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v0.17.2
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"tts_models": { | ||
"multilingual": { | ||
"multi-dataset": { | ||
"xtts_v1": { | ||
"description": "XTTS-v1 by Coqui with 13 languages and cross-language voice cloning.", | ||
"hf_url": [ | ||
"https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/model.pth", | ||
"https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/config.json", | ||
"https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/vocab.json" | ||
], | ||
"default_vocoder": null, | ||
"commit": "e9a1953e", | ||
"license": "CPML", | ||
"contact": "[email protected]", | ||
"tos_required": true | ||
}, | ||
"your_tts": { | ||
"description": "Your TTS model accompanying the paper https://arxiv.org/abs/2112.02418", | ||
"github_rls_url": "https://coqui.gateway.scarf.sh/v0.10.1_models/tts_models--multilingual--multi-dataset--your_tts.zip", | ||
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} | ||
} | ||
} | ||
} | ||
} |
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0.16.6 | ||
0.17.2 |
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from dataclasses import dataclass, field | ||
from typing import List | ||
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from TTS.tts.configs.shared_configs import BaseTTSConfig | ||
from TTS.tts.models.xtts import XttsArgs, XttsAudioConfig | ||
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@dataclass | ||
class XttsConfig(BaseTTSConfig): | ||
"""Defines parameters for XTTS TTS model. | ||
Args: | ||
model (str): | ||
Model name. Do not change unless you know what you are doing. | ||
model_args (XttsArgs): | ||
Model architecture arguments. Defaults to `XttsArgs()`. | ||
audio (XttsAudioConfig): | ||
Audio processing configuration. Defaults to `XttsAudioConfig()`. | ||
model_dir (str): | ||
Path to the folder that has all the XTTS 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_gpt_outputs (int): | ||
Number of samples taken from the autoregressive model, all of which are filtered using CLVP. | ||
As XTTS is a probabilistic model, more samples means a higher probability of creating something "great". | ||
Defaults to `16`. | ||
decoder_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`. | ||
decoder_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.xtts_config import XttsConfig | ||
>>> config = XttsConfig() | ||
""" | ||
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model: str = "xtts" | ||
# model specific params | ||
model_args: XttsArgs = field(default_factory=XttsArgs) | ||
audio: XttsAudioConfig = field(default_factory=XttsAudioConfig) | ||
model_dir: str = None | ||
languages: List[str] = field( | ||
default_factory=lambda: ["en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn"] | ||
) | ||
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# inference params | ||
temperature: float = 0.2 | ||
length_penalty: float = 1.0 | ||
repetition_penalty: float = 2.0 | ||
top_k: int = 50 | ||
top_p: float = 0.8 | ||
cond_free_k: float = 2.0 | ||
diffusion_temperature: float = 1.0 | ||
num_gpt_outputs: int = 16 | ||
decoder_iterations: int = 30 | ||
decoder_sampler: str = "ddim" |
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