User guide¶
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.
from fastabx import zerospeech_abx
item, features, frequency = "./triphone-dev-clean.item", "./hubert-l11-dev-clean", 50
abx_error_rate = zerospeech_abx(
item,
features,
max_size_group=10,
speaker="within",
context="within",
distance="angular",
frequency=frequency,
seed=0,
)
print(abx_error_rate)
# 0.033783210627340875
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)
When the labels and the features both live in a single table (a CSV file, or a polars or pandas DataFrame),
use Dataset.from_dataframe and point feature_columns at the columns holding the features.
import polars as pl
from fastabx import Dataset
df = pl.DataFrame(
{
"label": ["a", "a", "b", "b"],
"x": [0.1, 0.2, 1.0, 1.1],
"y": [0.0, 0.1, 0.9, 1.2],
}
)
dataset = Dataset.from_dataframe(df, feature_columns=["x", "y"])
The ABX Task is built 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 weighted=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] [-V] --max-size-group MAX_SIZE_GROUP
[--max-x-across MAX_X_ACROSS] [--frequency FREQUENCY]
[--speaker {within,across}] [--context {within,any}]
[--distance {angular,euclidean,kl_symmetric,identical}]
[--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
-V, --version show program's version number and exit
--max-size-group MAX_SIZE_GROUP
Maximum number of A, B, or X in a cell. Set to 10 in
the original ZeroSpeech ABX. Disabled if negative
value. (default: None)
--max-x-across MAX_X_ACROSS
With 'across', maximum number of X given (A, B). Set
to 5 in the original ZeroSpeech ABX. Disabled if
negative value. (default: None)
--frequency FREQUENCY
Feature frequency (in Hz) (default: 50)
--speaker {within,across}
Speaker mode (default: within)
--context {within,any}
Context mode (default: within)
--distance {angular,euclidean,kl_symmetric,identical}
Distance (default: angular)
--seed SEED Random seed (default: 0)
Motivation¶
Simple and generic API
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.