1. 资源下载
源码地址
模型下载地址：
large-v3模型：https://huggingface.co/Systran/faster-whisper-large-v3/tree/main large-v2模型：https://huggingface.co/guillaumekln/faster-whisper-large-v2/tree/main large-v2模型：https://huggingface.co/guillaumekln/faster-whisper-large-v1/tree/main medium模型：https://huggingface.co/guillaumekln/faster-whisper-medium/tree/main small模型：https://huggingface.co/guillaumekln/faster-whisper-small/tree/main base模型：https://huggingface.co/guillaumekln/faster-whisper-base/tree/main tiny模型：https://huggingface.co/guillaumekln/faster-whisper-tiny/tree/main 下载cuBLAS and cuDNN
https://github.com/Purfview/whisper-standalone-win/releases/tag/libs 2. 创建环境
在conda环境中创建python运行环境
conda create -n faster_whisper python=3.9 # python版本要求3.8到3.11 激活虚拟环境
conda activate faster_whisper 安装faster-whisper依赖
pip install faster-whisper 3. 运行
执行完以上步骤后，我们可以写代码了
from faster_whisper import WhisperModel model_size = "large-v3" path = r"D:\Works\Python\Faster_Whisper\model\small" # Run on GPU with FP16 model = WhisperModel(model_size_or_path=path, device="cuda", local_files_only=True) # or run on GPU with INT8 # model = WhisperModel(model_size, device="cuda", compute_type="int8_float16") # or run on CPU with INT8 # model = WhisperModel(model_size, device="cpu", compute_type="int8") segments, info = model.transcribe("C:\\Users\\21316\\Documents\\录音\\test.wav", beam_size=5, language="zh", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=1000)) print("Detected language '%s' with probability %f" % (info.language, info.language_probability)) for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) 说明：
local_files_only=True 表示加载本地模型 model_size_or_path=path 指定加载模型路径 device="cuda" 指定使用cuda compute_type="int8_float16" 量化为8位 language="zh" 指定音频语言 vad_filter=True 开启vad vad_parameters=dict(min_silence_duration_ms=1000) 设置vad参数 更多内容欢迎访问博客
对应视频内容欢迎访问视频
