models/bs_roformer/mel_band_roformer.py

from functools import partial

import torch
from torch import nn, einsum, Tensor
from torch.nn import Module, ModuleList
import torch.nn.functional as F

from models.bs_roformer.attend import Attend

from beartype.typing import Tuple, Optional, List, Callable
from beartype import beartype

from rotary_embedding_torch import RotaryEmbedding

from einops import rearrange, pack, unpack, reduce, repeat
from einops.layers.torch import Rearrange

from librosa import filters


# helper functions

def exists(val):
    return val is not None


def default(v, d):
    return v if exists(v) else d


def pack_one(t, pattern):
    return pack([t], pattern)


def unpack_one(t, ps, pattern):
    return unpack(t, ps, pattern)[0]


def pad_at_dim(t, pad, dim=-1, value=0.):
    dims_from_right = (- dim - 1) if dim < 0 else (t.ndim - dim - 1)
    zeros = ((0, 0) * dims_from_right)
    return F.pad(t, (*zeros, *pad), value=value)


def l2norm(t):
    return F.normalize(t, dim=-1, p=2)


# norm

class RMSNorm(Module):
    def __init__(self, dim):
        super().__init__()
        self.scale = dim ** 0.5
        self.gamma = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        return F.normalize(x, dim=-1) * self.scale * self.gamma


# attention

class FeedForward(Module):
    def __init__(
            self,
            dim,
            mult=4,
            dropout=0.
    ):
        super().__init__()
        dim_inner = int(dim * mult)
        self.net = nn.Sequential(
            RMSNorm(dim),
            nn.Linear(dim, dim_inner),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(dim_inner, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        return self.net(x)


class Attention(Module):
    def __init__(
            self,
            dim,
            heads=8,
            dim_head=64,
            dropout=0.,
            rotary_embed=None,
            flash=True
    ):
        super().__init__()
        self.heads = heads
        self.scale = dim_head ** -0.5
        dim_inner = heads * dim_head

        self.rotary_embed = rotary_embed

        self.attend = Attend(flash=flash, dropout=dropout)

        self.norm = RMSNorm(dim)
        self.to_qkv = nn.Linear(dim, dim_inner * 3, bias=False)

        self.to_gates = nn.Linear(dim, heads)

        self.to_out = nn.Sequential(
            nn.Linear(dim_inner, dim, bias=False),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        x = self.norm(x)

        q, k, v = rearrange(self.to_qkv(x), 'b n (qkv h d) -> qkv b h n d', qkv=3, h=self.heads)

        if exists(self.rotary_embed):
            q = self.rotary_embed.rotate_queries_or_keys(q)
            k = self.rotary_embed.rotate_queries_or_keys(k)

        out = self.attend(q, k, v)

        gates = self.to_gates(x)
        out = out * rearrange(gates, 'b n h -> b h n 1').sigmoid()

        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)


class LinearAttention(Module):
    """
    this flavor of linear attention proposed in https://arxiv.org/abs/2106.09681 by El-Nouby et al.
    """

    @beartype
    def __init__(
            self,
            *,
            dim,
            dim_head=32,
            heads=8,
            scale=8,
            flash=False,
            dropout=0.
    ):
        super().__init__()
        dim_inner = dim_head * heads
        self.norm = RMSNorm(dim)

        self.to_qkv = nn.Sequential(
            nn.Linear(dim, dim_inner * 3, bias=False),
            Rearrange('b n (qkv h d) -> qkv b h d n', qkv=3, h=heads)
        )

        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))

        self.attend = Attend(
            scale=scale,
            dropout=dropout,
            flash=flash
        )

        self.to_out = nn.Sequential(
            Rearrange('b h d n -> b n (h d)'),
            nn.Linear(dim_inner, dim, bias=False)
        )

    def forward(
            self,
            x
    ):
        x = self.norm(x)

        q, k, v = self.to_qkv(x)

        q, k = map(l2norm, (q, k))
        q = q * self.temperature.exp()

        out = self.attend(q, k, v)

        return self.to_out(out)


class Transformer(Module):
    def __init__(
            self,
            *,
            dim,
            depth,
            dim_head=64,
            heads=8,
            attn_dropout=0.,
            ff_dropout=0.,
            ff_mult=4,
            norm_output=True,
            rotary_embed=None,
            flash_attn=True,
            linear_attn=False
    ):
        super().__init__()
        self.layers = ModuleList([])

        for _ in range(depth):
            if linear_attn:
                attn = LinearAttention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout, flash=flash_attn)
            else:
                attn = Attention(dim=dim, dim_head=dim_head, heads=heads, dropout=attn_dropout,
                                 rotary_embed=rotary_embed, flash=flash_attn)

            self.layers.append(ModuleList([
                attn,
                FeedForward(dim=dim, mult=ff_mult, dropout=ff_dropout)
            ]))

        self.norm = RMSNorm(dim) if norm_output else nn.Identity()

    def forward(self, x):

        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x

        return self.norm(x)


# bandsplit module

class BandSplit(Module):
    @beartype
    def __init__(
            self,
            dim,
            dim_inputs: Tuple[int, ...]
    ):
        super().__init__()
        self.dim_inputs = dim_inputs
        self.to_features = ModuleList([])

        for dim_in in dim_inputs:
            net = nn.Sequential(
                RMSNorm(dim_in),
                nn.Linear(dim_in, dim)
            )

            self.to_features.append(net)

    def forward(self, x):
        x = x.split(self.dim_inputs, dim=-1)

        outs = []
        for split_input, to_feature in zip(x, self.to_features):
            split_output = to_feature(split_input)
            outs.append(split_output)

        return torch.stack(outs, dim=-2)


def MLP(
        dim_in,
        dim_out,
        dim_hidden=None,
        depth=1,
        activation=nn.Tanh
):
    dim_hidden = default(dim_hidden, dim_in)

    net = []
    dims = (dim_in, *((dim_hidden,) * depth), dim_out)

    for ind, (layer_dim_in, layer_dim_out) in enumerate(zip(dims[:-1], dims[1:])):
        is_last = ind == (len(dims) - 2)

        net.append(nn.Linear(layer_dim_in, layer_dim_out))

        if is_last:
            continue

        net.append(activation())

    return nn.Sequential(*net)


class MaskEstimator(Module):
    @beartype
    def __init__(
            self,
            dim,
            dim_inputs: Tuple[int, ...],
            depth,
            mlp_expansion_factor=4
    ):
        super().__init__()
        self.dim_inputs = dim_inputs
        self.to_freqs = ModuleList([])
        dim_hidden = dim * mlp_expansion_factor

        for dim_in in dim_inputs:
            net = []

            mlp = nn.Sequential(
                MLP(dim, dim_in * 2, dim_hidden=dim_hidden, depth=depth),
                nn.GLU(dim=-1)
            )

            self.to_freqs.append(mlp)

    def forward(self, x):
        x = x.unbind(dim=-2)

        outs = []

        for band_features, mlp in zip(x, self.to_freqs):
            freq_out = mlp(band_features)
            outs.append(freq_out)

        return torch.cat(outs, dim=-1)


# main class

class MelBandRoformer(Module):

    @beartype
    def __init__(
            self,
            dim,
            *,
            depth,
            stereo=False,
            num_stems=1,
            time_transformer_depth=2,
            freq_transformer_depth=2,
            linear_transformer_depth=0,
            num_bands=60,
            dim_head=64,
            heads=8,
            attn_dropout=0.1,
            ff_dropout=0.1,
            flash_attn=True,
            dim_freqs_in=1025,
            sample_rate=44100,  # needed for mel filter bank from librosa
            stft_n_fft=2048,
            stft_hop_length=512,
            # 10ms at 44100Hz, from sections 4.1, 4.4 in the paper - @faroit recommends // 2 or // 4 for better reconstruction
            stft_win_length=2048,
            stft_normalized=False,
            stft_window_fn: Optional[Callable] = None,
            mask_estimator_depth=1,
            multi_stft_resolution_loss_weight=1.,
            multi_stft_resolutions_window_sizes: Tuple[int, ...] = (4096, 2048, 1024, 512, 256),
            multi_stft_hop_size=147,
            multi_stft_normalized=False,
            multi_stft_window_fn: Callable = torch.hann_window,
            match_input_audio_length=False,  # if True, pad output tensor to match length of input tensor
    ):
        super().__init__()

        self.stereo = stereo
        self.audio_channels = 2 if stereo else 1
        self.num_stems = num_stems

        self.layers = ModuleList([])

        transformer_kwargs = dict(
            dim=dim,
            heads=heads,
            dim_head=dim_head,
            attn_dropout=attn_dropout,
            ff_dropout=ff_dropout,
            flash_attn=flash_attn
        )

        time_rotary_embed = RotaryEmbedding(dim=dim_head)
        freq_rotary_embed = RotaryEmbedding(dim=dim_head)

        for _ in range(depth):
            tran_modules = []
            if linear_transformer_depth > 0:
                tran_modules.append(Transformer(depth=linear_transformer_depth, linear_attn=True, **transformer_kwargs))
            tran_modules.append(
                Transformer(depth=time_transformer_depth, rotary_embed=time_rotary_embed, **transformer_kwargs)
            )
            tran_modules.append(
                Transformer(depth=freq_transformer_depth, rotary_embed=freq_rotary_embed, **transformer_kwargs)
            )
            self.layers.append(nn.ModuleList(tran_modules))

        self.stft_window_fn = partial(default(stft_window_fn, torch.hann_window), stft_win_length)

        self.stft_kwargs = dict(
            n_fft=stft_n_fft,
            hop_length=stft_hop_length,
            win_length=stft_win_length,
            normalized=stft_normalized
        )

        freqs = torch.stft(torch.randn(1, 4096), **self.stft_kwargs, return_complex=True).shape[1]

        # create mel filter bank
        # with librosa.filters.mel as in section 2 of paper

        mel_filter_bank_numpy = filters.mel(sr=sample_rate, n_fft=stft_n_fft, n_mels=num_bands)

        mel_filter_bank = torch.from_numpy(mel_filter_bank_numpy)

        # for some reason, it doesn't include the first freq? just force a value for now

        mel_filter_bank[0][0] = 1.

        # In some systems/envs we get 0.0 instead of ~1.9e-18 in the last position,
        # so let's force a positive value

        mel_filter_bank[-1, -1] = 1.

        # binary as in paper (then estimated masks are averaged for overlapping regions)

        freqs_per_band = mel_filter_bank > 0
        assert freqs_per_band.any(dim=0).all(), 'all frequencies need to be covered by all bands for now'

        repeated_freq_indices = repeat(torch.arange(freqs), 'f -> b f', b=num_bands)
        freq_indices = repeated_freq_indices[freqs_per_band]

        if stereo:
            freq_indices = repeat(freq_indices, 'f -> f s', s=2)
            freq_indices = freq_indices * 2 + torch.arange(2)
            freq_indices = rearrange(freq_indices, 'f s -> (f s)')

        self.register_buffer('freq_indices', freq_indices, persistent=False)
        self.register_buffer('freqs_per_band', freqs_per_band, persistent=False)

        num_freqs_per_band = reduce(freqs_per_band, 'b f -> b', 'sum')
        num_bands_per_freq = reduce(freqs_per_band, 'b f -> f', 'sum')

        self.register_buffer('num_freqs_per_band', num_freqs_per_band, persistent=False)
        self.register_buffer('num_bands_per_freq', num_bands_per_freq, persistent=False)

        # band split and mask estimator

        freqs_per_bands_with_complex = tuple(2 * f * self.audio_channels for f in num_freqs_per_band.tolist())

        self.band_split = BandSplit(
            dim=dim,
            dim_inputs=freqs_per_bands_with_complex
        )

        self.mask_estimators = nn.ModuleList([])

        for _ in range(num_stems):
            mask_estimator = MaskEstimator(
                dim=dim,
                dim_inputs=freqs_per_bands_with_complex,
                depth=mask_estimator_depth
            )

            self.mask_estimators.append(mask_estimator)

        # for the multi-resolution stft loss

        self.multi_stft_resolution_loss_weight = multi_stft_resolution_loss_weight
        self.multi_stft_resolutions_window_sizes = multi_stft_resolutions_window_sizes
        self.multi_stft_n_fft = stft_n_fft
        self.multi_stft_window_fn = multi_stft_window_fn

        self.multi_stft_kwargs = dict(
            hop_length=multi_stft_hop_size,
            normalized=multi_stft_normalized
        )

        self.match_input_audio_length = match_input_audio_length

    def forward(
            self,
            raw_audio,
            target=None,
            return_loss_breakdown=False
    ):
        """
        einops

        b - batch
        f - freq
        t - time
        s - audio channel (1 for mono, 2 for stereo)
        n - number of 'stems'
        c - complex (2)
        d - feature dimension
        """

        device = raw_audio.device

        if raw_audio.ndim == 2:
            raw_audio = rearrange(raw_audio, 'b t -> b 1 t')

        batch, channels, raw_audio_length = raw_audio.shape

        istft_length = raw_audio_length if self.match_input_audio_length else None

        assert (not self.stereo and channels == 1) or (
                    self.stereo and channels == 2), 'stereo needs to be set to True if passing in audio signal that is stereo (channel dimension of 2). also need to be False if mono (channel dimension of 1)'

        # to stft

        raw_audio, batch_audio_channel_packed_shape = pack_one(raw_audio, '* t')

        stft_window = self.stft_window_fn(device=device)

        stft_repr = torch.stft(raw_audio, **self.stft_kwargs, window=stft_window, return_complex=True)
        stft_repr = torch.view_as_real(stft_repr)

        stft_repr = unpack_one(stft_repr, batch_audio_channel_packed_shape, '* f t c')
        stft_repr = rearrange(stft_repr,
                              'b s f t c -> b (f s) t c')  # merge stereo / mono into the frequency, with frequency leading dimension, for band splitting

        # index out all frequencies for all frequency ranges across bands ascending in one go

        batch_arange = torch.arange(batch, device=device)[..., None]

        # account for stereo

        x = stft_repr[batch_arange, self.freq_indices]

        # fold the complex (real and imag) into the frequencies dimension

        x = rearrange(x, 'b f t c -> b t (f c)')

        x = self.band_split(x)

        # axial / hierarchical attention

        for transformer_block in self.layers:

            if len(transformer_block) == 3:
                linear_transformer, time_transformer, freq_transformer = transformer_block

                x, ft_ps = pack([x], 'b * d')
                x = linear_transformer(x)
                x, = unpack(x, ft_ps, 'b * d')
            else:
                time_transformer, freq_transformer = transformer_block

            x = rearrange(x, 'b t f d -> b f t d')
            x, ps = pack([x], '* t d')

            x = time_transformer(x)

            x, = unpack(x, ps, '* t d')
            x = rearrange(x, 'b f t d -> b t f d')
            x, ps = pack([x], '* f d')

            x = freq_transformer(x)

            x, = unpack(x, ps, '* f d')

        num_stems = len(self.mask_estimators)

        masks = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
        masks = rearrange(masks, 'b n t (f c) -> b n f t c', c=2)

        # modulate frequency representation

        stft_repr = rearrange(stft_repr, 'b f t c -> b 1 f t c')

        # complex number multiplication

        stft_repr = torch.view_as_complex(stft_repr)
        masks = torch.view_as_complex(masks)

        masks = masks.type(stft_repr.dtype)

        # need to average the estimated mask for the overlapped frequencies

        scatter_indices = repeat(self.freq_indices, 'f -> b n f t', b=batch, n=num_stems, t=stft_repr.shape[-1])

        stft_repr_expanded_stems = repeat(stft_repr, 'b 1 ... -> b n ...', n=num_stems)
        masks_summed = torch.zeros_like(stft_repr_expanded_stems).scatter_add_(2, scatter_indices, masks)

        denom = repeat(self.num_bands_per_freq, 'f -> (f r) 1', r=channels)

        masks_averaged = masks_summed / denom.clamp(min=1e-8)

        # modulate stft repr with estimated mask

        stft_repr = stft_repr * masks_averaged

        # istft

        stft_repr = rearrange(stft_repr, 'b n (f s) t -> (b n s) f t', s=self.audio_channels)

        recon_audio = torch.istft(stft_repr, **self.stft_kwargs, window=stft_window, return_complex=False,
                                  length=istft_length)

        recon_audio = rearrange(recon_audio, '(b n s) t -> b n s t', b=batch, s=self.audio_channels, n=num_stems)

        if num_stems == 1:
            recon_audio = rearrange(recon_audio, 'b 1 s t -> b s t')

        # if a target is passed in, calculate loss for learning

        if not exists(target):
            return recon_audio

        if self.num_stems > 1:
            assert target.ndim == 4 and target.shape[1] == self.num_stems

        if target.ndim == 2:
            target = rearrange(target, '... t -> ... 1 t')

        target = target[..., :recon_audio.shape[-1]]  # protect against lost length on istft

        loss = F.l1_loss(recon_audio, target)

        multi_stft_resolution_loss = 0.

        for window_size in self.multi_stft_resolutions_window_sizes:
            res_stft_kwargs = dict(
                n_fft=max(window_size, self.multi_stft_n_fft),  # not sure what n_fft is across multi resolution stft
                win_length=window_size,
                return_complex=True,
                window=self.multi_stft_window_fn(window_size, device=device),
                **self.multi_stft_kwargs,
            )

            recon_Y = torch.stft(rearrange(recon_audio, '... s t -> (... s) t'), **res_stft_kwargs)
            target_Y = torch.stft(rearrange(target, '... s t -> (... s) t'), **res_stft_kwargs)

            multi_stft_resolution_loss = multi_stft_resolution_loss + F.l1_loss(recon_Y, target_Y)

        weighted_multi_resolution_loss = multi_stft_resolution_loss * self.multi_stft_resolution_loss_weight

        total_loss = loss + weighted_multi_resolution_loss

        if not return_loss_breakdown:
            return total_loss

        return total_loss, (loss, multi_stft_resolution_loss)