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following to Feature Request: Implementing Persistent Speaker Embeddings Across Conversations #227 #228

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following to Feature Request: Implementing Persistent Speaker Embeddings Across Conversations #227 #228

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1e1ae2a
speaker embeddings, redis writer
DmitriyG228 Jan 4, 2024
44a7257
speaker embeddings, redis writer
DmitriyG228 Jan 4, 2024
589034c
Merge branch 'juanmc2005:main' into main
DmitriyG228 Jun 1, 2024
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6 changes: 5 additions & 1 deletion src/diart/blocks/diarization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,7 +87,8 @@ def sample_rate(self) -> int:


class SpeakerDiarization(base.Pipeline):
def __init__(self, config: SpeakerDiarizationConfig | None = None):
def __init__(self, config: SpeakerDiarizationConfig | None = None, return_embeddings: bool = False):
self.return_embeddings = return_embeddings
self._config = SpeakerDiarizationConfig() if config is None else config

msg = f"Latency should be in the range [{self._config.step}, {self._config.duration}]"
Expand Down Expand Up @@ -225,6 +226,9 @@ def __call__(
agg_prediction = shifted_agg_prediction

outputs.append((agg_prediction, agg_waveform))
if self.return_embeddings:
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Why do we need to return centroids with each output? In order to have persistent centroids it would be enough to have a way of extracting them after inference and a way of setting them before inference. I think the best way to do this would be to expose getters and setters.

The thing is I don't want the RedisWriter to be exclusively for diarization. I would like for it to be compatible with VAD and in the future with transcription too.

Adding things at the end of each output is not a sustainable solution. With time, we could end up having huge tuples. An alternative would be to have different output objects per pipeline, like DiarizationOutput and VADOutput, but I want all sinks to be able to handle outputs seamlessly so they should remain at least similar enough. This would also require big changes to all inference and sink objects.

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@DmitriyG228 DmitriyG228 Jan 6, 2024 •
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I think, we need to have setnroings ducing inference in the real time application. Obviously, every iteration is overkill

it would be enough to have a way of extracting them after inference

My usecase is about global speakers identification in real - time environment, which means I want to obtain speaker embeddings at some points for vector store lookup. After inference is not enough. As for now, I think good idea is to obtain a centroid for each new speaker after it's well - converged. Getting embeddings at each iteration obviously is overkill, but that worka for me at this point, as I can keep only the last state of centroids downstream. Maybe, a better idea is to develop an on-demand method for that, but I am pretty new to real time application and not sure how to do it right. Exposing getters looks good to me, but I am not sure how to use them

outputs[-1] = outputs[-1] + (self.clustering.centers,) # extend output with speakers' embeddings


# Make place for new chunks in buffer if required
if len(self.chunk_buffer) == self.pred_aggregation.num_overlapping_windows:
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41 changes: 41 additions & 0 deletions src/diart/sinks.py
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Expand Up @@ -7,6 +7,7 @@
from pyannote.metrics.diarization import DiarizationErrorRate
from rx.core import Observer
from typing_extensions import Literal
import json


class WindowClosedException(Exception):
Expand Down Expand Up @@ -55,6 +56,46 @@ def on_error(self, error: Exception):
def on_completed(self):
self.patch()

######
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Remove blank comment


class RedisWriter(Observer):
def __init__(self, uri: Text, redis_client, patch_collar: float = 0.05):
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patch_collar isn't used here, right? In that case it should be removed

super().__init__()
self.uri = uri
self.redis_client = redis_client
self.conversation_id = uri # Assuming URI as a unique identifier for the conversation
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This attribute is not needed. self.uri is enough

self.patch_collar = patch_collar

def on_next(self, value: Union[Tuple, Annotation]):
if isinstance(value, tuple):
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Use _extract_prediction() here to get the annotation object instead of using an if statement

prediction, _ = value[:2]
# Process each segment in the prediction
for segment, _, label in prediction.itertracks(yield_label=True):
# Update last centroids for each speaker

# Write data to Redis queues
diarization_data = {
'start': segment.start,
'end': segment.end,
'speaker_id': label,
}
if len(value)==3:
diarization_data['centroids'] = value[-1].tolist()

self.redis_client.rpush(f'diarization_{self.conversation_id}', json.dumps(diarization_data))
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Let's not name this "diarization". We could also be writing the output of a VAD pipeline here


else:
prediction = value

def on_error(self, error: Exception):
# Handle error (optional)
pass

def on_completed(self):
# Handle completion (optional)
pass

#######
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Remove blank comment


class PredictionAccumulator(Observer):
def __init__(self, uri: Optional[Text] = None, patch_collar: float = 0.05):
Expand Down