starmen1265
29dd236bb9
- 将步骤5从 OCR 改为 Whisper 语音识别
- 更新 kb-whisper 支持自动音频转换
- 默认使用中文语言 (zh) 和 base 模型
- 更新输出格式为 {platform}_{id}.txt/.srt/.json
- 添加 FunASR 作为备用方案
404 lines
12 KiB
Python
Executable File
404 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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KB Whisper - 本地语音识别工具
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基于 Whisper.cpp 的音视频转文字
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功能：
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- 自动将视频/音频转换为 16kHz WAV 格式
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- 支持指定语言（默认中文）
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- 输出标准 JSON 格式供 KB Workflow 使用
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"""
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import argparse
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import json
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import os
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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import re
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def parse_arguments():
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"""解析命令行参数"""
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parser = argparse.ArgumentParser(
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description='KB Whisper - 本地语音识别工具',
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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示例:
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python whisper.py --input video.mp4
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python whisper.py --input video.mp4 --model base
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python whisper.py --input video.mp4 --output ./output --background
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"""
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)
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parser.add_argument(
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'--input',
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required=True,
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help='输入音视频文件路径'
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)
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parser.add_argument(
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'--model',
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default='base',
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choices=['tiny', 'base', 'small', 'medium', 'large'],
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help='Whisper 模型类型 (默认: base)'
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)
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parser.add_argument(
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'--output',
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help='输出目录 (默认: 输入文件所在目录)'
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)
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parser.add_argument(
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'--background',
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action='store_true',
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help='后台异步执行'
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)
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parser.add_argument(
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'--language',
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default='zh',
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help='指定语言 (默认: zh)，例如: en, zh, ja'
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)
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return parser.parse_args()
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def get_whisper_cpp_path():
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"""获取 whisper.cpp 路径"""
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# 优先从环境变量获取
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path = os.environ.get('WHISPER_CPP_PATH')
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if path:
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path = os.path.expanduser(path)
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if os.path.exists(path):
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return path
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# 尝试常见位置
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common_paths = [
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os.path.expanduser('~/whisper.cpp'),
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'/usr/local/bin/whisper',
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'/opt/whisper.cpp',
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]
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for cp in common_paths:
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if os.path.exists(cp):
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return cp
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raise FileNotFoundError(
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"未找到 whisper.cpp。请设置环境变量 WHISPER_CPP_PATH "
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"或安装到 ~/whisper.cpp\n"
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"安装方法: git clone https://github.com/ggerganov/whisper.cpp.git ~/whisper.cpp && cd ~/whisper.cpp && make"
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)
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def check_model(whisper_path, model):
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"""检查模型是否存在"""
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model_path = os.path.join(whisper_path, 'models', f'ggml-{model}.bin')
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if not os.path.exists(model_path):
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raise FileNotFoundError(
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f"模型文件不存在: {model_path}\n"
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f"请运行以下命令下载模型:\n"
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f"cd {whisper_path} && ./models/download-ggml-model.sh {model}"
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)
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return model_path
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def convert_to_wav(input_path, output_dir):
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"""将音视频转换为 16kHz WAV 格式
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Whisper 需要 WAV 格式的输入文件。
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"""
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input_path = os.path.abspath(input_path)
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# 如果已经是 WAV 且采样率正确，直接返回
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if input_path.lower().endswith('.wav'):
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try:
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result = subprocess.run(
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['ffprobe', '-v', 'error', '-show_entries',
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'stream=sample_rate', '-of', 'default=noprint_wrappers=1:nokey=1',
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input_path],
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capture_output=True, text=True
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)
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if result.returncode == 0 and int(result.stdout.strip()) == 16000:
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print(f"输入文件已是 16kHz WAV 格式，直接使用")
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return input_path
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except Exception:
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pass
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# 转换
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output_path = os.path.join(output_dir, f"{Path(input_path).stem}_audio.wav")
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print(f"转换音频格式: {input_path} -> {output_path}")
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try:
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subprocess.run([
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'ffmpeg', '-y', '-i', input_path,
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'-acodec', 'pcm_s16le',
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'-ar', '16000',
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'-ac', '1',
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'-loglevel', 'error',
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output_path
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], check=True, capture_output=True)
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print("音频转换完成")
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return output_path
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except subprocess.CalledProcessError as e:
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print(f"音频转换失败: {e}")
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raise RuntimeError(f"无法转换音频格式: {e}")
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except FileNotFoundError:
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raise RuntimeError("未找到 ffmpeg，请安装: sudo apt-get install ffmpeg")
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def run_whisper(whisper_path, audio_path, model, output_dir, language='zh'):
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"""运行 Whisper 语音识别
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Args:
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whisper_path: whisper.cpp 目录
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audio_path: WAV 音频文件路径
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model: 模型名称
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output_dir: 输出目录
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language: 语言代码
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"""
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# 输出文件名（使用原始输入文件名，不是 audio.wav）
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# 从 output_dir 和 audio_path 推断原始文件名
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original_stem = Path(audio_path).stem.replace('_audio', '')
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output_basename = original_stem
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# 构建命令
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cmd = [
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os.path.join(whisper_path, 'whisper-cli'),
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'-m', os.path.join(whisper_path, 'models', f'ggml-{model}.bin'),
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'-f', audio_path,
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'-of', os.path.join(output_dir, output_basename),
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'-l', language,
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'--output-txt',
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'--output-srt',
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'--output-json',
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]
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print(f"执行命令: {' '.join(cmd)}")
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# 运行
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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cwd=whisper_path,
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timeout=3600 # 1小时超时
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)
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if result.returncode != 0:
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# 检查是否有警告但仍成功
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if "error" in result.stderr.lower() and not os.path.exists(
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os.path.join(output_dir, f"{output_basename}.txt")
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):
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raise RuntimeError(
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f"Whisper 执行失败:\n"
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f"标准输出: {result.stdout}\n"
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f"错误输出: {result.stderr}"
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)
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return result
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def parse_srt_for_duration(srt_file):
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"""从 SRT 文件解析时长"""
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if not os.path.exists(srt_file):
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return 0
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with open(srt_file, 'r', encoding='utf-8') as f:
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content = f.read()
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# 找到最后一个时间戳
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pattern = r'(\d{2}:\d{2}:\d{2}),\d{3} --> \d{2}:\d{2}:\d{2},\d{3}'
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matches = re.findall(pattern, content)
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if not matches:
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return 0
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# 解析最后一个时间戳
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last_time = matches[-1]
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h, m, s = last_time.split(':')
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return int(h) * 3600 + int(m) * 60 + int(s)
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def create_workflow_json(input_file, output_dir, model, language, duration):
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"""创建标准格式的 JSON 输出，供 KB Workflow 使用"""
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input_path = Path(input_file)
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basename = input_path.stem
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txt_file = os.path.join(output_dir, f"{basename}.txt")
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srt_file = os.path.join(output_dir, f"{basename}.srt")
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json_file = os.path.join(output_dir, f"{basename}.json")
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# 读取文本内容
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text_content = ""
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if os.path.exists(txt_file):
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with open(txt_file, 'r', encoding='utf-8') as f:
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text_content = f.read().strip()
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# 解析 SRT 获取分段
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segments = []
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if os.path.exists(srt_file):
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with open(srt_file, 'r', encoding='utf-8') as f:
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content = f.read()
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# SRT 格式: 序号 -> 时间 -> 文本
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pattern = r'(\d+)\n(\d{2}:\d{2}:\d{2},\d{3}) --> (\d{2}:\d{2}:\d{2},\d{3})\n(.+?)(?=\n\n|\Z)'
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matches = re.findall(pattern, content, re.DOTALL)
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def parse_time(t):
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h, m, s_ms = t.split(':')
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s, ms = s_ms.split(',')
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return float(h) * 3600 + float(m) * 60 + float(s) + float(ms) / 1000
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for idx, (num, start, end, text) in enumerate(matches):
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segments.append({
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"id": idx,
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"start": round(parse_time(start), 2),
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"end": round(parse_time(end), 2),
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"text": text.strip()
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})
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# 构建 JSON
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workflow_json = {
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"success": True,
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"tool": "whisper",
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"model": model,
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"language": language,
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"input_file": str(input_path),
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"output_files": {
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"txt": f"{basename}.txt",
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"srt": f"{basename}.srt",
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"json": f"{basename}.json"
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},
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"duration_seconds": duration,
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"segments": segments,
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"text": text_content
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}
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# 写入 JSON
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with open(json_file, 'w', encoding='utf-8') as f:
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json.dump(workflow_json, f, ensure_ascii=False, indent=2)
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return workflow_json
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def main():
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"""主函数"""
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args = parse_arguments()
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# 检查输入文件
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input_file = os.path.abspath(args.input)
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if not os.path.exists(input_file):
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print(f"错误: 输入文件不存在: {input_file}", file=sys.stderr)
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sys.exit(1)
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# 获取输出目录
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if args.output:
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output_dir = os.path.abspath(args.output)
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else:
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output_dir = os.path.dirname(input_file)
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os.makedirs(output_dir, exist_ok=True)
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# 获取 whisper.cpp 路径
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try:
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whisper_path = get_whisper_cpp_path()
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print(f"✓ Whisper.cpp 路径: {whisper_path}")
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except FileNotFoundError as e:
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print(f"✗ 错误: {e}", file=sys.stderr)
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sys.exit(1)
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# 检查模型
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try:
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check_model(whisper_path, args.model)
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print(f"✓ 使用模型: {args.model}")
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except FileNotFoundError as e:
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print(f"✗ 错误: {e}", file=sys.stderr)
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sys.exit(1)
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# 后台执行
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if args.background:
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print("启动后台处理...")
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log_file = os.path.join(output_dir, f"{Path(input_file).stem}_whisper.log")
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with open(log_file, 'w') as log:
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subprocess.Popen(
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[sys.executable, __file__,
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'--input', input_file,
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'--model', args.model,
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'--output', output_dir,
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'--language', args.language],
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stdout=log,
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stderr=log,
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start_new_session=True
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)
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print(f"✓ 后台任务已启动")
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print(f" 日志: {log_file}")
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print(f" 输出: {output_dir}")
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result = {
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"success": True,
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"background": True,
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"status": "running",
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"log_file": log_file,
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"output_dir": output_dir
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}
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print(json.dumps(result, ensure_ascii=False, indent=2))
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return
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# 前台执行
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print(f"\n📁 处理文件: {input_file}")
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print(f"📁 输出目录: {output_dir}")
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try:
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# 1. 转换为 WAV
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print(f"\n🔄 步骤1: 转换音频格式...")
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audio_path = convert_to_wav(input_file, output_dir)
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# 2. 运行 Whisper
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print(f"\n🎙️ 步骤2: 语音识别 (语言: {args.language})...")
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run_whisper(whisper_path, audio_path, args.model, output_dir, args.language)
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# 3. 清理临时 WAV 文件
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if audio_path != input_file and audio_path.endswith('_audio.wav'):
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try:
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os.remove(audio_path)
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print("✓ 清理临时文件")
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except Exception:
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pass
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# 4. 获取时长并创建 JSON
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print(f"\n📝 步骤3: 生成输出文件...")
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basename = Path(input_file).stem
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srt_file = os.path.join(output_dir, f"{basename}.srt")
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duration = parse_srt_for_duration(srt_file)
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workflow_json = create_workflow_json(
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input_file, output_dir, args.model, args.language, duration
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)
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# 输出结果
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print(f"\n✅ 处理完成!")
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print(f" 时长: {duration} 秒")
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print(f" 分段数: {len(workflow_json['segments'])}")
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print(f"\n📄 输出文件:")
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for fmt in ['txt', 'srt', 'json']:
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fpath = os.path.join(output_dir, f"{basename}.{fmt}")
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if os.path.exists(fpath):
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size = os.path.getsize(fpath)
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print(f" • {fmt.upper()}: {fpath} ({size:,} bytes)")
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print(f"\n📋 Workflow JSON:")
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print(json.dumps({
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"success": workflow_json["success"],
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"tool": "whisper",
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"text_length": len(workflow_json["text"]),
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"segments_count": len(workflow_json["segments"])
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}, ensure_ascii=False, indent=2))
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except Exception as e:
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print(f"\n✗ 错误: {e}", file=sys.stderr)
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sys.exit(1)
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if __name__ == '__main__':
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main()
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