ChromaScale

class torchaudio.prototype.transforms.ChromaScale(sample_rate: int, n_freqs: int, *, n_chroma: int = 12, tuning: float = 0.0, ctroct: float = 5.0, octwidth: 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.

Converts spectrogram to chromagram.

Args:

sample_rate (int): Sample rate of audio signal. n_freqs (int): Number of frequency bins in STFT. See n_fft in Spectrogram. 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)
>>> spectrogram_transform = transforms.Spectrogram(n_fft=1024)
>>> spectrogram = spectrogram_transform(waveform)
>>> chroma_transform = transforms.ChromaScale(sample_rate=sample_rate, n_freqs=1024 // 2 + 1)
>>> chroma_spectrogram = chroma_transform(spectrogram)

See also:

torchaudio.prototype.functional.chroma_filterbank() — function used to generate the filter bank.

forward(x: Tensor) → Tensor

Parameters

specgram (torch.Tensor) – Spectrogram of dimension (…, n_freqs, time).

Returns

Chroma spectrogram of size (…, n_chroma, time).

Return type

torch.Tensor