feat: add fbank_only property to WeSpeaker models

CHANGELOG.md

@@ -6,15 +6,16 @@
 
 - feat(task): add option to cache task training metadata to speed up training (with [@clement-pages](https://github.com/clement-pages/))
 - feat(model): add `receptive_field`, `num_frames` and `dimension` to models (with [@Bilal-Rahou](https://github.com/Bilal-Rahou))
+- feat(model): add `fbank_only` property to `WeSpeaker` models
 - feat(util): add `Powerset.permutation_mapping` to help with permutation in powerset space (with [@FrenchKrab](https://github.com/FrenchKrab))
-- feat(sample): add sample file at `pyannote.audio.sample.SAMPLE_FILE` 
+- feat(sample): add sample file at `pyannote.audio.sample.SAMPLE_FILE`
 - feat(metric): add `reduce` option to `diarization_error_rate` metric (with [@Bilal-Rahou](https://github.com/Bilal-Rahou))
 - feat(pipeline): add `Waveform` and `SampleRate` preprocessors
 
 ### Fixes
 
-- fix(task): fix random generators and their reproducibility  (with [@FrenchKrab](https://github.com/FrenchKrab))
-- fix(task): fix estimation of training set size  (with [@FrenchKrab](https://github.com/FrenchKrab))
+- fix(task): fix random generators and their reproducibility (with [@FrenchKrab](https://github.com/FrenchKrab))
+- fix(task): fix estimation of training set size (with [@FrenchKrab](https://github.com/FrenchKrab))
 
 ### Improvements
 

pyannote/audio/models/embedding/wespeaker/__init__.py

@@ -25,6 +25,7 @@
 from typing import Optional
 
 import torch
+import torch.nn.functional as F
 import torchaudio.compliance.kaldi as kaldi
 
 from pyannote.audio.core.model import Model
@@ -39,16 +40,33 @@
 
 
 class BaseWeSpeakerResNet(Model):
+    """Base class for WeSpeaker's ResNet models
+
+    Parameters
+    ----------
+    fbank_centering_span : float, optional
+        Span of the fbank centering window (in seconds).
+        Defaults (None) to use whole input.
+
+    See also
+    --------
+    torchaudio.compliance.kaldi.fbank
+
+    """
+
     def __init__(
         self,
         sample_rate: int = 16000,
         num_channels: int = 1,
         num_mel_bins: int = 80,
-        frame_length: int = 25,
-        frame_shift: int = 10,
+        frame_length: float = 25.0,  # in milliseconds
+        frame_shift: float = 10.0,  # in milliseconds
+        round_to_power_of_two: bool = True,
+        snip_edges: bool = True,
         dither: float = 0.0,
         window_type: str = "hamming",
         use_energy: bool = False,
+        fbank_centering_span: Optional[float] = None,
         task: Optional[Task] = None,
     ):
         super().__init__(sample_rate=sample_rate, num_channels=num_channels, task=task)
@@ -60,21 +78,38 @@ def __init__(
             "frame_length",
             "frame_shift",
             "dither",
+            "round_to_power_of_two",
+            "snip_edges",
             "window_type",
             "use_energy",
+            "fbank_centering_span",
         )
 
         self._fbank = partial(
             kaldi.fbank,
             num_mel_bins=self.hparams.num_mel_bins,
             frame_length=self.hparams.frame_length,
+            round_to_power_of_two=self.hparams.round_to_power_of_two,
             frame_shift=self.hparams.frame_shift,
+            snip_edges=self.hparams.snip_edges,
             dither=self.hparams.dither,
             sample_frequency=self.hparams.sample_rate,
             window_type=self.hparams.window_type,
             use_energy=self.hparams.use_energy,
         )
 
+    @property
+    def fbank_only(self) -> bool:
+        """Whether to only extract fbank features"""
+        return getattr(self, "_fbank_only", False)
+
+    @fbank_only.setter
+    def fbank_only(self, value: bool):
+        if hasattr(self, "receptive_field"):
+            del self.receptive_field
+
+        self._fbank_only = value
+
     def compute_fbank(self, waveforms: torch.Tensor) -> torch.Tensor:
         """Extract fbank features
 
@@ -85,6 +120,7 @@ def compute_fbank(self, waveforms: torch.Tensor) -> torch.Tensor:
         Returns
         -------
         fbank : (batch_size, num_frames, num_mel_bins)
+            fbank features
 
         Source: https://github.com/wenet-e2e/wespeaker/blob/45941e7cba2c3ea99e232d02bedf617fc71b0dad/wespeaker/bin/infer_onnx.py#L30C1-L50
         """
@@ -98,11 +134,37 @@ def compute_fbank(self, waveforms: torch.Tensor) -> torch.Tensor:
 
         features = torch.vmap(self._fbank)(waveforms.to(fft_device)).to(device)
 
-        return features - torch.mean(features, dim=1, keepdim=True)
+        # center features with global average
+        if self.hparams.fbank_centering_span is None:
+            return features - torch.mean(features, dim=1, keepdim=True)
+
+        # center features with running average
+        window_size = int(self.hparams.sample_rate * self.hparams.frame_length * 0.001)
+        step_size = int(self.hparams.sample_rate * self.hparams.frame_shift * 0.001)
+        kernel_size = conv1d_num_frames(
+            num_samples=int(
+                self.hparams.fbank_centering_span * self.hparams.sample_rate
+            ),
+            kernel_size=window_size,
+            stride=step_size,
+            padding=0,
+            dilation=1,
+        )
+        return features - F.avg_pool1d(
+            features.transpose(1, 2),
+            kernel_size=2 * (kernel_size // 2) + 1,
+            stride=1,
+            padding=kernel_size // 2,
+            count_include_pad=False,
+        ).transpose(1, 2)
 
     @property
     def dimension(self) -> int:
         """Dimension of output"""
+
+        if self.fbank_only:
+            return self.hparams.num_mel_bins
+
         return self.resnet.embed_dim
 
     @lru_cache
@@ -122,13 +184,19 @@ def num_frames(self, num_samples: int) -> int:
         window_size = int(self.hparams.sample_rate * self.hparams.frame_length * 0.001)
         step_size = int(self.hparams.sample_rate * self.hparams.frame_shift * 0.001)
 
+        # TODO: take round_to_power_of_two and snip_edges into account
+
         num_frames = conv1d_num_frames(
             num_samples=num_samples,
             kernel_size=window_size,
             stride=step_size,
             padding=0,
             dilation=1,
         )
+
+        if self.fbank_only:
+            return num_frames
+
         return self.resnet.num_frames(num_frames)
 
     def receptive_field_size(self, num_frames: int = 1) -> int:
@@ -144,8 +212,13 @@ def receptive_field_size(self, num_frames: int = 1) -> int:
         receptive_field_size : int
             Receptive field size.
         """
+
         receptive_field_size = num_frames
-        receptive_field_size = self.resnet.receptive_field_size(receptive_field_size)
+
+        if not self.fbank_only:
+            receptive_field_size = self.resnet.receptive_field_size(
+                receptive_field_size
+            )
 
         window_size = int(self.hparams.sample_rate * self.hparams.frame_length * 0.001)
         step_size = int(self.hparams.sample_rate * self.hparams.frame_shift * 0.001)
@@ -172,9 +245,11 @@ def receptive_field_center(self, frame: int = 0) -> int:
             Index of receptive field center.
         """
         receptive_field_center = frame
-        receptive_field_center = self.resnet.receptive_field_center(
-            frame=receptive_field_center
-        )
+
+        if not self.fbank_only:
+            receptive_field_center = self.resnet.receptive_field_center(
+                frame=receptive_field_center
+            )
 
         window_size = int(self.hparams.sample_rate * self.hparams.frame_length * 0.001)
         step_size = int(self.hparams.sample_rate * self.hparams.frame_shift * 0.001)
@@ -189,14 +264,79 @@ def receptive_field_center(self, frame: int = 0) -> int:
     def forward(
         self, waveforms: torch.Tensor, weights: Optional[torch.Tensor] = None
     ) -> torch.Tensor:
+        """Extract speaker embeddings
+
+        Parameters
+        ----------
+        waveforms : torch.Tensor
+            Batch of waveforms with shape (batch, channel, sample)
+        weights : (batch, frames) or (batch, speakers, frames) torch.Tensor, optional
+            Batch of weights passed to statistics pooling layer.
+
+        Returns
+        -------
+        embeddings : (batch, dimension) or (batch, speakers, dimension) torch.Tensor
+            Batch of embeddings.
         """
 
+        fbank = self.compute_fbank(waveforms)
+        if self.fbank_only:
+            return fbank
+
+        return self.resnet(fbank, weights=weights)[1]
+
+    def forward_frames(self, waveforms: torch.Tensor) -> torch.Tensor:
+        """Extract frame-wise embeddings
+
         Parameters
         ----------
         waveforms : torch.Tensor
             Batch of waveforms with shape (batch, channel, sample)
-        weights : torch.Tensor, optional
-            Batch of weights with shape (batch, frame).
+
+        Returns
+        -------
+        embeddings : (batch, ..., embedding_frames) torch.Tensor
+            Batch of frame-wise embeddings.
+        """
+        fbank = self.compute_fbank(waveforms)
+        return self.resnet.forward_frames(fbank)
+
+    def forward_embedding(
+        self, frames: torch.Tensor, weights: torch.Tensor = None
+    ) -> torch.Tensor:
+        """Extract speaker embeddings from frame-wise embeddings
+
+        Parameters
+        ----------
+        frames : torch.Tensor
+            Batch of frames with shape (batch, ..., embedding_frames).
+        weights : (batch, frames) or (batch, speakers, frames) torch.Tensor, optional
+            Batch of weights passed to statistics pooling layer.
+
+        Returns
+        -------
+        embeddings : (batch, dimension) or (batch, speakers, dimension) torch.Tensor
+            Batch of embeddings.
+
+        """
+        return self.resnet.forward_embedding(frames, weights=weights)[1]
+
+    def forward(
+        self, waveforms: torch.Tensor, weights: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """Extract speaker embeddings
+
+        Parameters
+        ----------
+        waveforms : torch.Tensor
+            Batch of waveforms with shape (batch, channel, sample)
+        weights : (batch, frames) or (batch, speakers, frames) torch.Tensor, optional
+            Batch of weights passed to statistics pooling layer.
+
+        Returns
+        -------
+        embeddings : (batch, dimension) or (batch, speakers, dimension) torch.Tensor
+            Batch of embeddings.
         """
 
         fbank = self.compute_fbank(waveforms)

pyannote/audio/models/embedding/wespeaker/resnet.py

@@ -344,12 +344,64 @@ def receptive_field_center(self, frame: int = 0) -> int:
 
         return receptive_field_center
 
-    def forward(self, x: torch.Tensor, weights: Optional[torch.Tensor] = None):
+    def forward_frames(self, fbank: torch.Tensor) -> torch.Tensor:
+        """Extract frame-wise embeddings
+
+        Parameters
+        ----------
+        fbanks : (batch, frames, features) torch.Tensor
+            Batch of fbank features
+
+        Returns
+        -------
+        embeddings : (batch, ..., embedding_frames) torch.Tensor
+            Batch of frame-wise embeddings.
+
         """
+        fbank = fbank.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+        fbank = fbank.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(fbank)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        return out
+
+    def forward_embedding(
+        self, frames: torch.Tensor, weights: torch.Tensor = None
+    ) -> torch.Tensor:
+        """Extract speaker embeddings
 
         Parameters
         ----------
-        x : (batch, frames, features) torch.Tensor
+        frames : torch.Tensor
+            Batch of frames with shape (batch, ..., embedding_frames).
+        weights : (batch, frames) or (batch, speakers, frames) torch.Tensor, optional
+            Batch of weights passed to statistics pooling layer.
+
+        Returns
+        -------
+        embeddings : (batch, dimension) or (batch, speakers, dimension) torch.Tensor
+            Batch of embeddings.
+        """
+
+        stats = self.pool(frames, weights=weights)
+
+        embed_a = self.seg_1(stats)
+        if self.two_emb_layer:
+            out = F.relu(embed_a)
+            out = self.seg_bn_1(out)
+            embed_b = self.seg_2(out)
+            return embed_a, embed_b
+        else:
+            return torch.tensor(0.0), embed_a
+
+    def forward(self, fbank: torch.Tensor, weights: Optional[torch.Tensor] = None):
+        """Extract speaker embeddings
+
+        Parameters
+        ----------
+        fbank : (batch, frames, features) torch.Tensor
             Batch of features
         weights : (batch, frames) torch.Tensor, optional
             Batch of weights
@@ -358,10 +410,9 @@ def forward(self, x: torch.Tensor, weights: Optional[torch.Tensor] = None):
         -------
         embedding : (batch, embedding_dim) torch.Tensor
         """
-        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
-
-        x = x.unsqueeze_(1)
-        out = F.relu(self.bn1(self.conv1(x)))
+        fbank = fbank.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+        fbank = fbank.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(fbank)))
         out = self.layer1(out)
         out = self.layer2(out)
         out = self.layer3(out)