whisper-small-ct2 OPENAI ct2 语言识别模型实例，python环境请自行下载

import ctranslate2 import librosa import transformers import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" # Load and resample the audio file. audio, _ = librosa.load("audio.wav", sr=16000, mono=True) # Compute the features of the first 30 seconds of audio. processor = transformers.WhisperProcessor.from_pretrained("whisper-small") inputs = processor(audio, return_tensors="np", sampling_rate=16000) features = ctranslate2.StorageView.from_array(inputs.input_features) # Load the model on CPU. model = ctranslate2.models.Whisper("whisper-small-ct2") # Detect the language. results = model.detect_language(features) language, probability = results[0][0] print("Detected language %s with probability %f" % (language, probability)) # Describe the task in the prompt. # See the prompt format in https://github.com/openai/whisper. prompt = processor.tokenizer.convert_tokens_to_ids( [ "<|startoftranscript|>", language, "<|transcribe|>", "<|notimestamps|>", # Remove this token to generate timestamps. ] ) # Run generation for the 30-second window. results = model.generate(features, [prompt]) transcription = processor.decode(results[0].sequences_ids[0]) print(transcription)