mirror of https://github.com/coqui-ai/TTS.git
292 lines
12 KiB
Python
292 lines
12 KiB
Python
import time
|
|
from typing import List
|
|
|
|
import numpy as np
|
|
import pysbd
|
|
import torch
|
|
|
|
from TTS.config import load_config
|
|
from TTS.tts.models import setup_model as setup_tts_model
|
|
from TTS.tts.utils.speakers import SpeakerManager
|
|
|
|
# pylint: disable=unused-wildcard-import
|
|
# pylint: disable=wildcard-import
|
|
from TTS.tts.utils.synthesis import synthesis, trim_silence
|
|
from TTS.utils.audio import AudioProcessor
|
|
from TTS.vocoder.models import setup_model as setup_vocoder_model
|
|
from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input
|
|
|
|
|
|
class Synthesizer(object):
|
|
def __init__(
|
|
self,
|
|
tts_checkpoint: str,
|
|
tts_config_path: str,
|
|
tts_speakers_file: str = "",
|
|
vocoder_checkpoint: str = "",
|
|
vocoder_config: str = "",
|
|
encoder_checkpoint: str = "",
|
|
encoder_config: str = "",
|
|
use_cuda: bool = False,
|
|
) -> None:
|
|
"""General 🐸 TTS interface for inference. It takes a tts and a vocoder
|
|
model and synthesize speech from the provided text.
|
|
|
|
The text is divided into a list of sentences using `pysbd` and synthesize
|
|
speech on each sentence separately.
|
|
|
|
If you have certain special characters in your text, you need to handle
|
|
them before providing the text to Synthesizer.
|
|
|
|
TODO: set the segmenter based on the source language
|
|
|
|
Args:
|
|
tts_checkpoint (str): path to the tts model file.
|
|
tts_config_path (str): path to the tts config file.
|
|
vocoder_checkpoint (str, optional): path to the vocoder model file. Defaults to None.
|
|
vocoder_config (str, optional): path to the vocoder config file. Defaults to None.
|
|
encoder_checkpoint (str, optional): path to the speaker encoder model file. Defaults to `""`,
|
|
encoder_config (str, optional): path to the speaker encoder config file. Defaults to `""`,
|
|
use_cuda (bool, optional): enable/disable cuda. Defaults to False.
|
|
"""
|
|
self.tts_checkpoint = tts_checkpoint
|
|
self.tts_config_path = tts_config_path
|
|
self.tts_speakers_file = tts_speakers_file
|
|
self.vocoder_checkpoint = vocoder_checkpoint
|
|
self.vocoder_config = vocoder_config
|
|
self.encoder_checkpoint = encoder_checkpoint
|
|
self.encoder_config = encoder_config
|
|
self.use_cuda = use_cuda
|
|
|
|
self.tts_model = None
|
|
self.vocoder_model = None
|
|
self.speaker_manager = None
|
|
self.num_speakers = 0
|
|
self.tts_speakers = {}
|
|
self.d_vector_dim = 0
|
|
self.seg = self._get_segmenter("en")
|
|
self.use_cuda = use_cuda
|
|
|
|
if self.use_cuda:
|
|
assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
|
|
self._load_tts(tts_checkpoint, tts_config_path, use_cuda)
|
|
self.output_sample_rate = self.tts_config.audio["sample_rate"]
|
|
if vocoder_checkpoint:
|
|
self._load_vocoder(vocoder_checkpoint, vocoder_config, use_cuda)
|
|
self.output_sample_rate = self.vocoder_config.audio["sample_rate"]
|
|
|
|
@staticmethod
|
|
def _get_segmenter(lang: str):
|
|
"""get the sentence segmenter for the given language.
|
|
|
|
Args:
|
|
lang (str): target language code.
|
|
|
|
Returns:
|
|
[type]: [description]
|
|
"""
|
|
return pysbd.Segmenter(language=lang, clean=True)
|
|
|
|
def _load_speakers(self, speaker_file: str) -> None:
|
|
"""Load the SpeakerManager to organize multi-speaker TTS. It loads the speakers meta-data and the speaker
|
|
encoder if it is defined.
|
|
|
|
Args:
|
|
speaker_file (str): path to the speakers meta-data file.
|
|
"""
|
|
print("Loading speakers ...")
|
|
self.speaker_manager = SpeakerManager(
|
|
encoder_model_path=self.encoder_checkpoint, encoder_config_path=self.encoder_config
|
|
)
|
|
self.speaker_manager.load_d_vectors_file(self.tts_config.get("d_vector_file", speaker_file))
|
|
self.num_speakers = self.speaker_manager.num_speakers
|
|
self.d_vector_dim = self.speaker_manager.d_vector_dim
|
|
|
|
def _set_tts_speaker_file(self):
|
|
"""Set the TTS speaker file used by a multi-speaker model."""
|
|
# setup if multi-speaker settings are in the global model config
|
|
if hasattr(self.tts_config, "use_speaker_embedding") and self.tts_config.use_speaker_embedding is True:
|
|
if self.tts_config.use_d_vector_file:
|
|
self.tts_speakers_file = (
|
|
self.tts_speakers_file if self.tts_speakers_file else self.tts_config["d_vector_file"]
|
|
)
|
|
self.tts_config["d_vector_file"] = self.tts_speakers_file
|
|
else:
|
|
self.tts_speakers_file = (
|
|
self.tts_speakers_file if self.tts_speakers_file else self.tts_config["speakers_file"]
|
|
)
|
|
|
|
# setup if multi-speaker settings are in the model args config
|
|
if (
|
|
self.tts_speakers_file is None
|
|
and hasattr(self.tts_config, "model_args")
|
|
and hasattr(self.tts_config.model_args, "use_speaker_embedding")
|
|
and self.tts_config.model_args.use_speaker_embedding
|
|
):
|
|
_args = self.tts_config.model_args
|
|
if _args.use_d_vector_file:
|
|
self.tts_speakers_file = self.tts_speakers_file if self.tts_speakers_file else _args["d_vector_file"]
|
|
_args["d_vector_file"] = self.tts_speakers_file
|
|
else:
|
|
self.tts_speakers_file = self.tts_speakers_file if self.tts_speakers_file else _args["speakers_file"]
|
|
|
|
def _load_tts(self, tts_checkpoint: str, tts_config_path: str, use_cuda: bool) -> None:
|
|
"""Load the TTS model.
|
|
|
|
Args:
|
|
tts_checkpoint (str): path to the model checkpoint.
|
|
tts_config_path (str): path to the model config file.
|
|
use_cuda (bool): enable/disable CUDA use.
|
|
"""
|
|
# pylint: disable=global-statement
|
|
|
|
self.tts_config = load_config(tts_config_path)
|
|
self.use_phonemes = self.tts_config.use_phonemes
|
|
self.ap = AudioProcessor(verbose=False, **self.tts_config.audio)
|
|
|
|
self.tts_model = setup_tts_model(config=self.tts_config)
|
|
self.tts_model.load_checkpoint(self.tts_config, tts_checkpoint, eval=True)
|
|
if use_cuda:
|
|
self.tts_model.cuda()
|
|
self._set_tts_speaker_file()
|
|
|
|
def _load_vocoder(self, model_file: str, model_config: str, use_cuda: bool) -> None:
|
|
"""Load the vocoder model.
|
|
|
|
Args:
|
|
model_file (str): path to the model checkpoint.
|
|
model_config (str): path to the model config file.
|
|
use_cuda (bool): enable/disable CUDA use.
|
|
"""
|
|
self.vocoder_config = load_config(model_config)
|
|
self.vocoder_ap = AudioProcessor(verbose=False, **self.vocoder_config.audio)
|
|
self.vocoder_model = setup_vocoder_model(self.vocoder_config)
|
|
self.vocoder_model.load_checkpoint(self.vocoder_config, model_file, eval=True)
|
|
if use_cuda:
|
|
self.vocoder_model.cuda()
|
|
|
|
def split_into_sentences(self, text) -> List[str]:
|
|
"""Split give text into sentences.
|
|
|
|
Args:
|
|
text (str): input text in string format.
|
|
|
|
Returns:
|
|
List[str]: list of sentences.
|
|
"""
|
|
return self.seg.segment(text)
|
|
|
|
def save_wav(self, wav: List[int], path: str) -> None:
|
|
"""Save the waveform as a file.
|
|
|
|
Args:
|
|
wav (List[int]): waveform as a list of values.
|
|
path (str): output path to save the waveform.
|
|
"""
|
|
wav = np.array(wav)
|
|
self.ap.save_wav(wav, path, self.output_sample_rate)
|
|
|
|
def tts(self, text: str, speaker_idx: str = "", speaker_wav=None, style_wav=None) -> List[int]:
|
|
"""🐸 TTS magic. Run all the models and generate speech.
|
|
|
|
Args:
|
|
text (str): input text.
|
|
speaker_idx (str, optional): spekaer id for multi-speaker models. Defaults to "".
|
|
speaker_wav ():
|
|
style_wav ([type], optional): style waveform for GST. Defaults to None.
|
|
|
|
Returns:
|
|
List[int]: [description]
|
|
"""
|
|
start_time = time.time()
|
|
wavs = []
|
|
sens = self.split_into_sentences(text)
|
|
print(" > Text splitted to sentences.")
|
|
print(sens)
|
|
|
|
# handle multi-speaker
|
|
speaker_embedding = None
|
|
speaker_id = None
|
|
if self.tts_speakers_file:
|
|
if speaker_idx and isinstance(speaker_idx, str):
|
|
if self.tts_config.use_d_vector_file:
|
|
# get the speaker embedding from the saved d_vectors.
|
|
speaker_embedding = self.tts_model.speaker_manager.get_d_vectors_by_speaker(speaker_idx)[0]
|
|
else:
|
|
# get speaker idx from the speaker name
|
|
speaker_id = self.tts_model.speaker_manager.speaker_ids[speaker_idx]
|
|
|
|
elif not speaker_idx and not speaker_wav:
|
|
raise ValueError(
|
|
" [!] Look like you use a multi-speaker model. "
|
|
"You need to define either a `speaker_idx` or a `style_wav` to use a multi-speaker model."
|
|
)
|
|
else:
|
|
speaker_embedding = None
|
|
else:
|
|
if speaker_idx:
|
|
raise ValueError(
|
|
f" [!] Missing speaker.json file path for selecting speaker {speaker_idx}."
|
|
"Define path for speaker.json if it is a multi-speaker model or remove defined speaker idx. "
|
|
)
|
|
|
|
# compute a new d_vector from the given clip.
|
|
if speaker_wav is not None:
|
|
speaker_embedding = self.tts_model.speaker_manager.compute_d_vector_from_clip(speaker_wav)
|
|
|
|
use_gl = self.vocoder_model is None
|
|
|
|
for sen in sens:
|
|
# synthesize voice
|
|
outputs = synthesis(
|
|
model=self.tts_model,
|
|
text=sen,
|
|
CONFIG=self.tts_config,
|
|
use_cuda=self.use_cuda,
|
|
ap=self.ap,
|
|
speaker_id=speaker_id,
|
|
style_wav=style_wav,
|
|
enable_eos_bos_chars=self.tts_config.enable_eos_bos_chars,
|
|
use_griffin_lim=use_gl,
|
|
d_vector=speaker_embedding,
|
|
)
|
|
waveform = outputs["wav"]
|
|
mel_postnet_spec = outputs["outputs"]["model_outputs"][0].detach().cpu().numpy()
|
|
if not use_gl:
|
|
# denormalize tts output based on tts audio config
|
|
mel_postnet_spec = self.ap.denormalize(mel_postnet_spec.T).T
|
|
device_type = "cuda" if self.use_cuda else "cpu"
|
|
# renormalize spectrogram based on vocoder config
|
|
vocoder_input = self.vocoder_ap.normalize(mel_postnet_spec.T)
|
|
# compute scale factor for possible sample rate mismatch
|
|
scale_factor = [
|
|
1,
|
|
self.vocoder_config["audio"]["sample_rate"] / self.ap.sample_rate,
|
|
]
|
|
if scale_factor[1] != 1:
|
|
print(" > interpolating tts model output.")
|
|
vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input)
|
|
else:
|
|
vocoder_input = torch.tensor(vocoder_input).unsqueeze(0) # pylint: disable=not-callable
|
|
# run vocoder model
|
|
# [1, T, C]
|
|
waveform = self.vocoder_model.inference(vocoder_input.to(device_type))
|
|
if self.use_cuda and not use_gl:
|
|
waveform = waveform.cpu()
|
|
if not use_gl:
|
|
waveform = waveform.numpy()
|
|
waveform = waveform.squeeze()
|
|
|
|
# trim silence
|
|
waveform = trim_silence(waveform, self.ap)
|
|
|
|
wavs += list(waveform)
|
|
wavs += [0] * 10000
|
|
|
|
# compute stats
|
|
process_time = time.time() - start_time
|
|
audio_time = len(wavs) / self.tts_config.audio["sample_rate"]
|
|
print(f" > Processing time: {process_time}")
|
|
print(f" > Real-time factor: {process_time / audio_time}")
|
|
return wavs
|