import os
from dataclasses import dataclass
from typing import Optional
import numpy as np
from coqpit import Coqpit
from encodec import EncodecModel
from transformers import BertTokenizer
from TTS.tts.layers.bark.inference_funcs import (
codec_decode,
generate_coarse,
generate_fine,
generate_text_semantic,
generate_voice,
load_voice,
)
from TTS.tts.layers.bark.load_model import load_model
from TTS.tts.layers.bark.model import GPT
from TTS.tts.layers.bark.model_fine import FineGPT
from TTS.tts.models.base_tts import BaseTTS
@dataclass
class BarkAudioConfig(Coqpit):
sample_rate: int = 24000
output_sample_rate: int = 24000
[docs]
class Bark(BaseTTS):
def __init__(
self,
config: Coqpit,
tokenizer: BertTokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased"),
) -> None:
super().__init__(config=config, ap=None, tokenizer=None, speaker_manager=None, language_manager=None)
self.config.num_chars = len(tokenizer)
self.tokenizer = tokenizer
self.semantic_model = GPT(config.semantic_config)
self.coarse_model = GPT(config.coarse_config)
self.fine_model = FineGPT(config.fine_config)
self.encodec = EncodecModel.encodec_model_24khz()
self.encodec.set_target_bandwidth(6.0)
@property
def device(self):
return next(self.parameters()).device
def load_bark_models(self):
self.semantic_model, self.config = load_model(
ckpt_path=self.config.LOCAL_MODEL_PATHS["text"], device=self.device, config=self.config, model_type="text"
)
self.coarse_model, self.config = load_model(
ckpt_path=self.config.LOCAL_MODEL_PATHS["coarse"],
device=self.device,
config=self.config,
model_type="coarse",
)
self.fine_model, self.config = load_model(
ckpt_path=self.config.LOCAL_MODEL_PATHS["fine"], device=self.device, config=self.config, model_type="fine"
)
def train_step(
self,
):
pass
[docs]
def text_to_semantic(
self,
text: str,
history_prompt: Optional[str] = None,
temp: float = 0.7,
base=None,
allow_early_stop=True,
**kwargs,
):
"""Generate semantic array from text.
Args:
text: text to be turned into audio
history_prompt: history choice for audio cloning
temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy semantic array to be fed into `semantic_to_waveform`
"""
x_semantic = generate_text_semantic(
text,
self,
history_prompt=history_prompt,
temp=temp,
base=base,
allow_early_stop=allow_early_stop,
**kwargs,
)
return x_semantic
[docs]
def generate_audio(
self,
text: str,
history_prompt: Optional[str] = None,
text_temp: float = 0.7,
waveform_temp: float = 0.7,
base=None,
allow_early_stop=True,
**kwargs,
):
"""Generate audio array from input text.
Args:
text: text to be turned into audio
history_prompt: history choice for audio cloning
text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
Returns:
numpy audio array at sample frequency 24khz
"""
x_semantic = self.text_to_semantic(
text,
history_prompt=history_prompt,
temp=text_temp,
base=base,
allow_early_stop=allow_early_stop,
**kwargs,
)
audio_arr, c, f = self.semantic_to_waveform(
x_semantic, history_prompt=history_prompt, temp=waveform_temp, base=base
)
return audio_arr, [x_semantic, c, f]
[docs]
def generate_voice(self, audio, speaker_id, voice_dir):
"""Generate a voice from the given audio and text.
Args:
audio (str): Path to the audio file.
speaker_id (str): Speaker name.
voice_dir (str): Path to the directory to save the generate voice.
"""
if voice_dir is not None:
voice_dirs = [voice_dir]
try:
_ = load_voice(speaker_id, voice_dirs)
except (KeyError, FileNotFoundError):
output_path = os.path.join(voice_dir, speaker_id + ".npz")
os.makedirs(voice_dir, exist_ok=True)
generate_voice(audio, self, output_path)
def _set_voice_dirs(self, voice_dirs):
def_voice_dir = None
if isinstance(self.config.DEF_SPEAKER_DIR, str):
os.makedirs(self.config.DEF_SPEAKER_DIR, exist_ok=True)
if os.path.isdir(self.config.DEF_SPEAKER_DIR):
def_voice_dir = self.config.DEF_SPEAKER_DIR
_voice_dirs = [def_voice_dir] if def_voice_dir is not None else []
if voice_dirs is not None:
if isinstance(voice_dirs, str):
voice_dirs = [voice_dirs]
_voice_dirs = voice_dirs + _voice_dirs
return _voice_dirs
# TODO: remove config from synthesize
[docs]
def synthesize(
self, text, config, speaker_id="random", voice_dirs=None, **kwargs
): # pylint: disable=unused-argument
"""Synthesize speech with the given input text.
Args:
text (str): Input text.
config (BarkConfig): Config with inference parameters.
speaker_id (str): One of the available speaker names. If `random`, it generates a random speaker.
speaker_wav (str): Path to the speaker audio file for cloning a new voice. It is cloned and saved in
`voice_dirs` with the name `speaker_id`. Defaults to None.
voice_dirs (List[str]): List of paths that host reference audio files for speakers. Defaults to None.
**kwargs: Model specific inference settings used by `generate_audio()` and `TTS.tts.layers.bark.inference_funcs.generate_text_semantic().
Returns:
A dictionary of the output values with `wav` as output waveform, `deterministic_seed` as seed used at inference,
`text_input` as text token IDs after tokenizer, `voice_samples` as samples used for cloning, `conditioning_latents`
as latents used at inference.
"""
speaker_id = "random" if speaker_id is None else speaker_id
voice_dirs = self._set_voice_dirs(voice_dirs)
history_prompt = load_voice(self, speaker_id, voice_dirs)
outputs = self.generate_audio(text, history_prompt=history_prompt, **kwargs)
return_dict = {
"wav": outputs[0],
"text_inputs": text,
}
return return_dict
def eval_step(self):
...
def forward(self):
...
def inference(self):
...
@staticmethod
def init_from_config(config: "BarkConfig", **kwargs): # pylint: disable=unused-argument
return Bark(config)
# pylint: disable=unused-argument, redefined-builtin
[docs]
def load_checkpoint(
self,
config,
checkpoint_dir,
text_model_path=None,
coarse_model_path=None,
fine_model_path=None,
hubert_model_path=None,
hubert_tokenizer_path=None,
eval=False,
strict=True,
**kwargs,
):
"""Load a model checkpoints from a directory. This model is with multiple checkpoint files and it
expects to have all the files to be under the given `checkpoint_dir` with the rigth names.
If eval is True, set the model to eval mode.
Args:
config (TortoiseConfig): The model config.
checkpoint_dir (str): The directory where the checkpoints are stored.
ar_checkpoint_path (str, optional): The path to the autoregressive checkpoint. Defaults to None.
diff_checkpoint_path (str, optional): The path to the diffusion checkpoint. Defaults to None.
clvp_checkpoint_path (str, optional): The path to the CLVP checkpoint. Defaults to None.
vocoder_checkpoint_path (str, optional): The path to the vocoder checkpoint. Defaults to None.
eval (bool, optional): Whether to set the model to eval mode. Defaults to False.
strict (bool, optional): Whether to load the model strictly. Defaults to True.
"""
text_model_path = text_model_path or os.path.join(checkpoint_dir, "text_2.pt")
coarse_model_path = coarse_model_path or os.path.join(checkpoint_dir, "coarse_2.pt")
fine_model_path = fine_model_path or os.path.join(checkpoint_dir, "fine_2.pt")
hubert_model_path = hubert_model_path or os.path.join(checkpoint_dir, "hubert.pt")
hubert_tokenizer_path = hubert_tokenizer_path or os.path.join(checkpoint_dir, "tokenizer.pth")
self.config.LOCAL_MODEL_PATHS["text"] = text_model_path
self.config.LOCAL_MODEL_PATHS["coarse"] = coarse_model_path
self.config.LOCAL_MODEL_PATHS["fine"] = fine_model_path
self.config.LOCAL_MODEL_PATHS["hubert"] = hubert_model_path
self.config.LOCAL_MODEL_PATHS["hubert_tokenizer"] = hubert_tokenizer_path
self.load_bark_models()
if eval:
self.eval()