Mean Average Precision over words or n-grams with speech features

Compute the Mean Average Precision (MAP) with speech features.

This is the MAP@R from equation (3) of https://arxiv.org/abs/2003.08505.

Installation

This package is available on PyPI:

pip install speech-map

It is much more efficient to use the Faiss backend for the k-NN, instead of the naive PyTorch backend. Since Faiss is not available on PyPI, you can install this package in a conda environment with your conda variant:

• CPU version:

  micromamba create -f environment-cpu.yaml

• GPU version:

  CONDA_OVERRIDE_CUDA=12.6 micromamba create -f environment-gpu.yaml

Usage

CLI

❯ python -m speech_map --help
usage: __main__.py [-h] [--pooling {MEAN,MAX,MIN,HAMMING}] [--frequency FREQUENCY] [--backend {FAISS,TORCH}] features jsonl

Mean Average Precision over n-grams / words with speech features

positional arguments:
  features              Path to the directory with pre-computed features
  jsonl                 Path to the JSONL file with annotations

options:
  -h, --help            show this help message and exit
  --pooling {MEAN,MAX,MIN,HAMMING}
                        Pooling (default: MEAN)
  --frequency FREQUENCY
                        Feature frequency in Hz (default: 50 Hz)
  --backend {FAISS,TORCH}
                        KNN (default: FAISS)

Python API

You most probably need only two functions: build_embeddings_and_labels and mean_average_precision. Use them like this:

from speech_map import build_embeddings_and_labels, mean_average_precision

embeddings, labels = build_embeddings_and_labels(path_to_features, path_to_jsonl)
print(mean_average_precision(embeddings, labels))

In this example, path_to_features is a path to a directory containing features stored in individual PyTorch tensor files, and path_to_jsonl is the path to the JSONL annotations file.

You can also use those functions in a more advanced setting like this:

from speech_map import Pooling, build_embeddings_and_labels, mean_average_precision

embeddings, labels = build_embeddings_and_labels(
    path_to_features,
    path_to_jsonl,
    pooling=Pooling.MAX,
    frequency=100,
    feature_maker=my_model,
    file_extension=".wav",
)
print(mean_average_precision(embeddings, labels))

This is a minimal package, and you can easily go through the code in src/speech_map/core.py if you want to check the details.

Data

We distribute in data the words and n-grams annotations for LibriSpeech evaluation subsets. Decompress them with zstd.

We have not used the n-grams annotations recently; there is probably too much samples and they would need some clever subsampling.

References

MAP for speech representations:

@inproceedings{carlin11_interspeech,
  title     = {Rapid evaluation of speech representations for spoken term discovery},
  author    = {Michael A. Carlin and Samuel Thomas and Aren Jansen and Hynek Hermansky},
  year      = {2011},
  booktitle = {Interspeech 2011},
  pages     = {821--824},
  doi       = {10.21437/Interspeech.2011-304},
  issn      = {2958-1796},
}

Data and original implementation:

@inproceedings{algayres20_interspeech,
  title     = {Evaluating the Reliability of Acoustic Speech Embeddings},
  author    = {Robin Algayres and Mohamed Salah Zaiem and Benoît Sagot and Emmanuel Dupoux},
  year      = {2020},
  booktitle = {Interspeech 2020},
  pages     = {4621--4625},
  doi       = {10.21437/Interspeech.2020-2362},
  issn      = {2958-1796},
}