1、模型种类
whisper：有很多模型：tiny、base、small、medium、large等
faster_whisper:模型种类与whisper类似
2、模型安装
特别注意：whisper和faster_whisper中的模型，有两种获得方式。
①在网址：https://github.com/openai/whisper上有提示：pip install -U openai-whisper，下载结果为 .pt文件。在网址：https://github.com/SYSTRAN/faster-whisper上有提示：pip install faster-whisper，下载结果为.pt文件
②在网址：https://huggingface.co/，进行搜索 whisper，根据提示，可以下载 large-v3和large-v3-turbo，下载结果为文件，与①不同（特别注意）
3.模型运行
①按照①方法下载的模型：运行代码参考网址：https://github.com/openai/whisperhttps://github.com/openai/whisper ，示例如下：
import whisper
model = whisper.load_model("turbo")
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio("audio.mp3")
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
# print the recognized text
print(result.text)
以上代码，要求# load audio and pad/trim it to fit 30 seconds，提示：whisper模型要求一句话进行识别，如果音频时间太短，可能识别结果不准确，具体请自行尝试。
②按照①方法下载的模型：运行代码参考网址：https://github.com/SYSTRAN/faster-whisperhttps://github.com/SYSTRAN/faster-whisper ，示例如下：
from faster_whisper import WhisperModel
model_size = "large-v3"
# Run on GPU with FP16
model = WhisperModel(model_size, device="cuda", compute_type="float16")
# 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("audio.mp3", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
