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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.
"""Trainable audio embeddings with the same extraction policy as HEAR."""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Any
import torch
from torch.nn.utils.rnn import pad_sequence
from transformers import PreTrainedModel
from transformers.utils import ModelOutput
from .adapters import SpectrogramPatchAdapter, WaveformConvAdapter
from .adapters import resolve_adapter_spec
from .configuration_audio import AudioEmbeddingConfig
@dataclass
class AudioEmbeddingOutput(ModelOutput):
last_hidden_state: torch.Tensor | None = None
pooler_output: torch.Tensor | None = None
attention_mask: torch.Tensor | None = None
timestamps_ms: torch.Tensor | None = None
class AudioEmbeddingModel(PreTrainedModel):
config_class = AudioEmbeddingConfig
base_model_prefix = "adapter"
main_input_name = "input_values"
# RoPE modules are shared by every attention block. Save all buffer keys,
# so loading does not need special tied-buffer handling.
_supports_assign_param_buffer = False
def __init__(self, config: AudioEmbeddingConfig) -> None:
super().__init__(config)
adapter_config = config.to_adapter_config()
# Transformers 5 loads under a default meta device, but torchaudio's
# filter-bank constructors need real values. Build on CPU; HF subsequently
# loads the checkpoint tensors onto the requested device/dtype.
with torch.device("cpu"):
spec = resolve_adapter_spec(adapter_config)
adapter_type = (
SpectrogramPatchAdapter
if spec.adapter_key == "spectrogram_patch"
else WaveformConvAdapter
)
self.adapter = adapter_type(adapter_config, spec)
self.post_init()
def _init_weights(self, module: torch.nn.Module) -> None:
"""Preserve initialization performed by the research components themselves."""
def save_pretrained(
self, save_directory: str | Path, *args: Any, **kwargs: Any
) -> None:
if kwargs.get("state_dict") is None:
kwargs["state_dict"] = {
key: value.detach().clone().contiguous()
for key, value in self.state_dict().items()
}
return super().save_pretrained(save_directory, *args, **kwargs)
def forward(
self,
input_values: torch.Tensor,
attention_mask: torch.Tensor | None = None,
return_dict: bool | None = None,
) -> AudioEmbeddingOutput | tuple[torch.Tensor, ...]:
if input_values.ndim != 2 or min(input_values.shape) <= 0:
raise ValueError(
"input_values must have shape [batch, samples] with nonempty axes"
)
if (
not input_values.is_floating_point()
or not torch.isfinite(input_values).all()
):
raise ValueError(
"input_values must contain finite floating-point waveforms"
)
if attention_mask is None:
lengths = [input_values.shape[1]] * input_values.shape[0]
else:
if attention_mask.shape != input_values.shape:
raise ValueError(
"attention_mask must have the same shape as input_values"
)
if not torch.all((attention_mask == 0) | (attention_mask == 1)):
raise ValueError("attention_mask must contain only zeros and ones")
lengths_tensor = attention_mask.long().sum(dim=1)
expected = (
torch.arange(input_values.shape[1], device=attention_mask.device)[None]
< lengths_tensor[:, None]
)
if not torch.equal(attention_mask.bool(), expected) or torch.any(
lengths_tensor == 0
):
raise ValueError(
"attention_mask must describe nonempty, right-padded waveforms"
)
lengths = lengths_tensor.tolist()
# Clear non-buffer RoPE caches between calls: inference-mode caches cannot
# be reused for autograd, and .to(device/dtype) does not move these caches.
rope = self.adapter.encoder.rope
if rope is not None:
for name in ("cached_cos_sin", "cached_cos_sin_h", "cached_cos_sin_w"):
if hasattr(rope, name):
setattr(rope, name, None)
outputs = []
for waveform, length in zip(input_values, lengths):
if isinstance(self.adapter, SpectrogramPatchAdapter):
minimum = self.adapter.spectrogram.mel_spec.n_fft // 2 + 1
if length < minimum:
raise ValueError(
f"Audio requires at least {minimum} samples for this spectrogram; got {length}"
)
outputs.append(
self.adapter.extract(
waveform[:length], preset_name=self.config.extraction_preset
)
)
hidden = pad_sequence(
[item.timestamp_embeddings for item in outputs], batch_first=True
)
frame_lengths = torch.tensor(
[item.timestamp_embeddings.shape[0] for item in outputs],
device=hidden.device,
)
frame_mask = (
torch.arange(hidden.shape[1], device=hidden.device)[None]
< frame_lengths[:, None]
)
result = AudioEmbeddingOutput(
last_hidden_state=hidden,
pooler_output=torch.stack([item.scene_embedding for item in outputs]),
attention_mask=frame_mask.long(),
timestamps_ms=pad_sequence(
[item.timestamps_ms for item in outputs],
batch_first=True,
padding_value=-1.0,
),
)
return (
result
if (self.config.return_dict if return_dict is None else return_dict)
else result.to_tuple()
)
AudioEmbeddingModel.register_for_auto_class("AutoModel")
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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