Replace multi-resolution discriminator; update AdamW default config (#30

)

* New multi-resolution discriminator adopted from DAC

* Default optimizer params

* Bump version

* Update README.md

README.md

@@ -82,14 +82,10 @@ See [example notebook](notebooks%2FBark%2BVocos.ipynb).
 
 ## Pre-trained models
 
-The provided models were trained up to 2.5 million generator iterations, which resulted in slightly better objective
-scores
-compared to those reported in the paper.
-
 | Model Name                                                                          | Dataset       | Training Iterations | Parameters 
-|-------------------------------------------------------------------------------------|---------------|---------------------|------------|
-| [charactr/vocos-mel-24khz](https://huggingface.co/charactr/vocos-mel-24khz)         | LibriTTS      | 2.5 M               | 13.5 M     
-| [charactr/vocos-encodec-24khz](https://huggingface.co/charactr/vocos-encodec-24khz) | DNS Challenge | 2.5 M               | 7.9 M      
+|-------------------------------------------------------------------------------------|---------------|-------------------|------------|
+| [charactr/vocos-mel-24khz](https://huggingface.co/charactr/vocos-mel-24khz)         | LibriTTS      | 1M                | 13.5M
+| [charactr/vocos-encodec-24khz](https://huggingface.co/charactr/vocos-encodec-24khz) | DNS Challenge | 2M                | 7.9M
 
 ## Training
 

configs/vocos-encodec.yaml

@@ -22,7 +22,7 @@ model:
   class_path: vocos.experiment.VocosEncodecExp
   init_args:
     sample_rate: 24000
-    initial_learning_rate: 2e-4
+    initial_learning_rate: 5e-4
     mel_loss_coeff: 45
     mrd_loss_coeff: 1.0
     num_warmup_steps: 0 # Optimizers warmup steps

configs/vocos-imdct.yaml

@@ -22,7 +22,7 @@ model:
   class_path: vocos.experiment.VocosExp
   init_args:
     sample_rate: 24000
-    initial_learning_rate: 2e-4
+    initial_learning_rate: 5e-4
     mel_loss_coeff: 45
     mrd_loss_coeff: 0.1
     num_warmup_steps: 0 # Optimizers warmup steps

configs/vocos-resnet.yaml

@@ -22,7 +22,7 @@ model:
   class_path: vocos.experiment.VocosExp
   init_args:
     sample_rate: 24000
-    initial_learning_rate: 2e-4
+    initial_learning_rate: 5e-4
     mel_loss_coeff: 45
     mrd_loss_coeff: 0.1
     num_warmup_steps: 0 # Optimizers warmup steps

configs/vocos.yaml

@@ -22,7 +22,7 @@ model:
   class_path: vocos.experiment.VocosExp
   init_args:
     sample_rate: 24000
-    initial_learning_rate: 2e-4
+    initial_learning_rate: 5e-4
     mel_loss_coeff: 45
     mrd_loss_coeff: 0.1
     num_warmup_steps: 0 # Optimizers warmup steps

vocos/__init__.py

@@ -1,4 +1,4 @@
 from vocos.pretrained import Vocos
 
 
-__version__ = "0.0.4"
+__version__ = "0.1.0"

vocos/discriminators.py

@@ -1,12 +1,11 @@
 from typing import List, Optional, Tuple
 
 import torch
+from einops import rearrange
 from torch import nn
 from torch.nn import Conv2d
 from torch.nn.utils import weight_norm
-
-PeriodsType = Tuple[int, ...]
-ResolutionType = Tuple[int, int, int]
+from torchaudio.transforms import Spectrogram
 
 
 class MultiPeriodDiscriminator(nn.Module):
@@ -20,7 +19,7 @@ class MultiPeriodDiscriminator(nn.Module):
             Defaults to None.
     """
 
-    def __init__(self, periods: PeriodsType = (2, 3, 5, 7, 11), num_embeddings: Optional[int] = None):
+    def __init__(self, periods: Tuple[int, ...] = (2, 3, 5, 7, 11), num_embeddings: Optional[int] = None):
         super().__init__()
         self.discriminators = nn.ModuleList([DiscriminatorP(period=p, num_embeddings=num_embeddings) for p in periods])
 
@@ -104,30 +103,26 @@ def forward(
 class MultiResolutionDiscriminator(nn.Module):
     def __init__(
         self,
-        resolutions: Tuple[ResolutionType, ResolutionType, ResolutionType] = (
-            (1024, 256, 1024),
-            (2048, 512, 2048),
-            (512, 128, 512),
-        ),
+        fft_sizes: Tuple[int, ...] = (2048, 1024, 512),
         num_embeddings: Optional[int] = None,
     ):
         """
-        Multi-Resolution Discriminator module adapted from https://github.com/mindslab-ai/univnet.
+        Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec.
         Additionally, it allows incorporating conditional information with a learned embeddings table.
 
         Args:
-            resolutions (tuple[tuple[int, int, int]]): Tuple of resolutions for each discriminator.
-                Each resolution should be a tuple of (n_fft, hop_length, win_length).
+            fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512).
             num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
                 Defaults to None.
         """
+
         super().__init__()
         self.discriminators = nn.ModuleList(
-            [DiscriminatorR(resolution=r, num_embeddings=num_embeddings) for r in resolutions]
+            [DiscriminatorR(window_length=w, num_embeddings=num_embeddings) for w in fft_sizes]
         )
 
     def forward(
-        self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: Optional[torch.Tensor] = None
+        self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
     ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
         y_d_rs = []
         y_d_gs = []
@@ -148,40 +143,62 @@ def forward(
 class DiscriminatorR(nn.Module):
     def __init__(
         self,
-        resolution: Tuple[int, int, int],
-        channels: int = 64,
-        in_channels: int = 1,
+        window_length: int,
         num_embeddings: Optional[int] = None,
-        lrelu_slope: float = 0.1,
+        channels: int = 32,
+        hop_factor: float = 0.25,
+        bands: Tuple[Tuple[float, float], ...] = ((0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)),
     ):
         super().__init__()
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.lrelu_slope = lrelu_slope
-        self.convs = nn.ModuleList(
+        self.window_length = window_length
+        self.hop_factor = hop_factor
+        self.spec_fn = Spectrogram(
+            n_fft=window_length, hop_length=int(window_length * hop_factor), win_length=window_length, power=None
+        )
+        n_fft = window_length // 2 + 1
+        bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
+        self.bands = bands
+        convs = lambda: nn.ModuleList(
             [
-                weight_norm(nn.Conv2d(in_channels, channels, kernel_size=(7, 5), stride=(2, 2), padding=(3, 2))),
-                weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 1), padding=(2, 1))),
-                weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 2), padding=(2, 1))),
-                weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 1), padding=1)),
-                weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 2), padding=1)),
+                weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))),
+                weight_norm(nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))),
             ]
         )
+        self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
+
         if num_embeddings is not None:
             self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels)
             torch.nn.init.zeros_(self.emb.weight)
-        self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), padding=(1, 1)))
 
-    def forward(
-        self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None
-    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1)))
+
+    def spectrogram(self, x):
+        # Remove DC offset
+        x = x - x.mean(dim=-1, keepdims=True)
+        # Peak normalize the volume of input audio
+        x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
+        x = self.spec_fn(x)
+        x = torch.view_as_real(x)
+        x = rearrange(x, "b f t c -> b c t f")
+        # Split into bands
+        x_bands = [x[..., b[0] : b[1]] for b in self.bands]
+        return x_bands
+
+    def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None):
+        x_bands = self.spectrogram(x)
         fmap = []
-        x = self.spectrogram(x)
-        x = x.unsqueeze(1)
-        for l in self.convs:
-            x = l(x)
-            x = torch.nn.functional.leaky_relu(x, self.lrelu_slope)
-            fmap.append(x)
+        x = []
+        for band, stack in zip(x_bands, self.band_convs):
+            for i, layer in enumerate(stack):
+                band = layer(band)
+                band = torch.nn.functional.leaky_relu(band, 0.1)
+                if i > 0:
+                    fmap.append(band)
+            x.append(band)
+        x = torch.cat(x, dim=-1)
         if cond_embedding_id is not None:
             emb = self.emb(cond_embedding_id)
             h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
@@ -190,20 +207,5 @@ def forward(
         x = self.conv_post(x)
         fmap.append(x)
         x += h
-        x = torch.flatten(x, 1, -1)
 
         return x, fmap
-
-    def spectrogram(self, x: torch.Tensor) -> torch.Tensor:
-        n_fft, hop_length, win_length = self.resolution
-        magnitude_spectrogram = torch.stft(
-            x,
-            n_fft=n_fft,
-            hop_length=hop_length,
-            win_length=win_length,
-            window=None,  # interestingly rectangular window kind of works here
-            center=True,
-            return_complex=True,
-        ).abs()
-
-        return magnitude_spectrogram

vocos/experiment.py

@@ -78,8 +78,8 @@ def configure_optimizers(self):
             {"params": self.head.parameters()},
         ]
 
-        opt_disc = torch.optim.AdamW(disc_params, lr=self.hparams.initial_learning_rate)
-        opt_gen = torch.optim.AdamW(gen_params, lr=self.hparams.initial_learning_rate)
+        opt_disc = torch.optim.AdamW(disc_params, lr=self.hparams.initial_learning_rate, betas=(0.8, 0.9))
+        opt_gen = torch.optim.AdamW(gen_params, lr=self.hparams.initial_learning_rate, betas=(0.8, 0.9))
 
         max_steps = self.trainer.max_steps // 2  # Max steps per optimizer
         scheduler_disc = transformers.get_cosine_schedule_with_warmup(