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86dc2b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | # MIT License
#
# 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.
from __future__ import annotations
from dataclasses import dataclass
from typing import Callable
import torch
@dataclass(frozen=True)
class ExtractionPreset:
name: str
overlap: float
num_phases: int
PRESETS = {
preset.name: preset
for preset in (
ExtractionPreset("native_one_phase", 0.0, 1),
ExtractionPreset("overlap25_one_phase", 0.25, 1),
ExtractionPreset("overlap50_one_phase", 0.5, 1),
ExtractionPreset("native_two_phase", 0.0, 2),
ExtractionPreset("overlap25_two_phase", 0.25, 2),
ExtractionPreset("overlap50_two_phase", 0.5, 2),
)
}
def get_preset(name: str) -> ExtractionPreset:
try:
return PRESETS[name]
except KeyError as error:
raise ValueError(
f"Unknown extraction preset {name!r}. Expected one of {sorted(PRESETS)}"
) from error
def _window_starts(length: int, window: int, overlap: float) -> list[int]:
if length <= window:
return [0]
if not 0.0 <= overlap < 1.0:
raise ValueError(f"overlap must be in [0, 1), got {overlap}")
stride = max(1, round(window * (1.0 - overlap)))
if overlap == 0.0:
return list(range(0, length, stride))
starts = list(range(0, length - window + 1, stride))
final_start = length - window
if starts[-1] != final_start:
starts.append(final_start)
return starts
def _positive_triangular_weights(
length: int,
*,
device: torch.device,
dtype: torch.dtype,
) -> torch.Tensor:
positions = torch.arange(length, device=device, dtype=dtype)
return 1.0 - torch.abs((2.0 * positions) - (length - 1)) / (length + 1)
def fuse_context_windows(
tokens: torch.Tensor,
*,
max_context_tokens: int,
overlap: float,
encode_window: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
) -> torch.Tensor:
"""Contextualize a [frequency, time, dim] grid and fuse repeated tokens."""
if tokens.ndim != 3:
raise ValueError(
f"Expected token grid [frequency, time, dim], got {tuple(tokens.shape)}"
)
frequency, time, dimension = tokens.shape
max_time = max_context_tokens // frequency
if max_time <= 0:
raise ValueError(
f"Encoder context {max_context_tokens} cannot hold {frequency} frequency tokens"
)
if time == 0:
raise ValueError("Token grid has no time steps")
accumulator = torch.zeros_like(tokens)
denominator = torch.zeros(time, device=tokens.device, dtype=tokens.dtype)
for start in _window_starts(time, max_time, overlap):
end = min(time, start + max_time)
width = end - start
window = tokens[:, start:end, :]
flattened = window.reshape(1, frequency * width, dimension)
position_ids = torch.arange(
frequency * width,
device=tokens.device,
)
encoded = encode_window(flattened, position_ids)
if encoded.shape != flattened.shape:
raise ValueError(
"Encoder changed the token-grid shape: "
f"expected {tuple(flattened.shape)}, got {tuple(encoded.shape)}"
)
encoded_grid = encoded.reshape(frequency, width, dimension)
weights = _positive_triangular_weights(
width,
device=tokens.device,
dtype=tokens.dtype,
)
accumulator[:, start:end, :] += encoded_grid * weights.view(1, -1, 1)
denominator[start:end] += weights
if torch.any(denominator <= 0):
raise RuntimeError("At least one token received zero overlap weight")
fused = accumulator / denominator.view(1, -1, 1)
return fused.mean(dim=0)
def merge_phases(
phases: list[tuple[torch.Tensor, torch.Tensor]],
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
if not phases:
raise ValueError("At least one embedding phase is required")
embedding_dim = phases[0][0].shape[-1]
for embeddings, timestamps in phases:
if embeddings.ndim != 2 or embeddings.shape[-1] != embedding_dim:
raise ValueError("All phase embeddings must have shape [time, dimension]")
if timestamps.ndim != 1 or timestamps.shape[0] != embeddings.shape[0]:
raise ValueError("Each phase needs one timestamp per embedding")
merged_embeddings = torch.cat([phase[0] for phase in phases], dim=0)
merged_timestamps = torch.cat([phase[1] for phase in phases], dim=0)
order = torch.argsort(merged_timestamps, stable=True)
scene = torch.stack([embeddings.mean(dim=0) for embeddings, _ in phases]).mean(
dim=0
)
return merged_embeddings[order], merged_timestamps[order], scene
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