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import math
from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn.utils import weight_norm as legacy_weight_norm
from torch.nn.utils.parametrizations import weight_norm
def _rope(length: int, head_dim: int, base: float, device=None) -> Tensor:
frequencies = 1.0 / (
base ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim)
)
phases = torch.outer(torch.arange(length, device=device), frequencies)
values = torch.polar(torch.ones_like(phases), phases)
return torch.stack((values.real, values.imag), dim=-1).to(torch.bfloat16)
def _apply_rope(x: Tensor, values: Tensor) -> Tensor:
shaped = x.float().reshape(*x.shape[:-1], -1, 2)
values = values.view(1, shaped.shape[1], 1, shaped.shape[3], 2)
output = torch.stack(
(
shaped[..., 0] * values[..., 0] - shaped[..., 1] * values[..., 1],
shaped[..., 1] * values[..., 0] + shaped[..., 0] * values[..., 1],
),
dim=-1,
)
return output.flatten(3).to(x.dtype)
class ArkttsCodecRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = float(eps)
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
output = x.float() * torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps)
return output.to(x.dtype) * self.weight
class ArkttsCodecLayerScale(nn.Module):
def __init__(self, dim: int, init_values: float = 1e-2, inplace: bool = False):
super().__init__()
self.inplace = bool(inplace)
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
@dataclass
class ArkttsCodecTransformerConfig:
n_layer: int
n_head: int
dim: int
intermediate_size: int
n_local_heads: int = -1
head_dim: int = 64
rope_base: float = 10000
norm_eps: float = 1e-5
dropout_rate: float = 0.1
attn_dropout_rate: float = 0.1
channels_first: bool = True
def __post_init__(self):
if self.n_local_heads == -1:
self.n_local_heads = self.n_head
class ArkttsCodecAttention(nn.Module):
def __init__(self, config: ArkttsCodecTransformerConfig):
super().__init__()
total = (config.n_head + 2 * config.n_local_heads) * config.head_dim
self.wqkv = nn.Linear(config.dim, total, bias=False)
self.wo = nn.Linear(config.head_dim * config.n_head, config.dim, bias=False)
self.n_head = config.n_head
self.n_local_heads = config.n_local_heads
self.head_dim = config.head_dim
self.attn_dropout_rate = config.attn_dropout_rate
def forward(self, x, rope_values, mask):
batch, length, _ = x.shape
query_size = self.n_head * self.head_dim
kv_size = self.n_local_heads * self.head_dim
query, key, value = self.wqkv(x).split((query_size, kv_size, kv_size), dim=-1)
query = query.view(batch, length, self.n_head, self.head_dim)
key = key.view(batch, length, self.n_local_heads, self.head_dim)
value = value.view(batch, length, self.n_local_heads, self.head_dim)
query = _apply_rope(query, rope_values).transpose(1, 2)
key = _apply_rope(key, rope_values).transpose(1, 2)
value = value.transpose(1, 2)
repeat = self.n_head // self.n_local_heads
key = key.repeat_interleave(repeat, dim=1)
value = value.repeat_interleave(repeat, dim=1)
output = F.scaled_dot_product_attention(
query,
key,
value,
attn_mask=mask,
dropout_p=self.attn_dropout_rate if self.training else 0.0,
)
output = output.transpose(1, 2).contiguous().view(batch, length, query_size)
return self.wo(output)
class ArkttsCodecFeedForward(nn.Module):
def __init__(self, config: ArkttsCodecTransformerConfig):
super().__init__()
self.w1 = nn.Linear(config.dim, config.intermediate_size, bias=False)
self.w3 = nn.Linear(config.dim, config.intermediate_size, bias=False)
self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, x):
return self.w2(self.dropout(F.silu(self.w1(x)) * self.w3(x)))
class ArkttsCodecTransformerBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = ArkttsCodecAttention(config)
self.feed_forward = ArkttsCodecFeedForward(config)
self.ffn_norm = ArkttsCodecRMSNorm(config.dim, config.norm_eps)
self.attention_norm = ArkttsCodecRMSNorm(config.dim, config.norm_eps)
self.attention_layer_scale = ArkttsCodecLayerScale(config.dim, inplace=True)
self.ffn_layer_scale = ArkttsCodecLayerScale(config.dim, inplace=True)
def forward(self, x, rope_values, mask):
hidden = x + self.attention_layer_scale(
self.attention(self.attention_norm(x), rope_values, mask)
)
return hidden + self.ffn_layer_scale(self.feed_forward(self.ffn_norm(hidden)))
class ArkttsCodecWindowTransformer(nn.Module):
def __init__(self, config, input_dim: int, window_size: int | None, causal: bool = True):
super().__init__()
self.layers = nn.ModuleList([ArkttsCodecTransformerBlock(config) for _ in range(config.n_layer)])
self.norm = ArkttsCodecRMSNorm(config.dim, config.norm_eps)
self.window_size = window_size
self.causal = causal
self.channels_first = config.channels_first
self.input_proj = nn.Linear(input_dim, config.dim) if input_dim != config.dim else nn.Identity()
self.output_proj = nn.Linear(config.dim, input_dim) if input_dim != config.dim else nn.Identity()
self.look_ahead_conv = nn.Identity()
self.head_dim = config.head_dim
self.rope_base = config.rope_base
def forward(self, x, x_lens=None):
del x_lens
if self.channels_first:
x = x.transpose(1, 2)
x = self.look_ahead_conv(self.input_proj(x))
length = x.shape[1]
row = torch.arange(length, device=x.device)[:, None]
column = torch.arange(length, device=x.device)[None, :]
mask = column <= row
if self.window_size is not None:
mask &= column >= (row - self.window_size + 1).clamp_min(0)
mask = mask[None, None]
rope_values = _rope(length, self.head_dim, self.rope_base, x.device)
for layer in self.layers:
x = layer(x, rope_values, mask)
x = self.output_proj(self.norm(x))
return x.transpose(1, 2) if self.channels_first else x
def _extra_padding(x, kernel_size: int, stride: int, padding_total: int = 0) -> int:
length = x.shape[-1]
frames = (length - kernel_size + padding_total) / stride + 1
ideal = (math.ceil(frames) - 1) * stride + kernel_size - padding_total
return ideal - length
class ArkttsCausalConv1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation=1, stride=1, groups=1):
super().__init__()
self.conv = nn.Conv1d(
in_channels, out_channels, kernel_size, stride=stride, dilation=dilation, groups=groups
)
self.stride = stride
self.kernel_size = (kernel_size - 1) * dilation + 1
self.padding = self.kernel_size - self.stride
def forward(self, x):
right = _extra_padding(x, self.kernel_size, self.stride, self.padding)
return self.conv(F.pad(x, (self.padding, right))).contiguous()
def apply_weight_norm(self):
self.conv = weight_norm(self.conv)
return self
class ArkttsCausalConvTranspose1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1):
super().__init__()
self.conv = nn.ConvTranspose1d(
in_channels, out_channels, kernel_size, stride=stride, dilation=dilation
)
self.stride = stride
self.kernel_size = kernel_size
def forward(self, x):
x = self.conv(x)
crop = self.kernel_size - self.stride
return x[..., : x.shape[-1] - crop].contiguous() if crop else x.contiguous()
def apply_weight_norm(self):
self.conv = weight_norm(self.conv)
return self
def _causal_wn_conv(*args, **kwargs):
return ArkttsCausalConv1d(*args, **kwargs).apply_weight_norm()
def _causal_wn_transpose(*args, **kwargs):
return ArkttsCausalConvTranspose1d(*args, **kwargs).apply_weight_norm()
@torch.jit.script
def _arktts_snake(x: Tensor, alpha: Tensor) -> Tensor:
shape = x.shape
x = x.reshape(shape[0], shape[1], -1)
x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
return x.reshape(shape)
class ArkttsSnake1d(nn.Module):
def __init__(self, channels):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, channels, 1))
def forward(self, x):
return _arktts_snake(x, self.alpha)
class ArkttsResidualUnit(nn.Module):
def __init__(self, dim: int, dilation: int):
super().__init__()
self.block = nn.Sequential(
ArkttsSnake1d(dim),
_causal_wn_conv(dim, dim, kernel_size=7, dilation=dilation),
ArkttsSnake1d(dim),
_causal_wn_conv(dim, dim, kernel_size=1),
)
def forward(self, x):
output = self.block(x)
difference = x.shape[-1] - output.shape[-1]
if difference > 0:
x = x[..., :-difference]
return x + output
class ArkttsEncoderBlock(nn.Module):
def __init__(self, dim: int, stride: int, transformer_layers: int):
super().__init__()
modules = [
ArkttsResidualUnit(dim // 2, 1),
ArkttsResidualUnit(dim // 2, 3),
ArkttsResidualUnit(dim // 2, 9),
ArkttsSnake1d(dim // 2),
_causal_wn_conv(dim // 2, dim, kernel_size=2 * stride, stride=stride),
]
if transformer_layers:
config = ArkttsCodecTransformerConfig(
n_layer=transformer_layers,
n_head=dim // 64,
dim=dim,
intermediate_size=dim * 3,
)
modules.append(ArkttsCodecWindowTransformer(config, dim, window_size=512))
else:
modules.append(nn.Identity())
self.block = nn.Sequential(*modules)
def forward(self, x):
return self.block(x)
class ArkttsEncoder(nn.Module):
def __init__(self):
super().__init__()
dim = 64
modules = [_causal_wn_conv(1, dim, kernel_size=7)]
for stride, transformer_layers in zip((2, 4, 8, 8), (0, 0, 0, 4)):
dim *= 2
modules.append(ArkttsEncoderBlock(dim, stride, transformer_layers))
modules.extend((ArkttsSnake1d(dim), _causal_wn_conv(dim, 1024, kernel_size=3)))
self.block = nn.Sequential(*modules)
def forward(self, x):
return self.block(x)
class ArkttsDecoderBlock(nn.Module):
def __init__(self, input_dim: int, output_dim: int, stride: int):
super().__init__()
self.block = nn.Sequential(
ArkttsSnake1d(input_dim),
_causal_wn_transpose(input_dim, output_dim, kernel_size=2 * stride, stride=stride),
ArkttsResidualUnit(output_dim, 1),
ArkttsResidualUnit(output_dim, 3),
ArkttsResidualUnit(output_dim, 9),
)
def forward(self, x):
return self.block(x)
class ArkttsDecoder(nn.Module):
def __init__(self):
super().__init__()
channels = 1536
modules = [_causal_wn_conv(1024, channels, kernel_size=7)]
for index, stride in enumerate((8, 8, 4, 2)):
input_dim = channels // (2**index)
output_dim = channels // (2 ** (index + 1))
modules.append(ArkttsDecoderBlock(input_dim, output_dim, stride))
modules.extend((ArkttsSnake1d(output_dim), _causal_wn_conv(output_dim, 1, kernel_size=7), nn.Tanh()))
self.model = nn.Sequential(*modules)
def forward(self, x):
return self.model(x)
class ArkttsVectorQuantizer(nn.Module):
def __init__(self, input_dim: int, codebook_size: int, codebook_dim: int):
super().__init__()
self.codebook_size = int(codebook_size)
self.codebook_dim = int(codebook_dim)
self.in_proj = legacy_weight_norm(nn.Conv1d(input_dim, codebook_dim, kernel_size=1))
self.out_proj = legacy_weight_norm(nn.Conv1d(codebook_dim, input_dim, kernel_size=1))
self.codebook = nn.Embedding(codebook_size, codebook_dim)
def decode_code(self, indices):
return F.embedding(indices, self.codebook.weight).transpose(1, 2)
def decode_latents(self, latents):
batch, _, length = latents.shape
flattened = latents.transpose(1, 2).reshape(batch * length, -1)
flattened = F.normalize(flattened)
codebook = F.normalize(self.codebook.weight)
distances = (
flattened.pow(2).sum(1, keepdim=True)
- 2 * flattened @ codebook.t()
+ codebook.pow(2).sum(1, keepdim=True).t()
)
indices = (-distances).argmax(1).view(batch, length)
return self.decode_code(indices), indices
def forward(self, z):
projected = self.in_proj(z)
quantized, indices = self.decode_latents(projected)
quantized_st = projected + (quantized - projected).detach()
return self.out_proj(quantized_st), indices, projected
class ArkttsResidualQuantizer(nn.Module):
def __init__(self, input_dim: int, n_codebooks: int, codebook_size: int, codebook_dim: int):
super().__init__()
self.n_codebooks = int(n_codebooks)
self.codebook_size = int(codebook_size)
self.quantizers = nn.ModuleList([
ArkttsVectorQuantizer(input_dim, codebook_size, codebook_dim)
for _ in range(n_codebooks)
])
def forward(self, z):
quantized_sum = 0.0
residual = z
codes = []
for quantizer in self.quantizers:
quantized, indices, _ = quantizer(residual)
quantized_sum = quantized_sum + quantized
residual = residual - quantized
codes.append(indices)
return quantized_sum, torch.stack(codes, dim=1)
def from_codes(self, codes):
output = 0.0
for index in range(codes.shape[1]):
projected = self.quantizers[index].decode_code(codes[:, index])
output = output + self.quantizers[index].out_proj(projected)
return output
class ArkttsConvNeXtBlock(nn.Module):
def __init__(self, dim: int):
super().__init__()
self.dwconv = ArkttsCausalConv1d(dim, dim, kernel_size=7, groups=dim)
self.norm = nn.LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, 4 * dim)
self.act = nn.GELU()
self.pwconv2 = nn.Linear(4 * dim, dim)
self.gamma = nn.Parameter(1e-6 * torch.ones(dim))
def forward(self, x):
residual = x
x = self.dwconv(x).permute(0, 2, 1)
x = self.pwconv2(self.act(self.pwconv1(self.norm(x))))
x = (self.gamma * x).permute(0, 2, 1)
return residual + x
class ArkttsDownsampleQuantizer(nn.Module):
def __init__(self, config=None):
super().__init__()
self.semantic_quantizer = ArkttsResidualQuantizer(1024, 1, 4096, 8)
self.quantizer = ArkttsResidualQuantizer(1024, 9, 1024, 8)
self.downsample = nn.Sequential(
nn.Sequential(
ArkttsCausalConv1d(1024, 1024, kernel_size=2, stride=2),
ArkttsConvNeXtBlock(1024),
),
nn.Sequential(
ArkttsCausalConv1d(1024, 1024, kernel_size=2, stride=2),
ArkttsConvNeXtBlock(1024),
),
)
self.upsample = nn.Sequential(
nn.Sequential(
ArkttsCausalConvTranspose1d(1024, 1024, kernel_size=2, stride=2),
ArkttsConvNeXtBlock(1024),
),
nn.Sequential(
ArkttsCausalConvTranspose1d(1024, 1024, kernel_size=2, stride=2),
ArkttsConvNeXtBlock(1024),
),
)
pre_transformer_config = ArkttsCodecTransformerConfig(
n_layer=8,
n_head=16,
dim=1024,
intermediate_size=3072,
)
post_transformer_config = ArkttsCodecTransformerConfig(
n_layer=int(getattr(config, "codec_post_n_layer", 8)),
n_head=int(getattr(config, "codec_post_n_head", 16)),
n_local_heads=int(getattr(config, "codec_post_n_local_heads", 8)),
dim=1024,
intermediate_size=int(getattr(config, "codec_post_intermediate_size", 1216)),
)
self.pre_module = ArkttsCodecWindowTransformer(
pre_transformer_config, 1024, window_size=128
)
self.post_module = ArkttsCodecWindowTransformer(
post_transformer_config, 1024, window_size=128
)
self.semantic_predictor_module = nn.Identity()
def forward(self, z):
original_length = z.shape[-1]
z = self.pre_module(self.downsample(z))
semantic, semantic_codes = self.semantic_quantizer(z)
residual, residual_codes = self.quantizer(z - semantic)
z = self.upsample(self.post_module(semantic + residual))
difference = original_length - z.shape[-1]
if difference > 0:
z = F.pad(z, (difference, 0))
elif difference < 0:
z = z[..., -difference:]
return z, torch.cat((semantic_codes, residual_codes), dim=1)
def decode(self, indices):
indices = indices.clone()
indices[:, 0].clamp_(0, self.semantic_quantizer.codebook_size - 1)
indices[:, 1:].clamp_(0, self.quantizer.codebook_size - 1)
semantic = self.semantic_quantizer.from_codes(indices[:, :1])
residual = self.quantizer.from_codes(indices[:, 1:])
return self.upsample(self.post_module(semantic + residual))
class ArkttsCodec(nn.Module):
sample_rate = 44100
hop_length = 512
frame_length = 2048
def __init__(self, config=None):
super().__init__()
self.encoder = ArkttsEncoder()
self.quantizer = ArkttsDownsampleQuantizer(config)
self.decoder = ArkttsDecoder()
@torch.inference_mode()
def encode(self, audio, audio_lengths=None):
if audio.ndim == 2:
audio = audio[:, None]
if audio.ndim != 3 or audio.shape[1] != 1:
raise ValueError("audio must have shape [B, 1, samples]")
original_length = audio.shape[-1]
right = math.ceil(original_length / self.frame_length) * self.frame_length - original_length
if right:
audio = F.pad(audio, (0, right))
if audio_lengths is None:
audio_lengths = torch.full(
(audio.shape[0],), original_length, device=audio.device, dtype=torch.long
)
encoded = self.encoder(audio)
_, codes = self.quantizer(encoded)
code_lengths = torch.ceil(audio_lengths.float() / self.frame_length).long()
max_codes = codes.shape[-1]
padded = torch.full_like(codes, -1)
for index, length in enumerate(code_lengths.tolist()):
padded[index, :, : min(length, max_codes)] = codes[index, :, : min(length, max_codes)]
return padded, code_lengths.clamp_max(max_codes)
@torch.inference_mode()
def decode(self, codes):
return self.decoder(self.quantizer.decode(codes.long()))
__all__ = ["ArkttsCodec"]
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