| 1 | from typing import Set, Tuple |
| 2 | |
| 3 | import torch |
| 4 | import torch.nn.functional as F |
| 5 | |
| 6 | from ltx_core.components.patchifiers import AudioPatchifier |
| 7 | from ltx_core.model.audio_vae.attention import AttentionType, make_attn |
| 8 | from ltx_core.model.audio_vae.causal_conv_2d import make_conv2d |
| 9 | from ltx_core.model.audio_vae.causality_axis import CausalityAxis |
| 10 | from ltx_core.model.audio_vae.downsample import build_downsampling_path |
| 11 | from ltx_core.model.audio_vae.ops import AudioProcessor, PerChannelStatistics |
| 12 | from ltx_core.model.audio_vae.resnet import ResnetBlock |
| 13 | from ltx_core.model.audio_vae.upsample import build_upsampling_path |
| 14 | from ltx_core.model.audio_vae.vocoder import Vocoder |
| 15 | from ltx_core.model.common.normalization import NormType, build_normalization_layer |
| 16 | from ltx_core.types import Audio, AudioLatentShape |
| 17 | |
| 18 | LATENT_DOWNSAMPLE_FACTOR = 4 |
| 19 | |
| 20 | |
| 21 | def build_mid_block( |
| 22 | channels: int, |
| 23 | temb_channels: int, |
| 24 | dropout: float, |
| 25 | norm_type: NormType, |
| 26 | causality_axis: CausalityAxis, |
| 27 | attn_type: AttentionType, |
| 28 | add_attention: bool, |
| 29 | ) -> torch.nn.Module: |
| 30 | """Build the middle block with two ResNet blocks and optional attention.""" |
| 31 | mid = torch.nn.Module() |
| 32 | mid.block_1 = ResnetBlock( |
| 33 | in_channels=channels, |
| 34 | out_channels=channels, |
| 35 | temb_channels=temb_channels, |
| 36 | dropout=dropout, |
| 37 | norm_type=norm_type, |
| 38 | causality_axis=causality_axis, |
| 39 | ) |
| 40 | mid.attn_1 = make_attn(channels, attn_type=attn_type, norm_type=norm_type) if add_attention else torch.nn.Identity() |
| 41 | mid.block_2 = ResnetBlock( |
| 42 | in_channels=channels, |
| 43 | out_channels=channels, |
| 44 | temb_channels=temb_channels, |
| 45 | dropout=dropout, |
| 46 | norm_type=norm_type, |
| 47 | causality_axis=causality_axis, |
| 48 | ) |
| 49 | return mid |
| 50 | |
| 51 | |
| 52 | def run_mid_block(mid: torch.nn.Module, features: torch.Tensor) -> torch.Tensor: |
| 53 | """Run features through the middle block.""" |
| 54 | features = mid.block_1(features, temb=None) |
| 55 | features = mid.attn_1(features) |
| 56 | return mid.block_2(features, temb=None) |
| 57 | |
| 58 | |
| 59 | class AudioEncoder(torch.nn.Module): |
| 60 | """ |
| 61 | Encoder that compresses audio spectrograms into latent representations. |
| 62 | The encoder uses a series of downsampling blocks with residual connections, |
| 63 | attention mechanisms, and configurable causal convolutions. |
| 64 | """ |
| 65 | |
| 66 | def __init__( # noqa: PLR0913 |
| 67 | self, |
| 68 | *, |
| 69 | ch: int, |
| 70 | ch_mult: Tuple[int, ...] = (1, 2, 4, 8), |
| 71 | num_res_blocks: int, |
| 72 | attn_resolutions: Set[int], |
| 73 | dropout: float = 0.0, |
| 74 | resamp_with_conv: bool = True, |
| 75 | in_channels: int, |
| 76 | resolution: int, |
| 77 | z_channels: int, |
| 78 | double_z: bool = True, |
| 79 | attn_type: AttentionType = AttentionType.VANILLA, |
| 80 | mid_block_add_attention: bool = True, |
| 81 | norm_type: NormType = NormType.GROUP, |
| 82 | causality_axis: CausalityAxis = CausalityAxis.WIDTH, |
| 83 | sample_rate: int = 16000, |
| 84 | mel_hop_length: int = 160, |
| 85 | n_fft: int = 1024, |
| 86 | is_causal: bool = True, |
| 87 | mel_bins: int = 64, |
| 88 | **_ignore_kwargs, |
| 89 | ) -> None: |
| 90 | """ |
| 91 | Initialize the Encoder. |
| 92 | Args: |
| 93 | Arguments are configuration parameters, loaded from the audio VAE checkpoint config |
| 94 | (audio_vae.model.params.ddconfig): |
| 95 | ch: Base number of feature channels used in the first convolution layer. |
| 96 | ch_mult: Multiplicative factors for the number of channels at each resolution level. |
| 97 | num_res_blocks: Number of residual blocks to use at each resolution level. |
| 98 | attn_resolutions: Spatial resolutions (e.g., in time/frequency) at which to apply attention. |
| 99 | resolution: Input spatial resolution of the spectrogram (height, width). |
| 100 | z_channels: Number of channels in the latent representation. |
| 101 | norm_type: Normalization layer type to use within the network (e.g., group, batch). |
| 102 | causality_axis: Axis along which convolutions should be causal (e.g., time axis). |
| 103 | sample_rate: Audio sample rate in Hz for the input signals. |
| 104 | mel_hop_length: Hop length used when computing the mel spectrogram. |
| 105 | n_fft: FFT size used to compute the spectrogram. |
| 106 | mel_bins: Number of mel-frequency bins in the input spectrogram. |
| 107 | in_channels: Number of channels in the input spectrogram tensor. |
| 108 | double_z: If True, predict both mean and log-variance (doubling latent channels). |
| 109 | is_causal: If True, use causal convolutions suitable for streaming setups. |
| 110 | dropout: Dropout probability used in residual and mid blocks. |
| 111 | attn_type: Type of attention mechanism to use in attention blocks. |
| 112 | resamp_with_conv: If True, perform resolution changes using strided convolutions. |
| 113 | mid_block_add_attention: If True, add an attention block in the mid-level of the encoder. |
| 114 | """ |
| 115 | super().__init__() |
| 116 | |
| 117 | self.per_channel_statistics = PerChannelStatistics(latent_channels=ch) |
| 118 | self.sample_rate = sample_rate |
| 119 | self.mel_hop_length = mel_hop_length |
| 120 | self.n_fft = n_fft |
| 121 | self.is_causal = is_causal |
| 122 | self.mel_bins = mel_bins |
| 123 | |
| 124 | self.patchifier = AudioPatchifier( |
| 125 | patch_size=1, |
| 126 | audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR, |
| 127 | sample_rate=sample_rate, |
| 128 | hop_length=mel_hop_length, |
| 129 | is_causal=is_causal, |
| 130 | ) |
| 131 | |
| 132 | self.ch = ch |
| 133 | self.temb_ch = 0 |
| 134 | self.num_resolutions = len(ch_mult) |
| 135 | self.num_res_blocks = num_res_blocks |
| 136 | self.resolution = resolution |
| 137 | self.in_channels = in_channels |
| 138 | self.z_channels = z_channels |
| 139 | self.double_z = double_z |
| 140 | self.norm_type = norm_type |
| 141 | self.causality_axis = causality_axis |
| 142 | self.attn_type = attn_type |
| 143 | |
| 144 | # downsampling |
| 145 | self.conv_in = make_conv2d( |
| 146 | in_channels, |
| 147 | self.ch, |
| 148 | kernel_size=3, |
| 149 | stride=1, |
| 150 | causality_axis=self.causality_axis, |
| 151 | ) |
| 152 | |
| 153 | self.non_linearity = torch.nn.SiLU() |
| 154 | |
| 155 | self.down, block_in = build_downsampling_path( |
| 156 | ch=ch, |
| 157 | ch_mult=ch_mult, |
| 158 | num_resolutions=self.num_resolutions, |
| 159 | num_res_blocks=num_res_blocks, |
| 160 | resolution=resolution, |
| 161 | temb_channels=self.temb_ch, |
| 162 | dropout=dropout, |
| 163 | norm_type=self.norm_type, |
| 164 | causality_axis=self.causality_axis, |
| 165 | attn_type=self.attn_type, |
| 166 | attn_resolutions=attn_resolutions, |
| 167 | resamp_with_conv=resamp_with_conv, |
| 168 | ) |
| 169 | |
| 170 | self.mid = build_mid_block( |
| 171 | channels=block_in, |
| 172 | temb_channels=self.temb_ch, |
| 173 | dropout=dropout, |
| 174 | norm_type=self.norm_type, |
| 175 | causality_axis=self.causality_axis, |
| 176 | attn_type=self.attn_type, |
| 177 | add_attention=mid_block_add_attention, |
| 178 | ) |
| 179 | |
| 180 | self.norm_out = build_normalization_layer(block_in, normtype=self.norm_type) |
| 181 | self.conv_out = make_conv2d( |
| 182 | block_in, |
| 183 | 2 * z_channels if double_z else z_channels, |
| 184 | kernel_size=3, |
| 185 | stride=1, |
| 186 | causality_axis=self.causality_axis, |
| 187 | ) |
| 188 | |
| 189 | def forward(self, spectrogram: torch.Tensor) -> torch.Tensor: |
| 190 | """ |
| 191 | Encode audio spectrogram into latent representations. |
| 192 | Args: |
| 193 | spectrogram: Input spectrogram of shape (batch, channels, time, frequency) |
| 194 | Returns: |
| 195 | Encoded latent representation of shape (batch, channels, frames, mel_bins) |
| 196 | """ |
| 197 | h = self.conv_in(spectrogram) |
| 198 | h = self._run_downsampling_path(h) |
| 199 | h = run_mid_block(self.mid, h) |
| 200 | h = self._finalize_output(h) |
| 201 | |
| 202 | return self._normalize_latents(h) |
| 203 | |
| 204 | def _run_downsampling_path(self, h: torch.Tensor) -> torch.Tensor: |
| 205 | for level in range(self.num_resolutions): |
| 206 | stage = self.down[level] |
| 207 | for block_idx in range(self.num_res_blocks): |
| 208 | h = stage.block[block_idx](h, temb=None) |
| 209 | if stage.attn: |
| 210 | h = stage.attn[block_idx](h) |
| 211 | |
| 212 | if level != self.num_resolutions - 1: |
| 213 | h = stage.downsample(h) |
| 214 | |
| 215 | return h |
| 216 | |
| 217 | def _finalize_output(self, h: torch.Tensor) -> torch.Tensor: |
| 218 | h = self.norm_out(h) |
| 219 | h = self.non_linearity(h) |
| 220 | return self.conv_out(h) |
| 221 | |
| 222 | def _normalize_latents(self, latent_output: torch.Tensor) -> torch.Tensor: |
| 223 | """ |
| 224 | Normalize encoder latents using per-channel statistics. |
| 225 | When the encoder is configured with ``double_z=True``, the final |
| 226 | convolution produces twice the number of latent channels, typically |
| 227 | interpreted as two concatenated tensors along the channel dimension |
| 228 | (e.g., mean and variance or other auxiliary parameters). |
| 229 | This method intentionally uses only the first half of the channels |
| 230 | (the "mean" component) as input to the patchifier and normalization |
| 231 | logic. The remaining channels are left unchanged by this method and |
| 232 | are expected to be consumed elsewhere in the VAE pipeline. |
| 233 | If ``double_z=False``, the encoder output already contains only the |
| 234 | mean latents and the chunking operation simply returns that tensor. |
| 235 | """ |
| 236 | means = torch.chunk(latent_output, 2, dim=1)[0] |
| 237 | latent_shape = AudioLatentShape( |
| 238 | batch=means.shape[0], |
| 239 | channels=means.shape[1], |
| 240 | frames=means.shape[2], |
| 241 | mel_bins=means.shape[3], |
| 242 | ) |
| 243 | latent_patched = self.patchifier.patchify(means) |
| 244 | latent_normalized = self.per_channel_statistics.normalize(latent_patched) |
| 245 | return self.patchifier.unpatchify(latent_normalized, latent_shape) |
| 246 | |
| 247 | |
| 248 | def encode_audio( |
| 249 | audio: Audio, |
| 250 | audio_encoder: AudioEncoder, |
| 251 | audio_processor: AudioProcessor | None = None, |
| 252 | ) -> torch.Tensor: |
| 253 | """Encode audio waveform into latent representation. |
| 254 | Args: |
| 255 | audio: Audio container with waveform tensor of shape (batch, channels, samples) and sampling rate. |
| 256 | audio_encoder: Audio encoder model |
| 257 | audio_processor: Audio processor model (optional, if not provided, it will be created from the audio encoder) |
| 258 | """ |
| 259 | dtype = next(audio_encoder.parameters()).dtype |
| 260 | device = next(audio_encoder.parameters()).device |
| 261 | |
| 262 | if audio_processor is None: |
| 263 | audio_processor = AudioProcessor( |
| 264 | target_sample_rate=audio_encoder.sample_rate, |
| 265 | mel_bins=audio_encoder.mel_bins, |
| 266 | mel_hop_length=audio_encoder.mel_hop_length, |
| 267 | n_fft=audio_encoder.n_fft, |
| 268 | ).to(device=device) |
| 269 | |
| 270 | mel_spectrogram = audio_processor.waveform_to_mel(audio.to(device=device)) |
| 271 | |
| 272 | latent = audio_encoder(mel_spectrogram.to(dtype=dtype)) |
| 273 | return latent |
| 274 | |
| 275 | |
| 276 | class AudioDecoder(torch.nn.Module): |
| 277 | """ |
| 278 | Symmetric decoder that reconstructs audio spectrograms from latent features. |
| 279 | The decoder mirrors the encoder structure with configurable channel multipliers, |
| 280 | attention resolutions, and causal convolutions. |
| 281 | """ |
| 282 | |
| 283 | def __init__( # noqa: PLR0913 |
| 284 | self, |
| 285 | *, |
| 286 | ch: int, |
| 287 | out_ch: int, |
| 288 | ch_mult: Tuple[int, ...] = (1, 2, 4, 8), |
| 289 | num_res_blocks: int, |
| 290 | attn_resolutions: Set[int], |
| 291 | resolution: int, |
| 292 | z_channels: int, |
| 293 | norm_type: NormType = NormType.GROUP, |
| 294 | causality_axis: CausalityAxis = CausalityAxis.WIDTH, |
| 295 | dropout: float = 0.0, |
| 296 | mid_block_add_attention: bool = True, |
| 297 | sample_rate: int = 16000, |
| 298 | mel_hop_length: int = 160, |
| 299 | is_causal: bool = True, |
| 300 | mel_bins: int | None = None, |
| 301 | ) -> None: |
| 302 | """ |
| 303 | Initialize the Decoder. |
| 304 | Args: |
| 305 | Arguments are configuration parameters, loaded from the audio VAE checkpoint config |
| 306 | (audio_vae.model.params.ddconfig): |
| 307 | - ch, out_ch, ch_mult, num_res_blocks, attn_resolutions |
| 308 | - resolution, z_channels |
| 309 | - norm_type, causality_axis |
| 310 | """ |
| 311 | super().__init__() |
| 312 | |
| 313 | # Internal behavioural defaults that are not driven by the checkpoint. |
| 314 | resamp_with_conv = True |
| 315 | attn_type = AttentionType.VANILLA |
| 316 | |
| 317 | # Per-channel statistics for denormalizing latents |
| 318 | self.per_channel_statistics = PerChannelStatistics(latent_channels=ch) |
| 319 | self.sample_rate = sample_rate |
| 320 | self.mel_hop_length = mel_hop_length |
| 321 | self.is_causal = is_causal |
| 322 | self.mel_bins = mel_bins |
| 323 | self.patchifier = AudioPatchifier( |
| 324 | patch_size=1, |
| 325 | audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR, |
| 326 | sample_rate=sample_rate, |
| 327 | hop_length=mel_hop_length, |
| 328 | is_causal=is_causal, |
| 329 | ) |
| 330 | |
| 331 | self.ch = ch |
| 332 | self.temb_ch = 0 |
| 333 | self.num_resolutions = len(ch_mult) |
| 334 | self.num_res_blocks = num_res_blocks |
| 335 | self.resolution = resolution |
| 336 | self.out_ch = out_ch |
| 337 | self.give_pre_end = False |
| 338 | self.tanh_out = False |
| 339 | self.norm_type = norm_type |
| 340 | self.z_channels = z_channels |
| 341 | self.channel_multipliers = ch_mult |
| 342 | self.attn_resolutions = attn_resolutions |
| 343 | self.causality_axis = causality_axis |
| 344 | self.attn_type = attn_type |
| 345 | |
| 346 | base_block_channels = ch * self.channel_multipliers[-1] |
| 347 | base_resolution = resolution // (2 ** (self.num_resolutions - 1)) |
| 348 | self.z_shape = (1, z_channels, base_resolution, base_resolution) |
| 349 | |
| 350 | self.conv_in = make_conv2d( |
| 351 | z_channels, base_block_channels, kernel_size=3, stride=1, causality_axis=self.causality_axis |
| 352 | ) |
| 353 | self.non_linearity = torch.nn.SiLU() |
| 354 | self.mid = build_mid_block( |
| 355 | channels=base_block_channels, |
| 356 | temb_channels=self.temb_ch, |
| 357 | dropout=dropout, |
| 358 | norm_type=self.norm_type, |
| 359 | causality_axis=self.causality_axis, |
| 360 | attn_type=self.attn_type, |
| 361 | add_attention=mid_block_add_attention, |
| 362 | ) |
| 363 | self.up, final_block_channels = build_upsampling_path( |
| 364 | ch=ch, |
| 365 | ch_mult=ch_mult, |
| 366 | num_resolutions=self.num_resolutions, |
| 367 | num_res_blocks=num_res_blocks, |
| 368 | resolution=resolution, |
| 369 | temb_channels=self.temb_ch, |
| 370 | dropout=dropout, |
| 371 | norm_type=self.norm_type, |
| 372 | causality_axis=self.causality_axis, |
| 373 | attn_type=self.attn_type, |
| 374 | attn_resolutions=attn_resolutions, |
| 375 | resamp_with_conv=resamp_with_conv, |
| 376 | initial_block_channels=base_block_channels, |
| 377 | ) |
| 378 | |
| 379 | self.norm_out = build_normalization_layer(final_block_channels, normtype=self.norm_type) |
| 380 | self.conv_out = make_conv2d( |
| 381 | final_block_channels, out_ch, kernel_size=3, stride=1, causality_axis=self.causality_axis |
| 382 | ) |
| 383 | |
| 384 | def forward(self, sample: torch.Tensor) -> torch.Tensor: |
| 385 | """ |
| 386 | Decode latent features back to audio spectrograms. |
| 387 | Args: |
| 388 | sample: Encoded latent representation of shape (batch, channels, frames, mel_bins) |
| 389 | Returns: |
| 390 | Reconstructed audio spectrogram of shape (batch, channels, time, frequency) |
| 391 | """ |
| 392 | sample, target_shape = self._denormalize_latents(sample) |
| 393 | |
| 394 | h = self.conv_in(sample) |
| 395 | h = run_mid_block(self.mid, h) |
| 396 | h = self._run_upsampling_path(h) |
| 397 | h = self._finalize_output(h) |
| 398 | |
| 399 | return self._adjust_output_shape(h, target_shape) |
| 400 | |
| 401 | def _denormalize_latents(self, sample: torch.Tensor) -> tuple[torch.Tensor, AudioLatentShape]: |
| 402 | latent_shape = AudioLatentShape( |
| 403 | batch=sample.shape[0], |
| 404 | channels=sample.shape[1], |
| 405 | frames=sample.shape[2], |
| 406 | mel_bins=sample.shape[3], |
| 407 | ) |
| 408 | |
| 409 | sample_patched = self.patchifier.patchify(sample) |
| 410 | sample_denormalized = self.per_channel_statistics.un_normalize(sample_patched) |
| 411 | sample = self.patchifier.unpatchify(sample_denormalized, latent_shape) |
| 412 | |
| 413 | target_frames = latent_shape.frames * LATENT_DOWNSAMPLE_FACTOR |
| 414 | if self.causality_axis != CausalityAxis.NONE: |
| 415 | target_frames = max(target_frames - (LATENT_DOWNSAMPLE_FACTOR - 1), 1) |
| 416 | |
| 417 | target_shape = AudioLatentShape( |
| 418 | batch=latent_shape.batch, |
| 419 | channels=self.out_ch, |
| 420 | frames=target_frames, |
| 421 | mel_bins=self.mel_bins if self.mel_bins is not None else latent_shape.mel_bins, |
| 422 | ) |
| 423 | |
| 424 | return sample, target_shape |
| 425 | |
| 426 | def _adjust_output_shape( |
| 427 | self, |
| 428 | decoded_output: torch.Tensor, |
| 429 | target_shape: AudioLatentShape, |
| 430 | ) -> torch.Tensor: |
| 431 | """ |
| 432 | Adjust output shape to match target dimensions for variable-length audio. |
| 433 | This function handles the common case where decoded audio spectrograms need to be |
| 434 | resized to match a specific target shape. |
| 435 | Args: |
| 436 | decoded_output: Tensor of shape (batch, channels, time, frequency) |
| 437 | target_shape: AudioLatentShape describing (batch, channels, time, mel bins) |
| 438 | Returns: |
| 439 | Tensor adjusted to match target_shape exactly |
| 440 | """ |
| 441 | # Current output shape: (batch, channels, time, frequency) |
| 442 | _, _, current_time, current_freq = decoded_output.shape |
| 443 | target_channels = target_shape.channels |
| 444 | target_time = target_shape.frames |
| 445 | target_freq = target_shape.mel_bins |
| 446 | |
| 447 | # Step 1: Crop first to avoid exceeding target dimensions |
| 448 | decoded_output = decoded_output[ |
| 449 | :, :target_channels, : min(current_time, target_time), : min(current_freq, target_freq) |
| 450 | ] |
| 451 | |
| 452 | # Step 2: Calculate padding needed for time and frequency dimensions |
| 453 | time_padding_needed = target_time - decoded_output.shape[2] |
| 454 | freq_padding_needed = target_freq - decoded_output.shape[3] |
| 455 | |
| 456 | # Step 3: Apply padding if needed |
| 457 | if time_padding_needed > 0 or freq_padding_needed > 0: |
| 458 | # PyTorch padding format: (pad_left, pad_right, pad_top, pad_bottom) |
| 459 | # For audio: pad_left/right = frequency, pad_top/bottom = time |
| 460 | padding = ( |
| 461 | 0, |
| 462 | max(freq_padding_needed, 0), # frequency padding (left, right) |
| 463 | 0, |
| 464 | max(time_padding_needed, 0), # time padding (top, bottom) |
| 465 | ) |
| 466 | decoded_output = F.pad(decoded_output, padding) |
| 467 | |
| 468 | # Step 4: Final safety crop to ensure exact target shape |
| 469 | decoded_output = decoded_output[:, :target_channels, :target_time, :target_freq] |
| 470 | |
| 471 | return decoded_output |
| 472 | |
| 473 | def _run_upsampling_path(self, h: torch.Tensor) -> torch.Tensor: |
| 474 | for level in reversed(range(self.num_resolutions)): |
| 475 | stage = self.up[level] |
| 476 | for block_idx, block in enumerate(stage.block): |
| 477 | h = block(h, temb=None) |
| 478 | if stage.attn: |
| 479 | h = stage.attn[block_idx](h) |
| 480 | |
| 481 | if level != 0 and hasattr(stage, "upsample"): |
| 482 | h = stage.upsample(h) |
| 483 | |
| 484 | return h |
| 485 | |
| 486 | def _finalize_output(self, h: torch.Tensor) -> torch.Tensor: |
| 487 | if self.give_pre_end: |
| 488 | return h |
| 489 | |
| 490 | h = self.norm_out(h) |
| 491 | h = self.non_linearity(h) |
| 492 | h = self.conv_out(h) |
| 493 | return torch.tanh(h) if self.tanh_out else h |
| 494 | |
| 495 | |
| 496 | def decode_audio(latent: torch.Tensor, audio_decoder: "AudioDecoder", vocoder: "Vocoder") -> Audio: |
| 497 | """ |
| 498 | Decode an audio latent representation using the provided audio decoder and vocoder. |
| 499 | Args: |
| 500 | latent: Input audio latent tensor. |
| 501 | audio_decoder: Model to decode the latent to waveform features. |
| 502 | vocoder: Model to convert decoded features to audio waveform. |
| 503 | Returns: |
| 504 | Decoded audio with waveform and sampling rate. |
| 505 | """ |
| 506 | decoded_audio = audio_decoder(latent) |
| 507 | waveform = vocoder(decoded_audio).squeeze(0).float() |
| 508 | return Audio(waveform=waveform, sampling_rate=vocoder.output_sampling_rate) |
| 509 |