V 3.1.7
1. 移除软件包自带的本地 whisper（需单独安装） 2. 重构版本底层依赖，移除外部依赖 3. 修复 首页 暗黑模式不兼容的问题 4. 修复 SD 合并提示词报错
This commit is contained in:
+12
-16
@@ -4,7 +4,6 @@ import json
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import os
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import sys
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import clip
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import getgrame
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import Push_back_Prompt
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import public_tools
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import shotSplit
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@@ -56,6 +55,8 @@ if sys.argv[1] == "-c":
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clip = clip.Clip(cript_directory, sys.argv[2], sys.argv[3])
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clip.MergeVideosAndClip()
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pass
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# 获取字体
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elif sys.argv[1] == "-f":
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# 获取本地已安装的字幕。然后返回
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@@ -77,22 +78,17 @@ elif sys.argv[1] == "-p":
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Push_back_Prompt.init(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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# 剪映抽帧
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elif sys.argv[1] == "-k":
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# print("")
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getgrame.init(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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elif sys.argv[1] == "-ka":
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shotSplit.get_fram(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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# 智能分镜。字幕识别
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elif sys.argv[1] == "-a":
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print("开始算法分镜：" + sys.argv[2] + " -- 输出文件夹：" + sys.argv[3])
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shotSplit.init(sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])
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# 本地提取音频
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elif sys.argv[1] == "-t":
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print("开始提取文字：" + sys.argv[2])
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shotSplit.GetTextTask(sys.argv[2], sys.argv[3], sys.argv[4])
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pass
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# # 智能分镜。字幕识别
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# elif sys.argv[1] == "-a":
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# print("开始算法分镜：" + sys.argv[2] + " -- 输出文件夹：" + sys.argv[3])
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# shotSplit.init(sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])
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# # 本地提取音频
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# elif sys.argv[1] == "-t":
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# print("开始提取文字：" + sys.argv[2])
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# shotSplit.GetTextTask(sys.argv[2], sys.argv[3], sys.argv[4])
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# pass
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@@ -1,36 +1,34 @@
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# -*- mode: python ; coding: utf-8 -*-
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from PyInstaller.building.datastruct import Tree
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from PyInstaller.utils.hooks import get_package_paths
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PACKAGE_DIRECTORY = get_package_paths('faster_whisper')[1]
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datas = [(PACKAGE_DIRECTORY, 'faster_whisper')]
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a = Analysis(
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['Lai.py'],
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pathex=[],
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binaries=[],
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datas=datas,
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datas=[],
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hiddenimports=[],
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hookspath=[],
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hooksconfig={},
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runtime_hooks=[],
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excludes=[],
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noarchive=False,
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optimize=0,
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)
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pyz = PYZ(a.pure)
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exe = EXE(
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pyz,
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a.scripts,
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a.binaries,
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a.datas,
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[],
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exclude_binaries=True,
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name='Lai',
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debug=False,
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bootloader_ignore_signals=False,
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strip=False,
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upx=True,
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upx_exclude=[],
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runtime_tmpdir=None,
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console=True,
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disable_windowed_traceback=False,
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argv_emulation=False,
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@@ -38,12 +36,3 @@ exe = EXE(
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codesign_identity=None,
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entitlements_file=None,
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)
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coll = COLLECT(
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exe,
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a.binaries,
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a.datas,
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strip=False,
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upx=True,
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upx_exclude=[],
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name='Lai',
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)
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@@ -1,11 +1,17 @@
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# -*- mode: python ; coding: utf-8 -*-
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from PyInstaller.building.datastruct import Tree
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from PyInstaller.utils.hooks import get_package_paths
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PACKAGE_DIRECTORY = get_package_paths('faster_whisper')[1]
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datas = [(PACKAGE_DIRECTORY, 'faster_whisper')]
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a = Analysis(
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['lama_inpaint.py'],
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['Lai.py'],
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pathex=[],
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binaries=[],
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datas=[],
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datas=datas,
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hiddenimports=[],
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hookspath=[],
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hooksconfig={},
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@@ -18,16 +24,13 @@ pyz = PYZ(a.pure)
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exe = EXE(
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pyz,
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a.scripts,
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a.binaries,
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a.datas,
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[],
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name='lama_inpaint',
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exclude_binaries=True,
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name='Lai',
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debug=False,
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bootloader_ignore_signals=False,
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strip=False,
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upx=True,
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upx_exclude=[],
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runtime_tmpdir=None,
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console=True,
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disable_windowed_traceback=False,
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argv_emulation=False,
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@@ -35,3 +38,12 @@ exe = EXE(
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codesign_identity=None,
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entitlements_file=None,
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)
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coll = COLLECT(
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exe,
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a.binaries,
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a.datas,
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strip=False,
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upx=True,
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upx_exclude=[],
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name='Lai',
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)
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@@ -0,0 +1,2 @@
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@echo off
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pyinstaller --upx-dir="C:\\Users\\27698\\Desktop\\upx-4.2.4-win64\upx.exe" local_whisper.py
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@@ -0,0 +1,170 @@
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# -*- coding: utf-8 -*-
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import io
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import os
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import sys
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import public_tools
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from pathlib import Path
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from huggingface_hub import hf_hub_download
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from faster_whisper import WhisperModel
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
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# 判断sys.argv 的长度，如果小于2，说明没有传入参数，设置初始参数
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# "C:\\Users\\27698\\Desktop\\LAITool\\resources\\scripts\\Lai.exe" -c "D:/来推项目集/7.4/娱乐：江湖大哥退休，去拍电影/scripts/output_crop_00001.json" "NVIDIA"
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# if len(sys.argv) < 2:
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# sys.argv = [
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# "C:\\Users\\27698\\Desktop\\LAITool\\resources\\scripts\\Lai.exe",
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# "-w",
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# "C:\\Users\\27698\\Desktop\\测试\\test\\mjTestoutput_crop_00001.mp4",
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# "C:\\Users\\27698\\Desktop\\测试\\test\data\\frame",
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# "C:\\Users\\27698\\Desktop\\测试\\test\\tmp\\input_crop",
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# 30,
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# "NVIDIA",
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# ]
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print(sys.argv)
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if len(sys.argv) < 2:
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print("Params: <runtime-config.json>")
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exit(0)
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if getattr(sys, "frozen", False):
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cript_directory = os.path.dirname(sys.executable)
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elif __file__:
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cript_directory = os.path.dirname(__file__)
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def GetText(out_folder, mp3_folder):
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text = []
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# 先获取模型
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print("正在下载或加载模型")
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sys.stdout.flush()
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model_path = Path(
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hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="preprocessor_config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="tokenizer.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="vocabulary.json",
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)
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model = WhisperModel(
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model_size_or_path=os.path.dirname(model_path),
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device="auto",
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local_files_only=True,
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)
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print("模型加载成功，开始识别")
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sys.stdout.flush()
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# 拿到指定文件夹里面的所有的MP3文件
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mp3_list = []
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for root, dirs, files in os.walk(mp3_folder):
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for file in files:
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if file.endswith(".mp3"):
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mp3_list.append(os.path.join(root, file))
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for mp in mp3_list:
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segments, info = model.transcribe(
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mp,
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beam_size=5,
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language="zh",
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vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=1000),
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)
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tmp_text = ""
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for segment in segments:
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tmp_text += segment.text + "。"
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print(mp + "识别完成")
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sys.stdout.flush()
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text.append(tmp_text)
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# 数据写出
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print("文本全部识别成功，正在写出")
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sys.stdout.flush()
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tools = public_tools.PublicTools()
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tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
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print("写出完成")
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sys.stdout.flush()
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def GetTextTask(out_folder, mp, name):
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text = []
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# 先获取模型
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print("正在下载或加载模型")
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sys.stdout.flush()
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model_path = Path(
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hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="preprocessor_config.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="tokenizer.json",
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)
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hf_hub_download(
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repo_id="Systran/faster-whisper-large-v3",
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filename="vocabulary.json",
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)
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model = WhisperModel(
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model_size_or_path=os.path.dirname(model_path),
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device="auto",
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local_files_only=True,
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)
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print("模型加载成功，开始识别")
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sys.stdout.flush()
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segments, info = model.transcribe(
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mp,
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beam_size=5,
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language="zh",
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vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=1000),
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)
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tmp_text = ""
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for segment in segments:
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tmp_text += segment.text + "。"
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print(mp + "识别完成")
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sys.stdout.flush()
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text.append(tmp_text)
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# 数据写出
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sys.stdout.flush()
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tools = public_tools.PublicTools()
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tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
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sys.stdout.flush()
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# GetTextTask(
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# "C:\\Users\\27698\\Desktop\\测试\\mjTest",
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# "C:\\Users\\27698\\Desktop\\测试\\mjTest\\data\\frame\\00001.mp4",
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# "00001",
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# )
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if sys.argv[1] == "-ts":
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GetText(
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sys.argv[2],
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sys.argv[3],
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)
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elif sys.argv[1] == "-t":
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GetTextTask(
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sys.argv[2],
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sys.argv[3],
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sys.argv[4],
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)
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else:
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print("Params: <runtime-config.json>")
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exit(0)
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@@ -0,0 +1,50 @@
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# -*- mode: python ; coding: utf-8 -*-
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from PyInstaller.building.datastruct import Tree
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from PyInstaller.utils.hooks import get_package_paths
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PACKAGE_DIRECTORY = get_package_paths('faster_whisper')[1]
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datas = [(PACKAGE_DIRECTORY, 'faster_whisper')]
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a = Analysis(
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['local_whisper.py'],
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pathex=[],
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binaries=[],
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datas=[],
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hiddenimports=[],
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hookspath=[],
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hooksconfig={},
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runtime_hooks=[],
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excludes=[],
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noarchive=False,
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optimize=0,
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)
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pyz = PYZ(a.pure)
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exe = EXE(
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pyz,
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a.scripts,
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[],
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exclude_binaries=True,
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name='local_whisper',
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debug=False,
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bootloader_ignore_signals=False,
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strip=False,
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upx=True,
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console=True,
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disable_windowed_traceback=False,
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argv_emulation=False,
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target_arch=None,
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codesign_identity=None,
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entitlements_file=None,
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)
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coll = COLLECT(
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exe,
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a.binaries,
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a.datas,
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strip=False,
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upx=True,
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upx_exclude=[],
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name='local_whisper',
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)
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@@ -0,0 +1,351 @@
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# 读取文件的方法
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import json
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import os
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import win32api
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import win32con
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import pywintypes
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import shutil
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import re
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class PublicTools:
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"""
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一些公用的基础方法
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"""
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def delete_path(self, path):
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"""
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删除指定路径的文件或者是文件夹
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"""
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# 检查路径是否存在
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if not os.path.exists(path):
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return
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# 检查路径是文件还是文件夹
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if os.path.isfile(path):
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# 是文件，执行删除
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try:
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os.remove(path)
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except Exception as e:
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raise e
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elif os.path.isdir(path):
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# 是文件夹，执行删除
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try:
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shutil.rmtree(path)
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except Exception as e:
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raise e
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else:
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raise
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def list_files_by_extension(self, folder_path, extension):
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"""
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读取指定文件夹下面的所有的指定拓展文件命的文件列表
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"""
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file_list = []
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for root, dirs, files in os.walk(folder_path):
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for file in files:
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if file.endswith(extension):
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file_list.append(os.path.join(root, file))
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elif file.endswith(extension.upper()):
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file_list.append(os.path.join(root, file))
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return file_list
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def get_fonts_from_registry(self, key_path):
|
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"""
|
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获取注册表中安装的字体文件
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"""
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font_names = []
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try:
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key = win32api.RegOpenKeyEx(
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(
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win32con.HKEY_LOCAL_MACHINE
|
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if "HKEY_LOCAL_MACHINE" in key_path
|
||||
else win32con.HKEY_CURRENT_USER
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),
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key_path.split("\\", 1)[1],
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0,
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win32con.KEY_READ,
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)
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i = 0
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||||
while True:
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try:
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value = win32api.RegEnumValue(key, i)
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font_name = value[0]
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# 使用正则表达式移除括号及其内容
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font_name = re.sub(r"\s*\([^)]*\)$", "", font_name)
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font_names.append(font_name)
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i += 1
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except pywintypes.error as e:
|
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if e.winerror == 259: # 没有更多的数据
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break
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else:
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raise
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finally:
|
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try:
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win32api.RegCloseKey(key)
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||||
except:
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pass
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return font_names
|
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|
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def get_installed_fonts(self):
|
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"""
|
||||
获取字体文件名称并返回
|
||||
"""
|
||||
system_fonts = self.get_fonts_from_registry(
|
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"HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Fonts"
|
||||
)
|
||||
user_fonts = self.get_fonts_from_registry(
|
||||
"HKEY_CURRENT_USER\\Software\\Microsoft\\Windows NT\\CurrentVersion\\Fonts"
|
||||
)
|
||||
all_fonts = list(set(system_fonts + user_fonts)) # 合并并去重
|
||||
return all_fonts
|
||||
|
||||
# 将RRGGBB转换为BBGGRR
|
||||
def convert_rrggbb_to_bbggrr(self, rrggbb):
|
||||
"""
|
||||
将RRGGBB转换为BBGGRR
|
||||
"""
|
||||
if len(rrggbb) == 7:
|
||||
rr = rrggbb[1:3]
|
||||
gg = rrggbb[3:5]
|
||||
bb = rrggbb[5:7]
|
||||
return bb + gg + rr
|
||||
else:
|
||||
return "Invalid input"
|
||||
|
||||
def write_to_file(self, arr, filename):
|
||||
with open(filename, "w",encoding='utf-8') as f:
|
||||
for item in arr:
|
||||
f.write("%s\n" % item)
|
||||
|
||||
|
||||
# 读取文件
|
||||
def read_file(fileType):
|
||||
txt_path = input(f"输入{fileType}文件路径：")
|
||||
txt_path = remove_prefix_and_suffix(txt_path, '"', '"')
|
||||
while txt_path.strip() == "":
|
||||
txt_path = input(f"输入{fileType}文件路径：")
|
||||
|
||||
while os.path.exists(txt_path) == False:
|
||||
print("文件路径不存在错误：")
|
||||
txt_path = input(f"输入{fileType}文件路径：")
|
||||
txt_path = remove_prefix_and_suffix(txt_path, '"', '"')
|
||||
return txt_path
|
||||
|
||||
|
||||
def format_time_ms(milliseconds):
|
||||
"""
|
||||
时间转换将ms->小时:分钟:秒.毫秒格式
|
||||
"""
|
||||
seconds = milliseconds / 1000
|
||||
# 计算小时、分钟和秒
|
||||
hours = int(seconds // 3600)
|
||||
minutes = int((seconds % 3600) // 60)
|
||||
seconds = seconds % 60
|
||||
# 格式化字符串
|
||||
# 使用`%02d`确保小时和分钟总是显示为两位数，`%.2f`确保秒数显示两位小数
|
||||
formatted_time = f"{hours}:{minutes:02d}:{seconds:05.2f}"
|
||||
return formatted_time
|
||||
|
||||
|
||||
# 删除满足条件的开头和结尾
|
||||
def remove_prefix_and_suffix(input_str, prefix_to_remove, suffix_to_remove):
|
||||
if input_str.startswith(prefix_to_remove):
|
||||
# 删除开头
|
||||
input_str = input_str[len(prefix_to_remove) :]
|
||||
|
||||
if input_str.endswith(suffix_to_remove):
|
||||
# 删除结尾
|
||||
input_str = input_str[: -len(suffix_to_remove)]
|
||||
|
||||
return input_str
|
||||
|
||||
|
||||
# 判断文件夹下面是不是有特定的文件夹
|
||||
def check_if_folder_exists(parent_folder, target_folder_name):
|
||||
# 获取文件夹列表
|
||||
subfolders = [f.name for f in os.scandir(parent_folder) if f.is_dir()]
|
||||
|
||||
# 检查特定文件夹是否存在
|
||||
if target_folder_name in subfolders:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
|
||||
# 检查指定文件夹中是否存在特定文件。
|
||||
def file_exists_in_folder(folder_path: str, file_name: str) -> bool:
|
||||
# 构建完整的文件路径
|
||||
file_path = os.path.join(folder_path, file_name)
|
||||
|
||||
# 返回文件是否存在
|
||||
return os.path.isfile(file_path)
|
||||
|
||||
|
||||
# 秒数转换，保留一位小数
|
||||
def convert_to_seconds(number, count):
|
||||
seconds = number / 1000000
|
||||
rounded_number = round(seconds, count)
|
||||
return rounded_number
|
||||
|
||||
|
||||
def is_empty(obj):
|
||||
if obj is None:
|
||||
return True
|
||||
elif isinstance(obj, str):
|
||||
return len(obj) == 0
|
||||
elif isinstance(obj, list):
|
||||
return len(obj) == 0
|
||||
elif isinstance(obj, dict):
|
||||
return len(obj) == 0
|
||||
return False
|
||||
|
||||
|
||||
def opt_dict(obj, key, default=None):
|
||||
if obj is None:
|
||||
return default
|
||||
if key in obj:
|
||||
v = obj[key]
|
||||
if not is_empty(v):
|
||||
return v
|
||||
return default
|
||||
|
||||
|
||||
def read_config(path, webui=True):
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
runtime_config = json.load(f)
|
||||
|
||||
if "config" not in runtime_config:
|
||||
print("no filed 'config' in json")
|
||||
return None
|
||||
|
||||
config = runtime_config["config"]
|
||||
if "webui" not in config:
|
||||
print("no filed 'webui' in 'config'")
|
||||
return None
|
||||
|
||||
setting_config_path = config["setting"]
|
||||
if not os.path.exists(setting_config_path):
|
||||
setting_config_path = "config/" + setting_config_path
|
||||
if not os.path.exists(setting_config_path):
|
||||
setting_config_path = "../" + setting_config_path
|
||||
|
||||
# read config
|
||||
with open(setting_config_path, "r", encoding="utf-8") as f:
|
||||
setting_config = json.load(f)
|
||||
|
||||
# set workspace parent:根目录
|
||||
if "workspace" in setting_config:
|
||||
setting_config["workspace"]["parent"] = runtime_config["workspace"]
|
||||
else:
|
||||
setting_config["workspace"] = {"parent": runtime_config["workspace"]}
|
||||
setting_config["video"] = opt_dict(runtime_config, "video")
|
||||
|
||||
# merge setting config
|
||||
if "setting" in config:
|
||||
setting_config.update(runtime_config["setting"])
|
||||
|
||||
# webui config
|
||||
if webui:
|
||||
webui_config_path = config["webui"]
|
||||
if not os.path.exists(webui_config_path):
|
||||
webui_config_path = "config/webui/" + webui_config_path
|
||||
if not os.path.exists(webui_config_path):
|
||||
webui_config_path = "../" + webui_config_path
|
||||
|
||||
with open(webui_config_path, "r", encoding="utf-8") as f:
|
||||
webui_config = json.load(f)
|
||||
|
||||
# merge webui config
|
||||
if "webui" in runtime_config:
|
||||
webui_config.update(runtime_config["webui"])
|
||||
|
||||
return webui_config, setting_config
|
||||
return setting_config
|
||||
|
||||
|
||||
TAG_MODE_NONE = ""
|
||||
|
||||
|
||||
# 工作路径
|
||||
class Workspace:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
root: str,
|
||||
input: str,
|
||||
output: str,
|
||||
input_crop: str,
|
||||
output_crop: str,
|
||||
input_tag: str,
|
||||
input_mask: str,
|
||||
input_crop_mask: str,
|
||||
crop_info: str,
|
||||
):
|
||||
self.root = root
|
||||
self.input = input
|
||||
self.output = output
|
||||
self.input_crop = input_crop
|
||||
self.output_crop = output_crop
|
||||
self.input_tag = input_tag
|
||||
self.input_mask = input_mask
|
||||
self.input_crop_mask = input_crop_mask
|
||||
self.crop_info = crop_info
|
||||
|
||||
|
||||
# 定义一个倍数函数
|
||||
def round_up(num, mul):
|
||||
return (num // mul + 1) * mul
|
||||
|
||||
|
||||
class SettingConfig:
|
||||
|
||||
def __init__(self, config: dict, workParent):
|
||||
self.config = config
|
||||
self.webui_work_api = None
|
||||
self.workParent = workParent
|
||||
|
||||
def to_dict(self):
|
||||
return self.__dict__
|
||||
|
||||
def get_tag_mode(self):
|
||||
tag_cfg = opt_dict(self.config, "tag")
|
||||
return opt_dict(tag_cfg, "mode", TAG_MODE_NONE)
|
||||
|
||||
def get_tag_actions(self):
|
||||
tag_cfg = opt_dict(self.config, "tag")
|
||||
return opt_dict(tag_cfg, "actions", [])
|
||||
|
||||
def get_workspace_config(self) -> Workspace:
|
||||
workspace_config = opt_dict(self.config, "workspace")
|
||||
tmp_config = opt_dict(workspace_config, "tmp")
|
||||
|
||||
input = opt_dict(workspace_config, "input", "input")
|
||||
output = opt_dict(workspace_config, "output", "output")
|
||||
workspace_parent = self.workParent
|
||||
|
||||
tmp_parent = opt_dict(tmp_config, "parent", "tmp")
|
||||
input_crop = opt_dict(tmp_config, "input_crop", "input_crop")
|
||||
output_crop = opt_dict(tmp_config, "output_crop", "output_crop")
|
||||
input_tag = opt_dict(tmp_config, "input_tag", "input_crop")
|
||||
input_mask = opt_dict(tmp_config, "input_mask", "input_mask")
|
||||
input_crop_mask = opt_dict(tmp_config, "input_crop_mask", "input_crop_mask")
|
||||
crop_info = opt_dict(tmp_config, "crop_info", "crop_info.txt")
|
||||
|
||||
tmp_path = os.path.join(workspace_parent, tmp_parent)
|
||||
|
||||
return Workspace(
|
||||
workspace_parent,
|
||||
os.path.join(workspace_parent, input),
|
||||
os.path.join(workspace_parent, output),
|
||||
os.path.join(tmp_path, input_crop),
|
||||
os.path.join(tmp_path, output_crop),
|
||||
os.path.join(tmp_path, input_tag),
|
||||
os.path.join(tmp_path, input_mask),
|
||||
os.path.join(tmp_path, input_crop_mask),
|
||||
os.path.join(tmp_path, crop_info),
|
||||
)
|
||||
|
||||
def enable_tag(self):
|
||||
tag_cfg = opt_dict(self.config, "tag")
|
||||
return opt_dict(tag_cfg, "enable", True)
|
||||
@@ -0,0 +1,307 @@
|
||||
# pip install scenedetect opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
|
||||
from scenedetect.video_manager import VideoManager
|
||||
from scenedetect.scene_manager import SceneManager
|
||||
from scenedetect.stats_manager import StatsManager
|
||||
from scenedetect.detectors.content_detector import ContentDetector
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
from huggingface_hub import hf_hub_download
|
||||
from faster_whisper import WhisperModel
|
||||
from pathlib import Path
|
||||
import public_tools
|
||||
|
||||
# 获取智能画面分割的时间或者秒数
|
||||
def find_scenes(video_path, sensitivity):
|
||||
print(
|
||||
"正在计算分镜数据" + "sensitivity：" + str(sensitivity) + "path : " + video_path
|
||||
)
|
||||
sys.stdout.flush()
|
||||
video_manager = VideoManager([video_path])
|
||||
stats_manager = StatsManager()
|
||||
scene_manager = SceneManager(stats_manager)
|
||||
|
||||
# 使用contect-detector
|
||||
scene_manager.add_detector(ContentDetector(threshold=float(sensitivity)))
|
||||
|
||||
shijian_list = []
|
||||
|
||||
try:
|
||||
video_manager.set_downscale_factor()
|
||||
video_manager.start()
|
||||
scene_manager.detect_scenes(frame_source=video_manager)
|
||||
scene_list = scene_manager.get_scene_list()
|
||||
print("分镜数据列表：")
|
||||
sys.stdout.flush()
|
||||
for i, scene in enumerate(scene_list):
|
||||
shijian_list.append([scene[0].get_timecode(), scene[1].get_timecode()])
|
||||
print(
|
||||
"Scene %2d: Start %s / Frame %d, End %s / Frame %d"
|
||||
% (
|
||||
i + 1,
|
||||
scene[0].get_timecode(),
|
||||
scene[0].get_frames(),
|
||||
scene[1].get_timecode(),
|
||||
scene[1].get_frames(),
|
||||
)
|
||||
)
|
||||
sys.stdout.flush()
|
||||
finally:
|
||||
video_manager.release()
|
||||
|
||||
return shijian_list
|
||||
|
||||
|
||||
# 如果不存在就创建
|
||||
def createDir(file_dir):
|
||||
# 如果不存在文件夹，就创建
|
||||
if not os.path.isdir(file_dir):
|
||||
os.mkdir(file_dir)
|
||||
|
||||
|
||||
# 切分一个视频
|
||||
def ClipVideo(video_path, out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
shijian_list = find_scenes(video_path, sensitivity) # 多组时间列表
|
||||
shijian_list_len = len(shijian_list)
|
||||
|
||||
print("总共有%s个场景" % str(shijian_list_len))
|
||||
sys.stdout.flush()
|
||||
video_list = []
|
||||
for i in range(0, shijian_list_len):
|
||||
start_time_str = shijian_list[i][0]
|
||||
end_time_str = shijian_list[i][1]
|
||||
|
||||
print("开始输出第" + str(i + 1) + "个分镜")
|
||||
video_name = "{:05d}".format(i + 1)
|
||||
out_video_file = os.path.join(out_folder, video_name + ".mp4")
|
||||
sys.stdout.flush()
|
||||
video_list.append(
|
||||
{
|
||||
"start_time_str": start_time_str,
|
||||
"end_time_str": end_time_str,
|
||||
"out_video_file": out_video_file,
|
||||
"video_name": video_name,
|
||||
}
|
||||
)
|
||||
|
||||
# 使用 ffmpeg 裁剪视频
|
||||
command = []
|
||||
command.append("ffmpeg")
|
||||
command.append("-i")
|
||||
command.append(video_path)
|
||||
command.append("-ss")
|
||||
command.append(start_time_str)
|
||||
command.append("-to")
|
||||
command.append(end_time_str)
|
||||
command.append("-c:v")
|
||||
|
||||
if gpu_type == "NVIDIA":
|
||||
command.append("h264_nvenc")
|
||||
elif gpu_type == "AMD":
|
||||
command.append("h264_amf")
|
||||
else:
|
||||
command.append("libx264")
|
||||
|
||||
command.append("-preset")
|
||||
command.append("fast")
|
||||
command.append("-c:a")
|
||||
command.append("copy")
|
||||
command.append(out_video_file)
|
||||
command.append("-loglevel")
|
||||
command.append("error")
|
||||
|
||||
subprocess.run(
|
||||
command,
|
||||
check=True,
|
||||
stderr=subprocess.PIPE,
|
||||
)
|
||||
|
||||
print("分镜输出完成。开始抽帧")
|
||||
sys.stdout.flush()
|
||||
for vi in video_list:
|
||||
h, m, s = vi["start_time_str"].split(":")
|
||||
start_seconds = int(h) * 3600 + int(m) * 60 + float(s)
|
||||
|
||||
h, m, s = vi["end_time_str"].split(":")
|
||||
end_seconds = int(h) * 3600 + int(m) * 60 + float(s)
|
||||
print("正在抽帧：" + vi["video_name"])
|
||||
sys.stdout.flush()
|
||||
subprocess.run(
|
||||
[
|
||||
"ffmpeg",
|
||||
"-ss",
|
||||
str((end_seconds - start_seconds) / 2),
|
||||
"-i",
|
||||
vi["out_video_file"],
|
||||
"-frames:v",
|
||||
"1",
|
||||
os.path.join(image_out_folder, vi["video_name"] + ".png"),
|
||||
"-loglevel",
|
||||
"error",
|
||||
]
|
||||
)
|
||||
|
||||
print("抽帧完成，开始识别文案")
|
||||
sys.stdout.flush()
|
||||
return video_list
|
||||
|
||||
|
||||
def SplitAudio(video_out_folder, video_list):
|
||||
# ffmpeg -i input_file.mp4 -vn -ab 128k output_file.mp3
|
||||
print("正在分离音频!!")
|
||||
mp3_list = []
|
||||
sys.stdout.flush()
|
||||
for v in video_list:
|
||||
mp3_path = os.path.join(video_out_folder, v["video_name"] + ".mp3")
|
||||
mp3_list.append(mp3_path)
|
||||
subprocess.run(
|
||||
[
|
||||
"ffmpeg",
|
||||
"-i",
|
||||
v["out_video_file"],
|
||||
"-vn",
|
||||
"-ab",
|
||||
"128k",
|
||||
mp3_path,
|
||||
"-loglevel",
|
||||
"error",
|
||||
],
|
||||
check=True,
|
||||
)
|
||||
return mp3_list
|
||||
|
||||
|
||||
def GetText(out_folder, mp3_list):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
for mp in mp3_list:
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
print("文本全部识别成功，正在写出")
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
|
||||
print("写出完成")
|
||||
sys.stdout.flush()
|
||||
|
||||
|
||||
def GetTextTask(out_folder, mp, name):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
|
||||
sys.stdout.flush()
|
||||
|
||||
|
||||
def get_fram(video_path, out_path, sensitivity):
|
||||
try:
|
||||
shijian_list = find_scenes(video_path, sensitivity) # 多组时间列表
|
||||
print("总共有%s个场景" % str(len(shijian_list)))
|
||||
print("开始输出json")
|
||||
print(shijian_list)
|
||||
# 将数组中的消息写道json文件中
|
||||
with open(out_path, "w") as file:
|
||||
# 将数组写入到指定的json文件
|
||||
json.dump(shijian_list, file)
|
||||
print("输出完成")
|
||||
except Exception as e:
|
||||
print("出现错误" + str(e))
|
||||
exit(0)
|
||||
|
||||
|
||||
def init(video_path, video_out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
v_l = ClipVideo(
|
||||
video_path, video_out_folder, image_out_folder, sensitivity, gpu_type
|
||||
)
|
||||
|
||||
# 开始分离音频
|
||||
m_l = SplitAudio(video_out_folder, v_l)
|
||||
# 开始识别字幕
|
||||
GetText(os.path.dirname(video_out_folder), m_l)
|
||||
@@ -1,298 +0,0 @@
|
||||
accelerate==0.30.1
|
||||
addict==2.4.0
|
||||
aiofiles==23.2.1
|
||||
aiohttp==3.8.6
|
||||
aiosignal==1.3.1
|
||||
alibabacloud-bailian20230601==1.6.1
|
||||
alibabacloud-credentials==0.3.3
|
||||
alibabacloud-endpoint-util==0.0.3
|
||||
alibabacloud-gateway-spi==0.0.1
|
||||
alibabacloud-openapi-util==0.2.2
|
||||
alibabacloud-tea==0.3.6
|
||||
alibabacloud-tea-openapi==0.3.9
|
||||
alibabacloud-tea-util==0.3.12
|
||||
alibabacloud-tea-xml==0.0.2
|
||||
aliyun-python-sdk-core==2.15.0
|
||||
aliyun-python-sdk-kms==2.16.2
|
||||
altair==5.3.0
|
||||
altgraph==0.17.4
|
||||
annotated-types==0.6.0
|
||||
anthropic==0.26.1
|
||||
antlr4-python3-runtime==4.9.3
|
||||
anyio==4.3.0
|
||||
APScheduler==3.10.4
|
||||
arxiv==2.1.0
|
||||
astor==0.8.1
|
||||
asttokens==2.4.1
|
||||
async-timeout==4.0.3
|
||||
attrdict==2.0.1
|
||||
attrs==23.2.0
|
||||
av==11.0.0
|
||||
azure-cognitiveservices-speech==1.37.0
|
||||
Babel==2.15.0
|
||||
backports.tarfile==1.1.1
|
||||
baidu-aip==4.16.13
|
||||
bce-python-sdk==0.9.11
|
||||
beautifulsoup4==4.12.3
|
||||
bidict==0.23.1
|
||||
blinker==1.8.2
|
||||
broadscope-bailian==1.3.1
|
||||
cachetools==5.3.3
|
||||
certifi==2024.2.2
|
||||
cffi==1.16.0
|
||||
cfgv==3.4.0
|
||||
chardet==5.2.0
|
||||
charset-normalizer==3.3.2
|
||||
chatgpt-tool-hub==0.5.0
|
||||
cheroot==10.0.1
|
||||
click==8.1.7
|
||||
colorama==0.4.6
|
||||
coloredlogs==15.0.1
|
||||
comtypes==1.4.2
|
||||
contourpy==1.2.1
|
||||
controlnet-aux==0.0.3
|
||||
crcmod==1.7
|
||||
cryptography==42.0.5
|
||||
cssselect==1.2.0
|
||||
cssutils==2.11.0
|
||||
ctranslate2==4.1.0
|
||||
curl_cffi==0.6.4
|
||||
cx-Logging==3.1.0
|
||||
cx_Freeze==6.15.16
|
||||
cycler==0.12.1
|
||||
Cython==3.0.10
|
||||
dashscope==1.19.2
|
||||
datasets==2.18.0
|
||||
decorator==4.4.2
|
||||
diffusers==0.27.2
|
||||
dill==0.3.8
|
||||
dingtalk-stream==0.18.1
|
||||
distlib==0.3.8
|
||||
distro==1.9.0
|
||||
dnspython==2.6.1
|
||||
dulwich==0.22.1
|
||||
easydict==1.13
|
||||
edge-tts==6.1.12
|
||||
einops==0.7.0
|
||||
elevenlabs==1.0.3
|
||||
email_validator==2.1.1
|
||||
et-xmlfile==1.1.0
|
||||
exceptiongroup==1.2.1
|
||||
executing==2.0.1
|
||||
fastapi==0.108.0
|
||||
fastapi-cli==0.0.2
|
||||
faster-whisper==1.0.1
|
||||
feedparser==6.0.10
|
||||
ffmpy==0.3.2
|
||||
filelock==3.13.1
|
||||
fire==0.6.0
|
||||
Flask==3.0.3
|
||||
flask-babel==4.0.0
|
||||
flatbuffers==24.3.7
|
||||
fonttools==4.53.0
|
||||
frozenlist==1.4.1
|
||||
fsspec==2024.2.0
|
||||
future==1.0.0
|
||||
gast==0.5.4
|
||||
google-ai-generativelanguage==0.6.4
|
||||
google-api-core==2.19.0
|
||||
google-api-python-client==2.130.0
|
||||
google-auth==2.29.0
|
||||
google-auth-httplib2==0.2.0
|
||||
google-generativeai==0.5.4
|
||||
googleapis-common-protos==1.63.0
|
||||
gradio==4.21.0
|
||||
gradio_client==0.12.0
|
||||
grpcio==1.64.0
|
||||
grpcio-status==1.62.2
|
||||
gTTS==2.5.1
|
||||
h11==0.14.0
|
||||
HTMLParser==0.0.2
|
||||
httpcore==1.0.5
|
||||
httplib2==0.22.0
|
||||
httptools==0.6.1
|
||||
httpx==0.27.0
|
||||
huggingface-hub==0.23.2
|
||||
humanfriendly==10.0
|
||||
identify==2.5.36
|
||||
idna==3.6
|
||||
imageio==2.34.0
|
||||
imageio-ffmpeg==0.4.9
|
||||
imgaug==0.4.0
|
||||
importlib_metadata==7.0.2
|
||||
importlib_resources==6.4.0
|
||||
install==1.3.5
|
||||
IOPaint==1.3.3
|
||||
ipython==8.24.0
|
||||
itsdangerous==2.2.0
|
||||
jaraco.context==5.3.0
|
||||
jaraco.functools==4.0.1
|
||||
jedi==0.19.1
|
||||
Jinja2==3.1.3
|
||||
jiter==0.4.0
|
||||
jmespath==0.10.0
|
||||
jsonschema==4.22.0
|
||||
jsonschema-specifications==2023.12.1
|
||||
kiwisolver==1.4.5
|
||||
langid==1.1.6
|
||||
lazy_loader==0.4
|
||||
lief==0.14.1
|
||||
linkai==0.0.6.0
|
||||
lmdb==1.4.1
|
||||
loguru==0.7.2
|
||||
lxml==5.2.2
|
||||
markdown-it-py==3.0.0
|
||||
MarkupSafe==2.1.5
|
||||
matplotlib==3.9.0
|
||||
matplotlib-inline==0.1.7
|
||||
mdurl==0.1.2
|
||||
modelscope==1.13.1
|
||||
more-itertools==10.2.0
|
||||
moviepy==1.0.3
|
||||
mpmath==1.3.0
|
||||
multidict==6.0.5
|
||||
multiprocess==0.70.16
|
||||
networkx==3.2.1
|
||||
nodeenv==1.8.0
|
||||
Nuitka==2.1.2
|
||||
numpy==1.24.2
|
||||
omegaconf==2.3.0
|
||||
onnxruntime==1.17.1
|
||||
openai==0.27.8
|
||||
opencv-contrib-python==4.6.0.66
|
||||
opencv-python==4.6.0.66
|
||||
opencv-python-headless==4.9.0.80
|
||||
openpyxl==3.1.2
|
||||
opt-einsum==3.3.0
|
||||
optionaldict==0.1.2
|
||||
ordered-set==4.1.0
|
||||
orjson==3.10.3
|
||||
oss2==2.18.4
|
||||
packaging==24.0
|
||||
paddleocr==2.7.3
|
||||
paddlepaddle==2.6.1
|
||||
pandas==2.2.1
|
||||
parso==0.8.4
|
||||
pdf2docx==0.5.8
|
||||
pefile==2023.2.7
|
||||
peft==0.7.1
|
||||
piexif==1.1.3
|
||||
pillow==10.3.0
|
||||
platformdirs==4.2.0
|
||||
pre-commit==3.7.1
|
||||
premailer==3.10.0
|
||||
proglog==0.1.10
|
||||
prompt-toolkit==3.0.43
|
||||
proto-plus==1.23.0
|
||||
protobuf==3.20.2
|
||||
psutil==5.9.8
|
||||
pure-eval==0.2.2
|
||||
pyarrow==15.0.1
|
||||
pyarrow-hotfix==0.6
|
||||
pyasn1==0.6.0
|
||||
pyasn1_modules==0.4.0
|
||||
pyclipper==1.3.0.post5
|
||||
pycparser==2.21
|
||||
pycryptodome==3.20.0
|
||||
pydantic==2.5.3
|
||||
pydantic_core==2.14.6
|
||||
pydub==0.25.1
|
||||
Pygments==2.18.0
|
||||
pyinstaller==6.5.0
|
||||
pyinstaller-hooks-contrib==2024.3
|
||||
PyJWT==2.8.0
|
||||
PyMuPDF==1.24.5
|
||||
PyMuPDFb==1.24.3
|
||||
pyOpenSSL==24.1.0
|
||||
pyoxidizer==0.24.0
|
||||
pyparsing==3.1.2
|
||||
pypiwin32==223
|
||||
pypng==0.20220715.0
|
||||
PyQRCode==1.2.1
|
||||
pyreadline3==3.4.1
|
||||
pytesseract==0.3.10
|
||||
python-dateutil==2.9.0.post0
|
||||
python-docx==1.1.2
|
||||
python-dotenv==1.0.1
|
||||
python-engineio==4.9.1
|
||||
python-multipart==0.0.9
|
||||
python-socketio==5.7.2
|
||||
pyttsx3==2.90
|
||||
pytz==2024.1
|
||||
pywin32==306
|
||||
pywin32-ctypes==0.2.2
|
||||
PyYAML==6.0.1
|
||||
qrcode==7.4.2
|
||||
rapidfuzz==3.9.3
|
||||
rarfile==4.2
|
||||
referencing==0.35.1
|
||||
regex==2024.5.15
|
||||
requests==2.31.0
|
||||
rich==13.7.1
|
||||
rpds-py==0.18.1
|
||||
rsa==4.9
|
||||
ruff==0.4.7
|
||||
safetensors==0.4.3
|
||||
scenedetect==0.6.3
|
||||
scikit-image==0.23.2
|
||||
scipy==1.12.0
|
||||
semantic-version==2.10.0
|
||||
sgmllib3k==1.0.0
|
||||
shapely==2.0.4
|
||||
shellingham==1.5.4
|
||||
simple-websocket==1.0.0
|
||||
simplejson==3.19.2
|
||||
six==1.16.0
|
||||
sniffio==1.3.1
|
||||
sortedcontainers==2.4.0
|
||||
soupsieve==2.5
|
||||
SpeechRecognition==3.10.4
|
||||
stack-data==0.6.3
|
||||
starlette==0.32.0.post1
|
||||
sympy==1.12
|
||||
tenacity==8.2.3
|
||||
termcolor==2.4.0
|
||||
tifffile==2024.5.22
|
||||
tiktoken==0.4.0
|
||||
timm==1.0.3
|
||||
tokenizers==0.19.1
|
||||
tomli==2.0.1
|
||||
tomlkit==0.12.0
|
||||
toolz==0.12.1
|
||||
torch==2.1.2+cu118
|
||||
torchvision==0.16.2+cu118
|
||||
tqdm==4.66.2
|
||||
traitlets==5.14.3
|
||||
transformers==4.41.2
|
||||
typer==0.12.3
|
||||
typer-config==1.4.0
|
||||
typing_extensions==4.10.0
|
||||
tzdata==2024.1
|
||||
tzlocal==5.2
|
||||
ujson==5.9.0
|
||||
uritemplate==4.1.1
|
||||
urllib3==2.2.1
|
||||
utility==1.0
|
||||
uvicorn==0.29.0
|
||||
virtualenv==20.26.2
|
||||
visualdl==2.5.3
|
||||
watchfiles==0.21.0
|
||||
wcwidth==0.2.13
|
||||
web.py==0.62
|
||||
websocket-client==1.2.0
|
||||
websockets==11.0.3
|
||||
wechatpy==1.8.18
|
||||
Werkzeug==3.0.3
|
||||
wikipedia==1.4.0
|
||||
win32-setctime==1.1.0
|
||||
wolframalpha==5.0.0
|
||||
wsproto==1.2.0
|
||||
xlrd==2.0.1
|
||||
xmltodict==0.13.0
|
||||
xxhash==3.4.1
|
||||
yacs==0.1.8
|
||||
yapf==0.40.2
|
||||
yarl==1.9.4
|
||||
zhipuai==2.1.0.20240521
|
||||
zipp==3.18.1
|
||||
zstandard==0.22.0
|
||||
+111
-111
@@ -8,8 +8,8 @@ import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
from huggingface_hub import hf_hub_download
|
||||
from faster_whisper import WhisperModel
|
||||
# from huggingface_hub import hf_hub_download
|
||||
# from faster_whisper import WhisperModel
|
||||
import public_tools
|
||||
from pathlib import Path
|
||||
|
||||
@@ -174,111 +174,111 @@ def SplitAudio(video_out_folder, video_list):
|
||||
return mp3_list
|
||||
|
||||
|
||||
def GetText(out_folder, mp3_list):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
for mp in mp3_list:
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
# def GetText(out_folder, mp3_list):
|
||||
# text = []
|
||||
# # 先获取模型
|
||||
# print("正在下载或加载模型")
|
||||
# sys.stdout.flush()
|
||||
# model_path = Path(
|
||||
# hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="preprocessor_config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="tokenizer.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="vocabulary.json",
|
||||
# )
|
||||
# model = WhisperModel(
|
||||
# model_size_or_path=os.path.dirname(model_path),
|
||||
# device="auto",
|
||||
# local_files_only=True,
|
||||
# )
|
||||
# print("模型加载成功，开始识别")
|
||||
# sys.stdout.flush()
|
||||
# for mp in mp3_list:
|
||||
# segments, info = model.transcribe(
|
||||
# mp,
|
||||
# beam_size=5,
|
||||
# language="zh",
|
||||
# vad_filter=True,
|
||||
# vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
# )
|
||||
# tmp_text = ""
|
||||
# for segment in segments:
|
||||
# tmp_text += segment.text + "。"
|
||||
# print(mp + "识别完成")
|
||||
# sys.stdout.flush()
|
||||
# text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
print("文本全部识别成功，正在写出")
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
|
||||
print("写出完成")
|
||||
sys.stdout.flush()
|
||||
# # 数据写出
|
||||
# print("文本全部识别成功，正在写出")
|
||||
# sys.stdout.flush()
|
||||
# tools = public_tools.PublicTools()
|
||||
# tools.write_to_file(text, os.path.join(out_folder, "文案.txt"))
|
||||
# print("写出完成")
|
||||
# sys.stdout.flush()
|
||||
|
||||
|
||||
def GetTextTask(out_folder, mp, name):
|
||||
text = []
|
||||
# 先获取模型
|
||||
print("正在下载或加载模型")
|
||||
sys.stdout.flush()
|
||||
model_path = Path(
|
||||
hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="preprocessor_config.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="tokenizer.json",
|
||||
)
|
||||
hf_hub_download(
|
||||
repo_id="Systran/faster-whisper-large-v3",
|
||||
filename="vocabulary.json",
|
||||
)
|
||||
model = WhisperModel(
|
||||
model_size_or_path=os.path.dirname(model_path),
|
||||
device="auto",
|
||||
local_files_only=True,
|
||||
)
|
||||
print("模型加载成功，开始识别")
|
||||
sys.stdout.flush()
|
||||
segments, info = model.transcribe(
|
||||
mp,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
vad_filter=True,
|
||||
vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
)
|
||||
tmp_text = ""
|
||||
for segment in segments:
|
||||
tmp_text += segment.text + "。"
|
||||
print(mp + "识别完成")
|
||||
sys.stdout.flush()
|
||||
text.append(tmp_text)
|
||||
# def GetTextTask(out_folder, mp, name):
|
||||
# text = []
|
||||
# # 先获取模型
|
||||
# print("正在下载或加载模型")
|
||||
# sys.stdout.flush()
|
||||
# model_path = Path(
|
||||
# hf_hub_download(repo_id="Systran/faster-whisper-large-v3", filename="model.bin")
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="preprocessor_config.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="tokenizer.json",
|
||||
# )
|
||||
# hf_hub_download(
|
||||
# repo_id="Systran/faster-whisper-large-v3",
|
||||
# filename="vocabulary.json",
|
||||
# )
|
||||
# model = WhisperModel(
|
||||
# model_size_or_path=os.path.dirname(model_path),
|
||||
# device="auto",
|
||||
# local_files_only=True,
|
||||
# )
|
||||
# print("模型加载成功，开始识别")
|
||||
# sys.stdout.flush()
|
||||
# segments, info = model.transcribe(
|
||||
# mp,
|
||||
# beam_size=5,
|
||||
# language="zh",
|
||||
# vad_filter=True,
|
||||
# vad_parameters=dict(min_silence_duration_ms=1000),
|
||||
# )
|
||||
# tmp_text = ""
|
||||
# for segment in segments:
|
||||
# tmp_text += segment.text + "。"
|
||||
# print(mp + "识别完成")
|
||||
# sys.stdout.flush()
|
||||
# text.append(tmp_text)
|
||||
|
||||
# 数据写出
|
||||
sys.stdout.flush()
|
||||
tools = public_tools.PublicTools()
|
||||
tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
|
||||
sys.stdout.flush()
|
||||
# # 数据写出
|
||||
# sys.stdout.flush()
|
||||
# tools = public_tools.PublicTools()
|
||||
# tools.write_to_file(text, os.path.join(out_folder, name + ".txt"))
|
||||
# sys.stdout.flush()
|
||||
|
||||
|
||||
def get_fram(video_path, out_path, sensitivity):
|
||||
@@ -297,12 +297,12 @@ def get_fram(video_path, out_path, sensitivity):
|
||||
exit(0)
|
||||
|
||||
|
||||
def init(video_path, video_out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
v_l = ClipVideo(
|
||||
video_path, video_out_folder, image_out_folder, sensitivity, gpu_type
|
||||
)
|
||||
# def init(video_path, video_out_folder, image_out_folder, sensitivity, gpu_type):
|
||||
# v_l = ClipVideo(
|
||||
# video_path, video_out_folder, image_out_folder, sensitivity, gpu_type
|
||||
# )
|
||||
|
||||
# 开始分离音频
|
||||
m_l = SplitAudio(video_out_folder, v_l)
|
||||
# 开始识别字幕
|
||||
GetText(os.path.dirname(video_out_folder), m_l)
|
||||
# # 开始分离音频
|
||||
# m_l = SplitAudio(video_out_folder, v_l)
|
||||
# # 开始识别字幕
|
||||
# GetText(os.path.dirname(video_out_folder), m_l)
|
||||
|
||||
Reference in New Issue
Block a user