HDemucs

class torchaudio.models.HDemucs(sources: List[str], audio_channels: int = 2, channels: int = 48, growth: int = 2, nfft: int = 4096, depth: int = 6, freq_emb: float = 0.2, emb_scale: int = 10, emb_smooth: bool = True, kernel_size: int = 8, time_stride: int = 2, stride: int = 4, context: int = 1, context_enc: int = 0, norm_starts: int = 4, norm_groups: int = 4, dconv_depth: int = 2, dconv_comp: int = 4, dconv_attn: int = 4, dconv_lstm: int = 4, dconv_init: float = 0.0001)

Hybrid Demucs model from Hybrid Spectrogram and Waveform Source Separation [Défossez, 2021].

See also

• torchaudio.pipelines.SourceSeparationBundle: Source separation pipeline with pre-trained models.

Parameters:

• sources (List[str]) – list of source names. List can contain the following source options: ["bass", "drums", "other", "mixture", "vocals"].

• audio_channels (int, optional) – input/output audio channels. (Default: 2)

• channels (int, optional) – initial number of hidden channels. (Default: 48)

• growth (int, optional) – increase the number of hidden channels by this factor at each layer. (Default: 2)

• nfft (int, optional) – number of fft bins. Note that changing this requires careful computation of various shape parameters and will not work out of the box for hybrid models. (Default: 4096)

• depth (int, optional) – number of layers in encoder and decoder (Default: 6)

• freq_emb (float, optional) – add frequency embedding after the first frequency layer if > 0, the actual value controls the weight of the embedding. (Default: 0.2)

• emb_scale (int, optional) – equivalent to scaling the embedding learning rate (Default: 10)

• emb_smooth (bool, optional) – initialize the embedding with a smooth one (with respect to frequencies). (Default: True)

• kernel_size (int, optional) – kernel_size for encoder and decoder layers. (Default: 8)

• time_stride (int, optional) – stride for the final time layer, after the merge. (Default: 2)

• stride (int, optional) – stride for encoder and decoder layers. (Default: 4)

• context (int, optional) – context for 1x1 conv in the decoder. (Default: 4)

• context_enc (int, optional) – context for 1x1 conv in the encoder. (Default: 0)

• norm_starts (int, optional) – layer at which group norm starts being used. decoder layers are numbered in reverse order. (Default: 4)

• norm_groups (int, optional) – number of groups for group norm. (Default: 4)

• dconv_depth (int, optional) – depth of residual DConv branch. (Default: 2)

• dconv_comp (int, optional) – compression of DConv branch. (Default: 4)

• dconv_attn (int, optional) – adds attention layers in DConv branch starting at this layer. (Default: 4)

• dconv_lstm (int, optional) – adds a LSTM layer in DConv branch starting at this layer. (Default: 4)

• dconv_init (float, optional) – initial scale for the DConv branch LayerScale. (Default: 1e-4)

Tutorials using HDemucs:

Music Source Separation with Hybrid Demucs

Music Source Separation with Hybrid Demucs

Methods

forward

HDemucs.forward(input: Tensor)

HDemucs forward call

Parameters:

input (torch.Tensor) – input mixed tensor of shape (batch_size, channel, num_frames)

Returns:

Tensor

output tensor split into sources of shape (batch_size, num_sources, channel, num_frames)

Factory Functions

hdemucs_low	Builds low nfft (1024) version of HDemucs, suitable for sample rates around 8 kHz.
hdemucs_medium	Builds medium nfft (2048) version of HDemucs, suitable for sample rates of 16-32 kHz.
hdemucs_high	Builds medium nfft (4096) version of HDemucs, suitable for sample rates of 44.1-48 kHz.