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#
# Copyright (c) 2026 audio-embeddings contributors
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""Serializable architecture settings; no Hydra execution at inference time."""
from __future__ import annotations
from copy import deepcopy
from typing import Any
from transformers import PretrainedConfig
from .adapters import SPECTROGRAM_TARGETS, WAVEFORM_TARGETS
from .extraction import get_preset
class AudioEmbeddingConfig(PretrainedConfig):
model_type = "audio_embeddings"
def __init__(
self,
model_target: str = "src.models.best_rq2_module.BestRQ2Module",
encoder_kwargs: dict[str, Any] | None = None,
spectrogram_kwargs: dict[str, Any] | None = None,
patch_embed_kwargs: dict[str, Any] | None = None,
feature_encoder_kwargs: dict[str, Any] | None = None,
sampling_rate: int = 16000,
spectrogram_adjustment_mode: str = "pad",
extraction_preset: str = "overlap50_two_phase",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if model_target not in {*SPECTROGRAM_TARGETS, *WAVEFORM_TARGETS}:
raise ValueError(f"Unsupported model_target: {model_target!r}")
if not isinstance(sampling_rate, int) or sampling_rate <= 0:
raise ValueError("sampling_rate must be a positive integer")
if spectrogram_adjustment_mode not in {"pad", "truncate"}:
raise ValueError("spectrogram_adjustment_mode must be 'pad' or 'truncate'")
get_preset(extraction_preset)
self.model_target = model_target
self.encoder_kwargs = deepcopy(
encoder_kwargs
or {
"embed_dim": 768,
"num_patches": 128,
}
)
self.spectrogram_kwargs = deepcopy(spectrogram_kwargs or {})
self.patch_embed_kwargs = deepcopy(patch_embed_kwargs or {})
self.feature_encoder_kwargs = deepcopy(feature_encoder_kwargs or {})
# Remote configuration must contain data, never Python expressions.
if isinstance(self.feature_encoder_kwargs.get("conv_layers_spec"), str):
raise ValueError(
"conv_layers_spec must be a JSON list, not a Python expression"
)
self.sampling_rate = sampling_rate
self.spectrogram_adjustment_mode = spectrogram_adjustment_mode
self.extraction_preset = extraction_preset
self.hidden_size = int(self.encoder_kwargs["embed_dim"])
if self.hidden_size <= 0:
raise ValueError("encoder_kwargs.embed_dim must be positive")
def to_adapter_config(self) -> dict[str, Any]:
net: dict[str, Any] = {"encoder": deepcopy(self.encoder_kwargs)}
if self.model_target in SPECTROGRAM_TARGETS:
net.update(
spectrogram=deepcopy(self.spectrogram_kwargs),
patch_embed=deepcopy(self.patch_embed_kwargs),
)
else:
net.update(
feature_encoder=deepcopy(self.feature_encoder_kwargs),
sampling={"sample_rate": self.sampling_rate},
)
return {
"data": {"target_sample_rate": self.sampling_rate},
"model": {
"_target_": self.model_target,
"spectrogram_adjustment_mode": self.spectrogram_adjustment_mode,
"net": net,
},
}
AudioEmbeddingConfig.register_for_auto_class()
from .adapters import __name__ as _bundled_adapters # noqa: F401
from .extraction import __name__ as _bundled_extraction # noqa: F401
from .patch_embed import __name__ as _bundled_patch_embed # noqa: F401
from .spectrogram import __name__ as _bundled_spectrogram # noqa: F401
from .vit import __name__ as _bundled_vit # noqa: F401
from .rope import __name__ as _bundled_rope # noqa: F401
from .transformer import __name__ as _bundled_transformer # noqa: F401
from .normalization import __name__ as _bundled_normalization # noqa: F401
from .waveform_feature_encoder import __name__ as _bundled_waveform_feature_encoder # noqa: F401
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