das-Peak

vericlient.daspeak.client.DaspeakClient

Bases: Client

Class to interact with the Daspeak API.

Source code in src/vericlient/daspeak/client.py

class DaspeakClient(Client):
    """Class to interact with the Daspeak API."""

    def __init__(
        self,
        apikey: str | None = None,
        timeout: int | None = None,
        environment: str | None = None,
        location: str | None = None,
        url: str | None = None,
        headers: dict | None = None,
    ) -> None:
        """Create the DaspeakClient class.

        Args:
            apikey: The API key to use
            timeout: The timeout to use in the requests
            environment: The environment to use
            location: The location to use
            url: The URL to use in case of a custom target
            headers: The headers to be used in the requests

        """
        api = APIs.DASPEAK
        super().__init__(
            api=api,
            apikey=apikey,
            timeout=timeout,
            environment=environment,
            location=location,
            url=url,
            headers=headers,
        )
        self._exceptions = [
            "AudioInputException",
            "SignalNoiseRatioException",
            "VoiceDurationIsNotEnoughException",
            "InvalidChannelException",
            "InsufficientQuality",
            "CalibrationNotAvailable",
            "ServerError",
            "InvalidCredential",
            "UnsupportedMediaType",
            "ModelNotAvailable",
        ]
        self._compare_functions_map = {
            CompareCredential2AudioInput: self._compare_credential2audio,
            CompareAudio2AudioInput: self._compare_audio2audio,
            CompareCredential2CredentialInput: self._compare_credential2credential,
            CompareAudio2CredentialsInput: self._compare_audio2credentials,
            CompareCredential2CredentialsInput: self._compare_credential2credentials,
        }
        self._exception_map = {
            "SignalNoiseRatioException": SignalNoiseRatioError,
            "VoiceDurationIsNotEnoughException": self._handle_voice_duration_error,
            "InvalidChannelException": InvalidSpecifiedChannelError,
            "InsufficientQuality": InsufficientQualityError,
            "CalibrationNotAvailable": self._handle_calibration_error,
            "InvalidCredential": InvalidCredentialError,
            "UnsupportedMediaType": UnsupportedMediaTypeError,
            "ModelNotAvailable": self._handle_model_not_available_error,
        }
        self._audio_input_errors = {
            "more channels than": TooManyAudioChannelsError,
            "unsupported codec": UnsupportedAudioCodecError,
            "sample rate": UnsupportedSampleRateError,
            "duration is longer": AudioDurationTooLongError,
        }

    def alive(self) -> bool:
        """Check if the service is alive.

        Returns
            bool: True if the service is alive, False otherwise

        """
        response = self._get(endpoint=DaspeakEndpoints.ALIVE.value)
        accepted_status_code = 200
        return response.status_code == accepted_status_code

    def _handle_error_response(self, response: Response) -> None:
        """Handle error responses from the API."""
        response_json = self._error_payload(response)

        exception = response_json.get("exception")
        if not exception or exception not in self._exceptions:
            self._raise_server_error(response)

        if exception == "AudioInputException":
            error_message = response_json.get("error", "")
            for error_text, error_class in self._audio_input_errors.items():
                if error_text in error_message:
                    raise error_class
            raise ValueError(error_message)

        handler = self._exception_map.get(exception)
        if handler:
            if isinstance(handler, type) and issubclass(handler, VeriClientError):
                raise handler
            handler(response_json)

        raise ValueError(response_json.get("error", "Unknown error"))

    def _handle_voice_duration_error(self, response_json: dict) -> None:
        error_message = response_json.get("error", "")
        net_speech_detected = float(error_message.split(" ")[-3].replace("s", ""))
        raise NetSpeechDurationIsNotEnoughError(net_speech_detected)

    def _handle_model_not_available_error(self, response_json: dict) -> None:
        error_message = response_json.get("error", "")
        model_hash = error_message.rsplit(" ", 1)[-1]
        raise ModelNotAvailableError(model_hash)

    def _handle_calibration_error(self, response_json: dict) -> None:
        error_message = response_json.get("error", "")
        calibration = str(error_message.split(" ")[2])
        raise CalibrationNotAvailableError(calibration)

    def get_models(self) -> ModelsOutput:
        """Get the models available biometrics models in the service.

        Returns:
            The response from the service

        """
        response = self._get(endpoint=DaspeakEndpoints.MODELS.value)
        return ModelsOutput(**response.json())

    def get_model_metadata(self, data_model: GetModelMetadataInput) -> GetModelMetadataOutput:
        """Get the metadata of a biometrics model.

        Args:
            data_model: The hash of the model to describe

        Returns:
            The response from the service

        Raises:
            ModelNotAvailableError: If no model exists with that hash

        """
        response = self._post(
            endpoint=DaspeakEndpoints.MODELS_METADATA.value,
            data={"hash": data_model.hash},
        )
        return GetModelMetadataOutput(**response.json())

    def get_model_calibrations(self, data_model: GetModelCalibrationsInput) -> GetModelCalibrationsOutput:
        """Get the calibration modes a biometrics model supports.

        Any of the returned values is valid as the `calibration` argument of
        `generate_credential` and of the comparison inputs.

        Args:
            data_model: The hash of the model to list the calibrations of

        Returns:
            The response from the service

        Raises:
            ModelNotAvailableError: If no model exists with that hash

        """
        response = self._post(
            endpoint=DaspeakEndpoints.MODELS_CALIBRATION.value,
            data={"hash": data_model.hash},
        )
        return GetModelCalibrationsOutput(**response.json())

    def get_model_metadata_from_credential(
        self,
        data_model: GetModelMetadataFromCredentialInput,
    ) -> GetModelMetadataFromCredentialOutput:
        """Get the metadata of the model a credential was generated with.

        Useful to find out whether a stored credential is still compatible with the models
        the service currently offers.

        Args:
            data_model: The credential to read the originating model from

        Returns:
            The response from the service

        Raises:
            InvalidCredentialError: If the credential is not valid

        """
        response = self._post(
            endpoint=DaspeakEndpoints.MODELS_METADATA_FROM_CREDENTIAL.value,
            data={"credential": data_model.credential},
        )
        return GetModelMetadataFromCredentialOutput(**response.json())

    def generate_credential(self, data_model: GenerateCredentialInput) -> GenerateCredentialOutput:
        """Generate a credential from a WAV file.

        Args:
            data_model: The data required to generate the credential

        Returns:
            The response from the service

        Raises:
            ValueError: If the `data_model` is not an instance of `GenerateCredentialInput`
            TooManyAudioChannelsError: If the audio has more channels than the service supports
            UnsupportedAudioCodecError: If the audio has an unsupported codec
            UnsupportedSampleRateError: If the audio has an unsupported sample rate
            AudioDurationTooLongError: If the audio duration is longer than the service supports
            SignalNoiseRatioError: If the signal-to-noise ratio is too low
            NetSpeechDurationIsNotEnoughError: If the net speech duration is not enough
            InvalidSpecifiedChannelError: If the specified channel is invalid
            InsufficientQualityError: If the audio quality is insufficient
            CalibrationNotAvailableError: If the calibration is not available
            UnsupportedMediaTypeError: If the media type is not supported

        """
        endpoint = DaspeakEndpoints.MODELS_HASH_CREDENTIAL_AUDIO.value.replace("<hash>", data_model.hash)
        audio = get_virtual_file(data_model.audio)
        files = {
            "audio": ("audio", audio, "audio/wav"),
        }
        data = {
            "channel": data_model.channel,
            "calibration": data_model.calibration,
        }
        response = self._post(endpoint=endpoint, data=data, files=files)
        return GenerateCredentialOutput(**response.json())

    def compare(  # noqa: D417
        self,
        data_model: CompareInput,
    ) -> (
        CompareCredential2AudioOutput
        | CompareAudio2AudioOutput
        | CompareCredential2CredentialOutput
        | CompareAudio2CredentialsOutput
        | CompareCredential2CredentialsOutput
    ):
        """Compare two sets of data based on the provided input.

        Args:
            data_model (CompareCredential2AudioInput | CompareAudio2AudioInput | CompareCredential2CredentialInput | \
                        CompareAudio2CredentialsInput | CompareCredential2CredentialsInput):
                The data required to compare the audio files or credentials

        Returns:
            The response from the service, depending on the input type.

        Raises:
            ValueError: If the `data_model` is not an instance of `CompareInput`
            TooManyAudioChannelsError: If the audio has more channels than the service supports
            UnsupportedAudioCodecError: If the audio has an unsupported codec
            UnsupportedSampleRateError: If the audio has an unsupported sample rate
            AudioDurationTooLongError: If the audio duration is longer than the service supports
            SignalNoiseRatioError: If the signal-to-noise ratio is too low
            NetSpeechDurationIsNotEnoughError: If the net speech duration is not enough
            InvalidSpecifiedChannelError: If the specified channel is invalid
            InsufficientQualityError: If the audio quality is insufficient
            CalibrationNotAvailableError: If the calibration is not available
            InvalidCredentialError: If the credential is invalid
            UnsupportedMediaTypeError: If the media type is not supported

        """
        try:
            func = self._compare_functions_map.get(type(data_model))
            return func(data_model)
        except AttributeError as e:
            error = "data_model must be an instance of CompareInput"
            raise TypeError(error) from e

    def _compare_credential2audio(
        self,
        data_model: CompareCredential2AudioInput,
    ) -> CompareCredential2AudioOutput:
        """Compare a credential with an audio file.

        Args:
            data_model: The data required to compare the credential with the audio

        Returns:
            CompareCredential2AudioOutput: The response from the service

        """
        endpoint = DaspeakEndpoints.SIMILARITY_CREDENTIAL2AUDIO.value
        audio = get_virtual_file(data_model.audio_to_evaluate)
        files = {
            "audio_to_evaluate": ("audio", audio, "audio/wav"),
        }
        data = {
            "credential_reference": data_model.credential_reference,
            "channel": data_model.channel,
            "calibration": data_model.calibration,
        }
        response = self._post(endpoint=endpoint, data=data, files=files)
        return CompareCredential2AudioOutput(**response.json())

    def _compare_audio2audio(self, data_model: CompareAudio2AudioInput) -> CompareAudio2AudioOutput:
        """Compare two audio files.

        Args:
            data_model: The data required to compare the audio files

        Returns:
            CompareAudio2AudioOutput: The response from the service

        """
        endpoint = DaspeakEndpoints.SIMILARITY_AUDIO2AUDIO.value
        audio_reference = get_virtual_file(data_model.audio_reference)
        audio_to_evaluate = get_virtual_file(data_model.audio_to_evaluate)
        files = {
            "audio_reference": ("audio", audio_reference, "audio/wav"),
            "audio_to_evaluate": ("audio", audio_to_evaluate, "audio/wav"),
        }
        data = {
            "channel_reference": data_model.channel_reference,
            "channel_to_evaluate": data_model.channel_to_evaluate,
            "calibration": data_model.calibration,
        }
        response = self._post(endpoint=endpoint, data=data, files=files)
        return CompareAudio2AudioOutput(**response.json())

    def _compare_credential2credential(
        self,
        data_model: CompareCredential2CredentialInput,
    ) -> CompareCredential2CredentialOutput:
        """Compare two credentials.

        Args:
            data_model: The data required to compare the credentials

        Returns:
            CompareCredential2CredentialOutput: The response from the service

        """
        endpoint = DaspeakEndpoints.SIMILARITY_CREDENTIAL2CREDENTIAL.value
        data = {
            "credential_reference": data_model.credential_reference,
            "credential_to_evaluate": data_model.credential_to_evaluate,
            "calibration": data_model.calibration,
        }
        response = self._post(endpoint=endpoint, data=data)
        return CompareCredential2CredentialOutput(**response.json())

    def _compare_audio2credentials(
        self,
        data_model: CompareAudio2CredentialsInput,
    ) -> CompareAudio2CredentialsOutput:
        """Compare an audio file with a list of credentials.

        Args:
            data_model: The data required to compare the audio file with the credentials

        Returns:
            CompareAudio2CredentialsOutput: The response from the service

        """
        endpoint = DaspeakEndpoints.IDENTIFICATION_AUDIO2CREDENTIALS.value
        audio = get_virtual_file(data_model.audio_to_evaluate)
        files = {
            "audio_to_evaluate": ("audio", audio, "audio/wav"),
        }
        credential_list = json.dumps(data_model.credential_list)
        data = {
            "credential_list": credential_list,
            "channel": data_model.channel,
            "calibration": data_model.calibration,
        }
        response = self._post(endpoint=endpoint, data=data, files=files)
        return CompareAudio2CredentialsOutput(**response.json())

    def _compare_credential2credentials(
        self,
        data_model: CompareCredential2CredentialsInput,
    ) -> CompareCredential2CredentialsOutput:
        endpoint = DaspeakEndpoints.IDENTIFICATION_CREDENTIAL2CREDENTIALS.value
        credential_list = json.dumps(data_model.credential_list)
        data = {
            "credential_to_evaluate": data_model.credential_to_evaluate,
            "credential_list": credential_list,
            "calibration": data_model.calibration,
        }
        response = self._post(endpoint=endpoint, data=data)
        return CompareCredential2CredentialsOutput(**response.json())

alive

alive()

Check if the service is alive.

Returns bool: True if the service is alive, False otherwise

Source code in src/vericlient/daspeak/client.py

def alive(self) -> bool:
    """Check if the service is alive.

    Returns
        bool: True if the service is alive, False otherwise

    """
    response = self._get(endpoint=DaspeakEndpoints.ALIVE.value)
    accepted_status_code = 200
    return response.status_code == accepted_status_code

get_models

get_models()

Get the models available biometrics models in the service.

Returns:

Type	Description
ModelsOutput	The response from the service

Source code in src/vericlient/daspeak/client.py

def get_models(self) -> ModelsOutput:
    """Get the models available biometrics models in the service.

    Returns:
        The response from the service

    """
    response = self._get(endpoint=DaspeakEndpoints.MODELS.value)
    return ModelsOutput(**response.json())

get_model_metadata

get_model_metadata(data_model)

Get the metadata of a biometrics model.

Parameters:

Name	Type	Description	Default
data_model	GetModelMetadataInput	The hash of the model to describe	required

Returns:

Type	Description
GetModelMetadataOutput	The response from the service

Raises:

Type	Description
ModelNotAvailableError	If no model exists with that hash

Source code in src/vericlient/daspeak/client.py

def get_model_metadata(self, data_model: GetModelMetadataInput) -> GetModelMetadataOutput:
    """Get the metadata of a biometrics model.

    Args:
        data_model: The hash of the model to describe

    Returns:
        The response from the service

    Raises:
        ModelNotAvailableError: If no model exists with that hash

    """
    response = self._post(
        endpoint=DaspeakEndpoints.MODELS_METADATA.value,
        data={"hash": data_model.hash},
    )
    return GetModelMetadataOutput(**response.json())

get_model_calibrations

get_model_calibrations(data_model)

Get the calibration modes a biometrics model supports.

Any of the returned values is valid as the calibration argument of generate_credential and of the comparison inputs.

Parameters:

Name	Type	Description	Default
data_model	GetModelCalibrationsInput	The hash of the model to list the calibrations of	required

Returns:

Type	Description
GetModelCalibrationsOutput	The response from the service

Raises:

Type	Description
ModelNotAvailableError	If no model exists with that hash

Source code in src/vericlient/daspeak/client.py

def get_model_calibrations(self, data_model: GetModelCalibrationsInput) -> GetModelCalibrationsOutput:
    """Get the calibration modes a biometrics model supports.

    Any of the returned values is valid as the `calibration` argument of
    `generate_credential` and of the comparison inputs.

    Args:
        data_model: The hash of the model to list the calibrations of

    Returns:
        The response from the service

    Raises:
        ModelNotAvailableError: If no model exists with that hash

    """
    response = self._post(
        endpoint=DaspeakEndpoints.MODELS_CALIBRATION.value,
        data={"hash": data_model.hash},
    )
    return GetModelCalibrationsOutput(**response.json())

get_model_metadata_from_credential

get_model_metadata_from_credential(data_model)

Get the metadata of the model a credential was generated with.

Useful to find out whether a stored credential is still compatible with the models the service currently offers.

Parameters:

Name	Type	Description	Default
data_model	GetModelMetadataFromCredentialInput	The credential to read the originating model from	required

Returns:

Type	Description
GetModelMetadataFromCredentialOutput	The response from the service

Raises:

Type	Description
InvalidCredentialError	If the credential is not valid

Source code in src/vericlient/daspeak/client.py

def get_model_metadata_from_credential(
    self,
    data_model: GetModelMetadataFromCredentialInput,
) -> GetModelMetadataFromCredentialOutput:
    """Get the metadata of the model a credential was generated with.

    Useful to find out whether a stored credential is still compatible with the models
    the service currently offers.

    Args:
        data_model: The credential to read the originating model from

    Returns:
        The response from the service

    Raises:
        InvalidCredentialError: If the credential is not valid

    """
    response = self._post(
        endpoint=DaspeakEndpoints.MODELS_METADATA_FROM_CREDENTIAL.value,
        data={"credential": data_model.credential},
    )
    return GetModelMetadataFromCredentialOutput(**response.json())

generate_credential

generate_credential(data_model)

Generate a credential from a WAV file.

Parameters:

Name	Type	Description	Default
data_model	GenerateCredentialInput	The data required to generate the credential	required

Returns:

Type	Description
GenerateCredentialOutput	The response from the service

Raises:

Type	Description
ValueError	If the data_model is not an instance of GenerateCredentialInput
TooManyAudioChannelsError	If the audio has more channels than the service supports
UnsupportedAudioCodecError	If the audio has an unsupported codec
UnsupportedSampleRateError	If the audio has an unsupported sample rate
AudioDurationTooLongError	If the audio duration is longer than the service supports
SignalNoiseRatioError	If the signal-to-noise ratio is too low
NetSpeechDurationIsNotEnoughError	If the net speech duration is not enough
InvalidSpecifiedChannelError	If the specified channel is invalid
InsufficientQualityError	If the audio quality is insufficient
CalibrationNotAvailableError	If the calibration is not available
UnsupportedMediaTypeError	If the media type is not supported

Source code in src/vericlient/daspeak/client.py

def generate_credential(self, data_model: GenerateCredentialInput) -> GenerateCredentialOutput:
    """Generate a credential from a WAV file.

    Args:
        data_model: The data required to generate the credential

    Returns:
        The response from the service

    Raises:
        ValueError: If the `data_model` is not an instance of `GenerateCredentialInput`
        TooManyAudioChannelsError: If the audio has more channels than the service supports
        UnsupportedAudioCodecError: If the audio has an unsupported codec
        UnsupportedSampleRateError: If the audio has an unsupported sample rate
        AudioDurationTooLongError: If the audio duration is longer than the service supports
        SignalNoiseRatioError: If the signal-to-noise ratio is too low
        NetSpeechDurationIsNotEnoughError: If the net speech duration is not enough
        InvalidSpecifiedChannelError: If the specified channel is invalid
        InsufficientQualityError: If the audio quality is insufficient
        CalibrationNotAvailableError: If the calibration is not available
        UnsupportedMediaTypeError: If the media type is not supported

    """
    endpoint = DaspeakEndpoints.MODELS_HASH_CREDENTIAL_AUDIO.value.replace("<hash>", data_model.hash)
    audio = get_virtual_file(data_model.audio)
    files = {
        "audio": ("audio", audio, "audio/wav"),
    }
    data = {
        "channel": data_model.channel,
        "calibration": data_model.calibration,
    }
    response = self._post(endpoint=endpoint, data=data, files=files)
    return GenerateCredentialOutput(**response.json())

compare

compare(data_model)

Compare two sets of data based on the provided input.

Parameters:

Name	Type	Description	Default
data_model	CompareCredential2AudioInput | CompareAudio2AudioInput | CompareCredential2CredentialInput | CompareAudio2CredentialsInput | CompareCredential2CredentialsInput	The data required to compare the audio files or credentials	required

Returns:

Type	Description
CompareCredential2AudioOutput | CompareAudio2AudioOutput | CompareCredential2CredentialOutput | CompareAudio2CredentialsOutput | CompareCredential2CredentialsOutput	The response from the service, depending on the input type.

Raises:

Type	Description
ValueError	If the data_model is not an instance of CompareInput
TooManyAudioChannelsError	If the audio has more channels than the service supports
UnsupportedAudioCodecError	If the audio has an unsupported codec
UnsupportedSampleRateError	If the audio has an unsupported sample rate
AudioDurationTooLongError	If the audio duration is longer than the service supports
SignalNoiseRatioError	If the signal-to-noise ratio is too low
NetSpeechDurationIsNotEnoughError	If the net speech duration is not enough
InvalidSpecifiedChannelError	If the specified channel is invalid
InsufficientQualityError	If the audio quality is insufficient
CalibrationNotAvailableError	If the calibration is not available
InvalidCredentialError	If the credential is invalid
UnsupportedMediaTypeError	If the media type is not supported

Source code in src/vericlient/daspeak/client.py

def compare(  # noqa: D417
    self,
    data_model: CompareInput,
) -> (
    CompareCredential2AudioOutput
    | CompareAudio2AudioOutput
    | CompareCredential2CredentialOutput
    | CompareAudio2CredentialsOutput
    | CompareCredential2CredentialsOutput
):
    """Compare two sets of data based on the provided input.

    Args:
        data_model (CompareCredential2AudioInput | CompareAudio2AudioInput | CompareCredential2CredentialInput | \
                    CompareAudio2CredentialsInput | CompareCredential2CredentialsInput):
            The data required to compare the audio files or credentials

    Returns:
        The response from the service, depending on the input type.

    Raises:
        ValueError: If the `data_model` is not an instance of `CompareInput`
        TooManyAudioChannelsError: If the audio has more channels than the service supports
        UnsupportedAudioCodecError: If the audio has an unsupported codec
        UnsupportedSampleRateError: If the audio has an unsupported sample rate
        AudioDurationTooLongError: If the audio duration is longer than the service supports
        SignalNoiseRatioError: If the signal-to-noise ratio is too low
        NetSpeechDurationIsNotEnoughError: If the net speech duration is not enough
        InvalidSpecifiedChannelError: If the specified channel is invalid
        InsufficientQualityError: If the audio quality is insufficient
        CalibrationNotAvailableError: If the calibration is not available
        InvalidCredentialError: If the credential is invalid
        UnsupportedMediaTypeError: If the media type is not supported

    """
    try:
        func = self._compare_functions_map.get(type(data_model))
        return func(data_model)
    except AttributeError as e:
        error = "data_model must be an instance of CompareInput"
        raise TypeError(error) from e

vericlient.daspeak.models

Module to define the models for the Daspeak API.

DaspeakResponse

Bases: BaseModel

Base class for the Daspeak API responses.

Attributes:

Name	Type	Description
version	str	The version of the API

Source code in src/vericlient/daspeak/models.py

class DaspeakResponse(BaseModel):
    """Base class for the Daspeak API responses.

    Attributes:
        version: The version of the API

    """

    version: str

ModelsOutput

Bases: DaspeakResponse

Output class for the get models endpoint.

Attributes:

Name	Type	Description
models	list	The available models in the service

Source code in src/vericlient/daspeak/models.py

class ModelsOutput(DaspeakResponse):
    """Output class for the get models endpoint.

    Attributes:
        models: The available models in the service

    """

    models: list

BiometricsModelMetadata

Bases: BaseModel

Metadata describing a biometrics model.

Attributes:

Name	Type	Description
hash	str	The hash that identifies the model
description	str	The human readable description of the model, such as its release

Source code in src/vericlient/daspeak/models.py

class BiometricsModelMetadata(BaseModel):
    """Metadata describing a biometrics model.

    Attributes:
        hash: The hash that identifies the model
        description: The human readable description of the model, such as its release

    """

    hash: str
    description: str

GetModelMetadataInput

Bases: BaseModel

Input class for the model metadata endpoint.

Attributes:

Name	Type	Description
hash	str	The hash of the biometrics model to describe

Source code in src/vericlient/daspeak/models.py

class GetModelMetadataInput(BaseModel):
    """Input class for the model metadata endpoint.

    Attributes:
        hash: The hash of the biometrics model to describe

    """

    hash: str

GetModelMetadataOutput

Bases: DaspeakResponse

Output class for the model metadata endpoint.

Attributes:

Name	Type	Description
metadata	BiometricsModelMetadata	The metadata of the model

Source code in src/vericlient/daspeak/models.py

class GetModelMetadataOutput(DaspeakResponse):
    """Output class for the model metadata endpoint.

    Attributes:
        metadata: The metadata of the model

    """

    metadata: BiometricsModelMetadata

GetModelCalibrationsInput

Bases: BaseModel

Input class for the model calibration endpoint.

Attributes:

Name	Type	Description
hash	str	The hash of the biometrics model to list the calibrations of

Source code in src/vericlient/daspeak/models.py

class GetModelCalibrationsInput(BaseModel):
    """Input class for the model calibration endpoint.

    Attributes:
        hash: The hash of the biometrics model to list the calibrations of

    """

    hash: str

GetModelCalibrationsOutput

Bases: DaspeakResponse

Output class for the model calibration endpoint.

Attributes:

Name	Type	Description
calibrations	list[str]	The calibration modes the model supports

Source code in src/vericlient/daspeak/models.py

class GetModelCalibrationsOutput(DaspeakResponse):
    """Output class for the model calibration endpoint.

    Attributes:
        calibrations: The calibration modes the model supports

    """

    calibrations: list[str]

GetModelMetadataFromCredentialInput

Bases: BaseModel

Input class for the model metadata from credential endpoint.

Attributes:

Name	Type	Description
credential	str	The credential to read the originating model from

Source code in src/vericlient/daspeak/models.py

class GetModelMetadataFromCredentialInput(BaseModel):
    """Input class for the model metadata from credential endpoint.

    Attributes:
        credential: The credential to read the originating model from

    """

    credential: str

GetModelMetadataFromCredentialOutput

Bases: DaspeakResponse

Output class for the model metadata from credential endpoint.

Attributes:

Name	Type	Description
metadata	BiometricsModelMetadata	The metadata of the model the credential was generated with

Source code in src/vericlient/daspeak/models.py

class GetModelMetadataFromCredentialOutput(DaspeakResponse):
    """Output class for the model metadata from credential endpoint.

    Attributes:
        metadata: The metadata of the model the credential was generated with

    """

    metadata: BiometricsModelMetadata

GenerateCredentialInput

Bases: BaseModel

Input class for the generate credential endpoint.

Attributes:

Name	Type	Description
audio	str | bytes	The audio to generate the credential with. It can be a path to a file or a bytes object with the audio content
hash	str	The hash of the biometrics model to use
channel	int	The nchannel of the audio if it is stereo
calibration	str	The calibration to use

Source code in src/vericlient/daspeak/models.py

class GenerateCredentialInput(BaseModel):
    """Input class for the generate credential endpoint.

    Attributes:
        audio: The audio to generate the credential with.
            It can be a path to a file or a bytes object
            with the audio content
        hash: The hash of the biometrics model to use
        channel: The `nchannel` of the audio if it is stereo
        calibration: The calibration to use

    """

    audio: str | bytes
    hash: str
    channel: int = 1
    calibration: str = "telephone-channel"

    @field_validator("audio")
    def must_be_str_or_bytes(cls, value: object):
        if not isinstance(value, (str, bytes)):
            error = "audio must be a string or a bytes object"
            raise TypeError(error)
        return value

    model_config = ConfigDict(arbitrary_types_allowed=True)

ModelMetadata

Bases: BaseModel

Metadata of the model used to generate the credential.

Attributes:

Name	Type	Description
hash	str	The hash of the model
mode	str	The mode of the model

Source code in src/vericlient/daspeak/models.py

class ModelMetadata(BaseModel):
    """Metadata of the model used to generate the credential.

    Attributes:
        hash: The hash of the model
        mode: The mode of the model

    """

    hash: str
    mode: str

GenerateCredentialOutput

Bases: DaspeakResponse

Output class for the generate credential endpoint.

Attributes:

Name	Type	Description
model	ModelMetadata	The model used to generate the credential
credential	str	The generated credential
authenticity	float	The authenticity of the audio sample used
input_audio_duration	float	The duration of the input audio
net_speech_duration	float	The duration of the speech in the audio

Source code in src/vericlient/daspeak/models.py

class GenerateCredentialOutput(DaspeakResponse):
    """Output class for the generate credential endpoint.

    Attributes:
        model: The model used to generate the credential
        credential: The generated credential
        authenticity: The authenticity of the audio sample used
        input_audio_duration: The duration of the input audio
        net_speech_duration: The duration of the speech in the audio

    """

    model: ModelMetadata
    credential: str
    authenticity: float
    input_audio_duration: float
    net_speech_duration: float

    @field_validator("authenticity")
    def round_authenticity(cls, value: float) -> float:
        return round(value, 3)

CompareInput

Bases: BaseModel

Base class for the similarity inputs.

Attributes:

Name	Type	Description
calibration	str	The calibration to use

Source code in src/vericlient/daspeak/models.py

class CompareInput(BaseModel):
    """Base class for the similarity inputs.

    Attributes:
        calibration: The calibration to use

    """

    calibration: str = "telephone-channel"

CompareOutput

Bases: DaspeakResponse

Base class for the similarity outputs.

Attributes:

Name	Type	Description
score	float	The similarity score between the two inputs

Source code in src/vericlient/daspeak/models.py

class CompareOutput(DaspeakResponse):
    """Base class for the similarity outputs.

    Attributes:
        score: The similarity score between the two inputs

    """

    score: float

    @field_validator("score")
    def round_value(cls, value: float) -> float:
        return round(value, 3)

CompareCredential2CredentialInput

Bases: CompareInput

Input class for the similarity credential to credential endpoint.

Attributes:

Name	Type	Description
credential_reference	str	The reference credential
credential_to_evaluate	str	The credential to evaluate
calibration	str	The calibration to use

Source code in src/vericlient/daspeak/models.py

class CompareCredential2CredentialInput(CompareInput):
    """Input class for the similarity credential to credential endpoint.

    Attributes:
        credential_reference: The reference credential
        credential_to_evaluate: The credential to evaluate
        calibration: The calibration to use

    """

    credential_reference: str
    credential_to_evaluate: str

CompareCredential2CredentialOutput

Bases: CompareOutput

Output class for the similarity credential to credential endpoint.

Attributes:

Name	Type	Description
calibration	str	The calibration used

Source code in src/vericlient/daspeak/models.py

class CompareCredential2CredentialOutput(CompareOutput):
    """Output class for the similarity credential to credential endpoint.

    Attributes:
        calibration: The calibration used

    """

    calibration: str

CompareCredential2AudioInput

Bases: CompareInput

Input class for the similarity credential to audio endpoint.

Attributes:

Name	Type	Description
credential_reference	str	The reference credential
audio_to_evaluate	str | bytes	The audio to evaluate. It can be a path to a file or a bytes object with the audio content
channel	int	The nchannel of the audio if it is stereo

Source code in src/vericlient/daspeak/models.py

class CompareCredential2AudioInput(CompareInput):
    """Input class for the similarity credential to audio endpoint.

    Attributes:
        credential_reference: The reference credential
        audio_to_evaluate: The audio to evaluate.
            It can be a path to a file or a bytes object
            with the audio content
        channel: The `nchannel` of the audio if it is stereo

    """

    credential_reference: str
    audio_to_evaluate: str | bytes
    channel: int = 1

    @field_validator("audio_to_evaluate")
    def must_be_str_or_bytes(cls, value: object):
        if not isinstance(value, (str, bytes)):
            error = "audio must be a string or a bytes object"
            raise TypeError(error)
        return value

    model_config = ConfigDict(arbitrary_types_allowed=True)

CompareCredential2AudioOutput

Bases: CompareOutput

Output class for the similarity credential to audio endpoint.

Attributes:

Name	Type	Description
model	ModelMetadata	The model used to generate the credential
calibration	str	The calibration used
authenticity_to_evaluate	float	The authenticity of the audio sample used
input_audio_duration	float	The duration of the input audio
net_speech_duration	float	The duration of the speech in the audio

Source code in src/vericlient/daspeak/models.py

class CompareCredential2AudioOutput(CompareOutput):
    """Output class for the similarity credential to audio endpoint.

    Attributes:
        model: The model used to generate the credential
        calibration: The calibration used
        authenticity_to_evaluate: The authenticity of the audio sample used
        input_audio_duration: The duration of the input audio
        net_speech_duration: The duration of the speech in the audio

    """

    model: ModelMetadata
    calibration: str
    authenticity_to_evaluate: float
    input_audio_duration_to_evaluate: float
    net_speech_duration_to_evaluate: float

    @field_validator("authenticity_to_evaluate")
    def round_value(cls, value: float) -> float:
        return round(value, 3)

CompareAudio2AudioInput

Bases: CompareInput

Input class for the similarity audio to audio endpoint.

Attributes:

Name	Type	Description
audio_reference	str | bytes	The reference audio. It can be a path to a file or a bytes object with the audio content
audio_to_evaluate	str | bytes	The audio to evaluate. It can be a path to a file or a bytes object with the audio content
channel_reference	int	The nchannel of the reference audio if it is stereo
channel_to_evaluate	int	The nchannel of the audio to evaluate if it is stereo

Source code in src/vericlient/daspeak/models.py

class CompareAudio2AudioInput(CompareInput):
    """Input class for the similarity audio to audio endpoint.

    Attributes:
        audio_reference: The reference audio.
            It can be a path to a file or a bytes object
            with the audio content
        audio_to_evaluate: The audio to evaluate.
            It can be a path to a file or a bytes object
            with the audio content
        channel_reference: The `nchannel` of the reference audio if it is stereo
        channel_to_evaluate: The `nchannel` of the audio to evaluate if it is stereo

    """

    audio_reference: str | bytes
    audio_to_evaluate: str | bytes
    channel_reference: int = 1
    channel_to_evaluate: int = 1

    @field_validator("audio_reference", "audio_to_evaluate")
    def audio_ref_must_be_str_or_bytes(cls, value: object):
        if not isinstance(value, (str, bytes)):
            error = "audio must be a string or a bytes object"
            raise TypeError(error)
        return value

    model_config = ConfigDict(arbitrary_types_allowed=True)

CompareAudio2AudioOutput

Bases: CompareOutput

Output class for the similarity audio to audio endpoint.

Attributes:

Name	Type	Description
model	ModelMetadata	The model used to generate the credential
calibration	str	The calibration used
authenticity_reference	float	The authenticity of the reference audio sample
authenticity_to_evaluate	float	The authenticity of the audio to evaluate
input_audio_duration_reference	float	The duration of the reference audio
input_audio_duration_to_evaluate	float	The duration of the audio to evaluate
net_speech_duration_reference	float	The duration of the speech in the reference audio
net_speech_duration_to_evaluate	float	The duration of the speech in the audio to evaluate

Source code in src/vericlient/daspeak/models.py

class CompareAudio2AudioOutput(CompareOutput):
    """Output class for the similarity audio to audio endpoint.

    Attributes:
        model: The model used to generate the credential
        calibration: The calibration used
        authenticity_reference: The authenticity of the reference audio sample
        authenticity_to_evaluate: The authenticity of the audio to evaluate
        input_audio_duration_reference: The duration of the reference audio
        input_audio_duration_to_evaluate: The duration of the audio to evaluate
        net_speech_duration_reference: The duration of the speech in the reference audio
        net_speech_duration_to_evaluate: The duration of the speech in the audio to evaluate

    """

    model: ModelMetadata
    calibration: str
    authenticity_reference: float
    authenticity_to_evaluate: float
    input_audio_duration_reference: float
    input_audio_duration_to_evaluate: float
    net_speech_duration_reference: float
    net_speech_duration_to_evaluate: float

    @field_validator("authenticity_reference", "authenticity_to_evaluate")
    def round_value(cls, value: float) -> float:
        return round(value, 3)

CompareAudio2CredentialsInput

Bases: CompareInput

Input class for the identification audio to credentials endpoint.

Attributes:

Name	Type	Description
audio_to_evaluate	str | bytes	The audio to evaluate against the credential list. It can be a path to a file or a bytes object with the audio content
credential_list	list[tuple[str, str]]	The credentials to compare the audio with. The list contains touples with two strings: the id and the credential
channel	int	The nchannel of the audio if it is stereo

Source code in src/vericlient/daspeak/models.py

class CompareAudio2CredentialsInput(CompareInput):
    """Input class for the identification audio to credentials endpoint.

    Attributes:
        audio_to_evaluate: The audio to evaluate against the credential list.
            It can be a path to a file or a bytes object
            with the audio content
        credential_list: The credentials to compare the audio with.
            The list contains touples with two strings: the id and the credential
        channel: The `nchannel` of the audio if it is stereo

    """

    audio_to_evaluate: str | bytes
    credential_list: list[tuple[str, str]]
    channel: int = 1

    @field_validator("audio_to_evaluate")
    def must_be_str_or_bytes(cls, value: object):
        if not isinstance(value, (str, bytes)):
            error = "audio must be a string or a bytes object"
            raise TypeError(error)
        return value

    @field_validator("credential_list")
    def validate_and_build_list_format(cls, value: list):
        if not value:
            error = "credential_list must not be empty"
            raise ValueError(error)
        error = "credential_list must contain touples with two strings"
        n_items = 2
        for item in value:
            if not isinstance(item, tuple) or len(item) != n_items:
                raise ValueError(error)
            if not all(isinstance(i, str) for i in item):
                raise ValueError(error)
        return [{"id": item[0], "credential": item[1]} for item in value]

    model_config = ConfigDict(arbitrary_types_allowed=True)

CompareAudio2CredentialsOutput

Bases: DaspeakResponse

Output class for the identification audio to credentials endpoint.

Attributes:

Name	Type	Description
result	dict	The result of the identification, a dictionary with the "id" and the "score" of the best match
scores	list[dict]	The whole list of scores for each credential. The list contains dictionaries with two keys (and values): "id" and "score"
calibration	str	The calibration used
model	ModelMetadata	The model used to generate the credential
authenticity_to_evaluate	float	The authenticity of the evaluated audio sample
input_audio_duration_to_evaluate	float	The duration of the evaluated audio
net_speech_duration_to_evaluate	float	The duration of the speech in the evaluated audio

Source code in src/vericlient/daspeak/models.py

class CompareAudio2CredentialsOutput(DaspeakResponse):
    """Output class for the identification audio to credentials endpoint.

    Attributes:
        result: The result of the identification, a dictionary with the "id" and the "score"
            of the best match
        scores: The whole list of scores for each credential.
            The list contains dictionaries with two keys (and values): "id" and "score"
        calibration: The calibration used
        model: The model used to generate the credential
        authenticity_to_evaluate: The authenticity of the evaluated audio sample
        input_audio_duration_to_evaluate: The duration of the evaluated audio
        net_speech_duration_to_evaluate: The duration of the speech in the evaluated audio

    """

    result: dict
    scores: list[dict]
    calibration: str
    model: ModelMetadata
    authenticity_to_evaluate: float
    input_audio_duration_to_evaluate: float
    net_speech_duration_to_evaluate: float

    @field_validator("authenticity_to_evaluate")
    def round_value(cls, value: float) -> float:
        return round(value, 3)

CompareCredential2CredentialsInput

Bases: CompareInput

Input class for the identification credential to credentials endpoint.

Attributes:

Name	Type	Description
credential_to_evaluate	str	The credential to evaluate against the credential list
credential_list	list[tuple[str, str]]	The credentials to compare the audio with. The list contains touples with two strings: the id and the credential

Source code in src/vericlient/daspeak/models.py

class CompareCredential2CredentialsInput(CompareInput):
    """Input class for the identification credential to credentials endpoint.

    Attributes:
        credential_to_evaluate: The credential to evaluate against the credential list
        credential_list: The credentials to compare the audio with.
            The list contains touples with two strings: the id and the credential

    """

    credential_to_evaluate: str
    credential_list: list[tuple[str, str]]

    @field_validator("credential_list")
    def validate_and_build_list_format(cls, value: list):
        if not value:
            error = "credential_list must not be empty"
            raise ValueError(error)
        error = "credential_list must contain touples with two strings"
        n_items = 2
        for item in value:
            if not isinstance(item, tuple) or len(item) != n_items:
                raise ValueError(error)
            if not all(isinstance(i, str) for i in item):
                raise ValueError(error)
        return [{"id": item[0], "credential": item[1]} for item in value]

CompareCredential2CredentialsOutput

Bases: DaspeakResponse

Output class for the identification credential to credentials endpoint.

Attributes:

Name	Type	Description
result	dict	The result of the identification, a dictionary with the "id" and the "score" of the best match
scores	list[dict]	The whole list of scores for each credential. The list contains dictionaries with two keys (and values): "id" and "score"
calibration	str	The calibration used

Source code in src/vericlient/daspeak/models.py

class CompareCredential2CredentialsOutput(DaspeakResponse):
    """Output class for the identification credential to credentials endpoint.

    Attributes:
        result: The result of the identification, a dictionary with the "id" and the "score"
            of the best match
        scores: The whole list of scores for each credential.
            The list contains dictionaries with two keys (and values): "id" and "score"
        calibration: The calibration used

    """

    result: dict
    scores: list[dict]
    calibration: str

vericlient.daspeak.exceptions

Module to define the exceptions for the Daspeak API.

DaspeakError

Bases: VeriClientError

Base class for exceptions in the Daspeak API.

Source code in src/vericlient/daspeak/exceptions.py

class DaspeakError(VeriClientError):
    """Base class for exceptions in the Daspeak API."""

    def __init__(self, message: str) -> None:
        super().__init__(message)

AudioInputError

Bases: DaspeakError

Exception raised for errors in the audio input.

Source code in src/vericlient/daspeak/exceptions.py

class AudioInputError(DaspeakError):
    """Exception raised for errors in the audio input."""

    def __init__(self, message: str) -> None:
        super().__init__(message)

TooManyAudioChannelsError

Bases: AudioInputError

Exception raised for too many audio channels.

Source code in src/vericlient/daspeak/exceptions.py

class TooManyAudioChannelsError(AudioInputError):
    """Exception raised for too many audio channels."""

    def __init__(self) -> None:
        message = "The maximum allowed number of audio channels is 2, and the audio provided has more channels"
        super().__init__(message)

UnsupportedSampleRateError

Bases: AudioInputError

Exception raised for unsupported sample rates.

Source code in src/vericlient/daspeak/exceptions.py

class UnsupportedSampleRateError(AudioInputError):
    """Exception raised for unsupported sample rates."""

    def __init__(self) -> None:
        message = "The sample rate of the audio is not supported, must be 8 Khz or 16 Khz"
        super().__init__(message)

AudioDurationTooLongError

Bases: AudioInputError

Exception raised for audio duration too long.

Source code in src/vericlient/daspeak/exceptions.py

class AudioDurationTooLongError(AudioInputError):
    """Exception raised for audio duration too long."""

    def __init__(self) -> None:
        message = "The audio duration is too long, must be less than 30 seconds"
        super().__init__(message)

UnsupportedAudioCodecError

Bases: AudioInputError

Exception raised for unsupported audio codec.

Source code in src/vericlient/daspeak/exceptions.py

class UnsupportedAudioCodecError(AudioInputError):
    """Exception raised for unsupported audio codec."""

    def __init__(self) -> None:
        message = "The audio codec is not supported. Supported codecs are: 'PCM_16', 'ULAW', 'ALAW'"
        super().__init__(message)

SignalNoiseRatioError

Bases: DaspeakError

Exception raised for errors in the signal noise ratio.

Source code in src/vericlient/daspeak/exceptions.py

class SignalNoiseRatioError(DaspeakError):
    """Exception raised for errors in the signal noise ratio."""

    def __init__(self) -> None:
        message = "Noise level of the audio exceeded"
        super().__init__(message)

NetSpeechDurationIsNotEnoughError

Bases: DaspeakError

Exception raised for errors in the net speech duration.

Source code in src/vericlient/daspeak/exceptions.py

class NetSpeechDurationIsNotEnoughError(DaspeakError):
    """Exception raised for errors in the net speech duration."""

    def __init__(self, net_speech_detected: float) -> None:
        message = (
            f"You need at least 3 seconds of speech to perform the operation, "
            f"but only {net_speech_detected} seconds were detected"
        )
        super().__init__(message)

InvalidSpecifiedChannelError

Bases: DaspeakError

Exception raised for invalid specified channel.

Source code in src/vericlient/daspeak/exceptions.py

class InvalidSpecifiedChannelError(DaspeakError):
    """Exception raised for invalid specified channel."""

    def __init__(self) -> None:
        message = "The specified channel is invalid, must be 1 or 2"
        super().__init__(message)

InsufficientQualityError

Bases: DaspeakError

Exception raised for insufficient quality.

Source code in src/vericlient/daspeak/exceptions.py

class InsufficientQualityError(DaspeakError):
    """Exception raised for insufficient quality."""

    def __init__(self) -> None:
        message = "The audio quality is insufficient or may contain more than one speaker"
        super().__init__(message)

CalibrationNotAvailableError

Bases: DaspeakError

Exception raised for calibration not available.

Source code in src/vericlient/daspeak/exceptions.py

class CalibrationNotAvailableError(DaspeakError):
    """Exception raised for calibration not available."""

    def __init__(self, calibration: str) -> None:
        message = f"The calibration {calibration} is not available"
        super().__init__(message)

ModelNotAvailableError

Bases: DaspeakError

Exception raised when the requested biometrics model does not exist.

Source code in src/vericlient/daspeak/exceptions.py

class ModelNotAvailableError(DaspeakError):
    """Exception raised when the requested biometrics model does not exist."""

    def __init__(self, model_hash: str) -> None:
        message = f"The biometrics model {model_hash} is not available"
        super().__init__(message)