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feat: auto slice videos (#151)
* feat: auto slice videos fix #150 * refactor: add copyright * docs: update readme * refactor: adjust icons * style: adjust style * style: substitue icons
This commit is contained in:
John Howe
26
README.md
26
README.md
@@ -4,12 +4,18 @@
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<img src="assets/headerLight.svg" alt="BILIVE" />
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</picture>
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*7 x 24 小时无人监守录制、渲染弹幕、识别字幕、自动上传，启动项目，人人都是录播员。*
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*7 x 24 小时无人监守录制、渲染弹幕、识别字幕、自动切片、自动上传，启动项目，人人都是录播员。*
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[:page_facing_up: Documentation](https://timerring.github.io/bilive/) |
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[:gear: Installation](#quick-start) |
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[:thinking: Reporting Issues](https://github.com/timerring/bilive/issues/new/choose)
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支持模型
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<img src="assets/openai.svg" alt="OpenAI whisper" width="60" height="60" />
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<img src="assets/zhipu-color.svg" alt="Zhipu GLM-4V-PLUS" width="60" height="60" />
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<img src="assets/gemini-brand-color.svg" alt="Google Gemini 1.5 Pro" width="60" height="60" />
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</div>
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## 1. Introduction
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@@ -29,6 +35,7 @@
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- **自动渲染弹幕**：自动转换xml为ass弹幕文件并且渲染到视频中形成**有弹幕版视频**并自动上传。
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- **硬件要求极低**：无需GPU，只需最基础的单核CPU搭配最低的运存即可完成录制，弹幕渲染，上传等等全部过程，无最低配置要求，10年前的电脑或服务器依然可以使用！
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- **( :tada: NEW)自动渲染字幕**(如需使用本功能，则需保证有 Nvidia 显卡)：采用 OpenAI 的开源模型 [`whisper`](https://github.com/openai/whisper)，自动识别视频内语音并转换为字幕渲染至视频中。
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- **( :tada: NEW)自动切片上传**：根据弹幕密度计算寻找高能片段并切片，结合多模态视频理解大模型 [`GLM-4V-PLUS`](https://bigmodel.cn/dev/api/normal-model/glm-4) 自动生成有意思的切片标题及内容，并且自动上传。
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项目架构流程如下：
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@@ -46,8 +53,13 @@ graph TD
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ifDanmaku -->|有弹幕| DanmakuFactory[DanmakuFactory]
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ifDanmaku -->|无弹幕| ffmpeg1[ffmpeg]
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DanmakuFactory[DanmakuFactory] --根据分辨率转换弹幕--> ffmpeg1[ffmpeg]
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ffmpeg1[ffmpeg] --渲染弹幕及字幕 --> Video[视频文件]
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Video[视频文件] --计算弹幕密度并切片--> GLM[多模态视频理解模型]
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GLM[多模态视频理解模型] --生成切片信息--> slice[视频切片]
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end
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ffmpeg1[ffmpeg] --渲染弹幕及字幕 --> uploadQueue[(上传队列)]
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slice[视频切片] --> uploadQueue[(上传队列)]
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Video[视频文件] --> uploadQueue[(上传队列)]
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User((用户))--upload-->startUpload(启动视频上传进程)
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startUpload(启动视频上传进程) <--扫描队列并上传视频--> uploadQueue[(上传队列)]
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@@ -110,8 +122,9 @@ pip install -r requirements.txt
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./setPath.sh && source ~/.bashrc
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```
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#### 3. 配置 whisper 模型
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#### 3. 配置 whisper 模型及 GLM-4V-PLUS 模型
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##### 3.1 whisper 模型
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项目默认采用 [`small`](https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt) 模型，请点击下载所需文件，并放置在 `src/subtitle/models` 文件夹中。
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> [!TIP]
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@@ -119,6 +132,11 @@ pip install -r requirements.txt
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> + 更多模型请参考 [whisper 参数模型](https://timerring.github.io/bilive/models.html) 部分。
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> + 更换模型方法请参考 [更换模型方法](https://timerring.github.io/bilive/models.html#更换模型方法) 部分。
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##### 3.2 GLM-4V-PLUS 模型
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> 此功能默认关闭，如果需要打开请将 `src/config.py` 文件中的 `AUTO_SLICE` 参数设置为 `True`
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在配置文件 `src/config.py` 中，`SLICE_DURATION` 以秒为单位设置切片时长（不建议超过 1 分钟），在项目的自动切片功能需要使用到智谱的 [`GLM-4V-PLUS`](https://bigmodel.cn/dev/api/normal-model/glm-4) 模型，请自行[注册账号](https://www.bigmodel.cn/invite?icode=shBtZUfNE6FfdMH1R6NybGczbXFgPRGIalpycrEwJ28%3D)并申请 API Key，填写到 `src/config.py` 文件中对应的 `Your_API_KEY` 中。
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#### 4. biliup-rs 登录
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@@ -176,7 +194,7 @@ logs # 日志文件夹
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```
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### Installation(无 GPU 版本)
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无 GPU 版本过程基本同上，可以跳过步骤 3，需要注意在执行步骤 5 **之前**完成以下设置将确保完全用 CPU 渲染视频弹幕。
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无 GPU 版本过程基本同上，可以跳过步骤 3 配置 whisper 的部分，需要注意在执行步骤 5 **之前**完成以下设置将确保完全用 CPU 渲染视频弹幕。
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1. 请将 `src/config.py` 文件中的 `GPU_EXIST` 参数设置为 `False`。（若不置为 `False` 且则会使用 CPU 推理，不推荐，可自行根据硬件条件进行尝试。）
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2. 将 `MODEL_TYPE` 调整为 `merge` 或者 `append`。
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# Copyright (c) 2024 bilive.
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# Copyright (c) 2024 bilive.
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import re
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from collections import defaultdict
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from src.config import SLICE_DURATION
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def parse_time(time_str):
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"""Convert ASS time format to seconds with milliseconds."""
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h, m, s = time_str.split(':')
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s, ms = s.split('.')
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return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 100
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def format_time(seconds):
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"""Format seconds to hh:mm:ss.xx."""
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h = int(seconds // 3600)
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m = int((seconds % 3600) // 60)
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s = int(seconds % 60)
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ms = int((seconds - int(seconds)) * 100)
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return f"{h:02}:{m:02}:{s:02}.{ms:02}"
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def extract_dialogues(file_path):
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"""Extract dialogue start times from the ASS file."""
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dialogues = []
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with open(file_path, 'r', encoding='utf-8') as file:
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for line in file:
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if line.startswith('Dialogue:'):
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parts = line.split(',')
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start_time = parse_time(parts[1].strip())
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dialogues.append(start_time)
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return dialogues
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|
||||
def calculate_density(dialogues, window_size=SLICE_DURATION):
|
||||
"""Calculate the maximum density of dialogues in a given window size."""
|
||||
time_counts = defaultdict(int)
|
||||
for time in dialogues:
|
||||
time_counts[time] += 1
|
||||
|
||||
max_density = 0
|
||||
max_start_time = 0
|
||||
|
||||
# Use a sliding window to calculate density
|
||||
sorted_times = sorted(time_counts.keys())
|
||||
for i in range(len(sorted_times)):
|
||||
start_time = sorted_times[i]
|
||||
end_time = start_time + window_size
|
||||
current_density = sum(count for time, count in time_counts.items() if start_time <= time < end_time)
|
||||
if current_density > max_density:
|
||||
max_density = current_density
|
||||
max_start_time = start_time
|
||||
|
||||
return max_start_time, max_density
|
||||
61
src/autoslice/slice_video.py
Normal file
61
src/autoslice/slice_video.py
Normal file
@@ -0,0 +1,61 @@
|
||||
# Copyright (c) 2024 bilive.
|
||||
|
||||
import subprocess
|
||||
from src.autoslice.calculate_density import extract_dialogues, calculate_density, format_time
|
||||
from src.config import Your_API_KEY, SLICE_DURATION
|
||||
import base64
|
||||
from zhipuai import ZhipuAI
|
||||
|
||||
def zhipu_glm_4v_plus_generate_title(video_path, artist):
|
||||
with open(video_path, 'rb') as video_file:
|
||||
video_base = base64.b64encode(video_file.read()).decode('utf-8')
|
||||
|
||||
client = ZhipuAI(api_key=Your_API_KEY)
|
||||
response = client.chat.completions.create(
|
||||
model="glm-4v-plus",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "video_url",
|
||||
"video_url": {
|
||||
"url" : video_base
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "text",
|
||||
"text": f"视频是{artist}的直播的切片，请根据该视频中的内容及弹幕信息，为这段视频起一个调皮并且吸引眼球的标题，注意标题中如果有“主播”请替换成{artist}。"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
)
|
||||
return response.choices[0].message.content.replace("《", "").replace("》", "")
|
||||
|
||||
# https://stackoverflow.com/questions/64849478/cant-insert-stream-metadata-into-mp4
|
||||
def inject_metadata(video_path, generate_title, output_path):
|
||||
"""Slice the video using ffmpeg."""
|
||||
command = [
|
||||
'ffmpeg',
|
||||
'-i', video_path,
|
||||
'-metadata:g', f'generate={generate_title}',
|
||||
'-c:v', 'copy',
|
||||
'-c:a', 'copy',
|
||||
output_path
|
||||
]
|
||||
subprocess.run(command)
|
||||
|
||||
def slice_video(video_path, start_time, output_path, duration=f'00:00:{SLICE_DURATION}'):
|
||||
"""Slice the video using ffmpeg."""
|
||||
command = [
|
||||
'ffmpeg',
|
||||
'-ss', format_time(start_time),
|
||||
'-i', video_path,
|
||||
'-t', duration,
|
||||
'-map_metadata', '-1',
|
||||
'-c:v', 'copy',
|
||||
'-c:a', 'copy',
|
||||
output_path
|
||||
]
|
||||
subprocess.run(command)
|
||||
@@ -3,10 +3,13 @@
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
from src.config import GPU_EXIST, SRC_DIR, MODEL_TYPE
|
||||
from src.config import GPU_EXIST, SRC_DIR, MODEL_TYPE, AUTO_SLICE, SLICE_DURATION
|
||||
from src.burn.generate_danmakus import get_resolution, process_danmakus
|
||||
from src.burn.generate_subtitles import generate_subtitles
|
||||
from src.burn.render_video import render_video
|
||||
from src.autoslice.slice_video import slice_video, inject_metadata, zhipu_glm_4v_plus_generate_title
|
||||
from src.autoslice.calculate_density import extract_dialogues, calculate_density, format_time
|
||||
from src.upload.extract_video_info import get_video_info
|
||||
import queue
|
||||
import threading
|
||||
import time
|
||||
@@ -52,7 +55,20 @@ def render_video_only(video_path):
|
||||
render_video(original_video_path, format_video_path, subtitle_font_size, subtitle_margin_v)
|
||||
print("complete danamku burning and wait for uploading!", flush=True)
|
||||
|
||||
# # Delete relative files
|
||||
if AUTO_SLICE:
|
||||
title, artist, date = get_video_info(format_video_path)
|
||||
slice_video_path = format_video_path[:-4] + '_slice.mp4'
|
||||
dialogues = extract_dialogues(ass_path)
|
||||
max_start_time, max_density = calculate_density(dialogues)
|
||||
formatted_time = format_time(max_start_time)
|
||||
print(f"The 30-second window with the highest density starts at {formatted_time} seconds with {max_density} danmakus.", flush=True)
|
||||
slice_video(format_video_path, max_start_time, slice_video_path)
|
||||
glm_title = zhipu_glm_4v_plus_generate_title(slice_video_path, artist)
|
||||
slice_video_flv_path = slice_video_path[:-4] + '.flv'
|
||||
inject_metadata(slice_video_path, glm_title, slice_video_flv_path)
|
||||
os.remove(slice_video_path)
|
||||
|
||||
# Delete relative files
|
||||
for remove_path in [original_video_path, xml_path, ass_path, srt_path, jsonl_path]:
|
||||
if os.path.exists(remove_path):
|
||||
os.remove(remove_path)
|
||||
@@ -63,6 +79,9 @@ def render_video_only(video_path):
|
||||
|
||||
with open(f"{SRC_DIR}/upload/uploadVideoQueue.txt", "a") as file:
|
||||
file.write(f"{format_video_path}\n")
|
||||
if AUTO_SLICE:
|
||||
print("complete slice video and wait for uploading!", flush=True)
|
||||
file.write(f"{slice_video_flv_path}\n")
|
||||
|
||||
class VideoRenderQueue:
|
||||
def __init__(self):
|
||||
|
||||
@@ -10,7 +10,10 @@ GPU_EXIST=True
|
||||
# Can be pipeline, append, merge
|
||||
MODEL_TYPE = "pipeline"
|
||||
Inference_Model = "small"
|
||||
|
||||
AUTO_SLICE = False
|
||||
SLICE_DURATION = 30
|
||||
# Apply for your own GLM-4v-Plus API key at https://www.bigmodel.cn/invite?icode=shBtZUfNE6FfdMH1R6NybGczbXFgPRGIalpycrEwJ28%3D
|
||||
Your_API_KEY = ""
|
||||
# ============================ Basic configuration ============================
|
||||
SRC_DIR = str(Path(os.path.abspath(__file__)).parent)
|
||||
BILIVE_DIR = str(Path(SRC_DIR).parent)
|
||||
|
||||
@@ -6,6 +6,8 @@ import yaml
|
||||
import codecs
|
||||
from datetime import datetime
|
||||
from src.upload.extract_video_info import generate_title, generate_desc, generate_tag, generate_source
|
||||
import subprocess
|
||||
import json
|
||||
|
||||
def generate_yaml_template(video_path):
|
||||
source = generate_source(video_path)
|
||||
@@ -31,6 +33,35 @@ def generate_yaml_template(video_path):
|
||||
}
|
||||
return yaml.dump(data, default_flow_style=False, sort_keys=False)
|
||||
|
||||
def generate_slice_yaml_template(video_path):
|
||||
command = [
|
||||
"ffprobe",
|
||||
"-v", "quiet",
|
||||
"-print_format", "json",
|
||||
"-show_format",
|
||||
video_path
|
||||
]
|
||||
output = subprocess.check_output(command, stderr=subprocess.STDOUT).decode('utf-8')
|
||||
parsed_output = json.loads(output)
|
||||
title = parsed_output["format"]["tags"]["generate"]
|
||||
data = {
|
||||
"line": "bda2",
|
||||
"limit": 5,
|
||||
"streamers": {
|
||||
video_path: {
|
||||
"copyright": 1,
|
||||
"source": "",
|
||||
"tid": 138,
|
||||
"cover": "",
|
||||
"title": title,
|
||||
"desc_format_id": 0,
|
||||
"desc": "",
|
||||
"dynamic": "",
|
||||
"tag": "直播切片"
|
||||
}
|
||||
}
|
||||
}
|
||||
return yaml.dump(data, default_flow_style=False, sort_keys=False)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# read the queue and upload the video
|
||||
|
||||
@@ -5,7 +5,7 @@ import os
|
||||
import sys
|
||||
from src.config import SRC_DIR, BILIVE_DIR
|
||||
from datetime import datetime
|
||||
from src.upload.generate_yaml import generate_yaml_template
|
||||
from src.upload.generate_yaml import generate_yaml_template, generate_slice_yaml_template
|
||||
from src.upload.extract_video_info import generate_title
|
||||
import time
|
||||
import fcntl
|
||||
@@ -87,6 +87,15 @@ def read_append_and_delete_lines(file_path):
|
||||
|
||||
print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} " + "deal with " + upload_video_path, flush=True)
|
||||
# check if the live is already uploaded
|
||||
if upload_video_path.endswith('.flv'):
|
||||
# upload slice video
|
||||
yaml_template = generate_slice_yaml_template(upload_video_path)
|
||||
yaml_file_path = SRC_DIR + "/upload/upload.yaml"
|
||||
with open(yaml_file_path, 'w', encoding='utf-8') as file:
|
||||
file.write(yaml_template)
|
||||
upload_video(upload_video_path, yaml_file_path)
|
||||
return
|
||||
else:
|
||||
query = generate_title(upload_video_path)
|
||||
result = subprocess.check_output(f"{SRC_DIR}/upload/biliup" + " -u " + f"{SRC_DIR}/upload/cookies.json" + " list", shell=True)
|
||||
upload_list = result.decode("utf-8").splitlines()
|
||||
|
||||
Reference in New Issue
Block a user