Model Configs
FaceConfig
Configuration for the facial expression model.
@dataclass
class FaceConfig(ModelConfigBase["FaceConfig"])
Arguments:
fps_pred
Optional[float] - Number of frames per second to process. Other frames will be omitted
from the response.
This configuration is only available for the batch API.prob_threshold
Optional[float] - Face detection probability threshold. Faces detected with a
probability less than this threshold will be omitted from the response.
This configuration is only available for the batch API.identify_faces
Optional[bool] - Whether to return identifiers for faces across frames.
If true, unique identifiers will be assigned to face bounding boxes to differentiate different faces.
If false, all faces will be tagged with an "unknown" ID.min_face_size
Optional[float] - Minimum bounding box side length in pixels to treat as a face.
Faces detected with a bounding box side length in pixels less than this threshold will be
omitted from the response.
This configuration is only available for the batch API.save_faces
Optional[bool] - Whether to extract and save the detected faces to the artifacts
directory included in the response.
This configuration is only available for the batch API.descriptions
Optional[Dict[str, Any]] - Configuration for Descriptions predictions.
Descriptions prediction can be enabled by setting "descriptions": {}.
Currently, Descriptions prediction cannot be further configured with any parameters.
If missing or null, no descriptions predictions will be generated.facs
Optional[Dict[str, Any]] - Configuration for FACS predictions.
FACS prediction can be enabled by setting "facs": {}.
Currently, FACS prediction cannot be further configured with any parameters.
If missing or null, no facs predictions will be generated.
get_model_type
Get the configuration model type.
@classmethod
def get_model_type(cls) -> ModelType
Returns:
ModelType
- Model type.
LanguageConfig
Configuration for the language emotion model.
@dataclass
class LanguageConfig(ModelConfigBase["LanguageConfig"])
Arguments:
granularity
Optional[str] - The granularity at which to generate predictions.
Accepted values areword
,sentence
,utterance
, orconversational_turn
.
The default isutterance
.
utterance
corresponds to a natural pause or break in conversation
conversational_turn
corresponds to a change in speaker.
This configuration is available for the streaming API, but only with valuesword
andsentence
.identify_speakers
Optional[bool] - Whether to return identifiers for speakers over time.
If true, unique identifiers will be assigned to spoken words to differentiate different speakers.
If false, all speakers will be tagged with an "unknown" ID.
This configuration is only available for the batch API.sentiment
Optional[Dict[str, Any]] - Configuration for Sentiment predictions.
Sentiment prediction can be enabled by setting "sentiment": {}.
Currently, Sentiment prediction cannot be further configured with any parameters.
If missing or null, no sentiment predictions will be generated.toxicity
Optional[Dict[str, Any]] - Configuration for Toxicity predictions.
Toxicity prediction can be enabled by setting "toxicity": {}.
Currently, Toxicity prediction cannot be further configured with any parameters.
If missing or null, no toxicity predictions will be generated.
get_model_type
Get the configuration model type.
@classmethod
def get_model_type(cls) -> ModelType
Returns:
ModelType
- Model type.
FacemeshConfig
Configuration for the facemesh model.
@dataclass
class FacemeshConfig(ModelConfigBase["FacemeshConfig"])
get_model_type
Get the configuration model type.
@classmethod
def get_model_type(cls) -> ModelType
Returns:
ModelType
- Model type.
ProsodyConfig
Configuration for the speech prosody model.
@dataclass
class ProsodyConfig(ModelConfigBase["ProsodyConfig"])
Arguments:
granularity
Optional[str] - The granularity at which to generate predictions.
Accepted values areword
,sentence
,utterance
, orconversational_turn
.
The default isutterance
.
utterance
corresponds to a natural pause or break in conversation
conversational_turn
corresponds to a change in speaker.
This configuration is only available for the batch API.identify_speakers
Optional[bool] - Whether to return identifiers for speakers over time. If true,
unique identifiers will be assigned to spoken words to differentiate different speakers. If false,
all speakers will be tagged with an "unknown" ID.
This configuration is only available for the batch API.window
Optional[Dict[str, float]] - Sliding window used to chunk audio.
This dictionary input takes two entries:length
andstep
representing
the width of the window in seconds and the step size in seconds.
This configuration is only available for the batch API.
get_model_type
Get the configuration model type.
@classmethod
def get_model_type(cls) -> ModelType
Returns:
ModelType
- Model type.
BurstConfig
Configuration for the vocal burst model.
@dataclass
class BurstConfig(ModelConfigBase["BurstConfig"])
get_model_type
Get the configuration model type.
@classmethod
def get_model_type(cls) -> ModelType
Returns:
ModelType
- Model type.
NerConfig (Batch API Only)
Configuration for the named-entity emotion model.
@dataclass
class NerConfig(ModelConfigBase["NerConfig"])
Arguments:
identify_speakers
Optional[bool] - Whether to return identifiers for speakers over time. If true,
unique identifiers will be assigned to spoken words to differentiate different speakers. If false,
all speakers will be tagged with an "unknown" ID.
This configuration is only available for the batch API.
get_model_type
Get the configuration model type.
@classmethod
def get_model_type(cls) -> ModelType
Returns:
ModelType
- Model type.
Updated 3 months ago