Updated wavlm and whisper models to fit maeb structure

[alisartazkhan]

Following Structure from Silky's w2v2 model file: https://github.com/embeddings-benchmark/mteb/blob/maeb/mteb/models/wav2vec2_models.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.

Run command:

import mteb
model_name = "microsoft/wavlm-large"
model = mteb.get_model(model_name=model_name)
print("model loaded")
tasks = mteb.get_tasks(tasks=["BeijingOpera"])
print("task loaded")
evaluation = mteb.MTEB(tasks=tasks)
results = evaluation.run(model)
print("eval complete")

[KennethEnevoldsen]

We need some linting and there seems to be a failing test