fix(task): fix support for "balance" option

CHANGELOG.md

@@ -51,6 +51,7 @@
   - 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

pyannote/audio/tasks/segmentation/mixins.py

@@ -421,7 +421,7 @@ def train__iter__helper(self, rng: random.Random, **filters):
         # indices of training files that matches domain filters
         training = self.metadata["subset"] == Subsets.index("train")
         for key, value in filters.items():
-            training &= self.metadata[key] == value
+            training &= self.metadata[key] == self.metadata_unique_values[key].index(value)
         file_ids = np.where(training)[0]
 
         # turn annotated duration into a probability distribution
@@ -485,16 +485,19 @@ def train__iter__(self):
             # create a subchunk generator for each combination of "balance" keys
             subchunks = dict()
             for product in itertools.product(
-                [self.metadata_unique_values[key] for key in balance]
+                *[self.metadata_unique_values[key] for key in balance]
             ):
+                # we iterate on the cartesian product of the values in metadata_unique_values
+                # eg: for balance=["database", "split"], with 2 databases and 2 splits:
+                # ("DIHARD", "A"), ("DIHARD", "B"), ("REPERE", "A"), ("REPERE", "B")
                 filters = {key: value for key, value in zip(balance, product)}
                 subchunks[product] = self.train__iter__helper(rng, **filters)
 
         while True:
             # select one subchunk generator at random (with uniform probability)
             # so that it is balanced on average
             if balance is not None:
-                chunks = subchunks[rng.choice(subchunks)]
+                chunks = subchunks[rng.choice(list(subchunks))]
 
             # generate random chunk
             yield next(chunks)

pyannote/audio/tasks/segmentation/multilabel.py

@@ -58,10 +58,10 @@ class MultiLabelSegmentation(SegmentationTaskMixin, Task):
         parts, only the remaining central part of each chunk is used for computing the
         loss during training, and for aggregating scores during inference.
         Defaults to 0. (i.e. no warm-up).
-    balance: str, optional
-        When provided, training samples are sampled uniformly with respect to that key.
-        For instance, setting `balance` to "uri" will make sure that each file will be
-        equally represented in the training samples.
+    balance: Sequence[Text], optional
+        When provided, training samples are sampled uniformly with respect to these keys.
+        For instance, setting `balance` to ["database","subset"] will make sure that each
+        database & subset combination will be equally represented in the training samples.
     weight: str, optional
         When provided, use this key to as frame-wise weight in loss function.
     batch_size : int, optional
@@ -87,7 +87,7 @@ def __init__(
         classes: Optional[List[str]] = None,
         duration: float = 2.0,
         warm_up: Union[float, Tuple[float, float]] = 0.0,
-        balance: Text = None,
+        balance: Sequence[Text] = None,
         weight: Text = None,
         batch_size: int = 32,
         num_workers: int = None,

pyannote/audio/tasks/segmentation/overlapped_speech_detection.py

@@ -59,10 +59,10 @@ class OverlappedSpeechDetection(SegmentationTaskMixin, Task):
         parts, only the remaining central part of each chunk is used for computing the
         loss during training, and for aggregating scores during inference.
         Defaults to 0. (i.e. no warm-up).
-    balance: str, optional
-        When provided, training samples are sampled uniformly with respect to that key.
-        For instance, setting `balance` to "uri" will make sure that each file will be
-        equally represented in the training samples.
+    balance: Sequence[Text], optional
+        When provided, training samples are sampled uniformly with respect to these keys.
+        For instance, setting `balance` to ["database","subset"] will make sure that each
+        database & subset combination will be equally represented in the training samples.
     overlap: dict, optional
         Controls how artificial chunks with overlapping speech are generated:
         - "probability" key is the probability of artificial overlapping chunks. Setting
@@ -98,7 +98,7 @@ def __init__(
         duration: float = 2.0,
         warm_up: Union[float, Tuple[float, float]] = 0.0,
         overlap: dict = OVERLAP_DEFAULTS,
-        balance: Text = None,
+        balance: Sequence[Text] = None,
         weight: Text = None,
         batch_size: int = 32,
         num_workers: int = None,

pyannote/audio/tasks/segmentation/speaker_diarization.py

@@ -86,10 +86,10 @@ class SpeakerDiarization(SegmentationTaskMixin, Task):
         parts, only the remaining central part of each chunk is used for computing the
         loss during training, and for aggregating scores during inference.
         Defaults to 0. (i.e. no warm-up).
-    balance: str, optional
-        When provided, training samples are sampled uniformly with respect to that key.
-        For instance, setting `balance` to "database" will make sure that each database
-        will be equally represented in the training samples.
+    balance: Sequence[Text], optional
+        When provided, training samples are sampled uniformly with respect to these keys.
+        For instance, setting `balance` to ["database","subset"] will make sure that each
+        database & subset combination will be equally represented in the training samples.
     weight: str, optional
         When provided, use this key as frame-wise weight in loss function.
     batch_size : int, optional
@@ -132,7 +132,7 @@ def __init__(
         max_speakers_per_frame: int = None,
         weigh_by_cardinality: bool = False,
         warm_up: Union[float, Tuple[float, float]] = 0.0,
-        balance: Text = None,
+        balance: Sequence[Text] = None,
         weight: Text = None,
         batch_size: int = 32,
         num_workers: int = None,

pyannote/audio/tasks/segmentation/voice_activity_detection.py

@@ -53,10 +53,10 @@ class VoiceActivityDetection(SegmentationTaskMixin, Task):
         parts, only the remaining central part of each chunk is used for computing the
         loss during training, and for aggregating scores during inference.
         Defaults to 0. (i.e. no warm-up).
-    balance: str, optional
-        When provided, training samples are sampled uniformly with respect to that key.
-        For instance, setting `balance` to "uri" will make sure that each file will be
-        equally represented in the training samples.
+    balance: Sequence[Text], optional
+        When provided, training samples are sampled uniformly with respect to these keys.
+        For instance, setting `balance` to ["database","subset"] will make sure that each
+        database & subset combination will be equally represented in the training samples.
     weight: str, optional
         When provided, use this key to as frame-wise weight in loss function.
     batch_size : int, optional
@@ -81,7 +81,7 @@ def __init__(
         protocol: Protocol,
         duration: float = 2.0,
         warm_up: Union[float, Tuple[float, float]] = 0.0,
-        balance: Text = None,
+        balance: Sequence[Text] = None,
         weight: Text = None,
         batch_size: int = 32,
         num_workers: int = None,