User guide

If you are coming from ABXpy or Libri-light / ZeroSpeech 2021 ABX, see this page.

Python API

The library provides one function that can be used out of the box: zerospeech_abx. This function computes the triphone or phoneme ABX, similarly as in past ZeroSpeech challenges. It is also available through a command line interface.

The main interface of the library consists of three classes: Dataset, Task, and Score. The Dataset is a simple wrapper to the underlying corpus: it is made of labels and of a way to access the representations. We provide several class methods to create a Dataset from arrays, CSV files, or using an item file and a function to extract representations.

from fastabx import Dataset

item, features, frequency = "./triphone-dev-clean.item", "./hubert-l11-dev-clean", 50
dataset = Dataset.from_item(item, features, frequency)

The ABX Task is build given a Dataset and the ON, BY and ACROSS conditions. It efficiently pre-computes all cell specifications using the lazy operations of the Polars library. The Task is an iterable where each member is an instance of a Cell. A Cell contains all instances of \(a\), \(b\), and \(x\) that satisfy the specified conditions for a particular value.

from fastabx import Task

task = Task(dataset, on="#phone", by=["next-phone", "prev-phone", "speaker"])

print(len(task))
# 117927
print(task[0])
# Cell(
#         ON(#phone_ax = AO, #phone_b = IH)
#         BY(next-phone_abx = NG)
#         BY(prev-phone_abx = L)
#         BY(speaker_abx = 6295)
# )

To control the size and number of cells, a Task can be instantiated with an additional Subsampler. The Subsampler implements the two subsampling methods done in Libri-Light. First, it can cap the number of \(a\), \(b\) and \(x\) independently in each cell (with max_size_group). Second, when ACROSS conditions are specified, it can limit the number of distinct values that \(x\) can take for the ON attribute (with max_x_across).

from fastabx import Subsampler, Task

task = Task(dataset, on="#phone", by=["next-phone", "prev-phone"], across=["speaker"])
print(len(task))
# 5437695

subsampler = Subsampler(max_size_group=10, max_x_across=5)
task = Task(
     dataset,
     on="#phone",
     by=["next-phone", "prev-phone"],
     across=["speaker"],
     subsampler=subsampler,
)
print(len(task))
# 1346484

Once the task is built, the actual evaluation is conducted using the Score class. A Score is instantiated with the Task and the name of a distance (such as “angular”, “euclidean”, etc.). After the scores of each Cell have been computed, they can be aggregated using the collapse method. The user can either obtain a final score by weighting according to cell size (using weigted=True), or they can aggregate by averaging across subsequent attributes (with levels=...).

from fastabx import Score

score = Score(task, "angular")
abx_error_rate = score.collapse(levels=[("prev-phone", "next-phone"), "speaker"])
print(abx_error_rate)
# 0.033783210627340875

CLI

This package also provides a command line interface, a simple wrapper that exposes the zerospeech_abx function.

❯ fastabx --help
usage: fastabx [-h] [--frequency FREQUENCY] [--speaker {within,across}] [--context {within,any}]
               [--distance {euclidean,cosine,angular,kl,kl_symmetric,identical,null}] [--max-size-group MAX_SIZE_GROUP]
               [--max-x-across MAX_X_ACROSS] [--seed SEED]
               item features

ZeroSpeech ABX

positional arguments:
  item                  Path to the item file
  features              Path to the features directory

options:
  -h, --help            show this help message and exit
  --frequency FREQUENCY
                        Feature frequency (in Hz) (default: 50)
  --speaker {within,across}
                        Speaker mode (default: within)
  --context {within,any}
                        Context mode (default: within)
  --distance {euclidean,cosine,angular,kl,kl_symmetric,identical,null}
                        Distance (default: cosine)
  --max-size-group MAX_SIZE_GROUP
                        Maximum number of A, B, or X in a cell (default: 10)
  --max-x-across MAX_X_ACROSS
                        With 'across', maximum number of X given (A, B) (default: 5)
  --seed SEED           Random seed (default: 0)

Motivation

  1. Simple and generic API

  2. As fast as possible

This library aims to be as clear and minimal as possible to make its maintenance easy, and the code readable and quick to understand. It should be easy to incorporate different components into one’s personal code, and not just use it as a black box.

At the same time, it must be as fast as possible to calculate the ABX, both in forming triplets and calculating the distances themselves, while offering the possibility to use any configuration of ON, BY, and ACROSS conditions.

The idea of creating yet again a new ABX library comes from the realization that the polars library efficiently and easily solves the difficulties associated with creating triplets.

We can write the creation of the triplets as some “join” and “select” operations on dataframes, then some “filter” for subsampling. With polars, the full query is built lazily and then processed end-to-end. The backend will run several optimizations for us, and can even run on GPU. We don’t have to worry anymore about how to built the triplets in a clever manner.

The computation of the distances is similar as what is done in Libri-Light and ZeroSpeech 2021. The distances functions have been modified to be more memory efficient by avoiding large broadcastings. The important change is that now the DTW is computed with a PyTorch C++ extension, with CPU (using OpenMP) and CUDA backends. The speedup is most noticeable on large cells, such as those obtained when running the Phoneme ABX without context conditions.