diart

Subpackages

• diart.blocks
  • diart.blocks.aggregation
  • diart.blocks.base
  • diart.blocks.clustering
  • diart.blocks.diarization
  • diart.blocks.embedding
  • diart.blocks.segmentation
  • diart.blocks.utils
  • diart.blocks.vad

Submodules

• diart.audio
• diart.features
• diart.functional
• diart.inference
• diart.mapping
• diart.models
• diart.operators
• diart.optim
• diart.progress
• diart.sinks
• diart.sources
• diart.utils

Package Contents

Classes

SpeakerDiarization	Represents a streaming audio pipeline
Pipeline	Represents a streaming audio pipeline
SpeakerDiarizationConfig	Configuration containing the required
PipelineConfig	Configuration containing the required
VoiceActivityDetection	Represents a streaming audio pipeline
VoiceActivityDetectionConfig	Configuration containing the required

class diart.SpeakerDiarization(config=None)

Bases: diart.blocks.base.Pipeline

Represents a streaming audio pipeline

Parameters:

config (SpeakerDiarizationConfig | None) –

property config: SpeakerDiarizationConfig

Return type:

SpeakerDiarizationConfig

static get_config_class()

Return type:

type

static suggest_metric()

Return type:

pyannote.metrics.base.BaseMetric

static hyper_parameters()

Return type:

Sequence[diart.blocks.base.HyperParameter]

set_timestamp_shift(shift)

Parameters:

shift (float) –

reset()

__call__(waveforms)

Diarize the next audio chunks of an audio stream.

Parameters:

waveforms (Sequence[SlidingWindowFeature]) – A sequence of consecutive audio chunks from an audio stream.

Returns:

Speaker diarization of each chunk alongside their corresponding audio.

Return type:

Sequence[tuple[Annotation, SlidingWindowFeature]]

class diart.Pipeline

Bases: abc.ABC

Represents a streaming audio pipeline

abstract property config: PipelineConfig

Return type:

PipelineConfig

abstract static get_config_class()

Return type:

type

abstract static suggest_metric()

Return type:

pyannote.metrics.base.BaseMetric

abstract static hyper_parameters()

Return type:

Sequence[HyperParameter]

abstract reset()

abstract set_timestamp_shift(shift)

Parameters:

shift (float) –

abstract __call__(waveforms)

Runs the next steps of the pipeline given a list of consecutive audio chunks.

Parameters:

waveforms (Sequence[SlidingWindowFeature]) – Consecutive chunk waveforms for the pipeline to ingest

Returns:

For each input waveform, a tuple containing the pipeline output and its respective audio

Return type:

Sequence[Tuple[Any, SlidingWindowFeature]]

class diart.SpeakerDiarizationConfig(segmentation=None, embedding=None, duration=5, step=0.5, latency=None, tau_active=0.6, rho_update=0.3, delta_new=1, gamma=3, beta=10, max_speakers=20, normalize_embedding_weights=False, device=None, sample_rate=16000, **kwargs)

Bases: diart.blocks.base.PipelineConfig

Configuration containing the required parameters to build and run a pipeline

Parameters:

• segmentation (diart.models.SegmentationModel | None) –

• embedding (diart.models.EmbeddingModel | None) –

• duration (float) –

• step (float) –

• latency (float | typing_extensions.Literal[max, min] | None) –

• tau_active (float) –

• rho_update (float) –

• delta_new (float) –

• gamma (float) –

• beta (float) –

• max_speakers (int) –

• normalize_embedding_weights (bool) –

• device (torch.device | None) –

• sample_rate (int) –

property duration: float

The duration of an input audio chunk (in seconds)

Return type:

float

property step: float

The step between two consecutive input audio chunks (in seconds)

Return type:

float

property latency: float

The algorithmic latency of the pipeline (in seconds). At time t of the audio stream, the pipeline will output predictions for time t - latency.

Return type:

float

property sample_rate: int

The sample rate of the input audio stream

Return type:

int

class diart.PipelineConfig

Bases: abc.ABC

Configuration containing the required parameters to build and run a pipeline

abstract property duration: float

The duration of an input audio chunk (in seconds)

Return type:

float

abstract property step: float

The step between two consecutive input audio chunks (in seconds)

Return type:

float

abstract property latency: float

The algorithmic latency of the pipeline (in seconds). At time t of the audio stream, the pipeline will output predictions for time t - latency.

Return type:

float

abstract property sample_rate: int

The sample rate of the input audio stream

Return type:

int

get_file_padding(filepath)

Parameters:

filepath (diart.audio.FilePath) –

Return type:

Tuple[float, float]

class diart.VoiceActivityDetection(config=None)

Bases: diart.blocks.base.Pipeline

Represents a streaming audio pipeline

Parameters:

config (VoiceActivityDetectionConfig | None) –

property config: diart.blocks.base.PipelineConfig

Return type:

diart.blocks.base.PipelineConfig

static get_config_class()

Return type:

type

static suggest_metric()

Return type:

pyannote.metrics.base.BaseMetric

static hyper_parameters()

Return type:

Sequence[diart.blocks.base.HyperParameter]

reset()

set_timestamp_shift(shift)

Parameters:

shift (float) –

__call__(waveforms)

Runs the next steps of the pipeline given a list of consecutive audio chunks.

Parameters:

waveforms (Sequence[SlidingWindowFeature]) – Consecutive chunk waveforms for the pipeline to ingest

Returns:

For each input waveform, a tuple containing the pipeline output and its respective audio

Return type:

Sequence[Tuple[Any, SlidingWindowFeature]]

class diart.VoiceActivityDetectionConfig(segmentation=None, duration=5, step=0.5, latency=None, tau_active=0.6, device=None, sample_rate=16000, **kwargs)

Bases: diart.blocks.base.PipelineConfig

Configuration containing the required parameters to build and run a pipeline

Parameters:

• segmentation (diart.models.SegmentationModel | None) –

• duration (float) –

• step (float) –

• latency (float | typing_extensions.Literal[max, min] | None) –

• tau_active (float) –

• device (torch.device | None) –

• sample_rate (int) –

property duration: float

The duration of an input audio chunk (in seconds)

Return type:

float

property step: float

The step between two consecutive input audio chunks (in seconds)

Return type:

float

property latency: float

The algorithmic latency of the pipeline (in seconds). At time t of the audio stream, the pipeline will output predictions for time t - latency.

Return type:

float

property sample_rate: int

The sample rate of the input audio stream

Return type:

int