Class swarmauri_standard.llms.HyperbolicAudioTTS.HyperbolicAudioTTS

swarmauri_standard.llms.HyperbolicAudioTTS.HyperbolicAudioTTS

HyperbolicAudioTTS(**data)

Bases: LLMBase

A class to interact with Hyperbolic's Text-to-Speech API, allowing for synchronous and asynchronous text-to-speech synthesis.

ATTRIBUTE	DESCRIPTION
api_key	The API key for accessing Hyperbolic's TTS service. TYPE: str
language	Language of the text. TYPE: Optional[str]
speaker	Specific speaker variant. TYPE: Optional[str]
speed	Speech speed control. TYPE: Optional[float]

Provider Resource: https://api.hyperbolic.xyz/v1/audio/generation Link to API KEYS: https://app.hyperbolic.xyz/settings

Initialize the HyperbolicAudioTTS class with the provided data.

Source code in swarmauri_standard/llms/HyperbolicAudioTTS.py

def __init__(self, **data):
    """
    Initialize the HyperbolicAudioTTS class with the provided data.
    """
    super().__init__(**data)
    self._headers = {
        "Authorization": f"Bearer {self.api_key.get_secret_value()}",
        "Content-Type": "application/json",
    }

api_key instance-attribute

api_key

allowed_languages class-attribute instance-attribute

allowed_languages = ['EN', 'ES', 'FR', 'ZH', 'JP', 'KR']

allowed_speakers class-attribute instance-attribute

allowed_speakers = {
    "EN": ["EN-US", "EN-BR", "EN-INDIA", "EN-AU"],
    "ES": ["ES"],
    "FR": ["FR"],
    "ZH": ["ZH"],
    "JP": ["JP"],
    "KR": ["KR"],
}

language class-attribute instance-attribute

language = None

speaker class-attribute instance-attribute

speaker = None

speed class-attribute instance-attribute

speed = Field(default=1.0, ge=0.1, le=5)

timeout class-attribute instance-attribute

timeout = 600.0

type class-attribute instance-attribute

type = 'HyperbolicAudioTTS'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = ''

resource class-attribute instance-attribute

resource = Field(default=LLM.value, frozen=True)

version class-attribute instance-attribute

version = '0.1.0'

allowed_models class-attribute instance-attribute

allowed_models = []

include_usage class-attribute instance-attribute

include_usage = True

BASE_URL class-attribute instance-attribute

BASE_URL = None

predict

predict(text, audio_path='output.mp3')

Synchronously converts text to speech.

Source code in swarmauri_standard/llms/HyperbolicAudioTTS.py

def predict(self, text: str, audio_path: str = "output.mp3") -> str:
    """
    Synchronously converts text to speech.
    """
    payload = self._prepare_payload(text)

    with httpx.Client(timeout=self.timeout) as client:
        response = client.post(self._BASE_URL, headers=self._headers, json=payload)
        response.raise_for_status()

        # Decode base64 audio
        audio_data = base64.b64decode(response.json()["audio"])

        with open(audio_path, "wb") as audio_file:
            audio_file.write(audio_data)

        return os.path.abspath(audio_path)

apredict async

apredict(text, audio_path='output.mp3')

Asynchronously converts text to speech.

Source code in swarmauri_standard/llms/HyperbolicAudioTTS.py

async def apredict(self, text: str, audio_path: str = "output.mp3") -> str:
    """
    Asynchronously converts text to speech.
    """
    payload = self._prepare_payload(text)

    async with httpx.AsyncClient(timeout=self.timeout) as client:
        response = await client.post(
            self._BASE_URL, headers=self._headers, json=payload
        )
        response.raise_for_status()

        # Decode base64 audio
        audio_data = base64.b64decode(response.json()["audio"])

        with open(audio_path, "wb") as audio_file:
            audio_file.write(audio_data)

        return os.path.abspath(audio_path)

batch

batch(text_path_dict)

Synchronously process multiple text-to-speech requests in batch mode.

Source code in swarmauri_standard/llms/HyperbolicAudioTTS.py

def batch(
    self,
    text_path_dict: Dict[str, str],
) -> List[str]:
    """
    Synchronously process multiple text-to-speech requests in batch mode.
    """
    return [
        self.predict(text=text, audio_path=path)
        for text, path in text_path_dict.items()
    ]

abatch async

abatch(text_path_dict, max_concurrent=5)

Asynchronously process multiple text-to-speech requests in batch mode.

Source code in swarmauri_standard/llms/HyperbolicAudioTTS.py

async def abatch(
    self,
    text_path_dict: Dict[str, str],
    max_concurrent=5,
) -> List[str]:
    """
    Asynchronously process multiple text-to-speech requests in batch mode.
    """
    semaphore = asyncio.Semaphore(max_concurrent)

    async def process_conversation(text, path) -> str:
        async with semaphore:
            return await self.apredict(text=text, audio_path=path)

    tasks = [
        process_conversation(text, path) for text, path in text_path_dict.items()
    ]
    return await asyncio.gather(*tasks)

stream

stream(text, audio_path='output.mp3')

Stream the text-to-speech output.

Source code in swarmauri_standard/llms/HyperbolicAudioTTS.py

def stream(
    self,
    text: str,
    audio_path: str = "output.mp3",
) -> str:
    """
    Stream the text-to-speech output.
    """
    raise NotImplementedError("Stream method is not implemented for HyperbolicTTS")

astream async

astream(text, audio_path='output.mp3')

Asynchronously stream the text-to-speech output.

Source code in swarmauri_standard/llms/HyperbolicAudioTTS.py

async def astream(
    self,
    text: str,
    audio_path: str = "output.mp3",
) -> str:
    """
    Asynchronously stream the text-to-speech output.
    """
    raise NotImplementedError("AStream method is not implemented for HyperbolicTTS")

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger

add_allowed_model

add_allowed_model(model)

Add a new model to the list of allowed models.

RAISES	DESCRIPTION
ValueError	If the model is already in the allowed models list.

Source code in swarmauri_base/llms/LLMBase.py

def add_allowed_model(self, model: str) -> None:
    """
    Add a new model to the list of allowed models.

    Raises:
        ValueError: If the model is already in the allowed models list.
    """
    if model in self.allowed_models:
        raise ValueError(f"Model '{model}' is already allowed.")
    self.allowed_models.append(model)

remove_allowed_model

remove_allowed_model(model)

Remove a model from the list of allowed models.

RAISES	DESCRIPTION
ValueError	If the model is not in the allowed models list.

Source code in swarmauri_base/llms/LLMBase.py

def remove_allowed_model(self, model: str) -> None:
    """
    Remove a model from the list of allowed models.

    Raises:
        ValueError: If the model is not in the allowed models list.
    """
    if model not in self.allowed_models:
        raise ValueError(f"Model '{model}' is not in the allowed models list.")
    self.allowed_models.remove(model)