108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
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from dataclasses import dataclass, field
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from typing import List
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from TTS.tts.configs.shared_configs import BaseTTSConfig
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from TTS.tts.models.xtts import XttsArgs, XttsAudioConfig
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@dataclass
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class XttsConfig(BaseTTSConfig):
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"""Defines parameters for XTTS 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 (XttsArgs):
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Model architecture arguments. Defaults to `XttsArgs()`.
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audio (XttsAudioConfig):
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Audio processing configuration. Defaults to `XttsAudioConfig()`.
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model_dir (str):
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Path to the folder that has all the XTTS 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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repetition_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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num_gpt_outputs (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 XTTS 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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gpt_cond_len (int):
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Secs audio to be used as conditioning for the autoregressive model. Defaults to `12`.
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gpt_cond_chunk_len (int):
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Audio chunk size in secs. Audio is split into chunks and latents are extracted for each chunk. Then the
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latents are averaged. Chunking improves the stability. It must be <= gpt_cond_len.
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If gpt_cond_len == gpt_cond_chunk_len, no chunking. Defaults to `4`.
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max_ref_len (int):
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Maximum number of seconds of audio to be used as conditioning for the decoder. Defaults to `10`.
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sound_norm_refs (bool):
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Whether to normalize the conditioning audio. Defaults to `False`.
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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.xtts_config import XttsConfig
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>>> config = XttsConfig()
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"""
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model: str = "xtts"
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# model specific params
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model_args: XttsArgs = field(default_factory=XttsArgs)
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audio: XttsAudioConfig = field(default_factory=XttsAudioConfig)
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model_dir: str = None
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languages: List[str] = field(
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default_factory=lambda: [
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"en",
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"es",
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"fr",
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"de",
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"it",
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"pt",
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"pl",
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"tr",
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"ru",
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"nl",
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"cs",
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"ar",
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"zh-cn",
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"hu",
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"ko",
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"ja",
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"hi",
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]
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)
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# inference params
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temperature: float = 0.85
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length_penalty: float = 1.0
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repetition_penalty: float = 2.0
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top_k: int = 50
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top_p: float = 0.85
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num_gpt_outputs: int = 1
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# cloning
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gpt_cond_len: int = 12
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gpt_cond_chunk_len: int = 4
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max_ref_len: int = 10
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sound_norm_refs: bool = False
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