Faster-Whisper 本地实时语音识别部署与实战指南

前言

要实现类似微信的语音输入功能，云端 API 虽准但涉及隐私和成本，本地模型则是免费且离线的最佳选择。这里记录一下使用 Faster-Whisper 进行本地实时语音转文本的部署过程。

一、环境准备

在虚拟环境中安装核心库。注意，标准库名为 pyaudio，而非某些教程中误写的变体。

pip install faster-whisper pyaudio

二、模型下载

Faster-Whisper 支持多种模型规格，从轻量级的 Tiny 到效果最好的 Large-v3。若服务器无法直连 Hugging Face，可手动下载以下文件至指定目录：

• config.json
• model.bin
• tokenizer.json
• vocabulary.json
• preprocessor_config.json

三、录音与转录脚本

下面是一个完整的 Python 示例，实现了持续录音、切片处理及实时转录。代码中使用了线程来避免录音阻塞，并启用了 VAD（语音活动检测）过滤静音片段。

# -*- coding: utf-8 -*-
import os
import sys
import time
import wave
import tempfile
import threading
import torch
import pyaudio
from faster_whisper import WhisperModel

AUDIO_BUFFER = 5

def record_audio(p, device):
    # 创建临时 WAV 文件
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        filename = f.name
        wave_file = wave.open(filename, "wb")
        wave_file.setnchannels(int(device["maxInputChannels"]))
        wave_file.setsampwidth(p.get_sample_size(pyaudio.paInt16))
        wave_file.setframerate(int(device["defaultSampleRate"]))

        def callback():
            wave_file.writeframes(in_data)
             (in_data, pyaudio.paContinue)

        :
            stream = p.(
                =pyaudio.paInt16,
                channels=(device[]),
                rate=(device[]),
                frames_per_buffer=,
                =,
                input_device_index=device[],
                stream_callback=callback,
            )
            time.sleep(AUDIO_BUFFER)
         Exception  e:
            ()
        :
               ():
                stream.stop_stream()
                stream.close()
            wave_file.close()
         filename

 ():
    :
        
        segments, info = model.transcribe(
            filename, beam_size=, language=, vad_filter=,
            vad_parameters=(min_silence_duration_ms=)
        )
         segment  segments:
            ( % (segment.start, segment.end, segment.text))
     Exception  e:
        ()
    :
         os.path.exists(filename):
            os.remove(filename)

 ():
    ()
     torch.cuda.is_available():
        device = 
        compute_type = 
        ()
    :
        device = 
        compute_type = 
        ()

    
    model_path = 
    :
        model = WhisperModel(model_path, device=device, compute_type=compute_type, local_files_only=)
        ()
     Exception  e:
        ()
        

     pyaudio.PyAudio()  p:
        :
            default_mic = p.get_default_input_device_info()
            ()
            ( * )
            ()
             :
                filename = record_audio(p, default_mic)
                thread = threading.Thread(target=whisper_audio, args=(filename, model))
                thread.start()
         OSError:
            ()
         KeyboardInterrupt:
            ()
         Exception  e:
            ()

 __name__ == :
    main()