument('--decode_mode', type=str, default="basic", choices=["basic", "jacobi"], help='mode for decoding')
parser.add_argument('--enable_history', action='store_true', default=True, help="if set, enables storing of history memory.")
llama_args = parser.parse_args()
model = Llama3(llama_args)
w_model, writer, writer_args, args, temperature, loop_profile = cli(None)
yolo_args = argsparser()
obj_dict = {0:'人',
1:'自行车',
2:'汽车',
3:'摩托车',
4:'飞机',
5:'巴士',
6:'火车',
7:'卡车',
8:'船',
9:'交通灯',
10:'消防栓',
11:'停止标志',
12:'停车收费表',
13:'长凳',
14:'鸟',
15:'猫',
16:'狗',
17:'马',
18:'羊',
19:'牛',
20:'大象',
21:'熊',
22:'斑马',
23:'长颈鹿',
24:'背包',
25:'雨伞',
26:'手提包',
27:'领带',
28:'手提箱',
29:'飞盘',
30:'滑雪板',
31:'滑雪板',
32:'运动球',
33:'风筝',
34:'棒球棒',
35:'棒球手套',
36:'滑板',
37:'冲浪板',
38:'网球拍',
39:'瓶子',
40:'酒杯',
41:'杯子',
42:'叉子',
43:'刀',
44:'勺子',
45:'碗',
46:'香蕉',
47:'苹果',
48:'三明治',
49:'橙子',
50:'西兰花',
51:'胡萝卜',
52:'热狗',
53:'披萨',
54:'甜甜圈',
55:'蛋糕',
56:'椅子',
57:'沙发',
58:'盆栽',
59:'床',
60:'餐桌',
61:'厕所',
62:'电视',
63:'笔记本电脑',
64:'老鼠',
65:'远程',
66:'键盘',
67:'手机',
68:'微波炉',
69:'烤箱',
70:'烤面包机',
71:'水槽',
72:'冰箱',
73:'书',
74:'时钟',
75:'花瓶',
76:'剪刀',
77:'泰迪熊',
78:'吹风机',
79:'牙刷'}
def main(audio_path):
question = run_whisper(audio_path, w_model, writer, writer_args, args, temperature, loop_profile)
raw_pic = requests.get("http://192.168.xxx.xxx:8080/photoaf.jpg").content
with open("./temp/pic.jpg", "wb") as pic:
pic.write(raw_pic)
yolo_list = yolo(yolo_args)
det_list = []
for i in yolo_list:
det_list.append(obj_dict[i])
if len(det_list):
see = obj = ", ".join(det_list)
else:
see = "什么都没有"
print(see)
q = "假设你看见了:" + see + "。我的问题是:" + question
model.input_str = q
tokens = model.encode_tokens()
model.stream_answer(tokens)
res = tts_only(model.answer_cur,"6097","普通")
print(res)
return res
app = FastAPI()
@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile = File(...)):
with open("received.wav", "wb") as out_file:
content = await file.read()
out_file.write(content)
send = main("received.wav")
return FileResponse(send)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
while 1:
cmd = input(">")
try:
exec(cmd)
except Exception as e:
print(e)
当然,导入的这些模块也需要做一点相应的修改。具体修改内容由于在之前的章节中都有提及,这里就不再赘述了。修改过的项目文件我会放在附件中,供大家下载测试。
webcam使用的是Android手机的一个小程序,可以把手机变成摄像头。大家只要在主程序中填入手机的对应ip即可。
最后,在运行前,要把C库的环境变量添加好:
export LD_LIBRARY_PATH=/opt/sophon/libsophon-0.5.0/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=./lib_soc:$LD_LIBRARY_PATH
export PYTHONPATH=$PWD:$PYTHONPATH
接着运行main.py
文件,运行一开始是加载各个模型,及初始化。非常漫长,需要几分钟的时间,大家耐心等待。当看到下面输出时,说明FastAPI服务器已经开始工作,我们就可以运行上位机的代码,开始愉快的玩耍了。
我在项目文件夹下写了一个run.sh文件,也可以直接通过这个文件来一次性加载虚拟环境并配置PATH。
![image.png](//file1.elecfans.com/web2/M00/FD/3E/wKgZomaY8JGAVBRfAAAWTgsit_E312.png)
项目源代码在下面可以自行下载。我在里面打包了除模型文件外的其余全部文件。也就是说,以下4个文件夹还需要大家自行去拉取:
BM1684X
bmodel
bmodels
model_file
*附件:jarvis_RADAX.7z