diff --git a/CHANGELOG.md b/CHANGELOG.md
index d63c193d0..346d8ad26 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,132 +1,167 @@
# Changelog
+## `develop` branch
+
+## Version 3.1.0 (2023-11-16)
+
+### TL;DR
+
+[`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) no longer requires [unpopular](https://github.com/pyannote/pyannote-audio/issues/1537) ONNX runtime
+
+### New features
+
+- feat(model): add WeSpeaker embedding wrapper based on PyTorch
+- feat(model): add support for multi-speaker statistics pooling
+- feat(pipeline): add `TimingHook` for profiling processing time
+- feat(pipeline): add `ArtifactHook` for saving internal steps
+- feat(pipeline): add support for list of hooks with `Hooks`
+- feat(utils): add `"soft"` option to `Powerset.to_multilabel`
+
+### Fixes
+
+- fix(pipeline): add missing "embedding" hook call in `SpeakerDiarization`
+- fix(pipeline): fix `AgglomerativeClustering` to honor `num_clusters` when provided
+- fix(pipeline): fix frame-wise speaker count exceeding `max_speakers` or detected `num_speakers` in `SpeakerDiarization` pipeline
+
+### Improvements
+
+- improve(pipeline): compute `fbank` on GPU when requested
+
+### Breaking changes
+
+- BREAKING(pipeline): rename `WeSpeakerPretrainedSpeakerEmbedding` to `ONNXWeSpeakerPretrainedSpeakerEmbedding`
+- BREAKING(setup): remove `onnxruntime` dependency.
+ You can still use ONNX `hbredin/wespeaker-voxceleb-resnet34-LM` but you will have to install `onnxruntime` yourself.
+- BREAKING(pipeline): remove `logging_hook` (use `ArtifactHook` instead)
+- BREAKING(pipeline): remove `onset` and `offset` parameter in `SpeakerDiarizationMixin.speaker_count`
+ You should now binarize segmentations before passing them to `speaker_count`
+
## Version 3.0.1 (2023-09-28)
- - fix(pipeline): fix WeSpeaker GPU support
+- fix(pipeline): fix WeSpeaker GPU support
## Version 3.0.0 (2023-09-26)
### Features and improvements
- - feat(pipeline): send pipeline to device with `pipeline.to(device)`
- - feat(pipeline): add `return_embeddings` option to `SpeakerDiarization` pipeline
- - feat(pipeline): make `segmentation_batch_size` and `embedding_batch_size` mutable in `SpeakerDiarization` pipeline (they now default to `1`)
- - feat(pipeline): add progress hook to pipelines
- - feat(task): add [powerset](https://www.isca-speech.org/archive/interspeech_2023/plaquet23_interspeech.html) support to `SpeakerDiarization` task
- - feat(task): add support for multi-task models
- - feat(task): add support for label scope in speaker diarization task
- - feat(task): add support for missing classes in multi-label segmentation task
- - feat(model): add segmentation model based on torchaudio self-supervised representation
- - feat(pipeline): check version compatibility at load time
- - improve(task): load metadata as tensors rather than pyannote.core instances
- - improve(task): improve error message on missing specifications
+- feat(pipeline): send pipeline to device with `pipeline.to(device)`
+- feat(pipeline): add `return_embeddings` option to `SpeakerDiarization` pipeline
+- feat(pipeline): make `segmentation_batch_size` and `embedding_batch_size` mutable in `SpeakerDiarization` pipeline (they now default to `1`)
+- feat(pipeline): add progress hook to pipelines
+- feat(task): add [powerset](https://www.isca-speech.org/archive/interspeech_2023/plaquet23_interspeech.html) support to `SpeakerDiarization` task
+- feat(task): add support for multi-task models
+- feat(task): add support for label scope in speaker diarization task
+- feat(task): add support for missing classes in multi-label segmentation task
+- feat(model): add segmentation model based on torchaudio self-supervised representation
+- feat(pipeline): check version compatibility at load time
+- improve(task): load metadata as tensors rather than pyannote.core instances
+- improve(task): improve error message on missing specifications
### Breaking changes
- - BREAKING(task): rename `Segmentation` task to `SpeakerDiarization`
- - BREAKING(pipeline): pipeline defaults to CPU (use `pipeline.to(device)`)
- - BREAKING(pipeline): remove `SpeakerSegmentation` pipeline (use `SpeakerDiarization` pipeline)
- - BREAKING(pipeline): remove `segmentation_duration` parameter from `SpeakerDiarization` pipeline (defaults to `duration` of segmentation model)
- - BREAKING(task): remove support for variable chunk duration for segmentation tasks
- - BREAKING(pipeline): remove support for `FINCHClustering` and `HiddenMarkovModelClustering`
- - BREAKING(setup): drop support for Python 3.7
- - BREAKING(io): channels are now 0-indexed (used to be 1-indexed)
- - BREAKING(io): multi-channel audio is no longer downmixed to mono by default.
- You should update how `pyannote.audio.core.io.Audio` is instantiated:
- * replace `Audio()` by `Audio(mono="downmix")`;
- * replace `Audio(mono=True)` by `Audio(mono="downmix")`;
- * replace `Audio(mono=False)` by `Audio()`.
- - BREAKING(model): get rid of (flaky) `Model.introspection`
- If, for some weird reason, you wrote some custom code based on that,
- you should instead rely on `Model.example_output`.
- - BREAKING(interactive): remove support for Prodigy recipes
-
+- BREAKING(task): rename `Segmentation` task to `SpeakerDiarization`
+- BREAKING(pipeline): pipeline defaults to CPU (use `pipeline.to(device)`)
+- BREAKING(pipeline): remove `SpeakerSegmentation` pipeline (use `SpeakerDiarization` pipeline)
+- BREAKING(pipeline): remove `segmentation_duration` parameter from `SpeakerDiarization` pipeline (defaults to `duration` of segmentation model)
+- BREAKING(task): remove support for variable chunk duration for segmentation tasks
+- BREAKING(pipeline): remove support for `FINCHClustering` and `HiddenMarkovModelClustering`
+- BREAKING(setup): drop support for Python 3.7
+- BREAKING(io): channels are now 0-indexed (used to be 1-indexed)
+- BREAKING(io): multi-channel audio is no longer downmixed to mono by default.
+ You should update how `pyannote.audio.core.io.Audio` is instantiated:
+ - replace `Audio()` by `Audio(mono="downmix")`;
+ - replace `Audio(mono=True)` by `Audio(mono="downmix")`;
+ - replace `Audio(mono=False)` by `Audio()`.
+- BREAKING(model): get rid of (flaky) `Model.introspection`
+ If, for some weird reason, you wrote some custom code based on that,
+ you should instead rely on `Model.example_output`.
+- BREAKING(interactive): remove support for Prodigy recipes
### Fixes and improvements
- - fix(pipeline): fix reproducibility issue with Ampere CUDA devices
- - fix(pipeline): fix support for IOBase audio
- - fix(pipeline): fix corner case with no speaker
- - fix(train): prevent metadata preparation to happen twice
- - fix(task): fix support for "balance" option
- - improve(task): shorten and improve structure of Tensorboard tags
+- fix(pipeline): fix reproducibility issue with Ampere CUDA devices
+- fix(pipeline): fix support for IOBase audio
+- fix(pipeline): fix corner case with no speaker
+- fix(train): prevent metadata preparation to happen twice
+- fix(task): fix support for "balance" option
+- improve(task): shorten and improve structure of Tensorboard tags
### Dependencies update
- - setup: switch to torch 2.0+, torchaudio 2.0+, soundfile 0.12+, lightning 2.0+, torchmetrics 0.11+
- - setup: switch to pyannote.core 5.0+, pyannote.database 5.0+, and pyannote.pipeline 3.0+
- - setup: switch to speechbrain 0.5.14+
+- setup: switch to torch 2.0+, torchaudio 2.0+, soundfile 0.12+, lightning 2.0+, torchmetrics 0.11+
+- setup: switch to pyannote.core 5.0+, pyannote.database 5.0+, and pyannote.pipeline 3.0+
+- setup: switch to speechbrain 0.5.14+
## Version 2.1.1 (2022-10-27)
- - BREAKING(pipeline): rewrite speaker diarization pipeline
- - feat(pipeline): add option to optimize for DER variant
- - feat(clustering): add support for NeMo speaker embedding
- - feat(clustering): add FINCH clustering
- - feat(clustering): add min_cluster_size hparams to AgglomerativeClustering
- - feat(hub): add support for private/gated models
- - setup(hub): switch to latest hugginface_hub API
- - fix(pipeline): fix support for missing reference in Resegmentation pipeline
- - fix(clustering) fix corner case where HMM.fit finds too little states
+- BREAKING(pipeline): rewrite speaker diarization pipeline
+- feat(pipeline): add option to optimize for DER variant
+- feat(clustering): add support for NeMo speaker embedding
+- feat(clustering): add FINCH clustering
+- feat(clustering): add min_cluster_size hparams to AgglomerativeClustering
+- feat(hub): add support for private/gated models
+- setup(hub): switch to latest hugginface_hub API
+- fix(pipeline): fix support for missing reference in Resegmentation pipeline
+- fix(clustering) fix corner case where HMM.fit finds too little states
## Version 2.0.1 (2022-07-20)
- - BREAKING: complete rewrite
- - feat: much better performance
- - feat: Python-first API
- - feat: pretrained pipelines (and models) on Huggingface model hub
- - feat: multi-GPU training with pytorch-lightning
- - feat: data augmentation with torch-audiomentations
- - feat: Prodigy recipe for model-assisted audio annotation
+- BREAKING: complete rewrite
+- feat: much better performance
+- feat: Python-first API
+- feat: pretrained pipelines (and models) on Huggingface model hub
+- feat: multi-GPU training with pytorch-lightning
+- feat: data augmentation with torch-audiomentations
+- feat: Prodigy recipe for model-assisted audio annotation
## Version 1.1.2 (2021-01-28)
- - fix: make sure master branch is used to load pretrained models (#599)
+- fix: make sure master branch is used to load pretrained models (#599)
## Version 1.1 (2020-11-08)
- - last release before complete rewriting
+- last release before complete rewriting
## Version 1.0.1 (2018-07-19)
- - fix: fix regression in Precomputed.__call__ (#110, #105)
+- fix: fix regression in Precomputed.**call** (#110, #105)
## Version 1.0 (2018-07-03)
- - chore: switch from keras to pytorch (with tensorboard support)
- - improve: faster & better traning (`AutoLR`, advanced learning rate schedulers, improved batch generators)
- - feat: add tunable speaker diarization pipeline (with its own tutorial)
- - chore: drop support for Python 2 (use Python 3.6 or later)
+- chore: switch from keras to pytorch (with tensorboard support)
+- improve: faster & better traning (`AutoLR`, advanced learning rate schedulers, improved batch generators)
+- feat: add tunable speaker diarization pipeline (with its own tutorial)
+- chore: drop support for Python 2 (use Python 3.6 or later)
## Version 0.3.1 (2017-07-06)
- - feat: add python 3 support
- - chore: rewrite neural speaker embedding using autograd
- - feat: add new embedding architectures
- - feat: add new embedding losses
- - chore: switch to Keras 2
- - doc: add tutorial for (MFCC) feature extraction
- - doc: add tutorial for (LSTM-based) speech activity detection
- - doc: add tutorial for (LSTM-based) speaker change detection
- - doc: add tutorial for (TristouNet) neural speaker embedding
+- feat: add python 3 support
+- chore: rewrite neural speaker embedding using autograd
+- feat: add new embedding architectures
+- feat: add new embedding losses
+- chore: switch to Keras 2
+- doc: add tutorial for (MFCC) feature extraction
+- doc: add tutorial for (LSTM-based) speech activity detection
+- doc: add tutorial for (LSTM-based) speaker change detection
+- doc: add tutorial for (TristouNet) neural speaker embedding
## Version 0.2.1 (2017-03-28)
- - feat: add LSTM-based speech activity detection
- - feat: add LSTM-based speaker change detection
- - improve: refactor LSTM-based speaker embedding
- - feat: add librosa basic support
- - feat: add SMORMS3 optimizer
+- feat: add LSTM-based speech activity detection
+- feat: add LSTM-based speaker change detection
+- improve: refactor LSTM-based speaker embedding
+- feat: add librosa basic support
+- feat: add SMORMS3 optimizer
## Version 0.1.4 (2016-09-26)
- - feat: add 'covariance_type' option to BIC segmentation
+- feat: add 'covariance_type' option to BIC segmentation
## Version 0.1.3 (2016-09-23)
- - chore: rename sequence generator in preparation of the release of
- TristouNet reproducible research package.
+- chore: rename sequence generator in preparation of the release of
+ TristouNet reproducible research package.
## Version 0.1.2 (2016-09-22)
- - first public version
+- first public version
diff --git a/pyannote/audio/cli/train.py b/pyannote/audio/cli/train.py
index 9ab8b1658..74041554b 100644
--- a/pyannote/audio/cli/train.py
+++ b/pyannote/audio/cli/train.py
@@ -115,7 +115,7 @@ def configure_optimizers(self):
checkpoint = ModelCheckpoint(
monitor=monitor,
mode=direction,
- save_top_k=None if monitor is None else 1,
+ save_top_k=None if monitor is None else 10,
every_n_epochs=1,
save_last=True,
save_weights_only=False,
diff --git a/pyannote/audio/models/blocks/pooling.py b/pyannote/audio/models/blocks/pooling.py
index debb05d13..22d736a03 100644
--- a/pyannote/audio/models/blocks/pooling.py
+++ b/pyannote/audio/models/blocks/pooling.py
@@ -1,6 +1,6 @@
# MIT License
#
-# Copyright (c) 2020 CNRS
+# Copyright (c) 2020- CNRS
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
@@ -26,6 +26,7 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
+from einops import rearrange
class StatsPool(nn.Module):
@@ -40,49 +41,91 @@ class StatsPool(nn.Module):
"""
- def forward(
- self, sequences: torch.Tensor, weights: Optional[torch.Tensor] = None
- ) -> torch.Tensor:
- """Forward pass
+ def _pool(self, sequences: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:
+ """Helper function to compute statistics pooling
+
+ Assumes that weights are already interpolated to match the number of frames
+ in sequences and that they encode the activation of only one speaker.
Parameters
----------
- sequences : (batch, channel, frames) torch.Tensor
- Sequences.
- weights : (batch, frames) torch.Tensor, optional
- When provided, compute weighted mean and standard deviation.
+ sequences : (batch, features, frames) torch.Tensor
+ Sequences of features.
+ weights : (batch, frames) torch.Tensor
+ (Already interpolated) weights.
Returns
-------
- output : (batch, 2 * channel) torch.Tensor
+ output : (batch, 2 * features) torch.Tensor
Concatenation of mean and (unbiased) standard deviation.
"""
- if weights is None:
- mean = sequences.mean(dim=2)
- std = sequences.std(dim=2, unbiased=True)
+ weights = weights.unsqueeze(dim=1)
+ # (batch, 1, frames)
- else:
- weights = weights.unsqueeze(dim=1)
- # (batch, 1, frames)
+ v1 = weights.sum(dim=2) + 1e-8
+ mean = torch.sum(sequences * weights, dim=2) / v1
+
+ dx2 = torch.square(sequences - mean.unsqueeze(2))
+ v2 = torch.square(weights).sum(dim=2)
+
+ var = torch.sum(dx2 * weights, dim=2) / (v1 - v2 / v1 + 1e-8)
+ std = torch.sqrt(var)
+
+ return torch.cat([mean, std], dim=1)
+
+ def forward(
+ self, sequences: torch.Tensor, weights: Optional[torch.Tensor] = None
+ ) -> torch.Tensor:
+ """Forward pass
- num_frames = sequences.shape[2]
- num_weights = weights.shape[2]
- if num_frames != num_weights:
- warnings.warn(
- f"Mismatch between frames ({num_frames}) and weights ({num_weights}) numbers."
- )
- weights = F.interpolate(
- weights, size=num_frames, mode="linear", align_corners=False
- )
+ Parameters
+ ----------
+ sequences : (batch, features, frames) torch.Tensor
+ Sequences of features.
+ weights : (batch, frames) or (batch, speakers, frames) torch.Tensor, optional
+ Compute weighted mean and standard deviation, using provided `weights`.
- v1 = weights.sum(dim=2)
- mean = torch.sum(sequences * weights, dim=2) / v1
+ Note
+ ----
+ `sequences` and `weights` might use a different number of frames, in which case `weights`
+ are interpolated linearly to reach the number of frames in `sequences`.
- dx2 = torch.square(sequences - mean.unsqueeze(2))
- v2 = torch.square(weights).sum(dim=2)
+ Returns
+ -------
+ output : (batch, 2 * features) or (batch, speakers, 2 * features) torch.Tensor
+ Concatenation of mean and (unbiased) standard deviation. When `weights` are
+ provided with the `speakers` dimension, `output` is computed for each speaker
+ separately and returned as (batch, speakers, 2 * channel)-shaped tensor.
+ """
- var = torch.sum(dx2 * weights, dim=2) / (v1 - v2 / v1)
- std = torch.sqrt(var)
+ if weights is None:
+ mean = sequences.mean(dim=-1)
+ std = sequences.std(dim=-1, correction=1)
+ return torch.cat([mean, std], dim=-1)
- return torch.cat([mean, std], dim=1)
+ if weights.dim() == 2:
+ has_speaker_dimension = False
+ weights = weights.unsqueeze(dim=1)
+ # (batch, frames) -> (batch, 1, frames)
+ else:
+ has_speaker_dimension = True
+
+ # interpolate weights if needed
+ _, _, num_frames = sequences.shape
+ _, _, num_weights = weights.shape
+ if num_frames != num_weights:
+ warnings.warn(
+ f"Mismatch between frames ({num_frames}) and weights ({num_weights}) numbers."
+ )
+ weights = F.interpolate(weights, size=num_frames, mode="nearest")
+
+ output = rearrange(
+ torch.vmap(self._pool, in_dims=(None, 1))(sequences, weights),
+ "speakers batch features -> batch speakers features",
+ )
+
+ if not has_speaker_dimension:
+ return output.squeeze(dim=1)
+
+ return output
diff --git a/pyannote/audio/models/embedding/__init__.py b/pyannote/audio/models/embedding/__init__.py
index 08f8a576c..2819096c2 100644
--- a/pyannote/audio/models/embedding/__init__.py
+++ b/pyannote/audio/models/embedding/__init__.py
@@ -21,6 +21,19 @@
# SOFTWARE.
+from .wespeaker import (
+ WeSpeakerResNet34,
+ WeSpeakerResNet152,
+ WeSpeakerResNet221,
+ WeSpeakerResNet293,
+)
from .xvector import XVectorMFCC, XVectorSincNet
-__all__ = ["XVectorSincNet", "XVectorMFCC"]
+__all__ = [
+ "XVectorSincNet",
+ "XVectorMFCC",
+ "WeSpeakerResNet34",
+ "WeSpeakerResNet152",
+ "WeSpeakerResNet221",
+ "WeSpeakerResNet293",
+]
diff --git a/pyannote/audio/models/embedding/wespeaker/LICENSE.WeSpeaker b/pyannote/audio/models/embedding/wespeaker/LICENSE.WeSpeaker
new file mode 100644
index 000000000..136492006
--- /dev/null
+++ b/pyannote/audio/models/embedding/wespeaker/LICENSE.WeSpeaker
@@ -0,0 +1,21 @@
+Copyright (c) 2021 Shuai Wang (wsstriving@gmail.com)
+2022 Zhengyang Chen (chenzhengyang117@gmail.com)
+2023 Bing Han (hanbing97@sjtu.edu.cn)
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+File `resnet.py` has been borrowed from WeSpeaker that is available under the Apache License, Version 2.0.
+
+The original file is available at https://github.com/wenet-e2e/wespeaker/blob/c20d765295359e681321625fbefc1a02e8794163/wespeaker/models/resnet.py
+
+Neither Shuai Wang (@wsstriving on Github) nor myself (Hervé Bredin, or @hbredin on Github) are lawyers, but we both agreed that putting this license file in this directory is enough to comply with the license. See https://github.com/pyannote/pyannote-audio/issues/1537#issuecomment-1808029836. If you know better about this potential MIT/Apache 2.0 compatibility issue, please let us know.
diff --git a/pyannote/audio/models/embedding/wespeaker/__init__.py b/pyannote/audio/models/embedding/wespeaker/__init__.py
new file mode 100644
index 000000000..603a88c64
--- /dev/null
+++ b/pyannote/audio/models/embedding/wespeaker/__init__.py
@@ -0,0 +1,236 @@
+# MIT License
+#
+# Copyright (c) 2023 CNRS
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+
+from functools import partial
+from typing import Optional
+
+import torch
+import torchaudio.compliance.kaldi as kaldi
+
+from pyannote.audio.core.model import Model
+from pyannote.audio.core.task import Task
+
+from .resnet import ResNet34, ResNet152, ResNet221, ResNet293
+
+
+class BaseWeSpeakerResNet(Model):
+ def __init__(
+ self,
+ sample_rate: int = 16000,
+ num_channels: int = 1,
+ num_mel_bins: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ dither: float = 0.0,
+ window_type: str = "hamming",
+ use_energy: bool = False,
+ task: Optional[Task] = None,
+ ):
+ super().__init__(sample_rate=sample_rate, num_channels=num_channels, task=task)
+
+ self.save_hyperparameters(
+ "sample_rate",
+ "num_channels",
+ "num_mel_bins",
+ "frame_length",
+ "frame_shift",
+ "dither",
+ "window_type",
+ "use_energy",
+ )
+
+ self._fbank = partial(
+ kaldi.fbank,
+ num_mel_bins=self.hparams.num_mel_bins,
+ frame_length=self.hparams.frame_length,
+ frame_shift=self.hparams.frame_shift,
+ dither=self.hparams.dither,
+ sample_frequency=self.hparams.sample_rate,
+ window_type=self.hparams.window_type,
+ use_energy=self.hparams.use_energy,
+ )
+
+ def compute_fbank(self, waveforms: torch.Tensor) -> torch.Tensor:
+ """Extract fbank features
+
+ Parameters
+ ----------
+ waveforms : (batch_size, num_channels, num_samples)
+
+ Returns
+ -------
+ fbank : (batch_size, num_frames, num_mel_bins)
+
+ Source: https://github.com/wenet-e2e/wespeaker/blob/45941e7cba2c3ea99e232d02bedf617fc71b0dad/wespeaker/bin/infer_onnx.py#L30C1-L50
+ """
+
+ waveforms = waveforms * (1 << 15)
+
+ # fall back to CPU for FFT computation when using MPS
+ # until FFT is fixed in MPS
+ device = waveforms.device
+ fft_device = torch.device("cpu") if device.type == "mps" else device
+
+ features = torch.vmap(self._fbank)(waveforms.to(fft_device)).to(device)
+
+ return features - torch.mean(features, dim=1, keepdim=True)
+
+ def forward(
+ self, waveforms: torch.Tensor, weights: torch.Tensor = None
+ ) -> torch.Tensor:
+ """
+
+ Parameters
+ ----------
+ waveforms : torch.Tensor
+ Batch of waveforms with shape (batch, channel, sample)
+ weights : torch.Tensor, optional
+ Batch of weights with shape (batch, frame).
+ """
+
+ fbank = self.compute_fbank(waveforms)
+ return self.resnet(fbank, weights=weights)[1]
+
+
+class WeSpeakerResNet34(BaseWeSpeakerResNet):
+ def __init__(
+ self,
+ sample_rate: int = 16000,
+ num_channels: int = 1,
+ num_mel_bins: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ dither: float = 0.0,
+ window_type: str = "hamming",
+ use_energy: bool = False,
+ task: Optional[Task] = None,
+ ):
+ super().__init__(
+ sample_rate=sample_rate,
+ num_channels=num_channels,
+ num_mel_bins=num_mel_bins,
+ frame_length=frame_length,
+ frame_shift=frame_shift,
+ dither=dither,
+ window_type=window_type,
+ use_energy=use_energy,
+ task=task,
+ )
+ self.resnet = ResNet34(
+ num_mel_bins, 256, pooling_func="TSTP", two_emb_layer=False
+ )
+
+
+class WeSpeakerResNet152(BaseWeSpeakerResNet):
+ def __init__(
+ self,
+ sample_rate: int = 16000,
+ num_channels: int = 1,
+ num_mel_bins: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ dither: float = 0.0,
+ window_type: str = "hamming",
+ use_energy: bool = False,
+ task: Optional[Task] = None,
+ ):
+ super().__init__(
+ sample_rate=sample_rate,
+ num_channels=num_channels,
+ num_mel_bins=num_mel_bins,
+ frame_length=frame_length,
+ frame_shift=frame_shift,
+ dither=dither,
+ window_type=window_type,
+ use_energy=use_energy,
+ task=task,
+ )
+ self.resnet = ResNet152(
+ num_mel_bins, 256, pooling_func="TSTP", two_emb_layer=False
+ )
+
+
+class WeSpeakerResNet221(BaseWeSpeakerResNet):
+ def __init__(
+ self,
+ sample_rate: int = 16000,
+ num_channels: int = 1,
+ num_mel_bins: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ dither: float = 0.0,
+ window_type: str = "hamming",
+ use_energy: bool = False,
+ task: Optional[Task] = None,
+ ):
+ super().__init__(
+ sample_rate=sample_rate,
+ num_channels=num_channels,
+ num_mel_bins=num_mel_bins,
+ frame_length=frame_length,
+ frame_shift=frame_shift,
+ dither=dither,
+ window_type=window_type,
+ use_energy=use_energy,
+ task=task,
+ )
+ self.resnet = ResNet221(
+ num_mel_bins, 256, pooling_func="TSTP", two_emb_layer=False
+ )
+
+
+class WeSpeakerResNet293(BaseWeSpeakerResNet):
+ def __init__(
+ self,
+ sample_rate: int = 16000,
+ num_channels: int = 1,
+ num_mel_bins: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ dither: float = 0.0,
+ window_type: str = "hamming",
+ use_energy: bool = False,
+ task: Optional[Task] = None,
+ ):
+ super().__init__(
+ sample_rate=sample_rate,
+ num_channels=num_channels,
+ num_mel_bins=num_mel_bins,
+ frame_length=frame_length,
+ frame_shift=frame_shift,
+ dither=dither,
+ window_type=window_type,
+ use_energy=use_energy,
+ task=task,
+ )
+ self.resnet = ResNet293(
+ num_mel_bins, 256, pooling_func="TSTP", two_emb_layer=False
+ )
+
+
+__all__ = [
+ "WeSpeakerResNet34",
+ "WeSpeakerResNet152",
+ "WeSpeakerResNet221",
+ "WeSpeakerResNet293",
+]
diff --git a/pyannote/audio/models/embedding/wespeaker/convert.py b/pyannote/audio/models/embedding/wespeaker/convert.py
new file mode 100644
index 000000000..34aec6092
--- /dev/null
+++ b/pyannote/audio/models/embedding/wespeaker/convert.py
@@ -0,0 +1,62 @@
+# MIT License
+#
+# Copyright (c) 2023 CNRS
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# Script used to convert from WeSpeaker to pyannote.audio
+
+import sys
+from pathlib import Path
+
+import pytorch_lightning as pl
+import torch
+
+import pyannote.audio.models.embedding.wespeaker as wespeaker
+from pyannote.audio import Model
+from pyannote.audio.core.task import Problem, Resolution, Specifications
+
+wespeaker_checkpoint_dir = sys.argv[1] # /path/to/wespeaker_cnceleb-resnet34-LM
+
+wespeaker_checkpoint = Path(wespeaker_checkpoint_dir) / "wespeaker.pt"
+
+depth = Path(wespeaker_checkpoint_dir).parts[-1].split("-")[-2][6:] # '34'
+Klass = getattr(wespeaker, f"WeSpeakerResNet{depth}") # WeSpeakerResNet34
+
+duration = 5.0 # whatever
+specifications = Specifications(
+ problem=Problem.REPRESENTATION, resolution=Resolution.CHUNK, duration=duration
+)
+
+state_dict = torch.load(wespeaker_checkpoint, map_location=torch.device("cpu"))
+state_dict.pop("projection.weight")
+
+model = Klass()
+model.resnet.load_state_dict(state_dict, strict=True)
+model.specifications = specifications
+
+checkpoint = {"state_dict": model.state_dict()}
+model.on_save_checkpoint(checkpoint)
+checkpoint["pytorch-lightning_version"] = pl.__version__
+
+pyannote_checkpoint = Path(wespeaker_checkpoint_dir) / "pytorch_model.bin"
+torch.save(checkpoint, pyannote_checkpoint)
+
+model = Model.from_pretrained(pyannote_checkpoint)
+print(model)
diff --git a/pyannote/audio/models/embedding/wespeaker/resnet.py b/pyannote/audio/models/embedding/wespeaker/resnet.py
new file mode 100644
index 000000000..54f95fa8b
--- /dev/null
+++ b/pyannote/audio/models/embedding/wespeaker/resnet.py
@@ -0,0 +1,302 @@
+# Copyright (c) 2021 Shuai Wang (wsstriving@gmail.com)
+# 2022 Zhengyang Chen (chenzhengyang117@gmail.com)
+# 2023 Bing Han (hanbing97@sjtu.edu.cn)
+# 2023 CNRS
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+from pyannote.audio.models.blocks.pooling import StatsPool
+
+
+class TSTP(nn.Module):
+ """
+ Temporal statistics pooling, concatenate mean and std, which is used in
+ x-vector
+ Comment: simple concatenation can not make full use of both statistics
+ """
+
+ def __init__(self, in_dim=0, **kwargs):
+ super(TSTP, self).__init__()
+ self.in_dim = in_dim
+ self.stats_pool = StatsPool()
+
+ def forward(self, features, weights: torch.Tensor = None):
+ """
+
+ Parameters
+ ----------
+ features : (batch, dimension, channel, frames) torch.Tensor
+ Batch of features
+ weights: (batch, frames) torch.Tensor, optional
+ Batch of weights
+
+ """
+
+ features = rearrange(
+ features,
+ "batch dimension channel frames -> batch (dimension channel) frames",
+ )
+
+ return self.stats_pool(features, weights=weights)
+
+ # # The last dimension is the temporal axis
+ # pooling_mean = features.mean(dim=-1)
+ # pooling_std = torch.sqrt(torch.var(features, dim=-1) + 1e-7)
+ # pooling_mean = pooling_mean.flatten(start_dim=1)
+ # pooling_std = pooling_std.flatten(start_dim=1)
+ # stats = torch.cat((pooling_mean, pooling_std), 1)
+ # return stats
+
+ def get_out_dim(self):
+ self.out_dim = self.in_dim * 2
+ return self.out_dim
+
+
+POOLING_LAYERS = {"TSTP": TSTP}
+
+
+class BasicBlock(nn.Module):
+ expansion = 1
+
+ def __init__(self, in_planes, planes, stride=1):
+ super(BasicBlock, self).__init__()
+ self.conv1 = nn.Conv2d(
+ in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
+ )
+ self.bn1 = nn.BatchNorm2d(planes)
+ self.conv2 = nn.Conv2d(
+ planes, planes, kernel_size=3, stride=1, padding=1, bias=False
+ )
+ self.bn2 = nn.BatchNorm2d(planes)
+
+ self.shortcut = nn.Sequential()
+ if stride != 1 or in_planes != self.expansion * planes:
+ self.shortcut = nn.Sequential(
+ nn.Conv2d(
+ in_planes,
+ self.expansion * planes,
+ kernel_size=1,
+ stride=stride,
+ bias=False,
+ ),
+ nn.BatchNorm2d(self.expansion * planes),
+ )
+
+ def forward(self, x):
+ out = F.relu(self.bn1(self.conv1(x)))
+ out = self.bn2(self.conv2(out))
+ out += self.shortcut(x)
+ out = F.relu(out)
+ return out
+
+
+class Bottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(self, in_planes, planes, stride=1):
+ super(Bottleneck, self).__init__()
+ self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
+ self.bn1 = nn.BatchNorm2d(planes)
+ self.conv2 = nn.Conv2d(
+ planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
+ )
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.conv3 = nn.Conv2d(
+ planes, self.expansion * planes, kernel_size=1, bias=False
+ )
+ self.bn3 = nn.BatchNorm2d(self.expansion * planes)
+
+ self.shortcut = nn.Sequential()
+ if stride != 1 or in_planes != self.expansion * planes:
+ self.shortcut = nn.Sequential(
+ nn.Conv2d(
+ in_planes,
+ self.expansion * planes,
+ kernel_size=1,
+ stride=stride,
+ bias=False,
+ ),
+ nn.BatchNorm2d(self.expansion * planes),
+ )
+
+ def forward(self, x):
+ out = F.relu(self.bn1(self.conv1(x)))
+ out = F.relu(self.bn2(self.conv2(out)))
+ out = self.bn3(self.conv3(out))
+ out += self.shortcut(x)
+ out = F.relu(out)
+ return out
+
+
+class ResNet(nn.Module):
+ def __init__(
+ self,
+ block,
+ num_blocks,
+ m_channels=32,
+ feat_dim=40,
+ embed_dim=128,
+ pooling_func="TSTP",
+ two_emb_layer=True,
+ ):
+ super(ResNet, self).__init__()
+ self.in_planes = m_channels
+ self.feat_dim = feat_dim
+ self.embed_dim = embed_dim
+ self.stats_dim = int(feat_dim / 8) * m_channels * 8
+ self.two_emb_layer = two_emb_layer
+
+ self.conv1 = nn.Conv2d(
+ 1, m_channels, kernel_size=3, stride=1, padding=1, bias=False
+ )
+ self.bn1 = nn.BatchNorm2d(m_channels)
+ self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1)
+ self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2)
+ self.layer3 = self._make_layer(block, m_channels * 4, num_blocks[2], stride=2)
+ self.layer4 = self._make_layer(block, m_channels * 8, num_blocks[3], stride=2)
+
+ self.pool = POOLING_LAYERS[pooling_func](
+ in_dim=self.stats_dim * block.expansion
+ )
+ self.pool_out_dim = self.pool.get_out_dim()
+ self.seg_1 = nn.Linear(self.pool_out_dim, embed_dim)
+ if self.two_emb_layer:
+ self.seg_bn_1 = nn.BatchNorm1d(embed_dim, affine=False)
+ self.seg_2 = nn.Linear(embed_dim, embed_dim)
+ else:
+ self.seg_bn_1 = nn.Identity()
+ self.seg_2 = nn.Identity()
+
+ def _make_layer(self, block, planes, num_blocks, stride):
+ strides = [stride] + [1] * (num_blocks - 1)
+ layers = []
+ for stride in strides:
+ layers.append(block(self.in_planes, planes, stride))
+ self.in_planes = planes * block.expansion
+ return nn.Sequential(*layers)
+
+ def forward(self, x: torch.Tensor, weights: torch.Tensor = None):
+ """
+
+ Parameters
+ ----------
+ x : (batch, frames, features) torch.Tensor
+ Batch of features
+ weights : (batch, frames) torch.Tensor, optional
+ Batch of weights
+
+ Returns
+ -------
+ 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)))
+ out = self.layer1(out)
+ out = self.layer2(out)
+ out = self.layer3(out)
+ out = self.layer4(out)
+
+ stats = self.pool(out, 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 ResNet18(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
+ return ResNet(
+ BasicBlock,
+ [2, 2, 2, 2],
+ feat_dim=feat_dim,
+ embed_dim=embed_dim,
+ pooling_func=pooling_func,
+ two_emb_layer=two_emb_layer,
+ )
+
+
+def ResNet34(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
+ return ResNet(
+ BasicBlock,
+ [3, 4, 6, 3],
+ feat_dim=feat_dim,
+ embed_dim=embed_dim,
+ pooling_func=pooling_func,
+ two_emb_layer=two_emb_layer,
+ )
+
+
+def ResNet50(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
+ return ResNet(
+ Bottleneck,
+ [3, 4, 6, 3],
+ feat_dim=feat_dim,
+ embed_dim=embed_dim,
+ pooling_func=pooling_func,
+ two_emb_layer=two_emb_layer,
+ )
+
+
+def ResNet101(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
+ return ResNet(
+ Bottleneck,
+ [3, 4, 23, 3],
+ feat_dim=feat_dim,
+ embed_dim=embed_dim,
+ pooling_func=pooling_func,
+ two_emb_layer=two_emb_layer,
+ )
+
+
+def ResNet152(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
+ return ResNet(
+ Bottleneck,
+ [3, 8, 36, 3],
+ feat_dim=feat_dim,
+ embed_dim=embed_dim,
+ pooling_func=pooling_func,
+ two_emb_layer=two_emb_layer,
+ )
+
+
+def ResNet221(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
+ return ResNet(
+ Bottleneck,
+ [6, 16, 48, 3],
+ feat_dim=feat_dim,
+ embed_dim=embed_dim,
+ pooling_func=pooling_func,
+ two_emb_layer=two_emb_layer,
+ )
+
+
+def ResNet293(feat_dim, embed_dim, pooling_func="TSTP", two_emb_layer=True):
+ return ResNet(
+ Bottleneck,
+ [10, 20, 64, 3],
+ feat_dim=feat_dim,
+ embed_dim=embed_dim,
+ pooling_func=pooling_func,
+ two_emb_layer=two_emb_layer,
+ )
diff --git a/pyannote/audio/pipelines/clustering.py b/pyannote/audio/pipelines/clustering.py
index a779016cb..b63ab214f 100644
--- a/pyannote/audio/pipelines/clustering.py
+++ b/pyannote/audio/pipelines/clustering.py
@@ -253,7 +253,6 @@ def __call__(
hard_clusters = np.zeros((num_chunks, num_speakers), dtype=np.int8)
soft_clusters = np.ones((num_chunks, num_speakers, 1))
centroids = np.mean(train_embeddings, axis=0, keepdims=True)
-
return hard_clusters, soft_clusters, centroids
train_clusters = self.cluster(
@@ -386,7 +385,8 @@ def cluster(
elif num_large_clusters > max_clusters:
num_clusters = max_clusters
- if num_clusters is not None:
+ # look for perfect candidate if necessary
+ if num_clusters is not None and num_large_clusters != num_clusters:
# switch stopping criterion from "inter-cluster distance" stopping to "iteration index"
_dendrogram = np.copy(dendrogram)
_dendrogram[:, 2] = np.arange(num_embeddings - 1)
diff --git a/pyannote/audio/pipelines/resegmentation.py b/pyannote/audio/pipelines/resegmentation.py
index bb71abf22..d01e5d65f 100644
--- a/pyannote/audio/pipelines/resegmentation.py
+++ b/pyannote/audio/pipelines/resegmentation.py
@@ -39,6 +39,7 @@
get_model,
)
from pyannote.audio.utils.permutation import mae_cost_func, permutate
+from pyannote.audio.utils.signal import binarize
class Resegmentation(SpeakerDiarizationMixin, Pipeline):
@@ -181,11 +182,17 @@ def apply(
hook("segmentation", segmentations)
- # estimate frame-level number of instantaneous speakers
- count = self.speaker_count(
+ # binarize segmentations before speaker counting
+ binarized_segmentations: SlidingWindowFeature = binarize(
segmentations,
onset=self.onset,
offset=self.offset,
+ initial_state=False,
+ )
+
+ # estimate frame-level number of instantaneous speakers
+ count = self.speaker_count(
+ binarized_segmentations,
warm_up=(self.warm_up, self.warm_up),
frames=self._frames,
)
diff --git a/pyannote/audio/pipelines/speaker_diarization.py b/pyannote/audio/pipelines/speaker_diarization.py
index 18b6565d3..354f6be7e 100644
--- a/pyannote/audio/pipelines/speaker_diarization.py
+++ b/pyannote/audio/pipelines/speaker_diarization.py
@@ -25,6 +25,8 @@
import functools
import itertools
import math
+import textwrap
+import warnings
from typing import Callable, Optional, Text, Union
import numpy as np
@@ -332,6 +334,9 @@ def iter_waveform_and_mask():
embedding_batches = []
+ if hook is not None:
+ hook("embeddings", None, total=batch_count, completed=0)
+
for i, batch in enumerate(batches, 1):
waveforms, masks = zip(*filter(lambda b: b[0] is not None, batch))
@@ -475,12 +480,19 @@ def apply(
hook("segmentation", segmentations)
# shape: (num_chunks, num_frames, local_num_speakers)
+ # binarize segmentation
+ if self._segmentation.model.specifications.powerset:
+ binarized_segmentations = segmentations
+ else:
+ binarized_segmentations: SlidingWindowFeature = binarize(
+ segmentations,
+ onset=self.segmentation.threshold,
+ initial_state=False,
+ )
+
# estimate frame-level number of instantaneous speakers
count = self.speaker_count(
- segmentations,
- onset=0.5
- if self._segmentation.model.specifications.powerset
- else self.segmentation.threshold,
+ binarized_segmentations,
frames=self._frames,
warm_up=(0.0, 0.0),
)
@@ -496,16 +508,6 @@ def apply(
return diarization
- # binarize segmentation
- if self._segmentation.model.specifications.powerset:
- binarized_segmentations = segmentations
- else:
- binarized_segmentations: SlidingWindowFeature = binarize(
- segmentations,
- onset=self.segmentation.threshold,
- initial_state=False,
- )
-
if self.klustering == "OracleClustering" and not return_embeddings:
embeddings = None
else:
@@ -530,6 +532,27 @@ def apply(
# hard_clusters: (num_chunks, num_speakers)
# centroids: (num_speakers, dimension)
+ # number of detected clusters is the number of different speakers
+ num_different_speakers = np.max(hard_clusters) + 1
+
+ # detected number of speakers can still be out of bounds
+ # (specifically, lower than `min_speakers`), since there could be too few embeddings
+ # to make enough clusters with a given minimum cluster size.
+ if num_different_speakers < min_speakers or num_different_speakers > max_speakers:
+ warnings.warn(textwrap.dedent(
+ f"""
+ The detected number of speakers ({num_different_speakers}) is outside
+ the given bounds [{min_speakers}, {max_speakers}]. This can happen if the
+ given audio file is too short to contain {min_speakers} or more speakers.
+ Try to lower the desired minimal number of speakers.
+ """
+ ))
+
+ # during counting, we could possibly overcount the number of instantaneous
+ # speakers due to segmentation errors, so we cap the maximum instantaneous number
+ # of speakers by the `max_speakers` value
+ count.data = np.minimum(count.data, max_speakers).astype(np.int8)
+
# reconstruct discrete diarization from raw hard clusters
# keep track of inactive speakers
@@ -585,6 +608,18 @@ def apply(
if not return_embeddings:
return diarization
+ # this can happen when we use OracleClustering
+ if centroids is None:
+ return diarization, None
+
+ # The number of centroids may be smaller than the number of speakers
+ # in the annotation. This can happen if the number of active speakers
+ # obtained from `speaker_count` for some frames is larger than the number
+ # of clusters obtained from `clustering`. In this case, we append zero embeddings
+ # for extra speakers
+ if len(diarization.labels()) > centroids.shape[0]:
+ centroids = np.pad(centroids, ((0, len(diarization.labels()) - centroids.shape[0]), (0, 0)))
+
# re-order centroids so that they match
# the order given by diarization.labels()
inverse_mapping = {label: index for index, label in mapping.items()}
@@ -592,11 +627,6 @@ def apply(
[inverse_mapping[label] for label in diarization.labels()]
]
- # FIXME: the number of centroids may be smaller than the number of speakers
- # in the annotation. This can happen if the number of active speakers
- # obtained from `speaker_count` for some frames is larger than the number
- # of clusters obtained from `clustering`. Will be fixed in the future
-
return diarization, centroids
def get_metric(self) -> GreedyDiarizationErrorRate:
diff --git a/pyannote/audio/pipelines/speaker_verification.py b/pyannote/audio/pipelines/speaker_verification.py
index 6b39679dd..c870ea622 100644
--- a/pyannote/audio/pipelines/speaker_verification.py
+++ b/pyannote/audio/pipelines/speaker_verification.py
@@ -386,7 +386,7 @@ def __call__(
return embeddings
-class WeSpeakerPretrainedSpeakerEmbedding(BaseInference):
+class ONNXWeSpeakerPretrainedSpeakerEmbedding(BaseInference):
"""Pretrained WeSpeaker speaker embedding
Parameters
@@ -398,7 +398,7 @@ class WeSpeakerPretrainedSpeakerEmbedding(BaseInference):
Usage
-----
- >>> get_embedding = WeSpeakerPretrainedSpeakerEmbedding("hbredin/wespeaker-voxceleb-resnet34-LM")
+ >>> get_embedding = ONNXWeSpeakerPretrainedSpeakerEmbedding("hbredin/wespeaker-voxceleb-resnet34-LM")
>>> assert waveforms.ndim == 3
>>> batch_size, num_channels, num_samples = waveforms.shape
>>> assert num_channels == 1
@@ -418,7 +418,7 @@ def __init__(
):
if not ONNX_IS_AVAILABLE:
raise ImportError(
- f"'onnxruntime' must be installed to use '{embedding}' embeddings. "
+ f"'onnxruntime' must be installed to use '{embedding}' embeddings."
)
super().__init__()
@@ -556,6 +556,7 @@ def compute_fbank(
for waveform in waveforms
]
)
+
return features - torch.mean(features, dim=1, keepdim=True)
def __call__(
@@ -578,12 +579,12 @@ def __call__(
batch_size, num_channels, num_samples = waveforms.shape
assert num_channels == 1
- features = self.compute_fbank(waveforms)
+ features = self.compute_fbank(waveforms.to(self.device))
_, num_frames, _ = features.shape
if masks is None:
embeddings = self.session_.run(
- output_names=["embs"], input_feed={"feats": features.numpy()}
+ output_names=["embs"], input_feed={"feats": features.numpy(force=True)}
)[0]
return embeddings
@@ -606,7 +607,7 @@ def __call__(
embeddings[f] = self.session_.run(
output_names=["embs"],
- input_feed={"feats": masked_feature.numpy()[None]},
+ input_feed={"feats": masked_feature.numpy(force=True)[None]},
)[0][0]
return embeddings
@@ -686,7 +687,7 @@ def min_num_samples(self) -> int:
try:
_ = self.model_(torch.randn(1, 1, middle).to(self.device))
upper = middle
- except RuntimeError:
+ except Exception:
lower = middle
middle = (lower + upper) // 2
@@ -744,7 +745,12 @@ def PretrainedSpeakerEmbedding(
>>> embeddings = get_embedding(waveforms, masks=masks)
"""
- if isinstance(embedding, str) and "speechbrain" in embedding:
+ if isinstance(embedding, str) and "pyannote" in embedding:
+ return PyannoteAudioPretrainedSpeakerEmbedding(
+ embedding, device=device, use_auth_token=use_auth_token
+ )
+
+ elif isinstance(embedding, str) and "speechbrain" in embedding:
return SpeechBrainPretrainedSpeakerEmbedding(
embedding, device=device, use_auth_token=use_auth_token
)
@@ -753,9 +759,10 @@ def PretrainedSpeakerEmbedding(
return NeMoPretrainedSpeakerEmbedding(embedding, device=device)
elif isinstance(embedding, str) and "wespeaker" in embedding:
- return WeSpeakerPretrainedSpeakerEmbedding(embedding, device=device)
+ return ONNXWeSpeakerPretrainedSpeakerEmbedding(embedding, device=device)
else:
+ # fallback to pyannote in case we are loading a local model
return PyannoteAudioPretrainedSpeakerEmbedding(
embedding, device=device, use_auth_token=use_auth_token
)
diff --git a/pyannote/audio/pipelines/utils/diarization.py b/pyannote/audio/pipelines/utils/diarization.py
index f494c6073..4a35f7049 100644
--- a/pyannote/audio/pipelines/utils/diarization.py
+++ b/pyannote/audio/pipelines/utils/diarization.py
@@ -117,13 +117,10 @@ def optimal_mapping(
else:
return mapped_hypothesis
- # TODO: get rid of onset/offset (binarization should be applied before calling speaker_count)
# TODO: get rid of warm-up parameter (trimming should be applied before calling speaker_count)
@staticmethod
def speaker_count(
- segmentations: SlidingWindowFeature,
- onset: float = 0.5,
- offset: float = None,
+ binarized_segmentations: SlidingWindowFeature,
warm_up: Tuple[float, float] = (0.1, 0.1),
frames: SlidingWindow = None,
) -> SlidingWindowFeature:
@@ -131,12 +128,8 @@ def speaker_count(
Parameters
----------
- segmentations : SlidingWindowFeature
- (num_chunks, num_frames, num_classes)-shaped scores.
- onset : float, optional
- Onset threshold. Defaults to 0.5
- offset : float, optional
- Offset threshold. Defaults to `onset`.
+ binarized_segmentations : SlidingWindowFeature
+ (num_chunks, num_frames, num_classes)-shaped binarized scores.
warm_up : (float, float) tuple, optional
Left/right warm up ratio of chunk duration.
Defaults to (0.1, 0.1), i.e. 10% on both sides.
@@ -151,10 +144,7 @@ def speaker_count(
(num_frames, 1)-shaped instantaneous speaker count
"""
- binarized: SlidingWindowFeature = binarize(
- segmentations, onset=onset, offset=offset, initial_state=False
- )
- trimmed = Inference.trim(binarized, warm_up=warm_up)
+ trimmed = Inference.trim(binarized_segmentations, warm_up=warm_up)
count = Inference.aggregate(
np.sum(trimmed, axis=-1, keepdims=True),
frames=frames,
@@ -197,7 +187,7 @@ def to_annotation(
min_duration_off=min_duration_off,
)
- return binarize(discrete_diarization)
+ return binarize(discrete_diarization).rename_tracks(generator="string")
@staticmethod
def to_diarization(
diff --git a/pyannote/audio/pipelines/utils/hook.py b/pyannote/audio/pipelines/utils/hook.py
index cb150ea4a..2a675d1c9 100644
--- a/pyannote/audio/pipelines/utils/hook.py
+++ b/pyannote/audio/pipelines/utils/hook.py
@@ -20,6 +20,7 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
+import time
from copy import deepcopy
from typing import Any, Mapping, Optional, Text
@@ -32,20 +33,49 @@
)
-def logging_hook(
- step_name: Text,
- step_artifact: Any,
- file: Optional[Mapping] = None,
- completed: Optional[int] = None,
- total: Optional[int] = None,
-):
- """Hook to save step_artifact as file[step_name]
+class ArtifactHook:
+ """Hook to save artifacts of each internal step
+
+ Parameters
+ ----------
+ artifacts: list of str, optional
+ List of steps to save. Defaults to all steps.
+ file_key: str, optional
+ Key used to store artifacts in `file`.
+ Defaults to "artifact".
+
+ Usage
+ -----
+ >>> with ArtifactHook() as hook:
+ ... output = pipeline(file, hook=hook)
+ # file["artifact"] contains a dict with artifacts of each step
- Useful for debugging purposes
"""
- if completed is None:
- file[step_name] = deepcopy(step_artifact)
+ def __init__(self, *artifacts, file_key: str = "artifact"):
+ self.artifacts = artifacts
+ self.file_key = file_key
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, *args):
+ pass
+
+ def __call__(
+ self,
+ step_name: Text,
+ step_artifact: Any,
+ file: Optional[Mapping] = None,
+ total: Optional[int] = None,
+ completed: Optional[int] = None,
+ ):
+ if (step_artifact is None) or (
+ self.artifacts and step_name not in self.artifacts
+ ):
+ return
+
+ file.setdefault(self.file_key, dict())[step_name] = deepcopy(step_artifact)
class ProgressHook:
@@ -64,11 +94,9 @@ class ProgressHook:
"""
def __init__(self, transient: bool = False):
- super().__init__()
self.transient = transient
def __enter__(self):
-
self.progress = Progress(
TextColumn("[progress.description]{task.description}"),
BarColumn(),
@@ -90,7 +118,6 @@ def __call__(
total: Optional[int] = None,
completed: Optional[int] = None,
):
-
if completed is None:
completed = total = 1
@@ -103,3 +130,95 @@ def __call__(
# force refresh when completed
if completed >= total:
self.progress.refresh()
+
+
+class TimingHook:
+ """Hook to compute processing time of internal steps
+
+ Parameters
+ ----------
+ file_key: str, optional
+ Key used to store processing time in `file`.
+ Defaults to "timing_hook".
+
+ Usage
+ -----
+ >>> with TimingHook() as hook:
+ ... output = pipeline(file, hook=hook)
+ # file["timing_hook"] contains processing time for each step
+ """
+
+ def __init__(self, file_key: str = "timing"):
+ self.file_key = file_key
+
+ def __enter__(self):
+ self._pipeline_start_time = time.time()
+ self._start_time = dict()
+ self._end_time = dict()
+ return self
+
+ def __exit__(self, *args):
+ _pipeline_end_time = time.time()
+ processing_time = dict()
+ processing_time["total"] = _pipeline_end_time - self._pipeline_start_time
+ for step_name, _start_time in self._start_time.items():
+ _end_time = self._end_time[step_name]
+ processing_time[step_name] = _end_time - _start_time
+
+ self._file[self.file_key] = processing_time
+
+ def __call__(
+ self,
+ step_name: Text,
+ step_artifact: Any,
+ file: Optional[Mapping] = None,
+ total: Optional[int] = None,
+ completed: Optional[int] = None,
+ ):
+ if not hasattr(self, "_file"):
+ self._file = file
+
+ if completed is None:
+ return
+
+ if completed == 0:
+ self._start_time[step_name] = time.time()
+
+ if completed >= total:
+ self._end_time[step_name] = time.time()
+
+
+class Hooks:
+ """List of hooks
+
+ Usage
+ -----
+ >>> with Hooks(ProgessHook(), TimingHook(), ArtifactHook()) as hook:
+ ... output = pipeline("audio.wav", hook=hook)
+
+ """
+
+ def __init__(self, *hooks):
+ self.hooks = hooks
+
+ def __enter__(self):
+ for hook in self.hooks:
+ if hasattr(hook, "__enter__"):
+ hook.__enter__()
+ return self
+
+ def __exit__(self, *args):
+ for hook in self.hooks:
+ if hasattr(hook, "__exit__"):
+ hook.__exit__(*args)
+
+ def __call__(
+ self,
+ step_name: Text,
+ step_artifact: Any,
+ file: Optional[Mapping] = None,
+ total: Optional[int] = None,
+ completed: Optional[int] = None,
+ ):
+ for hook in self.hooks:
+ hook(step_name, step_artifact, file=file, total=total, completed=completed)
diff --git a/pyannote/audio/utils/powerset.py b/pyannote/audio/utils/powerset.py
index 0f5cfb5bc..b75221e48 100644
--- a/pyannote/audio/utils/powerset.py
+++ b/pyannote/audio/utils/powerset.py
@@ -84,30 +84,36 @@ def build_cardinality(self) -> torch.Tensor:
powerset_k += 1
return cardinality
- def to_multilabel(self, powerset: torch.Tensor) -> torch.Tensor:
- """Convert predictions from (soft) powerset to (hard) multi-label
+ def to_multilabel(self, powerset: torch.Tensor, soft: bool = False) -> torch.Tensor:
+ """Convert predictions from powerset to multi-label
Parameter
---------
powerset : (batch_size, num_frames, num_powerset_classes) torch.Tensor
Soft predictions in "powerset" space.
+ soft : bool, optional
+ Return soft multi-label predictions. Defaults to False (i.e. hard predictions)
+ Assumes that `powerset` are "logits" (not "probabilities").
Returns
-------
multi_label : (batch_size, num_frames, num_classes) torch.Tensor
- Hard predictions in "multi-label" space.
+ Predictions in "multi-label" space.
"""
- hard_powerset = torch.nn.functional.one_hot(
- torch.argmax(powerset, dim=-1),
- self.num_powerset_classes,
- ).float()
+ if soft:
+ powerset_probs = torch.exp(powerset)
+ else:
+ powerset_probs = torch.nn.functional.one_hot(
+ torch.argmax(powerset, dim=-1),
+ self.num_powerset_classes,
+ ).float()
- return torch.matmul(hard_powerset, self.mapping)
+ return torch.matmul(powerset_probs, self.mapping)
- def forward(self, powerset: torch.Tensor) -> torch.Tensor:
+ def forward(self, powerset: torch.Tensor, soft: bool = False) -> torch.Tensor:
"""Alias for `to_multilabel`"""
- return self.to_multilabel(powerset)
+ return self.to_multilabel(powerset, soft=soft)
def to_powerset(self, multilabel: torch.Tensor) -> torch.Tensor:
"""Convert (hard) predictions from multi-label to powerset
diff --git a/requirements.txt b/requirements.txt
index 7829ada37..7e71fe024 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,6 @@ einops >=0.6.0
huggingface_hub >= 0.13.0
lightning >= 2.0.1
omegaconf >=2.1,<3.0
-onnxruntime-gpu >= 1.16.0
pyannote.core >= 5.0.0
pyannote.database >= 5.0.1
pyannote.metrics >= 3.2
diff --git a/tests/test_clustering.py b/tests/test_clustering.py
new file mode 100644
index 000000000..535da47de
--- /dev/null
+++ b/tests/test_clustering.py
@@ -0,0 +1,29 @@
+import numpy as np
+
+from pyannote.audio.pipelines.clustering import AgglomerativeClustering
+
+
+def test_agglomerative_clustering_num_cluster():
+ """
+ Make sure AgglomerativeClustering doesn't "over-merge" clusters when initial
+ clustering already matches target num_clusters, cf
+ https://github.com/pyannote/pyannote-audio/issues/1525
+ """
+
+ # 2 embeddings different enough
+ embeddings = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 2.0, 1.0, 2.0]])
+
+ # clustering with params that should yield 1 cluster per embedding
+ clustering = AgglomerativeClustering().instantiate(
+ {
+ "method": "centroid",
+ "min_cluster_size": 0,
+ "threshold": 0.0,
+ }
+ )
+
+ # request 2 clusters
+ clusters = clustering.cluster(
+ embeddings=embeddings, min_clusters=2, max_clusters=2, num_clusters=2
+ )
+ assert np.array_equal(clusters, np.array([0, 1]))
diff --git a/tests/test_stats_pool.py b/tests/test_stats_pool.py
new file mode 100644
index 000000000..e30262eda
--- /dev/null
+++ b/tests/test_stats_pool.py
@@ -0,0 +1,131 @@
+# MIT License
+#
+# Copyright (c) 2023- CNRS
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import torch
+
+from pyannote.audio.models.blocks.pooling import StatsPool
+
+
+def test_stats_pool_weightless():
+ x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
+ # (batch = 2, features = 2, frames = 2)
+
+ stats_pool = StatsPool()
+
+ y = stats_pool(x)
+ # (batch = 2, features = 4)
+
+ assert torch.equal(
+ torch.round(y, decimals=4),
+ torch.Tensor([[3.0, 3.0, 1.4142, 1.4142], [1.0, 1.0, 0.0, 0.0]]),
+ )
+
+
+def test_stats_pool_one_speaker():
+ x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
+ # (batch = 2, features = 2, frames = 2)
+
+ w = torch.Tensor(
+ [
+ [0.5, 0.01],
+ [0.2, 0.1],
+ ]
+ )
+ # (batch = 2, frames = 2)
+
+ stats_pool = StatsPool()
+
+ y = stats_pool(x, weights=w)
+ # (batch = 2, features = 4)
+
+ assert torch.equal(
+ torch.round(y, decimals=4),
+ torch.Tensor([[2.0392, 2.0392, 1.4142, 1.4142], [1.0, 1.0, 0.0, 0.0]]),
+ )
+
+
+def test_stats_pool_multi_speaker():
+ x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
+ # (batch = 2, features = 2, frames = 2)
+
+ w = torch.Tensor([[[0.1, 0.2], [0.2, 0.3]], [[0.001, 0.001], [0.2, 0.3]]])
+ # (batch = 2, speakers = 2, frames = 2)
+
+ stats_pool = StatsPool()
+
+ y = stats_pool(x, weights=w)
+ # (batch = 2, speakers = 2, features = 4)
+
+ assert torch.equal(
+ torch.round(y, decimals=4),
+ torch.Tensor(
+ [
+ [[3.3333, 3.3333, 1.4142, 1.4142], [3.2, 3.2, 1.4142, 1.4142]],
+ [[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]],
+ ]
+ ),
+ )
+
+
+def test_stats_pool_frame_mismatch():
+ x = torch.Tensor([[[2.0, 2.0], [2.0, 2.0]], [[1.0, 1.0], [1.0, 1.0]]])
+ # (batch = 2, features = 2, frames = 2)
+
+ stats_pool = StatsPool()
+ w = torch.Tensor(
+ [
+ [0.5, 0.5, 0.0],
+ [0.0, 0.5, 0.5],
+ ]
+ )
+ # (batch = 2, frames = 3)
+
+ y = stats_pool(x, weights=w)
+ # (batch = 2, features = 4)
+
+ assert torch.equal(
+ torch.round(y, decimals=4),
+ torch.Tensor([[2.0, 2.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]),
+ )
+
+
+def test_stats_pool_all_zero_weights():
+ x = torch.Tensor([[[2.0, 4.0], [2.0, 4.0]], [[1.0, 1.0], [1.0, 1.0]]])
+ # (batch = 2, features = 2, frames = 2)
+
+ w = torch.Tensor(
+ [
+ [0.5, 0.01],
+ [0.0, 0.0], # all zero weights
+ ]
+ )
+ # (batch = 2, frames = 2)
+
+ stats_pool = StatsPool()
+
+ y = stats_pool(x, weights=w)
+ # (batch = 2, features = 4)
+
+ assert torch.equal(
+ torch.round(y, decimals=4),
+ torch.Tensor([[2.0392, 2.0392, 1.4142, 1.4142], [0.0, 0.0, 0.0, 0.0]]),
+ )
diff --git a/tutorials/intro.ipynb b/tutorials/intro.ipynb
index 3793bfe09..75344267a 100644
--- a/tutorials/intro.ipynb
+++ b/tutorials/intro.ipynb
@@ -1,4066 +1,3888 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9-KmdPlBYnp6"
- },
- "source": [
- ""
]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# load groundtruth\n",
+ "from pyannote.database.util import load_rttm\n",
+ "_, groundtruth = load_rttm('ES2004a.rttm').popitem()\n",
+ "\n",
+ "# visualize groundtruth\n",
+ "groundtruth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "p_R9T9Y5Ynp9"
+ },
+ "source": [
+ "For the rest of this notebook, we will only listen to and visualize a one-minute long excerpt of the file (but will process the whole file anyway)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 230
},
+ "id": "bAHza4Y1Ynp-",
+ "outputId": "c4cc2369-bfe4-4ac2-bb71-37602e7c7a8a"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ai082p4HYnp7",
- "outputId": "bb673846-8b58-4743-cea2-6c6270632d7f",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "!pip install -qq https://github.com/pyannote/pyannote-audio/archive/refs/heads/develop.zip\n",
- "!pip install -qq ipython==7.34.0"
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyannote.core import Segment, notebook\n",
+ "# make notebook visualization zoom on 600s < t < 660s time range\n",
+ "EXCERPT = Segment(600, 660)\n",
+ "notebook.crop = EXCERPT\n",
+ "\n",
+ "# visualize excerpt groundtruth\n",
+ "groundtruth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L3FQXT5FYnp-"
+ },
+ "source": [
+ "This nice visualization is brought to you by [`pyannote.core`](http://pyannote.github.io/pyannote-core/) and basically indicates when each speaker speaks. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 62
},
+ "id": "rDhZ3bXEYnp-",
+ "outputId": "a82efe4e-2f9c-48bd-94fb-c62af3a3cb43"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "qggK-7VBYnp8"
- },
- "source": [
- "# Visualization with `pyannote.core`\n",
- "\n",
- "For the purpose of this notebook, we will download and use an audio file coming from the [AMI corpus](http://groups.inf.ed.ac.uk/ami/corpus/), which contains a conversation between 4 people in a meeting room."
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
]
- },
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyannote.audio import Audio \n",
+ "from IPython.display import Audio as IPythonAudio\n",
+ "waveform, sr = Audio(mono=\"downmix\").crop(DEMO_FILE, EXCERPT)\n",
+ "IPythonAudio(waveform.flatten(), rate=sr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hkzox7QIYnp_"
+ },
+ "source": [
+ "# Processing your own audio file (optional)\n",
+ "\n",
+ "In case you just want to go ahead with the demo file, skip this section entirely.\n",
+ "\n",
+ "In case you want to try processing your own audio file, proceed with running this section. It will offer you to upload an audio file (preferably a `wav` file but all formats supported by [`SoundFile`](https://pysoundfile.readthedocs.io/en/latest/) should work just fine)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3hmFmLzFYnp_"
+ },
+ "source": [
+ "## Upload audio file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xC05jFO_Ynp_",
+ "outputId": "c5502632-56ae-4adb-8bdc-112deedc8893"
+ },
+ "outputs": [],
+ "source": [
+ "import google.colab\n",
+ "own_file, _ = google.colab.files.upload().popitem()\n",
+ "OWN_FILE = {'audio': own_file}\n",
+ "notebook.reset()\n",
+ "\n",
+ "# load audio waveform and play it\n",
+ "waveform, sample_rate = Audio(mono=\"downmix\")(OWN_FILE)\n",
+ "IPythonAudio(data=waveform.squeeze(), rate=sample_rate, autoplay=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ctw4nLaPYnp_"
+ },
+ "source": [
+ "Simply replace `DEMO_FILE` by `OWN_FILE` in the rest of the notebook.\n",
+ "\n",
+ "Note, however, that unless you provide a groundtruth annotation in the next cell, you will (obviously) not be able to visualize groundtruth annotation nor evaluate the performance of the diarization pipeline quantitatively"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "x9AQgDzFYnp_"
+ },
+ "source": [
+ "## Upload groundtruth (optional)\n",
+ "\n",
+ "The groundtruth file is expected to use the RTTM format, with one line per speech turn with the following convention:\n",
+ "\n",
+ "```\n",
+ "SPEAKER {file_name} 1 {start_time} {duration} {speaker_name} \n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "iZaFudpDYnp_",
+ "outputId": "981274fa-e654-4091-c838-91c81f921e5d"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "uJWoQiJgYnp8",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "!wget -q http://groups.inf.ed.ac.uk/ami/AMICorpusMirror/amicorpus/ES2004a/audio/ES2004a.Mix-Headset.wav\n",
- "DEMO_FILE = {'uri': 'ES2004a.Mix-Headset', 'audio': 'ES2004a.Mix-Headset.wav'}"
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " Upload widget is only available when the cell has been executed in the\n",
+ " current browser session. Please rerun this cell to enable.\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "EPIapoCJYnp8"
- },
- "source": [
- "Because AMI is a benchmarking dataset, it comes with manual annotations (a.k.a *groundtruth*). \n",
- "Let us load and visualize the expected output of the speaker diarization pipeline.\n"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Saving sample.rttm to sample.rttm\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Mmm0Q22JYnp8",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [],
- "source": [
- "!wget -q https://raw.githubusercontent.com/pyannote/AMI-diarization-setup/main/only_words/rttms/test/ES2004a.rttm"
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "groundtruth_rttm, _ = google.colab.files.upload().popitem()\n",
+ "groundtruths = load_rttm(groundtruth_rttm)\n",
+ "if OWN_FILE['audio'] in groundtruths:\n",
+ " groundtruth = groundtruths[OWN_FILE['audio']]\n",
+ "else:\n",
+ " _, groundtruth = groundtruths.popitem()\n",
+ "groundtruth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5MclWK2GYnp_"
+ },
+ "source": [
+ "# Speaker diarization with `pyannote.pipeline`\n",
+ "\n",
+ "We are about to run a full speaker diarization pipeline, that includes speaker segmentation, speaker embedding, and a final clustering step. **Brace yourself!**\n",
+ "\n",
+ "To load the speaker diarization pipeline, \n",
+ "\n",
+ "* accept the user conditions on [hf.co/pyannote/speaker-diarization-3.0](https://hf.co/pyannote/speaker-diarization-3.0)\n",
+ "* accept the user conditions on [hf.co/pyannote/segmentation-3.0](https://hf.co/pyannote/segmentation-3.0)\n",
+ "* login using `notebook_login` below"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 301,
+ "referenced_widgets": [
+ "c8731777ce834e58a76a295076200cfc",
+ "859b12a6d95b4c6f987791ca848122b9",
+ "94756148d2e94a93ae233baba20af683",
+ "ba18cded436e486da34882d821d8f1eb",
+ "99898e6ee64a46bd832af112e79b58b7",
+ "79184c8c2a6f4b7493bb7f6983f18a09",
+ "ea95ffd922c0455d957120f034e541f8",
+ "13525aa369a9410a83343952ab511f3c",
+ "b2be65e192384c948fb8987d4cfca505",
+ "333b42ca7aa44788b1c22724eb11bcc3",
+ "0e382d66f09f4958a40baa7ab83c4ccb",
+ "6a45ce374e2e47ba9457d02e02522748",
+ "765485a1d3f941d28b79782dcffbf401",
+ "3499ef4dd9f243d9bef00b396e78ed69"
+ ]
},
+ "id": "r5u7VMb-YnqB",
+ "outputId": "c714a997-d4f8-417a-e5ad-0a4924333859"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 233
- },
- "id": "ToqCwl_FYnp9",
- "outputId": "a1d9631f-b198-44d1-ff6d-ec304125a9f4",
- "vscode": {
- "languageId": "python"
- }
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6e56329c30c0441c8d45df3975e75a76",
+ "version_major": 2,
+ "version_minor": 0
},
- "outputs": [
- {
- "data": {
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- "text/plain": [
- ""
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# load groundtruth\n",
- "from pyannote.database.util import load_rttm\n",
- "_, groundtruth = load_rttm('ES2004a.rttm').popitem()\n",
- "\n",
- "# visualize groundtruth\n",
- "groundtruth"
+ "text/plain": [
+ "VBox(children=(HTML(value=' "
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from pyannote.core import Segment, notebook\n",
- "# make notebook visualization zoom on 600s < t < 660s time range\n",
- "EXCERPT = Segment(600, 660)\n",
- "notebook.crop = EXCERPT\n",
- "\n",
- "# visualize excerpt groundtruth\n",
- "groundtruth"
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "diarization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DLhErS6wYnqB"
+ },
+ "source": [
+ "# Evaluation with `pyannote.metrics`\n",
+ "\n",
+ "Because groundtruth is available, we can evaluate the quality of the diarization pipeline by computing the [diarization error rate](http://pyannote.github.io/pyannote-metrics/reference.html#diarization)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "id": "vNHQRTUIYnqB"
+ },
+ "outputs": [],
+ "source": [
+ "from pyannote.metrics.diarization import DiarizationErrorRate\n",
+ "metric = DiarizationErrorRate()\n",
+ "der = metric(groundtruth, diarization)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "9d0vKQ0fYnqB",
+ "outputId": "9a664753-cd84-4211-9153-d33e929bb252"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "L3FQXT5FYnp-"
- },
- "source": [
- "This nice visualization is brought to you by [`pyannote.core`](http://pyannote.github.io/pyannote-core/) and basically indicates when each speaker speaks. "
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "diarization error rate = 19.8%\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f'diarization error rate = {100 * der:.1f}%')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Xz5QJV9nYnqB"
+ },
+ "source": [
+ "This implementation of diarization error rate is brought to you by [`pyannote.metrics`](http://pyannote.github.io/pyannote-metrics/).\n",
+ "\n",
+ "It can also be used to improve visualization by find the optimal one-to-one mapping between groundtruth and hypothesized speakers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 230
},
+ "id": "xMLf4mrYYnqB",
+ "outputId": "ed08bcc8-24c6-439c-a244-3a673ff480b0"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 62
- },
- "id": "rDhZ3bXEYnp-",
- "outputId": "a82efe4e-2f9c-48bd-94fb-c62af3a3cb43",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from pyannote.audio import Audio \n",
- "from IPython.display import Audio as IPythonAudio\n",
- "waveform, sr = Audio(mono=\"downmix\").crop(DEMO_FILE, EXCERPT)\n",
- "IPythonAudio(waveform.flatten(), rate=sr)"
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mapping = metric.optimal_mapping(groundtruth, diarization)\n",
+ "diarization.rename_labels(mapping=mapping)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 230
},
+ "id": "Z0ewsLlQYnqB",
+ "outputId": "8a8cd040-ee1d-48f7-d4be-eef9e08e9e55"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "hkzox7QIYnp_"
- },
- "source": [
- "# Processing your own audio file (optional)\n",
- "\n",
- "In case you just want to go ahead with the demo file, skip this section entirely.\n",
- "\n",
- "In case you want to try processing your own audio file, proceed with running this section. It will offer you to upload an audio file (preferably a `wav` file but all formats supported by [`SoundFile`](https://pysoundfile.readthedocs.io/en/latest/) should work just fine)."
+ "data": {
+ "image/png": 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]
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+ "metadata": {},
+ "output_type": "execute_result"
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+ "groundtruth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MxlrTbyPYnqB"
+ },
+ "source": [
+ "# Going further \n",
+ "\n",
+ "We have only scratched the surface in this introduction. \n",
+ "\n",
+ "More details can be found in the [`pyannote.audio` Github repository](https://github.com/pyannote/pyannote-audio).\n"
+ ]
+ }
+ ],
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+ "language": "python",
+ "name": "python3"
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+ "name": "ipython",
+ "version": 3
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- {
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- "id": "3hmFmLzFYnp_"
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- "source": [
- "## Upload audio file"
- ]
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- "outputId": "c5502632-56ae-4adb-8bdc-112deedc8893",
- "vscode": {
- "languageId": "python"
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- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " Upload widget is only available when the cell has been executed in the\n",
- " current browser session. Please rerun this cell to enable.\n",
- " \n",
- " "
- ],
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- ""
- ]
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- "metadata": {},
- "output_type": "display_data"
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- "output_type": "stream",
- "text": [
- "Saving sample.wav to sample.wav\n"
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- },
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- "\n",
- " \n",
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- "source": [
- "import google.colab\n",
- "own_file, _ = google.colab.files.upload().popitem()\n",
- "OWN_FILE = {'audio': own_file}\n",
- "notebook.reset()\n",
- "\n",
- "# load audio waveform and play it\n",
- "waveform, sample_rate = Audio(mono=\"downmix\")(OWN_FILE)\n",
- "IPythonAudio(data=waveform.squeeze(), rate=sample_rate, autoplay=True)"
- ]
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},
- {
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- "metadata": {
- "id": "ctw4nLaPYnp_"
- },
- "source": [
- "Simply replace `DEMO_FILE` by `OWN_FILE` in the rest of the notebook.\n",
- "\n",
- "Note, however, that unless you provide a groundtruth annotation in the next cell, you will (obviously) not be able to visualize groundtruth annotation nor evaluate the performance of the diarization pipeline quantitatively"
- ]
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- "source": [
- "## Upload groundtruth (optional)\n",
- "\n",
- "The groundtruth file is expected to use the RTTM format, with one line per speech turn with the following convention:\n",
- "\n",
- "```\n",
- "SPEAKER {file_name} 1 {start_time} {duration} {speaker_name} \n",
- "```"
- ]
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- "data": {
- "text/html": [
- "\n",
- " \n",
- " Upload widget is only available when the cell has been executed in the\n",
- " current browser session. Please rerun this cell to enable.\n",
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- "metadata": {},
- "output_type": "display_data"
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saving sample.rttm to sample.rttm\n"
- ]
- },
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- "else:\n",
- " _, groundtruth = groundtruths.popitem()\n",
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- "# Speaker diarization with `pyannote.pipeline`\n",
- "\n",
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- "\n",
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- "* accept the user conditions on [hf.co/pyannote/segmentation](https://hf.co/pyannote/segmentation)\n",
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+ "IPY_MODEL_0125df9fa8e14b3db0e2bce299529812",
+ "IPY_MODEL_e3169ca885e04536a709d5751173ce9a"
],
- "source": [
- "print(f'diarization error rate = {100 * der:.1f}%')"
- ]
+ "layout": "IPY_MODEL_70abdfd99be84f7b9b8d24fee9eec022"
+ }
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Xz5QJV9nYnqB"
- },
- "source": [
- "This implementation of diarization error rate is brought to you by [`pyannote.metrics`](http://pyannote.github.io/pyannote-metrics/).\n",
- "\n",
- "It can also be used to improve visualization by find the optimal one-to-one mapping between groundtruth and hypothesized speakers."
- ]
+ "c53a1cf68fcd4388abf1f0379891089a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 230
- },
- "id": "xMLf4mrYYnqB",
- "outputId": "ed08bcc8-24c6-439c-a244-3a673ff480b0",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
+ "c8731777ce834e58a76a295076200cfc": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "VBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "VBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "VBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_859b12a6d95b4c6f987791ca848122b9",
+ "IPY_MODEL_94756148d2e94a93ae233baba20af683",
+ "IPY_MODEL_ba18cded436e486da34882d821d8f1eb",
+ "IPY_MODEL_99898e6ee64a46bd832af112e79b58b7"
],
- "source": [
- "mapping = metric.optimal_mapping(groundtruth, diarization)\n",
- "diarization.rename_labels(mapping=mapping)"
- ]
+ "layout": "IPY_MODEL_79184c8c2a6f4b7493bb7f6983f18a09"
+ }
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 230
- },
- "id": "Z0ewsLlQYnqB",
- "outputId": "8a8cd040-ee1d-48f7-d4be-eef9e08e9e55",
- "vscode": {
- "languageId": "python"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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diff --git a/version.txt b/version.txt
index cb2b00e4f..fd2a01863 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-3.0.1
+3.1.0
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}