We support time-domain data augmentation via WavAugment and torchaudio libraries. They both leverage libsox to provide about 50 different audio effects like reverb, speed perturbation, pitch, etc.

Since WavAugment depends on libsox, it is an optional depedency for Lhotse, which can be installed using tools/ (for convenience, the script will also compile libsox from source - note that the WavAugment authors warn their library is untested on Mac).

Torchaudio also depends on libsox, but seems to provide it when installed via anaconda. This functionality is only available with PyTorch 1.7+ and torchaudio 0.7+.

Using Lhotse’s Python API, you can compose an arbitrary effect chain. On the other hand, for the CLI we provide a small number of predefined effect chains, such as pitch (pitch shifting), reverb (reverberation), and pitch_reverb_tdrop (pitch shift + reverberation + time dropout of a 50ms chunk).

Python usage


When using WavAugment or torchaudio data augmentation together with a multiprocessing executor (i.e. ProcessPoolExecutor), it is necessary to start it using the “spawn” context. Otherwise the process may hang (or terminate) on some systems due to libsox internals not handling forking well. Use: ProcessPoolExecutor(..., mp_context=multiprocessing.get_context("spawn")).

Lhotse’s FeatureExtractor and Cut offer convenience functions for feature extraction with data augmentation performed before that. These functions expose an optional parameter called augment_fn that has a signature like:

def augment_fn(audio: Union[np.ndarray, torch.Tensor], sampling_rate: int) -> np.ndarray: ...

For torchaudio we define a SoxEffectTransform class:

class lhotse.augmentation.SoxEffectTransform(effects)

Class-style wrapper for torchaudio SoX effect chains. It should be initialized with a config-like list of items that define SoX effect to be applied. It supports sampling randomized values for effect parameters through the RandomValue wrapper.

>>> audio = np.random.rand(16000)
>>> augment_fn = SoxEffectTransform(effects=[
>>>    ['reverb', 50, 50, RandomValue(0, 100)],
>>>    ['speed', RandomValue(0.9, 1.1)],
>>>    ['rate', 16000],
>>> ])
>>> augmented = augment_fn(audio, 16000)

See SoX manual or torchaudio.sox_effects.effect_names() for the list of possible effects. The parameters and the meaning of the values are explained in SoX manual/help.


Initialize self. See help(type(self)) for accurate signature.


Resolve a list of effects, replacing random distributions with samples from them. It converts every number to string to match the expectations of torchaudio.

Return type


We define a WavAugmenter class that is a thin wrapper over WavAugment. It can either be created with a predefined, or a user-supplied effect chain.

class lhotse.augmentation.WavAugmenter(effect_chain)

A wrapper class for WavAugment’s effect chain. You should construct the augment.EffectChain beforehand and pass it on to this class.

This class is only available when WavAugment is installed, as it is an optional dependency for Lhotse. It can be installed using the script in “<main-repo-directory>/tools/”

For more details on how to augment, see


Initialize self. See help(type(self)) for accurate signature.

static create_predefined(name, sampling_rate, **kwargs)

Create a WavAugmenter class with one of the predefined augmentation setups available in Lhotse. Some examples are: “pitch”, “reverb”, “pitch_reverb_tdrop”.

  • name (str) – the name of the augmentation setup.

  • sampling_rate (int) – expected sampling rate of the input audio.

Return type


CLI usage

To extract features from augmented audio, you can pass an extra --augmentation argument to lhotse feat extract.

lhotse feat extract -a pitch ...

You can create a dataset with both clean and augmented features by combining different variants of extracted features, e.g.:

lhotse feat extract audio.yml clean_feats/
lhotse feat extract -a pitch audio.yml pitch_feats/
lhotse feat extract -a reverb audio.yml reverb_feats/
lhotse yaml combine {clean,pitch,reverb}_feats/feature_manifest.yml.gz combined_feats.yml