Added Wav2Vec model, voice clustering task, VoxCeleb dataset subset (…

[sufen-f]

…embeddings-benchmark#2175)

• Added wav2vec model wrapper

• Added four w2v variants

• Update wav2vec_models.py

• Removed run.py test script

• Added subTask with small sample of dataset for testing

• Removed test portion of VoiceGender.py task

• add commit hash and bibtex

• make lint

• update models

• fix circular import

• make VoiceGender discoverable in get_tasks

• add a2a as category for clustering

• specify latest commit hash

• revert linting changes

• Based on feedback for model: updated w2v2 revisions and added torchaudio to .toml file

• Added Bibtex for dataset, set data to be test instead of training, shortened task_subtype

• Changed task from Voice Gender Clustering to Gender Clustering.

• Fixed mock audio clustering tests

• Added dataset metadata

• Linted

• Passed revision into the w2v2 loader

• passed lint check

• Linted

• Update VoiceGender.py

---

Code Quality

• Code Formatted: Format the code using make lint to maintain consistent style.

Documentation

• Updated Documentation: Add or update documentation to reflect the changes introduced in this PR.

Testing

• New Tests Added: Write tests to cover new functionality. Validate with make test-with-coverage.
• Tests Passed: Run tests locally using make test or make test-with-coverage to ensure no existing functionality is broken.

Adding datasets checklist

Reason for dataset addition: ...

• I have run the following models on the task (adding the results to the pr). These can be run using the mteb -m {model_name} -t {task_name} command.
  • sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  • intfloat/multilingual-e5-small
• I have checked that the performance is neither trivial (both models gain close to perfect scores) nor random (both models gain close to random scores).
• If the dataset is too big (e.g. >2048 examples), considering using self.stratified_subsampling() under dataset_transform()
• I have filled out the metadata object in the dataset file (find documentation on it here).
• Run tests locally to make sure nothing is broken using make test.
• Run the formatter to format the code using make lint.

Adding a model checklist

• I have filled out the ModelMeta object to the extent possible
• I have ensured that my model can be loaded using
  • mteb.get_model(model_name, revision) and
  • mteb.get_model_meta(model_name, revision)
• I have tested the implementation works on a representative set of tasks.