def compute_mean_std(manifest_path, num_samples, output_path): normalizer = FeatureNormalizer(mean_std_filepath=None, manifest_path=manifest_path, num_samples=num_samples) # 将计算的结果保存的文件中 normalizer.write_to_file(output_path) print('计算的均值和标准值已保存在 %s!' % output_path)
def compute_mean_std(manifest_path, num_samples, output_path): # 随机取指定的数量计算平均值归一化 normalizer = FeatureNormalizer(mean_std_filepath=None, manifest_path=manifest_path, num_samples=num_samples, num_workers=args.num_workers) # 将计算的结果保存的文件中 normalizer.write_to_file(output_path) print('计算的均值和标准值已保存在 %s!' % output_path)
def main(): print_arguments(args) audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type) def augment_and_featurize(audio_segment): return audio_featurizer.featurize(audio_segment) normalizer = FeatureNormalizer( mean_std_filepath=None, manifest_path=args.manifest_path, featurize_func=augment_and_featurize, num_samples=args.num_samples) normalizer.write_to_file(args.output_path)
def main(): print_arguments(args) augmentation_pipeline = AugmentationPipeline('{}') audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type) def augment_and_featurize(audio_segment): augmentation_pipeline.transform_audio(audio_segment) return audio_featurizer.featurize(audio_segment) # 随机取指定的数量计算平均值归一化 normalizer = FeatureNormalizer(mean_std_filepath=None, manifest_path=args.manifest_path, featurize_func=augment_and_featurize, num_samples=args.num_samples) # 将计算的结果保存的文件中 normalizer.write_to_file(args.output_path)