ChromaSpectrogram

class torchaudio.prototype.transforms.ChromaSpectrogram(sample_rate: int, n_fft: int, *, win_length: ~typing.Optional[int] = None, hop_length: ~typing.Optional[int] = None, pad: int = 0, window_fn: ~typing.Callable[[...], ~torch.Tensor] = <built-in method hann_window of type object>, power: float = 2.0, normalized: bool = False, wkwargs: ~typing.Optional[dict] = None, center: bool = True, pad_mode: str = 'reflect', n_chroma: int = 12, tuning: float = 0.0, ctroct: float = 5.0, octwidth: ~typing.Optional[float] = 2.0, norm: int = 2, base_c: bool = True)

DEPRECATED

Warning

This class is deprecated from version 2.8. It will be removed in the 2.9 release. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information.

Generates chromagram for audio signal.

Composes torchaudio.transforms.Spectrogram() and and torchaudio.prototype.transforms.ChromaScale().

Args:

sample_rate (int): Sample rate of audio signal. n_fft (int, optional): Size of FFT, creates n_fft // 2 + 1 bins. win_length (int or None, optional): Window size. (Default: n_fft) hop_length (int or None, optional): Length of hop between STFT windows. (Default: win_length // 2) pad (int, optional): Two sided padding of signal. (Default: 0) window_fn (Callable[…, torch.Tensor], optional): A function to create a window tensor

that is applied/multiplied to each frame/window. (Default: torch.hann_window)

power (float, optional): Exponent for the magnitude spectrogram,

(must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: 2)

normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: False) wkwargs (Dict[…, …] or None, optional): Arguments for window function. (Default: None) center (bool, optional): whether to pad waveform on both sides so

that the \(t\)-th frame is centered at time \(t \times \text{hop\_length}\). (Default: True)

pad_mode (string, optional): controls the padding method used when

center is True. (Default: "reflect")

n_chroma (int, optional): Number of chroma. (Default: 12) tuning (float, optional): Tuning deviation from A440 in fractions of a chroma bin. (Default: 0.0) ctroct (float, optional): Center of Gaussian dominance window to weight filters by, in octaves. (Default: 5.0) octwidth (float or None, optional): Width of Gaussian dominance window to weight filters by, in octaves.

If None, then disable weighting altogether. (Default: 2.0)

norm (int, optional): order of norm to normalize filter bank by. (Default: 2) base_c (bool, optional): If True, then start filter bank at C. Otherwise, start at A. (Default: True)

Example

>>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
>>> transform = transforms.ChromaSpectrogram(sample_rate=sample_rate, n_fft=400)
>>> chromagram = transform(waveform)  # (channel, n_chroma, time)

forward(waveform: Tensor) → Tensor

Parameters

waveform (Tensor) – Tensor of audio of dimension (…, time).

Returns

Chromagram of size (…, n_chroma, time).

Return type

Tensor