Merge branch 'develop' into use-mincpwer-instead-of-cpwer-in-separati…

…on-pipeline-tuto

CHANGELOG.md

@@ -4,13 +4,14 @@
 
 ### New features
 
+- feat: add support for `k-means` clustering
 - feat: add `"hidden"` option to `ProgressHook`
+- feat: add `FilterByNumberOfSpeakers` protocol files filter
 
 ### Fixes
 
 - fix: fix clipping issue in speech separation pipeline ([@joonaskalda](https://github.com/joonaskalda/))
 
-
 ## Version 3.3.2 (2024-09-11)
 
 ### Fixes

pyannote/audio/pipelines/clustering.py

@@ -22,7 +22,6 @@
 
 """Clustering pipelines"""
 
-
 import random
 from enum import Enum
 from typing import Optional, Tuple
@@ -35,6 +34,7 @@
 from scipy.cluster.hierarchy import fcluster, linkage
 from scipy.optimize import linear_sum_assignment
 from scipy.spatial.distance import cdist
+from sklearn.cluster import KMeans
 
 from pyannote.audio.core.io import AudioFile
 from pyannote.audio.pipelines.utils import oracle_segmentation
@@ -264,8 +264,8 @@ def __call__(
 
         train_clusters = self.cluster(
             train_embeddings,
-            min_clusters,
-            max_clusters,
+            min_clusters=min_clusters,
+            max_clusters=max_clusters,
             num_clusters=num_clusters,
         )
 
@@ -298,6 +298,8 @@ class AgglomerativeClustering(BaseClustering):
         Minimum cluster size
     """
 
+    expects_num_clusters: bool = False
+
     def __init__(
         self,
         metric: str = "cosine",
@@ -321,8 +323,8 @@ def __init__(
     def cluster(
         self,
         embeddings: np.ndarray,
-        min_clusters: int,
-        max_clusters: int,
+        min_clusters: Optional[int] = None,
+        max_clusters: Optional[int] = None,
         num_clusters: Optional[int] = None,
     ):
         """
@@ -471,9 +473,78 @@ def cluster(
         return clusters
 
 
+class KMeansClustering(BaseClustering):
+    """KMeans clustering
+
+    Parameters
+    ----------
+    metric : {"cosine", "euclidean"}, optional
+        Distance metric to use. Defaults to "cosine".
+
+    Hyper-parameters
+    ----------------
+    None
+    """
+
+    expects_num_clusters: bool = True
+
+    def __init__(
+        self,
+        metric: str = "cosine",
+    ):
+        if metric not in ["cosine", "euclidean"]:
+            raise ValueError(
+                f"Unsupported metric: {metric}. Must be 'cosine' or 'euclidean'."
+            )
+
+        super().__init__(metric=metric)
+
+    def cluster(
+        self,
+        embeddings: np.ndarray,
+        min_clusters: Optional[int] = None,
+        max_clusters: Optional[int] = None,
+        num_clusters: Optional[int] = None,
+    ):
+        """Perform KMeans clustering
+
+        Parameters
+        ----------
+        embeddings : (num_embeddings, dimension) array
+            Embeddings
+        num_clusters : int, optional
+            Expected number of clusters.
+
+        Returns
+        -------
+        clusters : (num_embeddings, ) array
+            0-indexed cluster indices.
+        """
+
+        if num_clusters is None:
+            raise ValueError("`num_clusters` must be provided.")
+
+        num_embeddings, _ = embeddings.shape
+        if num_embeddings < num_clusters:
+            # one cluster per embedding as int
+            return np.arange(num_embeddings, dtype=np.int32)
+
+        # unit-normalize embeddings to use 'euclidean' distance
+        if self.metric == "cosine":
+            with np.errstate(divide="ignore", invalid="ignore"):
+                embeddings /= np.linalg.norm(embeddings, axis=-1, keepdims=True)
+
+        # perform Kmeans clustering
+        return KMeans(
+            n_clusters=num_clusters, n_init=3, random_state=42, copy_x=False
+        ).fit_predict(embeddings)
+
+
 class OracleClustering(BaseClustering):
     """Oracle clustering"""
 
+    expects_num_clusters: bool = True
+
     def __call__(
         self,
         embeddings: Optional[np.ndarray] = None,
@@ -558,4 +629,5 @@ def __call__(
 
 class Clustering(Enum):
     AgglomerativeClustering = AgglomerativeClustering
+    KMeansClustering = KMeansClustering
     OracleClustering = OracleClustering

pyannote/audio/pipelines/speaker_diarization.py

@@ -27,7 +27,7 @@
 import math
 import textwrap
 import warnings
-from typing import Callable, Optional, Text, Union
+from typing import Callable, Mapping, Optional, Text, Union
 
 import numpy as np
 import torch
@@ -45,6 +45,7 @@
     SpeakerDiarizationMixin,
     get_model,
 )
+from pyannote.audio.pipelines.utils.diarization import set_num_speakers
 from pyannote.audio.utils.signal import binarize
 
 
@@ -177,6 +178,8 @@ def __init__(
             )
         self.clustering = Klustering.value(metric=metric)
 
+        self._expects_num_speakers = self.clustering.expects_num_clusters
+
     @property
     def segmentation_batch_size(self) -> int:
         return self._segmentation.batch_size
@@ -469,12 +472,25 @@ def apply(
         # setup hook (e.g. for debugging purposes)
         hook = self.setup_hook(file, hook=hook)
 
-        num_speakers, min_speakers, max_speakers = self.set_num_speakers(
+        num_speakers, min_speakers, max_speakers = set_num_speakers(
             num_speakers=num_speakers,
             min_speakers=min_speakers,
             max_speakers=max_speakers,
         )
 
+        # when using KMeans clustering (or equivalent), the number of speakers must
+        # be provided alongside the audio file. also, during pipeline training, we
+        # infer the number of speakers from the reference annotation to avoid the
+        # pipeline complaining about missing number of speakers.
+        if self._expects_num_speakers and num_speakers is None:
+            if isinstance(file, Mapping) and "annotation" in file:
+                num_speakers = len(file["annotation"].labels())
+
+            else:
+                raise ValueError(
+                    f"num_speakers must be provided when using {self.klustering} clustering"
+                )
+
         segmentations = self.get_segmentations(file, hook=hook)
         hook("segmentation", segmentations)
         #   shape: (num_chunks, num_frames, local_num_speakers)

pyannote/audio/pipelines/speech_separation.py

@@ -45,6 +45,7 @@
     SpeakerDiarizationMixin,
     get_model,
 )
+from pyannote.audio.pipelines.utils.diarization import set_num_speakers
 from pyannote.audio.utils.signal import binarize
 
 
@@ -489,7 +490,7 @@ def apply(
         # setup hook (e.g. for debugging purposes)
         hook = self.setup_hook(file, hook=hook)
 
-        num_speakers, min_speakers, max_speakers = self.set_num_speakers(
+        num_speakers, min_speakers, max_speakers = set_num_speakers(
             num_speakers=num_speakers,
             min_speakers=min_speakers,
             max_speakers=max_speakers,

pyannote/audio/pipelines/utils/diarization.py

@@ -31,7 +31,44 @@
 from pyannote.audio.utils.signal import Binarize
 
 
-# TODO: move to dedicated module
+def set_num_speakers(
+    num_speakers: Optional[int] = None,
+    min_speakers: Optional[int] = None,
+    max_speakers: Optional[int] = None,
+):
+    """Validate number of speakers
+
+    Parameters
+    ----------
+    num_speakers : int, optional
+        Number of speakers.
+    min_speakers : int, optional
+        Minimum number of speakers.
+    max_speakers : int, optional
+        Maximum number of speakers.
+
+    Returns
+    -------
+    num_speakers : int or None
+    min_speakers : int
+    max_speakers : int or np.inf
+    """
+
+    # override {min|max}_num_speakers by num_speakers when available
+    min_speakers = num_speakers or min_speakers or 1
+    max_speakers = num_speakers or max_speakers or np.inf
+
+    if min_speakers > max_speakers:
+        raise ValueError(
+            f"min_speakers must be smaller than (or equal to) max_speakers "
+            f"(here: min_speakers={min_speakers:g} and max_speakers={max_speakers:g})."
+        )
+    if min_speakers == max_speakers:
+        num_speakers = min_speakers
+
+    return num_speakers, min_speakers, max_speakers
+
+
 class SpeakerDiarizationMixin:
     """Defines a bunch of methods common to speaker diarization pipelines"""
 
@@ -58,20 +95,11 @@ def set_num_speakers(
         min_speakers : int
         max_speakers : int or np.inf
         """
-
-        # override {min|max}_num_speakers by num_speakers when available
-        min_speakers = num_speakers or min_speakers or 1
-        max_speakers = num_speakers or max_speakers or np.inf
-
-        if min_speakers > max_speakers:
-            raise ValueError(
-                f"min_speakers must be smaller than (or equal to) max_speakers "
-                f"(here: min_speakers={min_speakers:g} and max_speakers={max_speakers:g})."
-            )
-        if min_speakers == max_speakers:
-            num_speakers = min_speakers
-
-        return num_speakers, min_speakers, max_speakers
+        return set_num_speakers(
+            num_speakers=num_speakers,
+            min_speakers=min_speakers,
+            max_speakers=max_speakers,
+        )
 
     @staticmethod
     def optimal_mapping(

pyannote/audio/utils/protocol.py

@@ -21,8 +21,10 @@
 # SOFTWARE.
 
 from functools import partial
+from typing import Optional
 
 import torchaudio
+from pyannote.core import Annotation
 from pyannote.database import FileFinder, Protocol, get_annotated
 from pyannote.database.protocol import SpeakerVerificationProtocol
 
@@ -62,14 +64,12 @@ def check_protocol(protocol: Protocol) -> Protocol:
 
     # does protocol provide audio keys?
     if "audio" not in file:
-
         if "waveform" in file:
             if "sample_rate" not in file:
                 msg = f'Protocol {protocol.name} provides audio with "waveform" key but is missing a "sample_rate" key.'
                 raise ValueError(msg)
 
         else:
-
             file_finder = FileFinder()
             try:
                 _ = file_finder(file)
@@ -90,7 +90,6 @@ def check_protocol(protocol: Protocol) -> Protocol:
                 print(msg)
 
     if "waveform" not in file and "torchaudio.info" not in file:
-
         # use soundfile when available (it usually is faster than ffmpeg for getting info)
         backends = (
             torchaudio.list_audio_backends()
@@ -107,7 +106,6 @@ def check_protocol(protocol: Protocol) -> Protocol:
         print(msg)
 
     if "annotated" not in file:
-
         if "duration" not in file:
             protocol.preprocessors["duration"] = get_duration
 
@@ -143,3 +141,45 @@ def check_protocol(protocol: Protocol) -> Protocol:
     }
 
     return protocol, checks
+
+
+class FilterByNumberOfSpeakers:
+    """Filter files based on the number of speakers
+
+    Note
+    ----
+    Always returns True if `current_file` does not have an "annotation" key.
+
+    """
+
+    def __init__(
+        self,
+        num_speakers: Optional[int] = None,
+        min_speakers: Optional[int] = None,
+        max_speakers: Optional[int] = None,
+    ):
+        from pyannote.audio.pipelines.utils.diarization import set_num_speakers
+
+        self.num_speakers, self.min_speakers, self.max_speakers = set_num_speakers(
+            num_speakers=num_speakers,
+            min_speakers=min_speakers,
+            max_speakers=max_speakers,
+        )
+
+    def __call__(self, current_file: dict) -> bool:
+        if "annotation" not in current_file:
+            return True
+
+        annotation: Annotation = current_file["annotation"]
+        num_speakers: int = len(annotation.labels())
+
+        if self.num_speakers is not None and self.num_speakers != num_speakers:
+            return False
+
+        if self.min_speakers is not None and self.min_speakers > num_speakers:
+            return False
+
+        if self.max_speakers is not None and self.max_speakers < num_speakers:
+            return False
+
+        return True