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e044cab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 | """Matoub-82M: Kabyle text to a 24 kHz waveform.
A StyleTTS2 model fine-tuned from Kokoro-82M. The text and prosody modules are adapted
from `hexgrad/Kokoro-82M`'s `modules.py` (Apache-2.0), itself adapted from StyleTTS2's
`models.py` (MIT). Module attribute names are the published checkpoint's `state_dict`
keys; renaming one breaks `from_pretrained` for everybody who downloaded the release.
The speaker style is a 256-dim vector carried in the weights, so synthesis needs no
reference clip: the first 128 dimensions condition the waveform decoder and the second
128 condition duration and pitch. Style diffusion is not part of this checkpoint —
`lambda_diff` was 0.0 for every epoch — so there is no sampler to blend against and no
`alpha`/`beta` to set.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Final
import torch
from torch import Tensor, nn
from transformers import AlbertConfig, AlbertModel, PreTrainedModel
from transformers.utils.generic import ModelOutput
from .configuration_matoub import MatoubConfig
from .istftnet import AdainResBlk1d, Decoder
BATCHED_SEQUENCE_RANK: Final = 2
@dataclass
class MatoubOutput(ModelOutput):
"""Synthesised audio, and the frame count each input token was given.
`waveform` is right-padded to the longest item in the batch; `waveform_lengths` says
where each one ends.
"""
waveform: Tensor | None = None
waveform_lengths: Tensor | None = None
durations: Tensor | None = None
class LayerNorm(nn.Module):
"""Channel-last layer norm over a (batch, channels, time) tensor."""
def __init__(self, channels: int, eps: float = 1e-5) -> None:
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x: Tensor) -> Tensor:
x = x.transpose(1, -1)
x = nn.functional.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
class LinearNorm(nn.Module):
"""A linear layer under the attribute name the checkpoint stores it by."""
def __init__(self, in_dim: int, out_dim: int) -> None:
super().__init__()
self.linear_layer = nn.Linear(in_dim, out_dim)
def forward(self, x: Tensor) -> Tensor:
projected: Tensor = self.linear_layer(x)
return projected
class TextEncoder(nn.Module):
"""Phoneme ids to the acoustic features the decoder reads."""
def __init__(self, channels: int, kernel_size: int, depth: int, n_symbols: int) -> None:
super().__init__()
self.embedding = nn.Embedding(n_symbols, channels)
padding = (kernel_size - 1) // 2
self.cnn = nn.ModuleList(
[
nn.Sequential(
nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding),
LayerNorm(channels),
nn.LeakyReLU(0.2),
nn.Dropout(0.2),
)
for _ in range(depth)
]
)
self.lstm = nn.LSTM(channels, channels // 2, 1, batch_first=True, bidirectional=True)
def forward(self, input_ids: Tensor) -> Tensor:
x = self.embedding(input_ids).transpose(1, 2)
for block in self.cnn:
x = block(x)
encoded: Tensor
encoded, _ = self.lstm(x.transpose(1, 2))
return encoded.transpose(-1, -2)
class AdaLayerNorm(nn.Module):
"""Layer norm whose scale and shift are read off the style vector."""
def __init__(self, style_dim: int, channels: int, eps: float = 1e-5) -> None:
super().__init__()
self.channels = channels
self.eps = eps
self.fc = nn.Linear(style_dim, channels * 2)
def forward(self, x: Tensor, s: Tensor) -> Tensor:
x = x.transpose(-1, -2).transpose(1, -1)
h = self.fc(s).view(s.size(0), -1, 1)
gamma, beta = torch.chunk(h, chunks=2, dim=1)
gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
x = nn.functional.layer_norm(x, (self.channels,), eps=self.eps)
x = (1 + gamma) * x + beta
return x.transpose(1, -1).transpose(-1, -2)
class DurationEncoder(nn.Module):
"""Style-conditioned recurrent stack the duration head reads."""
def __init__(self, sty_dim: int, d_model: int, nlayers: int, dropout: float) -> None:
super().__init__()
self.lstms = nn.ModuleList()
for _ in range(nlayers):
self.lstms.append(
nn.LSTM(
d_model + sty_dim,
d_model // 2,
num_layers=1,
batch_first=True,
bidirectional=True,
)
)
self.lstms.append(AdaLayerNorm(sty_dim, d_model))
def forward(self, x: Tensor, style: Tensor) -> Tensor:
x = x.permute(2, 0, 1)
s = style.expand(x.shape[0], x.shape[1], -1)
x = torch.cat([x, s], dim=-1).transpose(0, 1).transpose(-1, -2)
for block in self.lstms:
if isinstance(block, AdaLayerNorm):
x = block(x.transpose(-1, -2), style).transpose(-1, -2)
x = torch.cat([x, s.permute(1, 2, 0)], dim=1)
else:
x, _ = block(x.transpose(-1, -2))
x = x.transpose(-1, -2)
return x.transpose(-1, -2)
class ProsodyPredictor(nn.Module):
"""Per-token duration, and the pitch and energy contours over the expanded frames."""
def __init__(
self, style_dim: int, d_hid: int, nlayers: int, max_dur: int, dropout: float
) -> None:
super().__init__()
self.text_encoder = DurationEncoder(
sty_dim=style_dim, d_model=d_hid, nlayers=nlayers, dropout=dropout
)
self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
self.duration_proj = LinearNorm(d_hid, max_dur)
self.shared = nn.LSTM(
d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True
)
self.F0 = nn.ModuleList(
[
AdainResBlk1d(d_hid, d_hid, style_dim),
AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True),
AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim),
]
)
self.N = nn.ModuleList(
[
AdainResBlk1d(d_hid, d_hid, style_dim),
AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True),
AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim),
]
)
self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
def contours(self, aligned: Tensor, s: Tensor) -> tuple[Tensor, Tensor]:
x, _ = self.shared(aligned.transpose(-1, -2))
pitch = x.transpose(-1, -2)
for block in self.F0:
pitch = block(pitch, s)
energy = x.transpose(-1, -2)
for block in self.N:
energy = block(energy, s)
return self.F0_proj(pitch).squeeze(1), self.N_proj(energy).squeeze(1)
class MatoubPreTrainedModel(PreTrainedModel):
config_class = MatoubConfig
base_model_prefix = "matoub"
main_input_name = "input_ids"
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear | nn.Conv1d | nn.ConvTranspose1d):
module.weight.data.normal_(mean=0.0, std=0.01)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=0.02)
class MatoubForTextToWaveform(MatoubPreTrainedModel):
"""`model(**tokenizer(text, return_tensors="pt")).waveform` — 24 kHz mono float32."""
voice: Tensor
def __init__(self, config: MatoubConfig) -> None:
super().__init__(config)
self.bert = AlbertModel(
AlbertConfig(
vocab_size=config.vocab_size,
hidden_size=config.plbert_hidden_size,
num_attention_heads=config.plbert_num_attention_heads,
intermediate_size=config.plbert_intermediate_size,
num_hidden_layers=config.plbert_num_hidden_layers,
max_position_embeddings=config.plbert_max_position_embeddings,
)
)
self.bert_encoder = nn.Linear(config.plbert_hidden_size, config.hidden_size)
self.predictor = ProsodyPredictor(
style_dim=config.style_dim,
d_hid=config.hidden_size,
nlayers=config.num_layers,
max_dur=config.max_duration,
dropout=config.dropout,
)
self.text_encoder = TextEncoder(
channels=config.hidden_size,
kernel_size=config.text_encoder_kernel_size,
depth=config.num_layers,
n_symbols=config.vocab_size,
)
self.decoder = Decoder(
dim_in=config.hidden_size,
style_dim=config.style_dim,
resblock_kernel_sizes=config.resblock_kernel_sizes,
upsample_rates=config.upsample_rates,
upsample_initial_channel=config.upsample_initial_channel,
resblock_dilation_sizes=config.resblock_dilation_sizes,
upsample_kernel_sizes=config.upsample_kernel_sizes,
gen_istft_n_fft=config.gen_istft_n_fft,
gen_istft_hop_size=config.gen_istft_hop_size,
sampling_rate=config.sampling_rate,
)
self.register_buffer("voice", torch.zeros(1, config.style_dim * 2))
self.post_init()
@property
def sampling_rate(self) -> int:
return int(self.config.sampling_rate)
def _synthesise(self, input_ids: Tensor, style: Tensor, speed: float) -> tuple[Tensor, Tensor]:
attention = torch.ones_like(input_ids)
bert_dur = self.bert(input_ids, attention_mask=attention).last_hidden_state
d_en = self.bert_encoder(bert_dur).transpose(-1, -2)
prosody_style = style[:, self.config.style_dim :]
acoustic_style = style[:, : self.config.style_dim]
d = self.predictor.text_encoder(d_en, prosody_style)
x, _ = self.predictor.lstm(d)
duration = torch.sigmoid(self.predictor.duration_proj(x)).sum(dim=-1) / speed
frames = torch.round(duration).clamp(min=1).long().squeeze(0)
indices = torch.repeat_interleave(
torch.arange(input_ids.shape[1], device=input_ids.device), frames
)
alignment = torch.zeros(
(input_ids.shape[1], indices.shape[0]), device=input_ids.device, dtype=d.dtype
)
alignment[indices, torch.arange(indices.shape[0], device=input_ids.device)] = 1
alignment = alignment.unsqueeze(0)
pitch, energy = self.predictor.contours(d.transpose(-1, -2) @ alignment, prosody_style)
asr = self.text_encoder(input_ids) @ alignment
waveform = self.decoder(asr, pitch, energy, acoustic_style).squeeze(1).squeeze(0)
return waveform, frames
@torch.no_grad()
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
speed: float = 1.0,
voice: Tensor | None = None,
return_dict: bool | None = None,
) -> MatoubOutput | tuple[Tensor, Tensor, Tensor]:
if speed <= 0:
message = f"speed must be positive, got {speed}"
raise ValueError(message)
if input_ids.dim() != BATCHED_SEQUENCE_RANK:
message = f"input_ids must be (batch, tokens), got shape {tuple(input_ids.shape)}"
raise ValueError(message)
limit = self.config.max_token_length
if input_ids.shape[1] > limit:
message = (
f"{input_ids.shape[1]} tokens exceeds the {limit} PL-BERT can position; "
f"synthesise one sentence at a time"
)
raise ValueError(message)
style = self.voice if voice is None else voice.to(self.voice.dtype)
if style.shape[-1] != self.config.style_dim * 2:
message = (
f"voice must be a {self.config.style_dim * 2}-dim style vector, "
f"got shape {tuple(style.shape)}"
)
raise ValueError(message)
style = style.reshape(1, -1).to(input_ids.device)
# Each item is synthesised on its own: the alignment matrix that expands tokens to
# frames is built from that item's own durations, so a padded row would be given
# frames of its own padding.
mask = torch.ones_like(input_ids) if attention_mask is None else attention_mask
waveforms: list[Tensor] = []
durations: list[Tensor] = []
for row, keep in zip(input_ids, mask, strict=True):
tokens = row[keep.bool()].unsqueeze(0)
waveform, frames = self._synthesise(tokens, style, speed)
waveforms.append(waveform)
durations.append(
nn.functional.pad(frames, (0, int(input_ids.shape[1] - frames.shape[0])))
)
lengths = torch.tensor([w.shape[0] for w in waveforms], device=input_ids.device)
longest = int(lengths.max())
audio = torch.stack([nn.functional.pad(w, (0, longest - w.shape[0])) for w in waveforms])
stacked_durations = torch.stack(durations)
if return_dict is False:
return audio, lengths, stacked_durations
return MatoubOutput(waveform=audio, waveform_lengths=lengths, durations=stacked_durations)
__all__ = ["MatoubForTextToWaveform", "MatoubOutput", "MatoubPreTrainedModel"]
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