推理的时候CPU占用很高，越到后面越高，而且不下来，导致推不动

[zxj329]

What is your question?

Code

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from argparse import ArgumentParser
import os
import soundfile as sf
import time
import numpy as np
import torchaudio
import torch
from funasr import AutoModel

def main():
save_dir = "/mnt/hdd2/ximalaya_16k_spk"
scp_dir = './wav.scp'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
parser = ArgumentParser()
parser.add_argument("-g", "--gpu_index", type=int, help="gpu index")
args = parser.parse_args()
gpu_id = args.gpu_index
with open(scp_dir, 'r') as f:
lines = f.readlines()
avg_file_num = len(lines)//8
process_files = lines[gpu_id*avg_file_num:(gpu_id+1)*avg_file_num]

if gpu_id == 8:
    process_files = lines[gpu_id*avg_file_num:]
trans_txt = './TRANS_'+str(gpu_id)+'.txt'
'''inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch', model_revision="v2.0.4",
    vad_model='iic/speech_fsmn_vad_zh-cn-16k-common-pytorch', vad_model_revision="v2.0.4",
    punc_model='iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', punc_model_revision="v2.0.4",
    spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
    spk_model_revision="v2.0.2",
    device='cuda:'+str(gpu_id),
    ncpu = 1
)'''
from funasr import AutoModel
model = AutoModel(
    model="paraformer-zh", model_revision="v2.0.4",
    vad_model="fsmn-vad", vad_model_revision="v2.0.4",
    punc_model="ct-punc-c", punc_model_revision="v2.0.4",
    spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
    spk_model_revision="v2.0.2",
    device='cuda:'+str(gpu_id),
    ncpu=1,
)

with open(trans_txt, 'w') as f:
    for process_file in process_files:
        cost_st = time.time()
        #rec_result = inference_pipeline(process_file.strip())
        rec_result = model.generate(input=process_file.strip(), batch_size_s=300)
        '''if os.path.exists(process_file.strip()):
            data, sr = torchaudio.load(process_file.strip())
            wav_name = process_file.strip().split('/')[-1]
            save_file = process_file.strip().split('/')[-2]
            count = 0
            spk_type = np.zeros(10)
            for resi in rec_result[0]['sentence_info']:
                spk_type[resi['spk']] = spk_type[resi['spk']] + 1
            max_index = np.argmax(spk_type)
            print("wav_dir:{}".format(process_file))
            for resi in rec_result[0]['sentence_info']:
                if (resi['spk']) == max_index:
                    save_file_spk = save_file
                    save_file_spk_dir = os.path.join(save_dir, save_file_spk)
                    if not os.path.exists(save_file_spk_dir):
                        os.mkdir(save_file_spk_dir)
                    text = resi['text']
                    st = int(resi['start'])*16
                    et = int(resi['end'])*16
                    wav_name_spk = save_file_spk + '_' + wav_name.replace('.wav', '_'+str(count)+'.wav')
                    save_wav_name_spk = os.path.join(save_file_spk_dir, wav_name_spk)
                    data_cut = data[:, st:et]
                    torchaudio.save(save_wav_name_spk, data_cut, sr)
                    #f.write(wav_name_spk+'\t'+save_file_spk+'\t'+text+'\t'+str(st)+'\t'+str(et)+'\t'+process_file.strip()+'\n')
                    f.write(wav_name_spk+'\t'+save_file_spk+'\t'+text+'\n')
                    count = count + 1
        cost_et = time.time()
        print("time_cost:{:.03f}".format(cost_et-cost_st))'''
print("ASR FINISHED\n")

if name == "main":
main()
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
93612 zhengxi+ 20 0 49.856g 5.562g 0.979g R 5705 2.2 18:12.87 python
78305 zhengxi+ 20 0 44064 5404 3532 R 21.1 0.0 0:53.40 top
56160 zhengxi+ 20 0 11.381g 77364 43004 S 5.3 0.0 0:58.93 node
1 root 20 0 225400 7760 5280 S 0.0 0.0 0:14.52 systemd
2 root 20 0 0 0 0 S 0.0 0.0 0:01.67 kthreadd

What have you tried?

What's your environment?

• Linux
• FunASR 1.1.12
• ModelScope 1.18.1:
• PyTorch 2.0.1+cu118:
• pip install funasr:
• Python version 3.8.20:
• GPU 3090
• CUDA/cuDNN version 11.8:

[palla-lin]

deep_update(kwargs, cfg) in auto_model.py is potentially problematic，
replace it with something to create new id for kwargs like kwargs = {**kwargs, **cfg}

[zxj329]

deep_update(kwargs, cfg) in auto_model.py is potentially problematic， replace it with something to create new id for kwargs like kwargs = {**kwargs, **cfg}
没看懂，啥意思
deep_update(model_conf, kwargs.get("model_conf", {}))
deep_update(model_conf, kwargs) in auto_model.py是这样子，我应该怎么改

[SFidea]

同样问题，CPU一直保持100%

[zxj329]

同样问题，CPU一直保持100%

我到后面都到4000%多了

[simin75simin]

我用那个caps包的，现在写了个ps脚本每5个长语音（10分钟1个）就重启一遍client，内存就不会爆。。以后调调gpu版本的看看会不会有类似行为。。

[LauraGPT]

多线程运行的么？

[zxj329]

多线程运行的么？
每个GPU起一个进程

[sdliang1013]

同样的问题刚开始还好好的, 几天后CPU飙到100%, 一直下不来, 识别率变的很低, 都是错乱的. 得重启服务才行.
是不是websocket连接久了, 某个逻辑进入了死循环?