TTS/tts/configs/tortoise_config.py

from dataclasses import dataclass, field

from TTS.tts.configs.shared_configs import BaseTTSConfig
from TTS.tts.models.tortoise import TortoiseArgs, TortoiseAudioConfig


@dataclass
class TortoiseConfig(BaseTTSConfig):
    """Defines parameters for Tortoise TTS model.

    Args:
        model (str):
            Model name. Do not change unless you know what you are doing.

        model_args (TortoiseArgs):
            Model architecture arguments. Defaults to `TortoiseArgs()`.

        audio (TortoiseAudioConfig):
            Audio processing configuration. Defaults to `TortoiseAudioConfig()`.

        model_dir (str):
            Path to the folder that has all the Tortoise models. Defaults to None.

        temperature (float):
            Temperature for the autoregressive model inference. Larger values makes predictions more creative sacrificing stability. Defaults to `0.2`.

        length_penalty (float):
            Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length,
            which in turn is used to divide the score of the sequence. Since the score is the log likelihood of the sequence (i.e. negative),
            length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences.

        reperation_penalty (float):
            The parameter for repetition penalty. 1.0 means no penalty. Defaults to `2.0`.

        top_p (float):
            If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
            Defaults to `0.8`.

        cond_free_k (float):
            Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
            As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
            Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k. Defaults to `2.0`.

        diffusion_temperature (float):
            Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
            are the "mean" prediction of the diffusion network and will sound bland and smeared.
            Defaults to `1.0`.

        num_autoregressive_samples (int):
            Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
            As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
            Defaults to `16`.

        diffusion_iterations (int):
            Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
            the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
            however. Defaults to `30`.

        sampler (str):
            Diffusion sampler to be used. `ddim` or `dpm++2m`. Defaults to `ddim`.
    Note:
        Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.

    Example:

        >>> from TTS.tts.configs.tortoise_config import TortoiseConfig
        >>> config = TortoiseConfig()
    """

    model: str = "tortoise"
    # model specific params
    model_args: TortoiseArgs = field(default_factory=TortoiseArgs)
    audio: TortoiseAudioConfig = field(default_factory=TortoiseAudioConfig)
    model_dir: str = None

    # settings
    temperature: float = 0.2
    length_penalty: float = 1.0
    repetition_penalty: float = 2.0
    top_p: float = 0.8
    cond_free_k: float = 2.0
    diffusion_temperature: float = 1.0

    # inference params
    num_autoregressive_samples: int = 16
    diffusion_iterations: int = 30
    sampler: str = "ddim"