这些标记序列可以用于模型的输入,从而进行推理。 audio.py load_audio load_audio函数的作用是将音频文件加载到内存中,并返回其采样率和音频信号数据。具体而言,该函数使用Python的wave库来读取.wav格式的音频文件,并使用scipy库中的resample函数将采样率转换为指定的采样率。最终,load_audio函数返回一个包含音频采样率...
model = whisper.load_model('tiny')tiny可以替换为上面提到的模型名称。定义语言检测器的函数 def lan_detector(audio_file): print('reading the audio file') audio = whisper.load_audio(audio_file) audio = whisper.pad_or_trim(audio) mel = whisper.log_mel_spectrogram(audio).to(model.device...
model = whisper.load_model("base") # 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).to(model.devi...
model = whisper.load_model("small") # load audio and pad/trim it to fit 30 seconds audio = whisper.load_audio("/Users/liuyue/wodfan/work/mydemo/b1.wav") audio = whisper.pad_or_trim(audio) # make log-Mel spectrogram and move to the same device as the model mel = whisper.log_mel...
对于语言识别,您首先需要使用whisper模块中的load_audio()方法加载音频。接下来,使用pad_or_trim()方法填充或修剪音频,该方法将音频文件填充或修剪到指定的持续时间。默认大小为 30 秒。 audio=whisper.load_audio("/content/harvard.wav")audio=whisper.pad_or_trim(audio) ...
audio = whisper.load_audio(audio_path) 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).to(model.device) # detect the spoken language
model=whisper.load_model('tiny') tiny可以替换为上面提到的模型名称。 定义语言检测器的函数 deflan_detector(audio_file): print('reading the audio file') audio=whisper.load_audio(audio_file) audio=whisper.pad_or_trim(audio) mel=whisper.log_mel_spectrogram(audio).to(model.device) ...
device ="cuda:0"if torch.cuda.is_available()else"cpu"audio = whisper.load_audio(audio_path) audio = whisper.pad_or_trim(audio) model = whisper.load_model("large-v2",download_root="./whisper_model/") mel = whisper.log_mel_spectrogram(audio).to(model.device) ...
result = model.transcribe("audio.mp3") print(result["text"]) 精细化使用: import whisper model = whisper.load_model("base") # load audio and pad/trim it to fit 30 seconds audio = whisper.load_audio("audio.mp3") audio = whisper.pad_or_trim(audio) ...
model=whisper.load_model('tiny') 1. tiny可以替换为上面提到的模型名称。 定义语言检测器的函数 复制 deflan_detector(audio_file):print('reading the audio file')audio=whisper.load_audio(audio_file)audio=whisper.pad_or_trim(audio)mel=whisper.log_mel_spectrogram(audio).to(model.device)_,probs=mo...