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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.
from typing import Optional
import torch
import torch.nn as nn
import torchaudio
class Spectrogram(nn.Module):
"""
Mel-frequency audio representation with optional temporal derivatives.
Args:
sample_rate (int): Sample rate of the audio.
n_fft (int): Size of FFT.
win_length (Optional[int]): Window length. Defaults to n_fft.
win_length_ms (Optional[float]): Window length in milliseconds. Overrides win_length if provided.
hop_length (Optional[int]): Hop length. Defaults to win_length // 2.
hop_length_ms (Optional[float]): Hop length in milliseconds. Overrides hop_length if provided.
n_mels (int): Number of mel filterbanks.
f_min (float): Minimum frequency.
f_max (Optional[float]): Maximum frequency.
power (float): Power of the magnitude.
representation (str): ``log_mel`` or phase-aware ``complex_mel``.
complex_log_dynamic_range_db (float): Retained complex-mel log range.
add_delta (bool): Append one temporal derivative per base channel.
add_delta_delta (bool): Append a second derivative per base channel.
delta_win_length (int): Odd regression window used for derivatives.
"""
def __init__(
self,
sample_rate: int = 32000,
n_fft: int = 4096,
win_length: Optional[int] = None,
win_length_ms: Optional[float] = None,
hop_length: Optional[int] = None,
hop_length_ms: Optional[float] = None,
n_mels: int = 128,
f_min: float = 0.0,
f_max: Optional[float] = None,
power: float = 2.0,
representation: str = "log_mel",
complex_log_dynamic_range_db: float = 80.0,
add_delta: bool = False,
add_delta_delta: bool = False,
delta_win_length: int = 5,
):
super().__init__()
if win_length is None:
if win_length_ms is None:
win_length = n_fft
else:
win_length = int(sample_rate * win_length_ms / 1000)
if hop_length is None:
if hop_length_ms is None:
hop_length = win_length // 2
else:
hop_length = int(sample_rate * hop_length_ms / 1000)
representation = representation.strip().lower().replace("-", "_")
if representation not in {"log_mel", "complex_mel"}:
raise ValueError(
"representation must be 'log_mel' or 'complex_mel', "
f"got {representation!r}"
)
if complex_log_dynamic_range_db <= 0.0:
raise ValueError(
"complex_log_dynamic_range_db must be positive, "
f"got {complex_log_dynamic_range_db}"
)
if delta_win_length < 3 or delta_win_length % 2 == 0:
raise ValueError(
f"delta_win_length must be an odd integer >= 3, got {delta_win_length}"
)
self.representation = representation
self.complex_log_dynamic_range_db = float(complex_log_dynamic_range_db)
self.add_delta = bool(add_delta)
self.add_delta_delta = bool(add_delta_delta)
self.delta_win_length = int(delta_win_length)
self.base_output_channels = 1 if representation == "log_mel" else 2
derivative_orders = int(self.add_delta) + int(self.add_delta_delta)
self.output_channels = self.base_output_channels * (1 + derivative_orders)
if representation == "log_mel":
self.mel_spec = torchaudio.transforms.MelSpectrogram(
sample_rate=sample_rate,
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
n_mels=n_mels,
f_min=f_min,
f_max=f_max,
power=power,
normalized=True,
)
self.amplitude_to_db = torchaudio.transforms.AmplitudeToDB()
else:
# Keep the historical ``mel_spec`` attribute for sample-rate and
# hop-length discovery in callbacks and HEAR adapters.
self.mel_spec = torchaudio.transforms.Spectrogram(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
power=None,
normalized=True,
)
self.mel_spec.sample_rate = sample_rate
self.amplitude_to_db = None
mel_fb = torchaudio.functional.melscale_fbanks(
n_freqs=n_fft // 2 + 1,
f_min=f_min,
f_max=float(sample_rate / 2 if f_max is None else f_max),
n_mels=n_mels,
sample_rate=sample_rate,
norm=None,
mel_scale="htk",
)
self.register_buffer("mel_fb", mel_fb)
def _complex_mel(self, x: torch.Tensor) -> torch.Tensor:
if x.shape[1] != 1:
raise ValueError(
"complex_mel expects mono waveform input [B, 1, T], "
f"got {tuple(x.shape)}"
)
complex_spec = self.mel_spec(x[:, 0])
mel_fb = self.mel_fb.to(dtype=complex_spec.dtype)
complex_mel = torch.matmul(
complex_spec.transpose(-1, -2),
mel_fb,
).transpose(-1, -2)
magnitude = complex_mel.abs()
eps = torch.finfo(magnitude.dtype).eps
reference = magnitude.amax(dim=(-2, -1), keepdim=True)
relative_magnitude = magnitude / reference.clamp_min(eps)
floor_ratio = 10.0 ** (-self.complex_log_dynamic_range_db / 20.0)
log_magnitude_db = 20.0 * torch.log10(relative_magnitude.clamp_min(floor_ratio))
log_radius = (
log_magnitude_db + self.complex_log_dynamic_range_db
) / self.complex_log_dynamic_range_db
log_radius = log_radius.clamp_(0.0, 1.0)
log_radius = torch.where(
reference > eps, log_radius, torch.zeros_like(log_radius)
)
unit_phase = complex_mel / magnitude.clamp_min(eps)
compressed = unit_phase * log_radius
return torch.stack([compressed.real, compressed.imag], dim=1)
def _append_deltas(self, spec: torch.Tensor) -> torch.Tensor:
if not self.add_delta and not self.add_delta_delta:
return spec
delta = torchaudio.functional.compute_deltas(
spec,
win_length=self.delta_win_length,
mode="replicate",
)
channels = [spec]
if self.add_delta:
channels.append(delta)
if self.add_delta_delta:
channels.append(
torchaudio.functional.compute_deltas(
delta,
win_length=self.delta_win_length,
mode="replicate",
)
)
return torch.cat(channels, dim=1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass.
Args:
x (torch.Tensor): Input waveform [B, C, T] or [B, T].
Returns:
torch.Tensor: Mel-frequency features [B, C, F, T].
"""
if x.ndim == 2:
x = x.unsqueeze(1)
if x.ndim != 3:
raise ValueError(f"Expected waveform [B, C, T], got {tuple(x.shape)}")
if self.representation == "complex_mel":
spec = self._complex_mel(x)
else:
spec = self.mel_spec(x)
spec = self.amplitude_to_db(spec)
return self._append_deltas(spec)
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