A Python library for audio data augmentation. Useful for making audio ML models work well in the real world, not just in the lab. - iver56/audiomentations
We don't want data augmentation to be a bottleneck in model training speed. Here is a comparison of the time it takes to run 1D convolution: Note: Not all transforms have a speedup this impressive compared to CPU. In general, running audio data augmentation on GPU is not always the best...
GitHub - felixchenfy/Speech-Commands-Classification-by-LSTM-PyTorch: Classification of 11 types of audio clips using MFCCs features and LSTM. Pretrained on Speech Command Dataset with intensive data augmentation. https://arxiv.org/pdf/1610.00087.pdf birdsong-cnn-pytorch https://github.com/yeyupiaoli...
【Python音频数据增广库】’Audiomentations - A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.' by Iver Jordal GitHub: http://t.cn/AiNF6qCG
Now that TorchAudio is installed, you can import it into your Python scripts or Jupyter notebooks to start using its functionality: import torchaudio Step 5: Load and Process Audio Data With TorchAudio, you can now load and process audio data using its various functions and transformations. For...
Fine-tuning via a NeMo pretrained model name# A model can be finetuned from an pre-trained NeMo model using the following command: pythonexamples/audio/audio_to_audio_train.py\--config-path=<pathtodirofconfigs>--config-name=<nameofconfigwithout.yaml>)\model.train_ds.manifest_filepath="<pa...
Input Augmentation:在循环层每一个时间步的输入拼接Site-Specific Speaker Embeddings。 Feature Gating:将深度网络层的激活值与Site-Specific Speaker Embeddings元素乘。 实验 数据: VCTK:44h,109 speakers 内部数据集:238h有声书,477 speakers,每人平均30min的音频 ...
Python Operators Defining an operation Defining a pipeline Running the pipeline and visualizing the results Variety of Python Operators Limitations of Python operators Processing GPU data with Python Operators CuPy operations Defining a pipeline Running the pipeline and visualizing the results Advanced: devic...
in these modalities can vary significantly and can be sensitive to the specific techniques employed in each domain. For instance, while certain augmentation techniques in computer vision can substantially enhance performance, their application to audio or EEG data may not consistently yield similar ...
Python Synthetic sounds datasets and real sounds datasets of waterflow sounds for the repo 'Neural-Texture-Sound-Synthesis-with-physically-driven-continuous-controls'. data-augmentationaudio-segmentationsynthetic-dataset-generationaudio-datasetssynthetic-datasetreal-datasetaudio-dataset-for-machine-learning ...