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Added Wavlm models (WIP) #2219

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1cf6859
Create wavlm_models.py
alisartazkhan Mar 3, 2025
0d5223f
Update overview.py
alisartazkhan Mar 3, 2025
217ceb7
Update mteb/models/wavlm_models.py
alisartazkhan Mar 3, 2025
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2 changes: 2 additions & 0 deletions mteb/models/overview.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,7 @@
voyage_models,
voyage_v,
wav2vec_models,
wavlm_models
)

logger = logging.getLogger(__name__)
Expand Down Expand Up @@ -138,6 +139,7 @@
voyage_models,
fa_models,
wav2vec_models,
wavlm_models
]
MODEL_REGISTRY = {}

Expand Down
271 changes: 271 additions & 0 deletions mteb/models/wavlm_models.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,271 @@
from __future__ import annotations

from functools import partial

import numpy as np
import torch
from datasets import Audio
from transformers import Wav2Vec2FeatureExtractor, WavLMModel

from mteb.encoder_interface import AudioEncoder, PromptType
from mteb.model_meta import ModelMeta


class WavlmWrapper(AudioEncoder):
def __init__(
self,
model_name: str,
model_revision: str,
device: str | None = None,
**kwargs,
):
super().__init__(device=device, **kwargs)
self.model_name = model_name
self.model_revision = model_revision

self.model = WavLMModel.from_pretrained(
self.model_name, revision=self.model_revision
)
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
self.model_name,
# revision=self.model_revision
)
self.embed_dim = self.model.config.hidden_size

if device:
self.model = self.model.to(device)

def get_audio_embeddings(
self, audio_files: list[Audio] | Audio, batch_size: int = 32, **kwargs
) -> np.ndarray:
if not isinstance(audio_files, list):
audio_files = [audio_files]

all_embeddings = []

for i in range(0, len(audio_files), batch_size):
batch = audio_files[i : i + batch_size]

audio_data = [file["array"] for file in batch]
sampling_rates = [file["sampling_rate"] for file in batch]

# Preprocess batch
inputs = self.feature_extractor(
audio_data,
sampling_rate=sampling_rates[0],
padding=True,
return_tensors="pt",
)

if hasattr(self, "device") and self.device:
inputs = {k: v.to(self.device) for k, v in inputs.items()}

# Get embeddings
with torch.no_grad():
outputs = self.model(
input_values=inputs["input_values"],
output_hidden_states=True,
return_dict=True,
)

hidden_states = outputs.hidden_states[-1]
batch_embeddings = hidden_states.mean(dim=1).cpu().numpy()
all_embeddings.append(batch_embeddings)

return np.vstack(all_embeddings)

def encode(
self,
audio_files: list[Audio],
*,
task_name: str,
prompt_type: PromptType | None = None,
batch_size: int = 32,
**kwargs,
) -> np.ndarray:
return self.get_audio_embeddings(audio_files, batch_size=batch_size, **kwargs)


wavlm_base = ModelMeta(
loader=partial(
WavlmWrapper,
model_name="microsoft/wavlm-base",
model_revision="efa81aae7ff777e464159e0f877d54eac5b84f81",
),
name="microsoft/wavlm-base",
languages=["eng"],
open_weights=True,
revision="efa81aae7ff777e464159e0f877d54eac5b84f81",
release_date="2022-07-19",
max_tokens=float("inf"),
n_parameters=94_700_000,
memory_usage_mb=361,
embed_dim=768,
license="MIT",
reference="https://huggingface.co/microsoft/wavlm-base",
similarity_fn_name="cosine",
framework=["PyTorch"],
use_instructions=False,
public_training_code=None,
public_training_data=None,
training_datasets=None,
modalities=["audio"],
)

wavlm_base_sd = ModelMeta(
loader=partial(
WavlmWrapper,
model_name="microsoft/wavlm-base-sd",
model_revision="fe13cca7e592cf0e11287cfede24e6999ac7dc4e",
),
name="microsoft/wavlm-base-sd",
languages=["eng"],
open_weights=True,
revision="fe13cca7e592cf0e11287cfede24e6999ac7dc4e",
release_date="2022-07-19",
max_tokens=float("inf"),
n_parameters=94_700_000,
memory_usage_mb=361,
embed_dim=768,
license="MIT",
reference="https://huggingface.co/microsoft/wavlm-base-sd",
similarity_fn_name="cosine",
framework=["PyTorch"],
use_instructions=False,
public_training_code=None,
public_training_data=None,
training_datasets=None,
modalities=["audio"],
)

wavlm_base_plus = ModelMeta(
loader=partial(
WavlmWrapper,
model_name="microsoft/wavlm-base-plus",
model_revision="4c66d4806a428f2e922ccfa1a962776e232d487b",
),
name="microsoft/wavlm-base-plus",
languages=["eng"],
open_weights=True,
revision="4c66d4806a428f2e922ccfa1a962776e232d487b",
release_date="2022-07-19",
max_tokens=float("inf"),
n_parameters=94_700_000,
memory_usage_mb=361,
embed_dim=768,
license="MIT",
reference="https://huggingface.co/microsoft/wavlm-base-plus",
similarity_fn_name="cosine",
framework=["PyTorch"],
use_instructions=False,
public_training_code=None,
public_training_data=None,
training_datasets=None,
modalities=["audio"],
)

wavlm_base_plus_sv = ModelMeta(
loader=partial(
WavlmWrapper,
model_name="microsoft/wavlm-base-plus-sv",
model_revision="feb593a6c23c1cc3d9510425c29b0a14d2b07b1e",
),
name="microsoft/wavlm-base-plus-sv",
languages=["eng"],
open_weights=True,
revision="feb593a6c23c1cc3d9510425c29b0a14d2b07b1e",
release_date="2022-07-19",
max_tokens=float("inf"),
n_parameters=94_700_000,
memory_usage_mb=361,
embed_dim=768,
license="MIT",
reference="https://huggingface.co/microsoft/wavlm-base-plus-sv",
similarity_fn_name="cosine",
framework=["PyTorch"],
use_instructions=False,
public_training_code=None,
public_training_data=None,
training_datasets=None,
modalities=["audio"],
)

wavlm_base_plus_sd = ModelMeta(
loader=partial(
WavlmWrapper,
model_name="microsoft/wavlm-base-plus-sd",
model_revision="5bd86f0662bd55704109a794c6a1b1790ea0f91a",
),
name="microsoft/wavlm-base-plus-sd",
languages=["eng"],
open_weights=True,
revision="5bd86f0662bd55704109a794c6a1b1790ea0f91a",
release_date="2022-07-19",
max_tokens=float("inf"),
n_parameters=94_700_000,
memory_usage_mb=361,
embed_dim=768,
license="MIT",
reference="https://huggingface.co/microsoft/wavlm-base-plus-sd",
similarity_fn_name="cosine",
framework=["PyTorch"],
use_instructions=False,
public_training_code=None,
public_training_data=None,
training_datasets=None,
modalities=["audio"],
)


wavlm_base_sv = ModelMeta(
loader=partial(
WavlmWrapper,
model_name="microsoft/wavlm-base-sv",
model_revision="0a23162ffc49adcf42bdf836a00cb2eb45af3601",
),
name="microsoft/wavlm-base-sv",
languages=["eng"],
open_weights=True,
revision="0a23162ffc49adcf42bdf836a00cb2eb45af3601",
release_date="2022-07-19",
max_tokens=float("inf"),
n_parameters=94_700_000,
memory_usage_mb=361,
embed_dim=768,
license="MIT",
reference="https://huggingface.co/microsoft/wavlm-base-sv",
similarity_fn_name="cosine",
framework=["PyTorch"],
use_instructions=False,
public_training_code=None,
public_training_data=None,
training_datasets=None,
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Can you add annotation of training_datasets?

modalities=["audio"],
)


wavlm_large = ModelMeta(
loader=partial(
WavlmWrapper,
model_name="microsoft/wavlm-large",
model_revision="c1423ed94bb01d80a3f5ce5bc39f6026a0f4828c",
),
name="microsoft/wavlm-large",
languages=["eng"],
open_weights=True,
revision="c1423ed94bb01d80a3f5ce5bc39f6026a0f4828c",
release_date="2022-07-19",
max_tokens=float("inf"),
n_parameters=316_620_000,
memory_usage_mb=1208,
embed_dim=1024,
license="MIT",
reference="https://huggingface.co/microsoft/wavlm-large",
similarity_fn_name="cosine",
framework=["PyTorch"],
use_instructions=False,
public_training_code=None,
public_training_data=None,
training_datasets=None,
modalities=["audio"],
)
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