88 lines
3.8 KiB
Python
88 lines
3.8 KiB
Python
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from dataclasses import dataclass, field
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from TTS.tts.configs.shared_configs import BaseTTSConfig
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from TTS.tts.models.tortoise import TortoiseArgs, TortoiseAudioConfig
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@dataclass
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class TortoiseConfig(BaseTTSConfig):
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"""Defines parameters for Tortoise TTS model.
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Args:
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model (str):
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Model name. Do not change unless you know what you are doing.
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model_args (TortoiseArgs):
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Model architecture arguments. Defaults to `TortoiseArgs()`.
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audio (TortoiseAudioConfig):
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Audio processing configuration. Defaults to `TortoiseAudioConfig()`.
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model_dir (str):
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Path to the folder that has all the Tortoise models. Defaults to None.
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temperature (float):
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Temperature for the autoregressive model inference. Larger values makes predictions more creative sacrificing stability. Defaults to `0.2`.
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length_penalty (float):
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Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to the sequence length,
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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),
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length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences.
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reperation_penalty (float):
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The parameter for repetition penalty. 1.0 means no penalty. Defaults to `2.0`.
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top_p (float):
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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.
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Defaults to `0.8`.
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cond_free_k (float):
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Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
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As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
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Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k. Defaults to `2.0`.
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diffusion_temperature (float):
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Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
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are the "mean" prediction of the diffusion network and will sound bland and smeared.
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Defaults to `1.0`.
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num_autoregressive_samples (int):
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Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
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As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
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Defaults to `16`.
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diffusion_iterations (int):
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Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
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the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
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however. Defaults to `30`.
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sampler (str):
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Diffusion sampler to be used. `ddim` or `dpm++2m`. Defaults to `ddim`.
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Note:
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Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
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Example:
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>>> from TTS.tts.configs.tortoise_config import TortoiseConfig
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>>> config = TortoiseConfig()
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"""
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model: str = "tortoise"
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# model specific params
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model_args: TortoiseArgs = field(default_factory=TortoiseArgs)
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audio: TortoiseAudioConfig = field(default_factory=TortoiseAudioConfig)
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model_dir: str = None
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# settings
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temperature: float = 0.2
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length_penalty: float = 1.0
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repetition_penalty: float = 2.0
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top_p: float = 0.8
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cond_free_k: float = 2.0
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diffusion_temperature: float = 1.0
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# inference params
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num_autoregressive_samples: int = 16
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diffusion_iterations: int = 30
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sampler: str = "ddim"
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