文章目录

• 本地部署Whisper实现语言转文字
• 1.前置条件
• 2.安装chocolatey
• 3.安装ffmpeg
• 4.安装whisper
• 5.测试用例
• 6.命令行用法
• 7.本地硬件受限，借用hugging face资源进行转译

  本地部署Whisper实现语言转文字

  1.前置条件

  环境windows10 64位

  

  2.安装chocolatey

  安装chocolatey目的是安装ffpeg
  

  以管理员身份运行PowerShell

  粘贴命令

  Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
  

  安装成功打入choco

  

  安装文件夹路径

  C:\ProgramData\chocolatey
  

  3.安装ffmpeg

  choco install ffmpeg
  

  

  4.安装whisper

  pip install git+https://github.com/openai/whisper.git
  

  安装完成运行

  pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git
  

  安装完成

  

  5.测试用例

  直接命令行

  whisper yoump3.mp3
  

  

  

  6.命令行用法

  以下命令将使用medium模型转录音频文件中的语音：

  whisper audio.flac audio.mp3 audio.wav --model medium
  

  默认设置（选择模型small）非常适合转录英语。要转录包含非英语语音的音频文件，您可以使用以下选项指定语言--language：

  whisper japanese.wav --language Japanese
  

  添加--task translate后将把演讲翻译成英文：

  whisper japanese.wav --language Japanese --task translate
  

  运行以下命令查看所有可用选项：

  whisper --help
  

  7.本地硬件受限，借用hugging face资源进行转译

  进入huggingface网址，往下拉

  https://huggingface.co/openai/whisper-large-v3
  

  

  粘贴上述代码

  import torch
  from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
  from datasets import load_dataset
  device = "cuda:0" if torch.cuda.is_available() else "cpu"
  torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
  model_id = "openai/whisper-large-v3"
  model = AutoModelForSpeechSeq2Seq.from_pretrained(
      model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
  )
  model.to(device)
  processor = AutoProcessor.from_pretrained(model_id)
  pipe = pipeline(
      "automatic-speech-recognition",
      model=model,
      tokenizer=processor.tokenizer,
      feature_extractor=processor.feature_extractor,
      max_new_tokens=128,
      chunk_length_s=30,
      batch_size=16,
      return_timestamps=True,
      torch_dtype=torch_dtype,
      device=device,
  )
  dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
  sample = dataset[0]["audio"]
  result = pipe(sample)
  print(result["text"])
  

  修改本地代码，将sample修改为，需要转录的录音，接入代理；

  import torch
  from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
  from datasets import load_dataset
  import os
  os.environ['CURL_CA_BUNDLE'] = ''
  os.environ["http_proxy"] = "http://127.0.0.1:7890"
  os.environ["https_proxy"] = "http://127.0.0.1:7890"
  device = "cuda:0" if torch.cuda.is_available() else "cpu"
  torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
  model_id = "openai/whisper-large-v3"
  model = AutoModelForSpeechSeq2Seq.from_pretrained(
      model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
  )
  model.to(device)
  processor = AutoProcessor.from_pretrained(model_id)
  pipe = pipeline(
      "automatic-speech-recognition",
      model=model,
      tokenizer=processor.tokenizer,
      feature_extractor=processor.feature_extractor,
      max_new_tokens=128,
      chunk_length_s=30,
      batch_size=16,
      return_timestamps=True,
      torch_dtype=torch_dtype,
      device=device,
  )
  dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
  sample = dataset[0]["audio"]
  result = pipe("myaudio")
  print(result["text"])
  

  借用huggingface的速度，速度取决于网速