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f o r m e r l y
i m p e r s o n a t i o n s
m o o d
p o p e
h e i r e s s
a c r e s
n e w s
w o m a n
c o u r t i n g
j o v e
a c c o u n t
w a n d e r e d
l a t t e r
a d o p t e d
w o u l d n ' t
s t r i v e
a f e a r ' d
b a d l y
c o u n t e d
i n v o l u n t a r i l y
t h e i r
g i r l s
j u m p e d
s t r i c t e s t
h e a l e r s
h a s
v i r g i n i a
a p p e a r s
c u t
r e d u c e d
b l i s t e r
r e m e m b e r e d
p r o m o t i o n
i n j u r e d
c l a i m s
f r o n t a l
n o r m a l
h e a l t h
p r o t e s t e d
b o l d l y
s p e n d i n g
m o o r i s h
r e l i g i o n
b e c o m e s
f u t u r e
s e x
h u r r y
s e m i c i r c l e
f o o t
e x p e r i e n c e s
f e e d
n u r s e s
s i t u a t i o n
c l i n g
c o u r t s h i p
g e r m a n s
p r o p e r t y
d i s a z e
a w a y
p r e v a i l e d
v o i c e
s o n ' s
u s e f u l n e s s
m o v e d
p u c k ' s
s p r u n g
r e a l i s t i c
n i n e t e e n
s u n k
e a s i l y
p e r q u i s i t e s
g e r m a n y
f e e l i n g
p r e c e d e n t
i n f i n i t e l y
b r o t h e r
r e a c h i n g
w i n e
i n f l u e n c e
i s n ' t
e n e m i e s
p e r f o r m e r s
p e r f o r m a n c e
e f f i c i e n t
o u t w a r d
e x c e e d i n g l y
c h u r c h
c o n c e a l m e n t
a n y t h i n g
c o m m e n t
c a n e
u n c o m m o n
g r a n d e u r
t r u l y
b o o k
d r i n k i n g
t i l l a g e
s a m p l e
l o v e l y
r u n n i n g
s a x o n s
w a s
e n t e r e d
a s t o n i s h m e n t
b r e a k
i r i s h
p o e t ' s
w i t n e s s
m e n t i o n e d
g r u m b l i n g
m a n n e r
r e t i r e m e n t
c a s u a l
s u p e r h u m a n
s a y s
s i c k
n a t u r e s
m a s t e r ' s
h u n t e r
i n s t r u c t i o n s
i m m e d i a t e
r e s u l t s
h e r
c h a i r
a c t
i n v e n t i v e n e s s
s w e d e n b o r g
m a k i n g
b o t t l e s
h o u s e s
p o e t
d a r k
l i e
i l l n e s s
a g e d
l e g i t i m a t e
p u l l
s t a t e m e n t s
c o m p a n i o n
i n t e r e s t s
t h e r e b y
m i n g l i n g
s e i z i n g
r e f l e c t i o n
f r e n c h
w i s h e s
f a c i n g
t e n t s
b r i t a i n
a l s o
t e m p t e d
s u p e r
i r o n
p e a s a n t
t h i n k i n g
s w e e t h e a r t s
f o l l o w i n g
r e q u i r e
p l e a s a n t l y
m e t a p h o r
v o l t a i r e a n
s p a r e
f l o o d
d e l i c a t e
c o n f i d e n t
t h i n k e r
c o o p e r ' s
t o d a y
p r e d e c e s s o r
v i e w s
o p e r a t i o n
d i n
o c c u r r e d
c o n t r a c t e d
s u f f e r i n g
h e r e
i n c e n t i v e
f o r c e
b u l l s
s o l a c e
i n c o m p r e h e n s i b l e
t r a i n
n u t s
i n q u i r e d
p u c k
s p e e c h
e s t e e m
d i m i n i s h i n g
i n o r g a n i c
i n t e l l i g e n t
q u i c k l y
m u s c l e
n a m e
e n d u r e
s t r i p e d
p a t t e r n
r e q u i r e m e n t s
t a l k s
a t t e n d e d
t e m p o r a l
i g n o b l e
v o i c e s
l e a v e s
n i h i l
m a s t e r
d a n g e r
f l a m e s
b o a t
g i v e s
l i b r a r y
p e n n i l e s s
a m e r i c a
h u s h
i ' m
w o r s e
s h a m e f a c e d
g l a n c e
m a n
t a c t
d e l i b e r a t e l y
t r u e
c l o t h
m a s q u e r a d e s
d i f f i d e n t
t i r e
b r e a t h i n g
w e a t h e r
r e j e c t
p a s t
t o s s e d
l i n e
p r e c i s e l y
s t r o k e
w o r k s
u s e d
a p p e a r e d
c o n t i g i t
g i r l
r e a l
w o o i n g
d o u b t
s i m i l a r i t y
d u r i n g
t e m p e r
c
l o c k e d
p e n k n i v e s
o m n i u m
e x c i t e m e n t
i n t o
v i c t i m s
r a n d o m
s t r i p e s
d i v i n i n g
l i k e n e s s
c a p a b l e
i m p o s t o r
h e l p
w a y
s p o i l e d
p r o p h e t s
s a c r e d
e n g a g e m e n t s
s a y i n g
e x t r a o r d i n a r y
i s
p r o v i d e n c e
c r a w l e y
d r a w
o p p o r t u n i t y
i m p r o v e d
g l a s t o n b u r y
s e a
c i g a r e t t e
e x h i b i t
p r o b a b l y
b e h i n d
h o m e
s w a i n s
j u l y
c h a i n s
d i f f i c u l t y
c l a i r v o y a n t
i
f a c u l t i e s
c o m i n g
r e t a l i a t o r y
r e s p o n d e d
t i m e s
e m p l o y e d
b r o w n
g l o u c e s t e r s
l u c y
s c o r n
a u g u s t
v i c i o u s
m u s h a
c r y i n g
c a r b i n e e r
d w a r f
g u a r d
t o m o r r o w
g o o d s
r e p u l s e d
r u b b e d
o p e r a t i n g
c a s e s
g a y
s a i d
u n k n o w n
s u b j e c t ' s
i n f i n i t e
p r o d u c i n g
h a s t e n e d
h i m
g i v i n g
e l s e
l a d y
n e v e r
c o n c e r n
s i l e n c e
m e a n l y
a s
c h a n c e d
f i n e r
r o m a n t i c
s h o o t
t h e s e
i t s e l f
d i s p o s e
b e t r a y
s h u d d e r i n g
a r e n t s c h i l d ' s
n e e d
a r t i s a n ' s
e s s e n t i a l l y
s t o p
t o o k
c o n v i n c e
i g n o r a n c e
p a t i e n c e
p r i v i l e g e d
s h a l l
r e f i n e m e n t
d e t e r m i n e s
m o v e m e n t s
m o n e y
a c c u s t o m e d
f o o t i n g
d r e a m t
c o l o r s
r e l a t i o n s
a v o i d
d i s c e r n m e n t
s t o c k
i n t e n d e d
r e c o r d s
l i v e
r i v e r
d e v i l
a m u s i n g
f i d d l e
i m p l i e s
a n s w e r
i n t e r e s t
e m p l o y m e n t
e n g a g e m e n t
m a s s i v e
h o r s e s
s i n t r y
a w a r e
p r e f e r m e n t
u g l y
f e v e r
t h e m s e l v e s
w r i t t e n
m a k e s
c r a w l e y ' s
b e l i e v e r
n a t i v e
m o n t h ' s
l a d e n
e x t i n g u i s h
l o u d
h u s b a n d ' s
t w i r l e d
h o n o r
f u n n y
l e a n e d
m a m m a
p l a n e t
s u f f u s e d
c o m p a n i o n s
f a m i l y
i t
i n c l i n e s
a b u n d a n t
g a l l e d
h e a r d
f a r e w e l l
a u t h o r i t a t i v e
a z u r e
o p e r a t e d
s w a m p
l a b o u r
u s e f u l
c u s h m a n
r e j o i c i n g
b a n k
r e a s o n s
h a s t e
g a r m e n t
a u d i e n c e
w e a r i n e s s
t h r e e
w o v e n
h o a r s e
w h o
i n c o m e s
p a r i s
s t u p i d
s w i r l i n g
c a r g o
d e c e i t
e m p r e s s e s
a c t u a t e d
w a t e r s
m a r v e l l o u s l y
l a n g u a g e
d a s h
o n
i n v e n t i o n
g r e a t l y
s
g i r l ' s
m a n a g e d
i n t e r r o g a t i o n
n e g l e c t e d
a s s ' s
d a n e s
o p i u m
t y p e s
d o m i n a t i n g
i m p r e s s i o n
p r o v i n c i a l i s m
v a n i t y
r e s u l t
h e a v i l y
a r i s e s
e n d
s c h o l a r s h i p
r i c h
r e s p o n d
e n t e r t a i n i n g
t a s t e
r e p r e s e n t
c h i a j a
s u f f e r e d
s u p p o r t
c i v i l i z a t i o n s
m a t t e r s
o b j e c t e d
m a l e
y o u r
o w n e d
e x i s t s
r e s i s t e d
b r i t o n s
l i n e r
f a r
g u i d e
d i s c o v e r e d
u n w i l l i n g
c u r l y
c o t t a g e
s h a r e
o b l i g e
i n v a r i a b l y
i n s t i n c t
d i s g u s t
a p p r o v a l
o p e r a t o r
c a r e f u l l y
n o r t h m e n
w i t h i n
m o t i o n
f o r t i f i e d
s h e f f i e l d
f o l l y
m a t e r i a l
o f f e r i n g
l a n d l a d y
b a r o c c o
w e a k
t i p p e d
p u r i t a n
m e l l o w
t h r a v e l s
i m p a r t
c a r r y
c o u n t r y
p o r k
p l a c e
r
s t r a i g h t
r o s e
w h o l e
i m p a d e
p r e f e r e n c e s
m a t u r i t y
s p e a k
c o u l d n ' t
d u k e
w a n t e d
s e e m i n g l y
e n o u g h
w a r m
l a k e ' s
b r a i n
g a t h e r
m o i s t e n
f o r
p h e n o m e n o n s
a r m e d
s n o r e d
p r a c t i c a l l y
a g e
e x p l a i n e d
f a r m y a r d
h y d r a u l i c
d r e w
w o o d e d
d i s e n g a g e
c o n v e r s a t i o n
n a r r o w n e s s
h u r r i e d l y
b r o o m
p o p u l a c e
d e a f
p u r e l y
g l o r i f i c a t i o n s
h i s t o r y
c a r n i v a l
h a s t i l y
e f f o r t
s u g g e s t i v e
u n a p p r o a c h a b l e
e a r t h
i n s t i n c t i v e
b r i g h t
d i v i n i t y
s c a l e
c l a y
m u s t
s e n t
w h o s e
a b s o r b e d
h e i g h t
g a z i n g
s t r o n g e s t
s y s t e m
w a r
s u b t l e r
l i s t e n e d
u n e q u i v o c a l
b o y s
j o y
e n t e r i n g
s e l d o m
a d m i r e d
w e a r
s t o r m
f e a t s
o u t r a g e o u s
s h a k e s p e a r e
f l i n t
e v e r y b o d y
w i t c h
m a g n e t i c
a l t h o u g h
s u c c e s s e s
t h e e
d i l a t e d
e v i l
r o a r s
d o
r e a l i t i e s
q u e s t i o n
c u l t i v a t o r
b u s i l y
u s i n g
s h o w i n g
b r i n g i n g
d o w n
l e t h a r g y
a t t e n t i o n
r o l l
g r a t i f i c a t i o n
c o n t e m p t
s u g g e s t e d
p o c k e t
b e d r o o m
r e g a i n e d
s t a n d
b e a r
p l a u s i b l e
m e a n i n g
t e l l
o r g a n
b r o k e
p u r s e
s o n s y
b a r
p u b l i c
h a l c y o n
c o n n e c t i o n
f o r c i n g
s i n k i n g
d i s g u i s e d
e v e r
e s t a b l i s h e d
e m p e r o r s
a s l e e p
y o u ' r e
s l i g h t
r e p l i e d
b o n h o m m e
l e s s o n
d i e d
d e l i g h t e d
f r e q u e n t
c a s t
f i l l e d
p r o b a b i l i t y
c o v e r e d
h a d
u n u s u a l
r i d
f a s h i o n
f i r e
e m b o d i m e n t
u n f o r t u n a t e l y
p e r f e c t i o n
d e s e r v e
f e e t
a t t a c k e d
f i r s t
r e a d i n g
b y
a u t h o r i t y
c r y
c o c k e
c o l o u r s
c o l d
m o n u m e n t s
s u m
s e r i e s
s e p a r a t e
r a r e l y
n o t h i n g
b l e n d e d
b o x w o o d
f e l l o w s
w o r d s
e n g l a n d
a f r a i d
c o o l i n g
t o n g u e
m a t r i m o n y
r a c e s
s w e l l
w i d
a n y
d i s t i n g u i s h e d
m a g n e t i s m
w a l k
a c k n o w l e d g e d
s u c c e s s i o n
w h i s p e r e d
b l e e d i n g
w o u l d
m e m b e r
d i s s a t i s f a c t i o n
t h r o w
i n t e l l e c t u a l
t a b l e
c h i l d r e n ' s
t r y i n g
b e g i n n i n
r e l u c t a n c e
g r o s s l y
g o o d
i m a g i n a t i o n
b e c o m e
b e a u t i f u l
g u n t e r
o t h e r s
f l a t t e r
m a s c u l i n e
t r o u b l e
b e h a v e d
p e r h a p s
m i t i g a t e d
f a l t e r e d
h a p p y
w a i s t
k n e w
t a m
c o u n t e n a n c e
l o d g i n g s
c o a l s
c o m p u l s i o n
e a r l y
s u r p r i s e d
d i m i n i s h e d
s h o w s
a g o
a c k n o w l e d g m e n t
p e d a n t
p r i v i l e g e
e n c h a n t e d
b a n t e r
p r o b a b l e
p r a y
d e s t i n y
p e r v e r s i t y
i n c u m b e n t
r h i n e
l i n e s
b u r i e d
w a s t e d
b a t h e d
s e v e n
c o a s t
s e c o n d l y
r o s e t t e
v e r s e s
p r e p a r e d
s w e e t h e a r t
s t e r n
c r e a t i v e
a p p r e c i a t e
c a u t i o u s
w h o m
d e l e c t u s
h a n d s o m e
m a r r i e d
y i e l d i n g
k i n g ' s
r e m i t t e d
s h o r e
f i e l d s
f u l l e s t
d i m
d e s c r i p t i o n
c o m p a n y
l u c k y
m i d
b e e r
c o n s c i o u s
d e c r e a s e d
p r e a c h e d
i n c l u d i n g
p r o t e c t e d
t h a t ' s
f o r m e r
m e a n t
j o i n t
| 0 |
coqui_public_repos/TTS/TTS | coqui_public_repos/TTS/TTS/utils/synthesizer.py | import os
import time
from typing import List
import numpy as np
import pysbd
import torch
from torch import nn
from TTS.config import load_config
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.models import setup_model as setup_tts_model
from TTS.tts.models.vits import Vits
# pylint: disable=unused-wildcard-import
# pylint: disable=wildcard-import
from TTS.tts.utils.synthesis import synthesis, transfer_voice, trim_silence
from TTS.utils.audio import AudioProcessor
from TTS.utils.audio.numpy_transforms import save_wav
from TTS.vc.models import setup_model as setup_vc_model
from TTS.vocoder.models import setup_model as setup_vocoder_model
from TTS.vocoder.utils.generic_utils import interpolate_vocoder_input
class Synthesizer(nn.Module):
def __init__(
self,
tts_checkpoint: str = "",
tts_config_path: str = "",
tts_speakers_file: str = "",
tts_languages_file: str = "",
vocoder_checkpoint: str = "",
vocoder_config: str = "",
encoder_checkpoint: str = "",
encoder_config: str = "",
vc_checkpoint: str = "",
vc_config: str = "",
model_dir: str = "",
voice_dir: str = None,
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, optional): path to the tts model file.
tts_config_path (str, optional): 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 `""`,
vc_checkpoint (str, optional): path to the voice conversion model file. Defaults to `""`,
vc_config (str, optional): path to the voice conversion config file. Defaults to `""`,
use_cuda (bool, optional): enable/disable cuda. Defaults to False.
"""
super().__init__()
self.tts_checkpoint = tts_checkpoint
self.tts_config_path = tts_config_path
self.tts_speakers_file = tts_speakers_file
self.tts_languages_file = tts_languages_file
self.vocoder_checkpoint = vocoder_checkpoint
self.vocoder_config = vocoder_config
self.encoder_checkpoint = encoder_checkpoint
self.encoder_config = encoder_config
self.vc_checkpoint = vc_checkpoint
self.vc_config = vc_config
self.use_cuda = use_cuda
self.tts_model = None
self.vocoder_model = None
self.vc_model = None
self.speaker_manager = None
self.tts_speakers = {}
self.language_manager = None
self.num_languages = 0
self.tts_languages = {}
self.d_vector_dim = 0
self.seg = self._get_segmenter("en")
self.use_cuda = use_cuda
self.voice_dir = voice_dir
if self.use_cuda:
assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
if tts_checkpoint:
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"]
if vc_checkpoint:
self._load_vc(vc_checkpoint, vc_config, use_cuda)
self.output_sample_rate = self.vc_config.audio["output_sample_rate"]
if model_dir:
if "fairseq" in model_dir:
self._load_fairseq_from_dir(model_dir, use_cuda)
self.output_sample_rate = self.tts_config.audio["sample_rate"]
else:
self._load_tts_from_dir(model_dir, use_cuda)
self.output_sample_rate = self.tts_config.audio["output_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_vc(self, vc_checkpoint: str, vc_config_path: str, use_cuda: bool) -> None:
"""Load the voice conversion model.
1. Load the model config.
2. Init the model from the config.
3. Load the model weights.
4. Move the model to the GPU if CUDA is enabled.
Args:
vc_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.vc_config = load_config(vc_config_path)
self.vc_model = setup_vc_model(config=self.vc_config)
self.vc_model.load_checkpoint(self.vc_config, vc_checkpoint)
if use_cuda:
self.vc_model.cuda()
def _load_fairseq_from_dir(self, model_dir: str, use_cuda: bool) -> None:
"""Load the fairseq model from a directory.
We assume it is VITS and the model knows how to load itself from the directory and there is a config.json file in the directory.
"""
self.tts_config = VitsConfig()
self.tts_model = Vits.init_from_config(self.tts_config)
self.tts_model.load_fairseq_checkpoint(self.tts_config, checkpoint_dir=model_dir, eval=True)
self.tts_config = self.tts_model.config
if use_cuda:
self.tts_model.cuda()
def _load_tts_from_dir(self, model_dir: str, use_cuda: bool) -> None:
"""Load the TTS model from a directory.
We assume the model knows how to load itself from the directory and there is a config.json file in the directory.
"""
config = load_config(os.path.join(model_dir, "config.json"))
self.tts_config = config
self.tts_model = setup_tts_model(config)
self.tts_model.load_checkpoint(config, checkpoint_dir=model_dir, eval=True)
if use_cuda:
self.tts_model.cuda()
def _load_tts(self, tts_checkpoint: str, tts_config_path: str, use_cuda: bool) -> None:
"""Load the TTS model.
1. Load the model config.
2. Init the model from the config.
3. Load the model weights.
4. Move the model to the GPU if CUDA is enabled.
5. Init the speaker manager in the 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)
if self.tts_config["use_phonemes"] and self.tts_config["phonemizer"] is None:
raise ValueError("Phonemizer is not defined in the TTS config.")
self.tts_model = setup_tts_model(config=self.tts_config)
if not self.encoder_checkpoint:
self._set_speaker_encoder_paths_from_tts_config()
self.tts_model.load_checkpoint(self.tts_config, tts_checkpoint, eval=True)
if use_cuda:
self.tts_model.cuda()
if self.encoder_checkpoint and hasattr(self.tts_model, "speaker_manager"):
self.tts_model.speaker_manager.init_encoder(self.encoder_checkpoint, self.encoder_config, use_cuda)
def _set_speaker_encoder_paths_from_tts_config(self):
"""Set the encoder paths from the tts model config for models with speaker encoders."""
if hasattr(self.tts_config, "model_args") and hasattr(
self.tts_config.model_args, "speaker_encoder_config_path"
):
self.encoder_checkpoint = self.tts_config.model_args.speaker_encoder_model_path
self.encoder_config = self.tts_config.model_args.speaker_encoder_config_path
def _load_vocoder(self, model_file: str, model_config: str, use_cuda: bool) -> None:
"""Load the vocoder model.
1. Load the vocoder config.
2. Init the AudioProcessor for the vocoder.
3. Init the vocoder model from the config.
4. Move the model to the GPU if CUDA is enabled.
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, pipe_out=None) -> 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.
pipe_out (BytesIO, optional): Flag to stdout the generated TTS wav file for shell pipe.
"""
# if tensor convert to numpy
if torch.is_tensor(wav):
wav = wav.cpu().numpy()
if isinstance(wav, list):
wav = np.array(wav)
save_wav(wav=wav, path=path, sample_rate=self.output_sample_rate, pipe_out=pipe_out)
def voice_conversion(self, source_wav: str, target_wav: str) -> List[int]:
output_wav = self.vc_model.voice_conversion(source_wav, target_wav)
return output_wav
def tts(
self,
text: str = "",
speaker_name: str = "",
language_name: str = "",
speaker_wav=None,
style_wav=None,
style_text=None,
reference_wav=None,
reference_speaker_name=None,
split_sentences: bool = True,
**kwargs,
) -> List[int]:
"""🐸 TTS magic. Run all the models and generate speech.
Args:
text (str): input text.
speaker_name (str, optional): speaker id for multi-speaker models. Defaults to "".
language_name (str, optional): language id for multi-language models. Defaults to "".
speaker_wav (Union[str, List[str]], optional): path to the speaker wav for voice cloning. Defaults to None.
style_wav ([type], optional): style waveform for GST. Defaults to None.
style_text ([type], optional): transcription of style_wav for Capacitron. Defaults to None.
reference_wav ([type], optional): reference waveform for voice conversion. Defaults to None.
reference_speaker_name ([type], optional): speaker id of reference waveform. Defaults to None.
split_sentences (bool, optional): split the input text into sentences. Defaults to True.
**kwargs: additional arguments to pass to the TTS model.
Returns:
List[int]: [description]
"""
start_time = time.time()
wavs = []
if not text and not reference_wav:
raise ValueError(
"You need to define either `text` (for sythesis) or a `reference_wav` (for voice conversion) to use the Coqui TTS API."
)
if text:
sens = [text]
if split_sentences:
print(" > Text splitted to sentences.")
sens = self.split_into_sentences(text)
print(sens)
# handle multi-speaker
if "voice_dir" in kwargs:
self.voice_dir = kwargs["voice_dir"]
kwargs.pop("voice_dir")
speaker_embedding = None
speaker_id = None
if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "name_to_id"):
if speaker_name and isinstance(speaker_name, str) and not self.tts_config.model == "xtts":
if self.tts_config.use_d_vector_file:
# get the average speaker embedding from the saved d_vectors.
speaker_embedding = self.tts_model.speaker_manager.get_mean_embedding(
speaker_name, num_samples=None, randomize=False
)
speaker_embedding = np.array(speaker_embedding)[None, :] # [1 x embedding_dim]
else:
# get speaker idx from the speaker name
speaker_id = self.tts_model.speaker_manager.name_to_id[speaker_name]
# handle Neon models with single speaker.
elif len(self.tts_model.speaker_manager.name_to_id) == 1:
speaker_id = list(self.tts_model.speaker_manager.name_to_id.values())[0]
elif not speaker_name and not speaker_wav:
raise ValueError(
" [!] Looks like you are using a multi-speaker model. "
"You need to define either a `speaker_idx` or a `speaker_wav` to use a multi-speaker model."
)
else:
speaker_embedding = None
else:
if speaker_name and self.voice_dir is None:
raise ValueError(
f" [!] Missing speakers.json file path for selecting speaker {speaker_name}."
"Define path for speaker.json if it is a multi-speaker model or remove defined speaker idx. "
)
# handle multi-lingual
language_id = None
if self.tts_languages_file or (
hasattr(self.tts_model, "language_manager")
and self.tts_model.language_manager is not None
and not self.tts_config.model == "xtts"
):
if len(self.tts_model.language_manager.name_to_id) == 1:
language_id = list(self.tts_model.language_manager.name_to_id.values())[0]
elif language_name and isinstance(language_name, str):
try:
language_id = self.tts_model.language_manager.name_to_id[language_name]
except KeyError as e:
raise ValueError(
f" [!] Looks like you use a multi-lingual model. "
f"Language {language_name} is not in the available languages: "
f"{self.tts_model.language_manager.name_to_id.keys()}."
) from e
elif not language_name:
raise ValueError(
" [!] Look like you use a multi-lingual model. "
"You need to define either a `language_name` or a `style_wav` to use a multi-lingual model."
)
else:
raise ValueError(
f" [!] Missing language_ids.json file path for selecting language {language_name}."
"Define path for language_ids.json if it is a multi-lingual model or remove defined language idx. "
)
# compute a new d_vector from the given clip.
if (
speaker_wav is not None
and self.tts_model.speaker_manager is not None
and hasattr(self.tts_model.speaker_manager, "encoder_ap")
and self.tts_model.speaker_manager.encoder_ap is not None
):
speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(speaker_wav)
vocoder_device = "cpu"
use_gl = self.vocoder_model is None
if not use_gl:
vocoder_device = next(self.vocoder_model.parameters()).device
if self.use_cuda:
vocoder_device = "cuda"
if not reference_wav: # not voice conversion
for sen in sens:
if hasattr(self.tts_model, "synthesize"):
outputs = self.tts_model.synthesize(
text=sen,
config=self.tts_config,
speaker_id=speaker_name,
voice_dirs=self.voice_dir,
d_vector=speaker_embedding,
speaker_wav=speaker_wav,
language=language_name,
**kwargs,
)
else:
# synthesize voice
outputs = synthesis(
model=self.tts_model,
text=sen,
CONFIG=self.tts_config,
use_cuda=self.use_cuda,
speaker_id=speaker_id,
style_wav=style_wav,
style_text=style_text,
use_griffin_lim=use_gl,
d_vector=speaker_embedding,
language_id=language_id,
)
waveform = outputs["wav"]
if not use_gl:
mel_postnet_spec = outputs["outputs"]["model_outputs"][0].detach().cpu().numpy()
# denormalize tts output based on tts audio config
mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
# 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.tts_model.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(vocoder_device))
if torch.is_tensor(waveform) and waveform.device != torch.device("cpu") and not use_gl:
waveform = waveform.cpu()
if not use_gl:
waveform = waveform.numpy()
waveform = waveform.squeeze()
# trim silence
if "do_trim_silence" in self.tts_config.audio and self.tts_config.audio["do_trim_silence"]:
waveform = trim_silence(waveform, self.tts_model.ap)
wavs += list(waveform)
wavs += [0] * 10000
else:
# get the speaker embedding or speaker id for the reference wav file
reference_speaker_embedding = None
reference_speaker_id = None
if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "name_to_id"):
if reference_speaker_name and isinstance(reference_speaker_name, str):
if self.tts_config.use_d_vector_file:
# get the speaker embedding from the saved d_vectors.
reference_speaker_embedding = self.tts_model.speaker_manager.get_embeddings_by_name(
reference_speaker_name
)[0]
reference_speaker_embedding = np.array(reference_speaker_embedding)[
None, :
] # [1 x embedding_dim]
else:
# get speaker idx from the speaker name
reference_speaker_id = self.tts_model.speaker_manager.name_to_id[reference_speaker_name]
else:
reference_speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(
reference_wav
)
outputs = transfer_voice(
model=self.tts_model,
CONFIG=self.tts_config,
use_cuda=self.use_cuda,
reference_wav=reference_wav,
speaker_id=speaker_id,
d_vector=speaker_embedding,
use_griffin_lim=use_gl,
reference_speaker_id=reference_speaker_id,
reference_d_vector=reference_speaker_embedding,
)
waveform = outputs
if not use_gl:
mel_postnet_spec = outputs[0].detach().cpu().numpy()
# denormalize tts output based on tts audio config
mel_postnet_spec = self.tts_model.ap.denormalize(mel_postnet_spec.T).T
# 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.tts_model.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(vocoder_device))
if torch.is_tensor(waveform) and waveform.device != torch.device("cpu"):
waveform = waveform.cpu()
if not use_gl:
waveform = waveform.numpy()
wavs = waveform.squeeze()
# 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
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/bin/fstunion-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Creates the union of two FSTs.
#include <cstring>
#include <memory>
#include <string>
#include <fst/flags.h>
#include <fst/log.h>
#include <fst/script/union.h>
int fstunion_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::FstClass;
using fst::script::MutableFstClass;
string usage = "Creates the union of two FSTs.\n\n Usage: ";
usage += argv[0];
usage += " in1.fst in2.fst [out.fst]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc < 3 || argc > 4) {
ShowUsage();
return 1;
}
const string in1_name = strcmp(argv[1], "-") != 0 ? argv[1] : "";
const string in2_name = strcmp(argv[2], "-") != 0 ? argv[2] : "";
const string out_name = argc > 3 ? argv[3] : "";
if (in1_name == "" && in2_name == "") {
LOG(ERROR) << argv[0] << ": Can't take both inputs from standard input";
return 1;
}
std::unique_ptr<MutableFstClass> fst1(MutableFstClass::Read(in1_name, true));
if (!fst1) return 1;
std::unique_ptr<FstClass> fst2(FstClass::Read(in2_name));
if (!fst2) return 1;
s::Union(fst1.get(), *fst2);
return !fst1->Write(out_name);
}
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/lib/properties.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Functions for updating property bits for various FST operations and
// string names of the properties.
#include <fst/properties.h>
#include <stddef.h>
#include <vector>
namespace fst {
// These functions determine the properties associated with the FST result of
// various finite-state operations. The property arguments correspond to the
// operation's FST arguments. The properties returned assume the operation
// modifies its first argument. Bitwise-and this result with kCopyProperties for
// the case when a new (possibly delayed) FST is instead constructed.
// Properties for a concatenatively-closed FST.
uint64 ClosureProperties(uint64 inprops, bool star, bool delayed) {
auto outprops = (kError | kAcceptor | kUnweighted | kAccessible) & inprops;
if (inprops & kUnweighted) outprops |= kUnweightedCycles;
if (!delayed) {
outprops |=
(kExpanded | kMutable | kCoAccessible | kNotTopSorted | kNotString) &
inprops;
}
if (!delayed || inprops & kAccessible) {
outprops |= (kNotAcceptor | kNonIDeterministic | kNonODeterministic |
kNotILabelSorted | kNotOLabelSorted | kWeighted |
kWeightedCycles | kNotAccessible | kNotCoAccessible) & inprops;
if ((inprops & kWeighted) && (inprops & kAccessible) &&
(inprops & kCoAccessible)) {
outprops |= kWeightedCycles;
}
}
return outprops;
}
// Properties for a complemented FST.
uint64 ComplementProperties(uint64 inprops) {
auto outprops = kAcceptor | kUnweighted | kUnweightedCycles | kNoEpsilons |
kNoIEpsilons | kNoOEpsilons | kIDeterministic |
kODeterministic | kAccessible;
outprops |=
(kError | kILabelSorted | kOLabelSorted | kInitialCyclic) & inprops;
if (inprops & kAccessible) {
outprops |= kNotILabelSorted | kNotOLabelSorted | kCyclic;
}
return outprops;
}
// Properties for a composed FST.
uint64 ComposeProperties(uint64 inprops1, uint64 inprops2) {
auto outprops = kError & (inprops1 | inprops2);
if (inprops1 & kAcceptor && inprops2 & kAcceptor) {
outprops |= kAcceptor | kAccessible;
outprops |= (kNoEpsilons | kNoIEpsilons | kNoOEpsilons | kAcyclic |
kInitialAcyclic) &
inprops1 & inprops2;
if (kNoIEpsilons & inprops1 & inprops2) {
outprops |= (kIDeterministic | kODeterministic) & inprops1 & inprops2;
}
} else {
outprops |= kAccessible;
outprops |= (kAcceptor | kNoIEpsilons | kAcyclic | kInitialAcyclic) &
inprops1 & inprops2;
if (kNoIEpsilons & inprops1 & inprops2) {
outprops |= kIDeterministic & inprops1 & inprops2;
}
}
return outprops;
}
// Properties for a concatenated FST.
uint64 ConcatProperties(uint64 inprops1, uint64 inprops2, bool delayed) {
auto outprops = (kAcceptor | kUnweighted | kUnweightedCycles | kAcyclic) &
inprops1 & inprops2;
outprops |= kError & (inprops1 | inprops2);
const bool empty1 = delayed; // Can the first FST be the empty machine?
const bool empty2 = delayed; // Can the second FST be the empty machine?
if (!delayed) {
outprops |= (kExpanded | kMutable | kNotTopSorted | kNotString) & inprops1;
outprops |= (kNotTopSorted | kNotString) & inprops2;
}
if (!empty1) outprops |= (kInitialAcyclic | kInitialCyclic) & inprops1;
if (!delayed || inprops1 & kAccessible) {
outprops |= (kNotAcceptor | kNonIDeterministic | kNonODeterministic |
kEpsilons | kIEpsilons | kOEpsilons | kNotILabelSorted |
kNotOLabelSorted | kWeighted | kWeightedCycles | kCyclic |
kNotAccessible | kNotCoAccessible) &
inprops1;
}
if ((inprops1 & (kAccessible | kCoAccessible)) ==
(kAccessible | kCoAccessible) &&
!empty1) {
outprops |= kAccessible & inprops2;
if (!empty2) outprops |= kCoAccessible & inprops2;
if (!delayed || inprops2 & kAccessible) {
outprops |= (kNotAcceptor | kNonIDeterministic | kNonODeterministic |
kEpsilons | kIEpsilons | kOEpsilons | kNotILabelSorted |
kNotOLabelSorted | kWeighted | kWeightedCycles | kCyclic |
kNotAccessible | kNotCoAccessible) &
inprops2;
}
}
return outprops;
}
// Properties for a determinized FST.
uint64 DeterminizeProperties(uint64 inprops, bool has_subsequential_label,
bool distinct_psubsequential_labels) {
auto outprops = kAccessible;
if ((kAcceptor & inprops) ||
((kNoIEpsilons & inprops) && distinct_psubsequential_labels) ||
(has_subsequential_label && distinct_psubsequential_labels)) {
outprops |= kIDeterministic;
}
outprops |= (kError | kAcceptor | kAcyclic | kInitialAcyclic | kCoAccessible |
kString) &
inprops;
if ((inprops & kNoIEpsilons) && distinct_psubsequential_labels) {
outprops |= kNoEpsilons & inprops;
}
if (inprops & kAccessible) {
outprops |= (kIEpsilons | kOEpsilons | kCyclic) & inprops;
}
if (inprops & kAcceptor) outprops |= (kNoIEpsilons | kNoOEpsilons) & inprops;
if ((inprops & kNoIEpsilons) && has_subsequential_label) {
outprops |= kNoIEpsilons;
}
return outprops;
}
// Properties for factored weight FST.
uint64 FactorWeightProperties(uint64 inprops) {
auto outprops = (kExpanded | kMutable | kError | kAcceptor | kAcyclic |
kAccessible | kCoAccessible) &
inprops;
if (inprops & kAccessible) {
outprops |= (kNotAcceptor | kNonIDeterministic | kNonODeterministic |
kEpsilons | kIEpsilons | kOEpsilons | kCyclic |
kNotILabelSorted | kNotOLabelSorted) &
inprops;
}
return outprops;
}
// Properties for an inverted FST.
uint64 InvertProperties(uint64 inprops) {
auto outprops = (kExpanded | kMutable | kError | kAcceptor | kNotAcceptor |
kEpsilons | kNoEpsilons | kWeighted | kUnweighted |
kWeightedCycles | kUnweightedCycles | kCyclic | kAcyclic |
kInitialCyclic | kInitialAcyclic | kTopSorted |
kNotTopSorted | kAccessible | kNotAccessible |
kCoAccessible | kNotCoAccessible | kString | kNotString) &
inprops;
if (kIDeterministic & inprops) outprops |= kODeterministic;
if (kNonIDeterministic & inprops) outprops |= kNonODeterministic;
if (kODeterministic & inprops) outprops |= kIDeterministic;
if (kNonODeterministic & inprops) outprops |= kNonIDeterministic;
if (kIEpsilons & inprops) outprops |= kOEpsilons;
if (kNoIEpsilons & inprops) outprops |= kNoOEpsilons;
if (kOEpsilons & inprops) outprops |= kIEpsilons;
if (kNoOEpsilons & inprops) outprops |= kNoIEpsilons;
if (kILabelSorted & inprops) outprops |= kOLabelSorted;
if (kNotILabelSorted & inprops) outprops |= kNotOLabelSorted;
if (kOLabelSorted & inprops) outprops |= kILabelSorted;
if (kNotOLabelSorted & inprops) outprops |= kNotILabelSorted;
return outprops;
}
// Properties for a projected FST.
uint64 ProjectProperties(uint64 inprops, bool project_input) {
auto outprops = kAcceptor;
outprops |= (kExpanded | kMutable | kError | kWeighted | kUnweighted |
kWeightedCycles | kUnweightedCycles |
kCyclic | kAcyclic | kInitialCyclic | kInitialAcyclic |
kTopSorted | kNotTopSorted | kAccessible | kNotAccessible |
kCoAccessible | kNotCoAccessible | kString | kNotString) &
inprops;
if (project_input) {
outprops |= (kIDeterministic | kNonIDeterministic | kIEpsilons |
kNoIEpsilons | kILabelSorted | kNotILabelSorted) &
inprops;
if (kIDeterministic & inprops) outprops |= kODeterministic;
if (kNonIDeterministic & inprops) outprops |= kNonODeterministic;
if (kIEpsilons & inprops) outprops |= kOEpsilons | kEpsilons;
if (kNoIEpsilons & inprops) outprops |= kNoOEpsilons | kNoEpsilons;
if (kILabelSorted & inprops) outprops |= kOLabelSorted;
if (kNotILabelSorted & inprops) outprops |= kNotOLabelSorted;
} else {
outprops |= (kODeterministic | kNonODeterministic | kOEpsilons |
kNoOEpsilons | kOLabelSorted | kNotOLabelSorted) &
inprops;
if (kODeterministic & inprops) outprops |= kIDeterministic;
if (kNonODeterministic & inprops) outprops |= kNonIDeterministic;
if (kOEpsilons & inprops) outprops |= kIEpsilons | kEpsilons;
if (kNoOEpsilons & inprops) outprops |= kNoIEpsilons | kNoEpsilons;
if (kOLabelSorted & inprops) outprops |= kILabelSorted;
if (kNotOLabelSorted & inprops) outprops |= kNotILabelSorted;
}
return outprops;
}
// Properties for a randgen FST.
uint64 RandGenProperties(uint64 inprops, bool weighted) {
auto outprops = kAcyclic | kInitialAcyclic | kAccessible | kUnweightedCycles;
outprops |= inprops & kError;
if (weighted) {
outprops |= kTopSorted;
outprops |=
(kAcceptor | kNoEpsilons | kNoIEpsilons | kNoOEpsilons |
kIDeterministic | kODeterministic | kILabelSorted | kOLabelSorted) &
inprops;
} else {
outprops |= kUnweighted;
outprops |= (kAcceptor | kILabelSorted | kOLabelSorted) & inprops;
}
return outprops;
}
// Properties for a replace FST.
uint64 ReplaceProperties(const std::vector<uint64>& inprops, ssize_t root,
bool epsilon_on_call, bool epsilon_on_return,
bool out_epsilon_on_call, bool out_epsilon_on_return,
bool replace_transducer, bool no_empty_fsts,
bool all_ilabel_sorted, bool all_olabel_sorted,
bool all_negative_or_dense) {
if (inprops.empty()) return kNullProperties;
uint64 outprops = 0;
for (auto inprop : inprops) outprops |= kError & inprop;
uint64 access_props = no_empty_fsts ? kAccessible | kCoAccessible : 0;
for (auto inprop : inprops) {
access_props &= (inprop & (kAccessible | kCoAccessible));
}
if (access_props == (kAccessible | kCoAccessible)) {
outprops |= access_props;
if (inprops[root] & kInitialCyclic) outprops |= kInitialCyclic;
uint64 props = 0;
bool string = true;
for (auto inprop : inprops) {
if (replace_transducer) props |= kNotAcceptor & inprop;
props |= (kNonIDeterministic | kNonODeterministic | kEpsilons |
kIEpsilons | kOEpsilons | kWeighted | kWeightedCycles |
kCyclic | kNotTopSorted | kNotString) & inprop;
if (!(inprop & kString)) string = false;
}
outprops |= props;
if (string) outprops |= kString;
}
bool acceptor = !replace_transducer;
bool ideterministic = !epsilon_on_call && epsilon_on_return;
bool no_iepsilons = !epsilon_on_call && !epsilon_on_return;
bool acyclic = true;
bool unweighted = true;
for (size_t i = 0; i < inprops.size(); ++i) {
if (!(inprops[i] & kAcceptor)) acceptor = false;
if (!(inprops[i] & kIDeterministic)) ideterministic = false;
if (!(inprops[i] & kNoIEpsilons)) no_iepsilons = false;
if (!(inprops[i] & kAcyclic)) acyclic = false;
if (!(inprops[i] & kUnweighted)) unweighted = false;
if (i != root && !(inprops[i] & kNoIEpsilons)) ideterministic = false;
}
if (acceptor) outprops |= kAcceptor;
if (ideterministic) outprops |= kIDeterministic;
if (no_iepsilons) outprops |= kNoIEpsilons;
if (acyclic) outprops |= kAcyclic;
if (unweighted) outprops |= kUnweighted;
if (inprops[root] & kInitialAcyclic) outprops |= kInitialAcyclic;
// We assume that all terminals are positive. The resulting ReplaceFst is
// known to be kILabelSorted when: (1) all sub-FSTs are kILabelSorted, (2) the
// input label of the return arc is epsilon, and (3) one of the 3 following
// conditions is satisfied:
//
// 1. the input label of the call arc is not epsilon
// 2. all non-terminals are negative, or
// 3. all non-terninals are positive and form a dense range containing 1.
if (all_ilabel_sorted && epsilon_on_return &&
(!epsilon_on_call || all_negative_or_dense)) {
outprops |= kILabelSorted;
}
// Similarly, the resulting ReplaceFst is known to be kOLabelSorted when: (1)
// all sub-FSTs are kOLabelSorted, (2) the output label of the return arc is
// epsilon, and (3) one of the 3 following conditions is satisfied:
//
// 1. the output label of the call arc is not epsilon
// 2. all non-terminals are negative, or
// 3. all non-terninals are positive and form a dense range containing 1.
if (all_olabel_sorted && out_epsilon_on_return &&
(!out_epsilon_on_call || all_negative_or_dense)) {
outprops |= kOLabelSorted;
}
return outprops;
}
// Properties for a relabeled FST.
uint64 RelabelProperties(uint64 inprops) {
static constexpr auto outprops =
kExpanded | kMutable | kError | kWeighted | kUnweighted |
kWeightedCycles | kUnweightedCycles | kCyclic | kAcyclic |
kInitialCyclic | kInitialAcyclic | kTopSorted | kNotTopSorted |
kAccessible | kNotAccessible | kCoAccessible | kNotCoAccessible |
kString | kNotString;
return outprops & inprops;
}
// Properties for a reversed FST (the superinitial state limits this set).
uint64 ReverseProperties(uint64 inprops, bool has_superinitial) {
auto outprops = (kExpanded | kMutable | kError | kAcceptor | kNotAcceptor |
kEpsilons | kIEpsilons | kOEpsilons | kUnweighted | kCyclic |
kAcyclic | kWeightedCycles | kUnweightedCycles) &
inprops;
if (has_superinitial) outprops |= kWeighted & inprops;
return outprops;
}
// Properties for re-weighted FST.
uint64 ReweightProperties(uint64 inprops) {
auto outprops = inprops & kWeightInvariantProperties;
outprops = outprops & ~kCoAccessible;
return outprops;
}
// Properties for an epsilon-removed FST.
uint64 RmEpsilonProperties(uint64 inprops, bool delayed) {
auto outprops = kNoEpsilons;
outprops |= (kError | kAcceptor | kAcyclic | kInitialAcyclic) & inprops;
if (inprops & kAcceptor) outprops |= kNoIEpsilons | kNoOEpsilons;
if (!delayed) {
outprops |= kExpanded | kMutable;
outprops |= kTopSorted & inprops;
}
if (!delayed || inprops & kAccessible) outprops |= kNotAcceptor & inprops;
return outprops;
}
// Properties for shortest path. This function computes how the properties of
// the output of shortest path need to be updated, given that 'props' is already
// known.
uint64 ShortestPathProperties(uint64 props, bool tree) {
auto outprops =
props | kAcyclic | kInitialAcyclic | kAccessible | kUnweightedCycles;
if (!tree) outprops |= kCoAccessible;
return outprops;
}
// Properties for a synchronized FST.
uint64 SynchronizeProperties(uint64 inprops) {
auto outprops = (kError | kAcceptor | kAcyclic | kAccessible | kCoAccessible |
kUnweighted | kUnweightedCycles) &
inprops;
if (inprops & kAccessible) {
outprops |= (kCyclic | kNotCoAccessible | kWeighted | kWeightedCycles) &
inprops;
}
return outprops;
}
// Properties for a unioned FST.
uint64 UnionProperties(uint64 inprops1, uint64 inprops2, bool delayed) {
auto outprops =
(kAcceptor | kUnweighted | kUnweightedCycles | kAcyclic | kAccessible) &
inprops1 & inprops2;
outprops |= kError & (inprops1 | inprops2);
outprops |= kInitialAcyclic;
bool empty1 = delayed; // Can the first FST be the empty machine?
bool empty2 = delayed; // Can the second FST be the empty machine?
if (!delayed) {
outprops |= (kExpanded | kMutable | kNotTopSorted) & inprops1;
outprops |= kNotTopSorted & inprops2;
}
if (!empty1 && !empty2) {
outprops |= kEpsilons | kIEpsilons | kOEpsilons;
outprops |= kCoAccessible & inprops1 & inprops2;
}
// Note kNotCoAccessible does not hold because of kInitialAcyclic option.
if (!delayed || inprops1 & kAccessible) {
outprops |=
(kNotAcceptor | kNonIDeterministic | kNonODeterministic | kEpsilons |
kIEpsilons | kOEpsilons | kNotILabelSorted | kNotOLabelSorted |
kWeighted | kWeightedCycles | kCyclic | kNotAccessible) &
inprops1;
}
if (!delayed || inprops2 & kAccessible) {
outprops |= (kNotAcceptor | kNonIDeterministic | kNonODeterministic |
kEpsilons | kIEpsilons | kOEpsilons | kNotILabelSorted |
kNotOLabelSorted | kWeighted | kWeightedCycles | kCyclic |
kNotAccessible | kNotCoAccessible) &
inprops2;
}
return outprops;
}
// Property string names (indexed by bit position).
const char* PropertyNames[] = {
// Binary.
"expanded", "mutable", "error", "", "", "", "", "", "", "", "", "", "", "",
"", "",
// Ternary.
"acceptor", "not acceptor", "input deterministic",
"non input deterministic", "output deterministic",
"non output deterministic", "input/output epsilons",
"no input/output epsilons", "input epsilons", "no input epsilons",
"output epsilons", "no output epsilons", "input label sorted",
"not input label sorted", "output label sorted", "not output label sorted",
"weighted", "unweighted", "cyclic", "acyclic", "cyclic at initial state",
"acyclic at initial state", "top sorted", "not top sorted", "accessible",
"not accessible", "coaccessible", "not coaccessible", "string",
"not string", "weighted cycles", "unweighted cycles"};
} // namespace fst
| 0 |
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core | coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/common/exceptions.h | // Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#pragma once
#include <algorithm>
#include <exception>
#include <iterator>
#include <stdexcept>
#include <string>
#include <vector>
#include "core/common/common.h"
#include "core/common/code_location.h"
namespace onnxruntime {
class NotImplementedException : public std::logic_error {
public:
explicit NotImplementedException(const char* _Message = "Function not yet implemented") noexcept : std::logic_error(_Message){};
explicit NotImplementedException(const std::string& _Message = "Function not yet implemented") noexcept : std::logic_error(_Message){};
};
class TypeMismatchException : public std::logic_error {
public:
TypeMismatchException() noexcept : logic_error("Type mismatch"){};
};
class OnnxRuntimeException : public std::exception {
public:
OnnxRuntimeException(const CodeLocation& location, const std::string& msg) noexcept
: OnnxRuntimeException(location, nullptr, msg) {
}
/**
Create a new exception that captures the location it was thrown from.
@param location Location in the source code the exception is being thrown from
@param failed_condition Optional string containing the condition that failed.
e.g. "tensor.Size() == input.Size()". May be nullptr.
@param msg Message containing additional information about the exception cause.
*/
OnnxRuntimeException(const CodeLocation& location, const char* failed_condition, const std::string& msg)
: location_{location} {
std::ostringstream ss;
ss << location.ToString(CodeLocation::kFilenameAndPath); // output full path in case just the filename is ambiguous
if (failed_condition != nullptr) {
ss << " " << failed_condition << " was false.";
}
ss << " " << msg << "\n";
if (!location.stacktrace.empty()) {
ss << "Stacktrace:\n";
// skip the first entry in the stacktrace as we have that information from location.ToString()
std::copy(++location.stacktrace.begin(), location.stacktrace.end(), std::ostream_iterator<std::string>(ss, "\n"));
}
what_ = ss.str();
}
const char* what() const noexcept override {
return what_.c_str();
}
private:
const CodeLocation location_;
const std::vector<std::string> stacktrace_;
std::string what_;
};
} // namespace onnxruntime | 0 |
coqui_public_repos/STT-examples | coqui_public_repos/STT-examples/mic_vad_streaming/requirements.txt | stt~=1.0.0
pyaudio~=0.2.11
webrtcvad~=2.0.10
halo~=0.0.18
numpy>=1.15.1
scipy>=1.1.0
pyautogui~=0.9.52 | 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/script/determinize.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#ifndef FST_SCRIPT_DETERMINIZE_H_
#define FST_SCRIPT_DETERMINIZE_H_
#include <tuple>
#include <fst/determinize.h>
#include <fst/script/fst-class.h>
#include <fst/script/weight-class.h>
namespace fst {
namespace script {
struct DeterminizeOptions {
const float delta;
const WeightClass &weight_threshold;
const int64_t state_threshold;
const int64_t subsequential_label;
const DeterminizeType det_type;
const bool increment_subsequential_label;
DeterminizeOptions(float delta, const WeightClass &weight_threshold,
int64_t state_threshold = kNoStateId,
int64_t subsequential_label = 0,
DeterminizeType det_type = DETERMINIZE_FUNCTIONAL,
bool increment_subsequential_label = false)
: delta(delta),
weight_threshold(weight_threshold),
state_threshold(state_threshold),
subsequential_label(subsequential_label),
det_type(det_type),
increment_subsequential_label(increment_subsequential_label) {}
};
using DeterminizeArgs = std::tuple<const FstClass &, MutableFstClass *,
const DeterminizeOptions &>;
template <class Arc>
void Determinize(DeterminizeArgs *args) {
using Weight = typename Arc::Weight;
const Fst<Arc> &ifst = *(std::get<0>(*args).GetFst<Arc>());
MutableFst<Arc> *ofst = std::get<1>(*args)->GetMutableFst<Arc>();
const auto &opts = std::get<2>(*args);
const auto weight_threshold = *(opts.weight_threshold.GetWeight<Weight>());
const fst::DeterminizeOptions<Arc> detargs(opts.delta, weight_threshold,
opts.state_threshold, opts.subsequential_label, opts.det_type,
opts.increment_subsequential_label);
Determinize(ifst, ofst, detargs);
}
void Determinize(const FstClass &ifst, MutableFstClass *ofst,
const DeterminizeOptions &opts);
} // namespace script
} // namespace fst
#endif // FST_SCRIPT_DETERMINIZE_H_
| 0 |
coqui_public_repos/STT/native_client/kenlm/lm | coqui_public_repos/STT/native_client/kenlm/lm/common/compare.hh | #ifndef LM_COMMON_COMPARE_H
#define LM_COMMON_COMPARE_H
#include "ngram.hh"
#include "../word_index.hh"
#include <functional>
#include <string>
namespace lm {
/**
* Abstract parent class for defining custom n-gram comparators.
*/
template <class Child> class Comparator : public std::binary_function<const void *, const void *, bool> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit Comparator(std::size_t order) : order_(order) {}
/**
* Applies the comparator using the Compare method that must be defined in any class that inherits from this class.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*
* @see ContextOrder::Compare
* @see PrefixOrder::Compare
* @see SuffixOrder::Compare
*/
inline bool operator()(const void *lhs, const void *rhs) const {
return static_cast<const Child*>(this)->Compare(static_cast<const WordIndex*>(lhs), static_cast<const WordIndex*>(rhs));
}
/** Gets the n-gram order defined for this comparator. */
std::size_t Order() const { return order_; }
protected:
std::size_t order_;
};
/**
* N-gram comparator that compares n-grams according to their reverse (suffix) order.
*
* This comparator compares n-grams lexicographically, one word at a time,
* beginning with the last word of each n-gram and ending with the first word of each n-gram.
*
* Some examples of n-gram comparisons as defined by this comparator:
* - a b c == a b c
* - a b c < a b d
* - a b c > a d b
* - a b c > a b b
* - a b c > x a c
* - a b c < x y z
*/
class SuffixOrder : public Comparator<SuffixOrder> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit SuffixOrder(std::size_t order) : Comparator<SuffixOrder>(order) {}
/**
* Compares two n-grams lexicographically, one word at a time,
* beginning with the last word of each n-gram and ending with the first word of each n-gram.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*/
inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const {
for (std::size_t i = order_ - 1; i != 0; --i) {
if (lhs[i] != rhs[i])
return lhs[i] < rhs[i];
}
return lhs[0] < rhs[0];
}
static const unsigned kMatchOffset = 1;
};
/**
* N-gram comparator that compares n-grams according to the reverse (suffix) order of the n-gram context.
*
* This comparator compares n-grams lexicographically, one word at a time,
* beginning with the penultimate word of each n-gram and ending with the first word of each n-gram;
* finally, this comparator compares the last word of each n-gram.
*
* Some examples of n-gram comparisons as defined by this comparator:
* - a b c == a b c
* - a b c < a b d
* - a b c < a d b
* - a b c > a b b
* - a b c > x a c
* - a b c < x y z
*/
class ContextOrder : public Comparator<ContextOrder> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit ContextOrder(std::size_t order) : Comparator<ContextOrder>(order) {}
/**
* Compares two n-grams lexicographically, one word at a time,
* beginning with the penultimate word of each n-gram and ending with the first word of each n-gram;
* finally, this comparator compares the last word of each n-gram.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*/
inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const {
for (int i = order_ - 2; i >= 0; --i) {
if (lhs[i] != rhs[i])
return lhs[i] < rhs[i];
}
return lhs[order_ - 1] < rhs[order_ - 1];
}
};
/**
* N-gram comparator that compares n-grams according to their natural (prefix) order.
*
* This comparator compares n-grams lexicographically, one word at a time,
* beginning with the first word of each n-gram and ending with the last word of each n-gram.
*
* Some examples of n-gram comparisons as defined by this comparator:
* - a b c == a b c
* - a b c < a b d
* - a b c < a d b
* - a b c > a b b
* - a b c < x a c
* - a b c < x y z
*/
class PrefixOrder : public Comparator<PrefixOrder> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit PrefixOrder(std::size_t order) : Comparator<PrefixOrder>(order) {}
/**
* Compares two n-grams lexicographically, one word at a time,
* beginning with the first word of each n-gram and ending with the last word of each n-gram.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*/
inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const {
for (std::size_t i = 0; i < order_; ++i) {
if (lhs[i] != rhs[i])
return lhs[i] < rhs[i];
}
return false;
}
static const unsigned kMatchOffset = 0;
};
template <class Range> struct SuffixLexicographicLess : public std::binary_function<const Range, const Range, bool> {
bool operator()(const Range first, const Range second) const {
for (const WordIndex *f = first.end() - 1, *s = second.end() - 1; f >= first.begin() && s >= second.begin(); --f, --s) {
if (*f < *s) return true;
if (*f > *s) return false;
}
return first.size() < second.size();
}
};
} // namespace lm
#endif // LM_COMMON_COMPARE_H
| 0 |
coqui_public_repos | coqui_public_repos/STT/RELEASE_NOTES.md | # General
This is the 1.4.0 release for Coqui STT, the deep learning toolkit for speech-to-text. In accordance with [semantic versioning](https://semver.org/), this version is backwards compatible with previous 1.x versions. The compatibility guarantees of our semantic versioning cover the deployment APIs: the C API and all the official language bindings: Python, Node.JS/ElectronJS and Java/Android. You can get started with Coqui STT 1.4.0 by following the steps in our [documentation](https://stt.readthedocs.io/).
Compatible pre-trained models are available in the [Coqui Model Zoo](https://coqui.ai/models).
We also include example audio files:
[audio-1.4.0.tar.gz](https://github.com/coqui-ai/STT/releases/download/v1.4.0/audio-1.4.0.tar.gz)
which can be used to test the engine, and checkpoint files for the English model (which are identical to the 1.0.0 checkpoint and provided here for convenience purposes):
[coqui-stt-1.4.0-checkpoint.tar.gz](https://github.com/coqui-ai/STT/releases/download/v1.4.0/coqui-stt-1.4.0-checkpoint.tar.gz)
which are under the Apache 2.0 license and can be used as the basis for further fine-tuning. Finally this release also includes a source code tarball:
[v1.4.0.tar.gz](https://github.com/coqui-ai/STT/archive/v1.4.0.tar.gz)
Under the [MPL-2.0 license](https://www.mozilla.org/en-US/MPL/2.0/). Note that this tarball is for archival purposes only since GitHub does not include submodules in the automatic tarballs. For usage and development with the source code, clone the repository using Git, following our [documentation](https://stt.readthedocs.io/).
# Notable changes
- Added experimental WebAssembly support
[With the new WASM package you can deploy Coqui STT directly in the browser](https://github.com/coqui-ai/STT-examples/tree/r1.0/wasm).
- Added ARMv7 and AArch64 Python wheels for Python 3.7 and 3.9
- Migrated .NET bindings to .NET Framework 4.8
- Rewritten audio processing logic in iOS demo app
# Documentation
Documentation is available on [stt.readthedocs.io](https://stt.readthedocs.io/).
# Contact/Getting Help
1. [GitHub Discussions](https://github.com/coqui-ai/STT/discussions/) - best place to ask questions, get support, and discuss anything related to 🐸STT with other users.
3. [Gitter](https://gitter.im/coqui-ai/) - You can also join our Gitter chat.
4. [Issues](https://github.com/coqui-ai/STT/issues) - If you have discussed a problem and identified a bug in 🐸STT, or if you have a feature request, please open an issue in our repo. Please make sure you search for an already existing issue beforehand!
# Contributors to 1.4.0 release
- Alessio Placitelli
- Anton Yaroshenko
- ChamathKB
- Ciaran O'Reilly
- Daniel Souza
- Danny Waser
- David Roundy
- Davidian1024
- Edresson Casanova
- Josh Meyer
- Mariano Gonzalez
- NanoNabla
- Reuben Morais
- Yanlong Wang
We’d also like to thank all the members of our [Gitter chat room](https://gitter.im/coqui-ai/STT) who have been helping to shape this release!
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions/special/sigma-fst.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/extensions/special/sigma-fst.h>
#include <fst/fst.h>
DEFINE_int64(sigma_fst_sigma_label, 0,
"Label of transitions to be interpreted as sigma ('any') "
"transitions");
DEFINE_string(sigma_fst_rewrite_mode, "auto",
"Rewrite both sides when matching? One of:"
" \"auto\" (rewrite iff acceptor), \"always\", \"never\"");
namespace fst {
const char sigma_fst_type[] = "sigma";
const char input_sigma_fst_type[] = "input_sigma";
const char output_sigma_fst_type[] = "output_sigma";
static FstRegisterer<StdSigmaFst> SigmaFst_StdArc_registerer;
static FstRegisterer<LogSigmaFst> SigmaFst_LogArc_registerer;
static FstRegisterer<Log64SigmaFst> SigmaFst_Log64Arc_registerer;
static FstRegisterer<StdInputSigmaFst> InputSigmaFst_StdArc_registerer;
static FstRegisterer<LogInputSigmaFst> InputSigmaFst_LogArc_registerer;
static FstRegisterer<Log64InputSigmaFst> InputSigmaFst_Log64Arc_registerer;
static FstRegisterer<StdOutputSigmaFst> OutputSigmaFst_StdArc_registerer;
static FstRegisterer<LogOutputSigmaFst> OutputSigmaFst_LogArc_registerer;
static FstRegisterer<Log64OutputSigmaFst> OutputSigmaFst_Log64Arc_registerer;
} // namespace fst
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-nodejs_15x_16k-linux-amd64-opt.yml | build:
template_file: test-linux-opt-base.tyml
docker_image: "ubuntu:16.04"
dependencies:
- "linux-amd64-cpu-opt"
- "test-training_16k-linux-amd64-py36m-opt"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${nodejs.packages_xenial.prep_15} && ${nodejs.packages_xenial.apt_pinning} && apt-get -qq update && apt-get -qq -y install ${nodejs.packages_xenial.apt}
args:
tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-node-tests.sh 15.x 16k"
workerType: "${docker.dsTests}"
metadata:
name: "DeepSpeech Linux AMD64 CPU NodeJS 15.x tests (16kHz)"
description: "Testing DeepSpeech for Linux/AMD64 on NodeJS v15.x, CPU only, optimized version (16kHz)"
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-python_38_tflite_16k-win-amd64-prod-opt.yml | build:
template_file: test-win-opt-base.tyml
dependencies:
- "win-amd64-tflite-opt"
- "test-training_16k-linux-amd64-py36m-opt"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${system.sox_win}
args:
tests_cmdline: "${system.homedir.win}/DeepSpeech/ds/taskcluster/tc-python_tflite-tests-prod.sh 3.8.1: 16k"
metadata:
name: "DeepSpeech Windows AMD64 TFLite Python v3.8 prod tests (16kHz)"
description: "Testing DeepSpeech for Windows/AMD64 on Python v3.8 on prod model, TFLite, optimized version (16kHz)"
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-nodejs_12x-raspbian-rpi3-opt.yml | build:
template_file: test-raspbian-opt-base.tyml
dependencies:
- "linux-rpi3-cpu-opt"
- "test-training_16k-linux-amd64-py36m-opt"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${nodejs.packages_buster.prep_12} && ${nodejs.packages_buster.apt_pinning} && apt-get -qq update && apt-get -qq -y install ${nodejs.packages_buster.apt}
args:
tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-node_tflite-tests.sh 12.x 16k"
metadata:
name: "DeepSpeech Raspbian RPi3/ARMv7 CPU NodeJS 12.x tests"
description: "Testing DeepSpeech for Raspbian RPi3/ARMv7 on NodeJS v12.x, CPU only, optimized version"
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/arcfilter.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Function objects to restrict which arcs are traversed in an FST.
#ifndef FST_ARCFILTER_H_
#define FST_ARCFILTER_H_
#include <fst/fst.h>
#include <fst/util.h>
namespace fst {
// True for all arcs.
template <class Arc>
class AnyArcFilter {
public:
bool operator()(const Arc &arc) const { return true; }
};
// True for (input/output) epsilon arcs.
template <class Arc>
class EpsilonArcFilter {
public:
bool operator()(const Arc &arc) const {
return arc.ilabel == 0 && arc.olabel == 0;
}
};
// True for input epsilon arcs.
template <class Arc>
class InputEpsilonArcFilter {
public:
bool operator()(const Arc &arc) const { return arc.ilabel == 0; }
};
// True for output epsilon arcs.
template <class Arc>
class OutputEpsilonArcFilter {
public:
bool operator()(const Arc &arc) const { return arc.olabel == 0; }
};
// True if specified label matches (doesn't match) when keep_match is
// true (false).
template <class Arc>
class LabelArcFilter {
public:
using Label = typename Arc::Label;
explicit LabelArcFilter(Label label, bool match_input = true,
bool keep_match = true)
: label_(label), match_input_(match_input), keep_match_(keep_match) {}
bool operator()(const Arc &arc) const {
const bool match = (match_input_ ? arc.ilabel : arc.olabel) == label_;
return keep_match_ ? match : !match;
}
private:
const Label label_;
const bool match_input_;
const bool keep_match_;
};
// True if specified labels match (don't match) when keep_match is true (false).
template <class Arc>
class MultiLabelArcFilter {
public:
using Label = typename Arc::Label;
explicit MultiLabelArcFilter(bool match_input = true, bool keep_match = true)
: match_input_(match_input), keep_match_(keep_match) {}
bool operator()(const Arc &arc) const {
const Label label = match_input_ ? arc.ilabel : arc.olabel;
const bool match = labels_.Find(label) != labels_.End();
return keep_match_ ? match : !match;
}
void AddLabel(Label label) { labels_.Insert(label); }
private:
CompactSet<Label, kNoLabel> labels_;
const bool match_input_;
const bool keep_match_;
};
} // namespace fst
#endif // FST_ARCFILTER_H_
| 0 |
coqui_public_repos/STT/native_client/kenlm/lm/common | coqui_public_repos/STT/native_client/kenlm/lm/common/test_data/toy0.arpa | \data\
ngram 1=5
ngram 2=7
ngram 3=7
\1-grams:
-0.90309 <unk> 0
0 <s> -0.30103
-0.46943438 a -0.30103
-0.5720968 </s> 0
-0.5720968 b -0.30103
\2-grams:
-0.37712017 <s> a -0.30103
-0.37712017 a a -0.30103
-0.2984526 b a -0.30103
-0.58682007 a </s> 0
-0.5220179 b </s> 0
-0.41574955 <s> b -0.30103
-0.58682007 a b -0.30103
\3-grams:
-0.14885087 <s> a a
-0.33741078 b a a
-0.124077894 <s> b a
-0.2997394 a b a
-0.42082912 b a </s>
-0.397617 a b </s>
-0.20102891 a a b
\end\
| 0 |
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$(MAKE) $(AM_MAKEFLAGS) INSTALL_PROGRAM="$(INSTALL_STRIP_PROGRAM)" \
install_sh_PROGRAM="$(INSTALL_STRIP_PROGRAM)" INSTALL_STRIP_FLAG=-s \
install; \
else \
$(MAKE) $(AM_MAKEFLAGS) INSTALL_PROGRAM="$(INSTALL_STRIP_PROGRAM)" \
install_sh_PROGRAM="$(INSTALL_STRIP_PROGRAM)" INSTALL_STRIP_FLAG=-s \
"INSTALL_PROGRAM_ENV=STRIPPROG='$(STRIP)'" install; \
fi
mostlyclean-generic:
clean-generic:
distclean-generic:
-test -z "$(CONFIG_CLEAN_FILES)" || rm -f $(CONFIG_CLEAN_FILES)
-test . = "$(srcdir)" || test -z "$(CONFIG_CLEAN_VPATH_FILES)" || rm -f $(CONFIG_CLEAN_VPATH_FILES)
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.PRECIOUS: Makefile
# Tell versions [3.59,3.63) of GNU make to not export all variables.
# Otherwise a system limit (for SysV at least) may be exceeded.
.NOEXPORT:
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-nodejs_11x-win-amd64-opt.yml | build:
template_file: test-win-opt-base.tyml
dependencies:
- "win-amd64-cpu-opt"
- "test-training_16k-linux-amd64-py36m-opt"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${system.sox_win} && ${nodejs.win.prep_11}
args:
tests_cmdline: "${system.homedir.win}/DeepSpeech/ds/taskcluster/tc-node-tests.sh 11.x 16k"
metadata:
name: "DeepSpeech Windows AMD64 CPU NodeJS 11.x tests"
description: "Testing DeepSpeech for Windows/AMD64 on NodeJS v11.x, CPU only, optimized version"
| 0 |
coqui_public_repos/TTS/docs/source | coqui_public_repos/TTS/docs/source/main_classes/model_api.md | # Model API
Model API provides you a set of functions that easily make your model compatible with the `Trainer`,
`Synthesizer` and `ModelZoo`.
## Base TTS Model
```{eval-rst}
.. autoclass:: TTS.model.BaseTrainerModel
:members:
```
## Base tts Model
```{eval-rst}
.. autoclass:: TTS.tts.models.base_tts.BaseTTS
:members:
```
## Base vocoder Model
```{eval-rst}
.. autoclass:: TTS.vocoder.models.base_vocoder.BaseVocoder
:members:
``` | 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/bin/fstunion.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
int fstunion_main(int argc, char **argv);
int main(int argc, char **argv) { return fstunion_main(argc, argv); }
| 0 |
coqui_public_repos/open-bible-scripts | coqui_public_repos/open-bible-scripts/data/luo.txt | https://ebible.org/Scriptures/luo_readaloud.zip
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https://downloads.open.bible/audio/dh/luoONLT20/luoONLT20_EXO_wav.zip
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https://downloads.open.bible/audio/dh/luoONLT20/luoONLT20_2JN_wav.zip
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| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/bin/fstreplace-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Performs the dynamic replacement of arcs in one FST with another FST,
// allowing for the definition of FSTs analogous to RTNs.
#include <cstring>
#include <string>
#include <vector>
#include <fst/flags.h>
#include <fst/script/getters.h>
#include <fst/script/replace.h>
DECLARE_string(call_arc_labeling);
DECLARE_string(return_arc_labeling);
DECLARE_int64(return_label);
DECLARE_bool(epsilon_on_replace);
void Cleanup(std::vector<fst::script::LabelFstClassPair> *pairs) {
for (const auto &pair : *pairs) {
delete pair.second;
}
pairs->clear();
}
int fstreplace_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::FstClass;
using fst::script::VectorFstClass;
using fst::ReplaceLabelType;
string usage = "Recursively replaces FST arcs with other FST(s).\n\n"
" Usage: ";
usage += argv[0];
usage += " root.fst rootlabel [rule1.fst label1 ...] [out.fst]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc < 4) {
ShowUsage();
return 1;
}
const string in_name = argv[1];
const string out_name = argc % 2 == 0 ? argv[argc - 1] : "";
auto *ifst = FstClass::Read(in_name);
if (!ifst) return 1;
std::vector<s::LabelFstClassPair> pairs;
// Note that if the root label is beyond the range of the underlying FST's
// labels, truncation will occur.
const auto root = atoll(argv[2]);
pairs.emplace_back(root, ifst);
for (auto i = 3; i < argc - 1; i += 2) {
ifst = FstClass::Read(argv[i]);
if (!ifst) {
Cleanup(&pairs);
return 1;
}
// Note that if the root label is beyond the range of the underlying FST's
// labels, truncation will occur.
const auto label = atoll(argv[i + 1]);
pairs.emplace_back(label, ifst);
}
ReplaceLabelType call_label_type;
if (!s::GetReplaceLabelType(FLAGS_call_arc_labeling, FLAGS_epsilon_on_replace,
&call_label_type)) {
LOG(ERROR) << argv[0] << ": Unknown or unsupported call arc replace "
<< "label type: " << FLAGS_call_arc_labeling;
}
ReplaceLabelType return_label_type;
if (!s::GetReplaceLabelType(FLAGS_return_arc_labeling,
FLAGS_epsilon_on_replace, &return_label_type)) {
LOG(ERROR) << argv[0] << ": Unknown or unsupported return arc replace "
<< "label type: " << FLAGS_return_arc_labeling;
}
s::ReplaceOptions opts(root, call_label_type, return_label_type,
FLAGS_return_label);
VectorFstClass ofst(ifst->ArcType());
s::Replace(pairs, &ofst, opts);
Cleanup(&pairs);
return !ofst.Write(out_name);
}
| 0 |
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