开源语音识别faster-whisper部署教程

1. 资源下载

模型下载地址：

markdown 复制代码

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

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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

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conda create -n faster_whisper python=3.9 # python版本要求3.8到3.11

激活虚拟环境

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conda activate faster_whisper

安装faster-whisper依赖

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pip install faster-whisper

3. 运行

执行完以上步骤后，我们可以写代码了

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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：

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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

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