User guide

ZeroSpeech 2021 ABX task

Python API

import torch
from fastabx import Dataset, Score, Subsampler, Task


item, features = "./triphone-dev-clean.item", "./features"
frequency, maker = 50, torch.load

dataset = Dataset.from_item(item, features, frequency, maker)
task = Task(dataset, on="#phone", by=["next-phone", "prev-phone", "speaker"], subsampler=Subsampler())
score = Score(task, "cosine")
print(score.collapse(levels=[("next-phone", "prev-phone"), "speaker"]))

CLI

❯ 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 https://github.com/pola-rs/polars 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 https://github.com/zerospeech/libri-light-abx2. 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 in 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.