@ -45,7 +45,7 @@ from tortoise.utils.device import get_device_name, set_device_name, get_device_c
MODELS [ ' dvae.pth ' ] = " https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth "
WHISPER_MODELS = [ " tiny " , " base " , " small " , " medium " , " large "
WHISPER_MODELS = [ " tiny " , " base " , " small " , " medium " , " large " , " large-v1 " , " large-v2 " ]
WHISPER_SPECIALIZED_MODELS = [ " tiny.en " , " base.en " , " small.en " , " medium.en " ]
WHISPER_BACKENDS = [ " openai/whisper " , " lightmare/whispercpp " , " m-bain/whisperx " ]
VOCODERS = [ ' univnet ' , ' bigvgan_base_24khz_100band ' , ' bigvgan_24khz_100band ' ]
@ -61,12 +61,15 @@ RESAMPLERS = {}
MIN_TRAINING_DURATION = 0.6
MAX_TRAINING_DURATION = 11.6097505669
MAX_TRAINING_CHAR_LENGTH = 200
VALLE_ENABLED = False
BARK_ENABLED = False
VERBOSE_DEBUG = True
import traceback
try :
from whisper . normalizers . english import EnglishTextNormalizer
from whisper . normalizers . basic import BasicTextNormalizer
@ -75,7 +78,7 @@ try:
print ( " Whisper detected " )
except Exception as e :
if VERBOSE_DEBUG :
print ( " Error: " , e )
print ( traceback . format_exc ( ) )
pass
try :
@ -90,12 +93,14 @@ try:
VALLE_ENABLED = True
except Exception as e :
if VERBOSE_DEBUG :
print ( " Error: " , e )
print ( traceback . format_exc ( ) )
pass
if VALLE_ENABLED :
TTSES . append ( ' vall-e ' )
# torchaudio.set_audio_backend('soundfile')
try :
import bark
from bark import text_to_semantic
@ -109,35 +114,10 @@ try:
BARK_ENABLED = True
except Exception as e :
if VERBOSE_DEBUG :
print ( " Error: " , e )
print ( traceback . format_exc ( ) )
pass
if BARK_ENABLED :
try :
from vocos import Vocos
VOCOS_ENABLED = True
print ( " Vocos detected " )
except Exception as e :
if VERBOSE_DEBUG :
print ( " Error: " , e )
VOCOS_ENABLED = False
try :
from hubert . hubert_manager import HuBERTManager
from hubert . pre_kmeans_hubert import CustomHubert
from hubert . customtokenizer import CustomTokenizer
hubert_manager = HuBERTManager ( )
hubert_manager . make_sure_hubert_installed ( )
hubert_manager . make_sure_tokenizer_installed ( )
HUBERT_ENABLED = True
print ( " HuBERT detected " )
except Exception as e :
if VERBOSE_DEBUG :
print ( " Error: " , e )
HUBERT_ENABLED = False
TTSES . append ( ' bark ' )
def semantic_to_audio_tokens (
@ -181,7 +161,32 @@ if BARK_ENABLED:
self . device = get_device_name ( )
if VOCOS_ENABLED :
try :
from vocos import Vocos
self . vocos_enabled = True
print ( " Vocos detected " )
except Exception as e :
if VERBOSE_DEBUG :
print ( traceback . format_exc ( ) )
self . vocos_enabled = False
try :
from hubert . hubert_manager import HuBERTManager
from hubert . pre_kmeans_hubert import CustomHubert
from hubert . customtokenizer import CustomTokenizer
hubert_manager = HuBERTManager ( )
hubert_manager . make_sure_hubert_installed ( )
hubert_manager . make_sure_tokenizer_installed ( )
self . hubert_enabled = True
print ( " HuBERT detected " )
except Exception as e :
if VERBOSE_DEBUG :
print ( traceback . format_exc ( ) )
self . hubert_enabled = False
if self . vocos_enabled :
self . vocos = Vocos . from_pretrained ( " charactr/vocos-encodec-24khz " ) . to ( self . device )
def create_voice ( self , voice ) :
@ -238,7 +243,7 @@ if BARK_ENABLED:
# generate semantic tokens
if HUBERT_ENABLED :
if self . hubert_enabled :
wav = wav . to ( self . device )
# Extract discrete codes from EnCodec
@ -426,7 +431,7 @@ def generate_bark(**kwargs):
idx_cache = { }
for i , file in enumerate ( os . listdir ( outdir ) ) :
filename = os . path . basename ( file )
extension = os . path . splitext ( filename ) [ 1 ]
extension = os . path . splitext ( filename ) [ - 1 ] [ 1 : ]
if extension != " .json " and extension != " .wav " :
continue
match = re . findall ( rf " ^ { cleanup_voice_name ( voice ) } _( \ d+)(?:.+?)? { extension } $ " , filename )
@ -672,18 +677,23 @@ def generate_valle(**kwargs):
voice_cache = { }
def fetch_voice ( voice ) :
if voice in voice_cache :
return voice_cache [ voice ]
voice_dir = f ' ./training/ { voice } /audio/ '
if not os . path . isdir ( voice_dir ) :
if not os . path . isdir ( voice_dir ) or len ( os . listdir ( voice_dir ) ) == 0 :
voice_dir = f ' ./voices/ { voice } / '
files = [ f ' { voice_dir } / { d } ' for d in os . listdir ( voice_dir ) if d [ - 4 : ] == " .wav " ]
# return files
return random . choice ( files )
voice_cache [ voice ] = random . choice ( files )
return voice_cache [ voice ]
def get_settings ( override = None ) :
settings = {
' ar_temp ' : float ( parameters [ ' temperature ' ] ) ,
' nar_temp ' : float ( parameters [ ' temperature ' ] ) ,
' max_ar_s ample s' : parameters [ ' num_autoregressive_samples ' ] ,
' max_ar_s tep s' : parameters [ ' num_autoregressive_samples ' ] ,
}
# could be better to just do a ternary on everything above, but i am not a professional
@ -697,7 +707,7 @@ def generate_valle(**kwargs):
continue
settings [ k ] = override [ k ]
settings [ ' reference ' ] = fetch_voice ( voice = selected_voice )
settings [ ' reference s ' ] = [ fetch_voice ( voice = selected_voice ) for _ in range ( 3 ) ]
return settings
if not parameters [ ' delimiter ' ] :
@ -723,7 +733,7 @@ def generate_valle(**kwargs):
idx_cache = { }
for i , file in enumerate ( os . listdir ( outdir ) ) :
filename = os . path . basename ( file )
extension = os . path . splitext ( filename ) [ 1 ]
extension = os . path . splitext ( filename ) [ - 1 ] [ 1 : ]
if extension != " .json " and extension != " .wav " :
continue
match = re . findall ( rf " ^ { voice } _( \ d+)(?:.+?)? { extension } $ " , filename )
@ -783,11 +793,14 @@ def generate_valle(**kwargs):
except Exception as e :
raise Exception ( " Prompt settings editing requested, but received invalid JSON " )
name = get_name ( line = line , candidate = 0 )
settings = get_settings ( override = override )
reference = settings [ ' reference ' ]
settings . pop ( " reference " )
references = settings [ ' references ' ]
settings . pop ( " references " )
settings [ ' out_path ' ] = f ' { outdir } / { cleanup_voice_name ( voice ) } _ { name } .wav '
gen = tts . inference ( cut_text , reference , * * settings )
gen = tts . inference ( cut_text , reference s , * * settings )
run_time = time . time ( ) - start_time
print ( f " Generating line took { run_time } seconds " )
@ -805,7 +818,7 @@ def generate_valle(**kwargs):
# save here in case some error happens mid-batch
#torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu(), sr)
soundfile . write ( f ' { outdir } / { cleanup_voice_name ( voice ) } _ { name } .wav ' , wav . cpu ( ) [ 0 , 0 ] , sr
#soundfile.write(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu()[0,0], sr
wav , sr = torchaudio . load ( f ' { outdir } / { cleanup_voice_name ( voice ) } _ { name } .wav ' )
audio_cache [ name ] = {
@ -1085,7 +1098,7 @@ def generate_tortoise(**kwargs):
idx_cache = { }
for i , file in enumerate ( os . listdir ( outdir ) ) :
filename = os . path . basename ( file )
extension = os . path . splitext ( filename ) [ 1 ]
extension = os . path . splitext ( filename ) [ - 1 ] [ 1 : ]
if extension != " .json " and extension != " .wav " :
continue
match = re . findall ( rf " ^ { voice } _( \ d+)(?:.+?)? { extension } $ " , filename )
@ -1605,30 +1618,18 @@ class TrainingState():
if args . tts_backend == " vall-e " :
keys [ ' lrs ' ] = [
' ar.lr ' , ' nar.lr ' ,
' ar-half.lr ' , ' nar-half.lr ' ,
' ar-quarter.lr ' , ' nar-quarter.lr ' ,
]
keys [ ' losses ' ] = [
' ar.loss ' , ' nar.loss ' , ' ar+nar.loss ' ,
' ar-half.loss ' , ' nar-half.loss ' , ' ar-half+nar-half.loss ' ,
' ar-quarter.loss ' , ' nar-quarter.loss ' , ' ar-quarter+nar-quarter.loss ' ,
# 'ar.loss', 'nar.loss', 'ar+nar.loss',
' ar.loss.nll ' , ' nar.loss.nll ' ,
' ar-half.loss.nll ' , ' nar-half.loss.nll ' ,
' ar-quarter.loss.nll ' , ' nar-quarter.loss.nll ' ,
]
keys [ ' accuracies ' ] = [
' ar.loss.acc ' , ' nar.loss.acc ' ,
' ar-half.loss.acc ' , ' nar-half.loss.acc ' ,
' ar-quarter.loss.acc ' , ' nar-quarter.loss.acc ' ,
]
keys [ ' precisions ' ] = [
' ar.loss.precision ' , ' nar.loss.precision ' ,
' ar-half.loss.precision ' , ' nar-half.loss.precision ' ,
' ar-quarter.loss.precision ' , ' nar-quarter.loss.precision ' ,
' ar.stats.acc ' , ' nar.loss.acc ' ,
]
keys [ ' grad_norms ' ] = [ ' ar.grad_norm ' , ' nar.grad_norm ' , ' ar-half.grad_norm ' , ' nar-half.grad_norm ' , ' ar-quarter.grad_norm ' , ' nar-quarter.grad_norm ' ]
keys [ ' precisions ' ] = [ ' ar.loss.precision ' , ' nar.loss.precision ' , ]
keys [ ' grad_norms ' ] = [ ' ar.grad_norm ' , ' nar.grad_norm ' ]
for k in keys [ ' lrs ' ] :
if k not in self . info :
@ -1746,7 +1747,8 @@ class TrainingState():
if args . tts_backend == " tortoise " :
logs = sorted ( [ f ' { self . training_dir } /finetune/ { d } ' for d in os . listdir ( f ' { self . training_dir } /finetune/ ' ) if d [ - 4 : ] == " .log " ] )
else :
logs = sorted ( [ f ' { self . training_dir } /logs/ { d } /log.txt ' for d in os . listdir ( f ' { self . training_dir } /logs/ ' ) ] )
log_dir = " logs "
logs = sorted ( [ f ' { self . training_dir } / { log_dir } / { d } /log.txt ' for d in os . listdir ( f ' { self . training_dir } / { log_dir } / ' ) ] )
if update :
logs = [ logs [ - 1 ] ]
@ -2219,6 +2221,8 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
files = get_voice ( voice , load_latents = False )
indir = f ' ./training/ { voice } / '
infile = f ' { indir } /whisper.json '
quantize_in_memory = args . tts_backend == " vall-e "
os . makedirs ( f ' { indir } /audio/ ' , exist_ok = True )
@ -2245,13 +2249,24 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
continue
results [ basename ] = result
waveform , sample_rate = torchaudio . load ( file )
# resample to the input rate, since it'll get resampled for training anyways
# this should also "help" increase throughput a bit when filling the dataloaders
waveform , sample_rate = resample ( waveform , sample_rate , TARGET_SAMPLE_RATE )
if waveform . shape [ 0 ] == 2 :
waveform = waveform [ : 1 ]
torchaudio . save ( f " { indir } /audio/ { basename } " , waveform , sample_rate , encoding = " PCM_S " , bits_per_sample = 16 )
if not quantize_in_memory :
waveform , sample_rate = torchaudio . load ( file )
# resample to the input rate, since it'll get resampled for training anyways
# this should also "help" increase throughput a bit when filling the dataloaders
waveform , sample_rate = resample ( waveform , sample_rate , TARGET_SAMPLE_RATE )
if waveform . shape [ 0 ] == 2 :
waveform = waveform [ : 1 ]
try :
kwargs = { }
if basename [ - 4 : ] == " .wav " :
kwargs [ ' encoding ' ] = " PCM_S "
kwargs [ ' bits_per_sample ' ] = 16
torchaudio . save ( f " { indir } /audio/ { basename } " , waveform , sample_rate , * * kwargs )
except Exception as e :
print ( e )
with open ( infile , ' w ' , encoding = " utf-8 " ) as f :
f . write ( json . dumps ( results , indent = ' \t ' ) )
@ -2317,6 +2332,9 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
segments = 0
for filename in results :
path = f ' ./voices/ { voice } / { filename } '
extension = os . path . splitext ( filename ) [ - 1 ] [ 1 : ]
out_extension = extension # "wav"
if not os . path . exists ( path ) :
path = f ' ./training/ { voice } / { filename } '
@ -2333,7 +2351,7 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
duration = num_frames / sample_rate
for segment in result [ ' segments ' ] :
file = filename . replace ( " .wav " , f " _ { pad ( segment [ ' id ' ] , 4 ) } . wav " )
file = filename . replace ( f" . { extension } " , f " _ { pad ( segment [ ' id ' ] , 4 ) } . { out_extension } " )
sliced , error = slice_waveform ( waveform , sample_rate , segment [ ' start ' ] + start_offset , segment [ ' end ' ] + end_offset , trim_silence )
if error :
@ -2341,12 +2359,17 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
print ( message )
messages . append ( message )
continue
#
if waveform . shape [ 0 ] == 2 :
waveform = waveform [ : 1 ]
torchaudio . save ( f " { indir } /audio/ { file } " , sliced , TARGET_SAMPLE_RATE , encoding = " PCM_S " , bits_per_sample = 16 )
kwargs = { }
if file [ - 4 : ] == " .wav " :
kwargs [ ' encoding ' ] = " PCM_S "
kwargs [ ' bits_per_sample ' ] = 16
torchaudio . save ( f " { indir } /audio/ { file } " , sliced , TARGET_SAMPLE_RATE , * * kwargs )
segments + = 1
@ -2462,18 +2485,32 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
errored = 0
messages = [ ]
normalize =
normalize = False # True
phonemize = should_phonemize ( )
lines = { ' training ' : [ ] , ' validation ' : [ ] }
segments = { }
quantize_in_memory = args . tts_backend == " vall-e "
if args . tts_backend != " tortoise " :
text_length = 0
audio_length = 0
start_offset = - 0.1
end_offset = 0.1
trim_silence = False
TARGET_SAMPLE_RATE = 22050
if args . tts_backend != " tortoise " :
TARGET_SAMPLE_RATE = 24000
if tts :
TARGET_SAMPLE_RATE = tts . input_sample_rate
for filename in tqdm ( results , desc = " Parsing results " ) :
use_segment = use_segments
extension = os . path . splitext ( filename ) [ - 1 ] [ 1 : ]
out_extension = extension # "wav"
result = results [ filename ]
lang = result [ ' language ' ]
language = LANGUAGES [ lang ] if lang in LANGUAGES else lang
@ -2481,8 +2518,8 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
# check if unsegmented text exceeds 200 characters
if not use_segment :
if len ( result [ ' text ' ] ) > 200 :
message = f " Text length too long ( 200 < { len ( result [ ' text ' ] ) } ), using segments: { filename } "
if len ( result [ ' text ' ] ) > MAX_TRAINING_CHAR_LENGTH :
message = f " Text length too long ( { MAX_TRAINING_CHAR_LENGTH } { len ( result [ ' text ' ] ) } ), using segments: { filename } "
print ( message )
messages . append ( message )
use_segment = True
@ -2490,13 +2527,15 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
# check if unsegmented audio exceeds 11.6s
if not use_segment :
path = f ' { indir } /audio/ { filename } '
if not . path . exists ( path ) :
if not quantize_in_memory and not os. path . exists ( path ) :
messages . append ( f " Missing source audio: { filename } " )
errored + = 1
continue
metadata = torchaudio . info ( path )
duration = metadata . num_frames / metadata . sample_rate
duration = 0
for segment in result [ ' segments ' ] :
duration = max ( duration , result [ ' segments ' ] [ segment ] [ ' end ' ] )
if duration > = MAX_TRAINING_DURATION :
message = f " Audio too large, using segments: { filename } "
print ( message )
@ -2511,19 +2550,36 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
if duration < = MIN_TRAINING_DURATION or MAX_TRAINING_DURATION < = duration :
continue
path = f ' { indir } /audio/ ' + filename . replace ( " .wav " , f " _ { pad ( segment [ ' id ' ] , 4 ) } . wav " )
path = f ' { indir } /audio/ ' + filename . replace ( f" . { extension } " , f " _ { pad ( segment [ ' id ' ] , 4 ) } . { out_extension } " )
if os . path . exists ( path ) :
continue
exists = False
break
if not :
if not quantize_in_memory and not exists:
tmp = { }
tmp [ filename ] = result
print ( f " Audio not segmented, segmenting: { filename } " )
message = slice_dataset ( voice , results = tmp )
print ( message )
messages = messages + message . split ( " \n " )
waveform = None
if quantize_in_memory :
path = f ' { indir } /audio/ { filename } '
if not os . path . exists ( path ) :
path = f ' ./voices/ { voice } / { filename } '
if not os . path . exists ( path ) :
message = f " Audio not found: { path } "
print ( message )
messages . append ( message )
#continue
else :
waveform = torchaudio . load ( path )
waveform = resample ( waveform [ 0 ] , waveform [ 1 ] , TARGET_SAMPLE_RATE )
if not use_segment :
segments [ filename ] = {
@ -2533,13 +2589,18 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
' normalizer ' : normalizer ,
' phonemes ' : result [ ' phonemes ' ] if ' phonemes ' in result else None
}
if waveform :
segments [ filename ] [ ' waveform ' ] = waveform
else :
for segment in result [ ' segments ' ] :
duration = segment [ ' end ' ] - segment [ ' start ' ]
if duration < = MIN_TRAINING_DURATION or MAX_TRAINING_DURATION < = duration :
continue
segments [ filename . replace ( " .wav " , f " _ { pad ( segment [ ' id ' ] , 4 ) } .wav " ) ] = {
file = filename . replace ( f " . { extension } " , f " _ { pad ( segment [ ' id ' ] , 4 ) } . { out_extension } " )
segments [ file ] = {
' text ' : segment [ ' text ' ] ,
' lang ' : lang ,
' language ' : language ,
@ -2547,22 +2608,27 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
' phonemes ' : segment [ ' phonemes ' ] if ' phonemes ' in segment else None
}
if waveform :
sliced , error = slice_waveform ( waveform [ 0 ] , waveform [ 1 ] , segment [ ' start ' ] + start_offset , segment [ ' end ' ] + end_offset , trim_silence )
if error :
message = f " { error } , skipping... { file } "
print ( message )
messages . append ( message )
segments [ file ] [ ' error ' ] = error
#continue
else :
segments [ file ] [ ' waveform ' ] = ( sliced , waveform [ 1 ] )
jobs = {
' quantize ' : [ [ ] , [ ] ] ,
' phonemize ' : [ [ ] , [ ] ] ,
}
for file in tqdm ( segments , desc = " Parsing segments " ) :
extension = os . path . splitext ( file ) [ - 1 ] [ 1 : ]
result = segments [ file ]
path = f ' { indir } /audio/ { file } '
if not os . path . exists ( path ) :
message = f " Missing segment, skipping... { file } "
print ( message )
messages . append ( message )
errored + = 1
continue
text = result [ ' text ' ]
lang = result [ ' lang ' ]
language = result [ ' language ' ]
@ -2573,28 +2639,20 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
normalized = normalizer ( text ) if normalize else text
if len ( text ) > 200 :
message = f " Text length too long ( 200 < { len ( text ) } ), skipping... { file } "
if len ( text ) > MAX_TRAINING_CHAR_LENGTH :
message = f " Text length too long ( { MAX_TRAINING_CHAR_LENGTH } { len ( text ) } ), skipping... { file } "
print ( message )
messages . append ( message )
errored + = 1
continue
waveform , sample_rate = torchaudio . load ( path )
num_channels , num_frames = waveform . shape
duration = num_frames / sample_rate
# num_channels, num_frames = waveform.shape
#duration = num_frames / sample_rate
error = validate_waveform ( waveform , sample_rate )
if error :
message = f " { error } , skipping... { file } "
print ( message )
messages . append ( message )
errored + = 1
continue
culled = len ( text ) < text_length
if not culled and audio_length > 0
#if not culled and audio_length > 0:
# culled = duration < audio_length
line = f ' audio/ { file } | { phonemes if phonemize and phonemes else text } '
@ -2605,17 +2663,8 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
os . makedirs ( f ' { indir } /valle/ ' , exist_ok = True )
qnt_file = f ' { indir } /valle/ { file . replace ( " .wav " , " .qnt.pt " ) } '
if not os . path . exists ( qnt_file ) :
jobs [ ' quantize ' ] [ 0 ] . append ( qnt_file )
jobs [ ' quantize ' ] [ 1 ] . append ( ( waveform , sample_rate ) )
"""
quantized = valle_quantize ( waveform , sample_rate ) . cpu ( )
torch . save ( quantized , f ' { indir } /valle/ { file . replace ( " .wav " , " .qnt.pt " ) } ' )
print ( " Quantized: " , file )
"""
phn_file = f ' { indir } /valle/ { file . replace ( " .wav " , " .phn.txt " ) } '
#phn_file = f'{indir}/valle/{file.replace(f".{extension}",".phn.txt")}'
phn_file = f ' ./training/valle/data/ { voice } / { file . replace ( f " . { extension } " , " .phn.txt " ) } '
if not os . path . exists ( phn_file ) :
jobs [ ' phonemize ' ] [ 0 ] . append ( phn_file )
jobs [ ' phonemize ' ] [ 1 ] . append ( normalized )
@ -2625,13 +2674,46 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
print ( " Phonemized: " , file , normalized , text )
"""
#qnt_file = f'{indir}/valle/{file.replace(f".{extension}",".qnt.pt")}'
qnt_file = f ' ./training/valle/data/ { voice } / { file . replace ( f " . { extension } " , " .qnt.pt " ) } '
if ' error ' not in result :
if not quantize_in_memory and not os . path . exists ( path ) :
message = f " Missing segment, skipping... { file } "
print ( message )
messages . append ( message )
errored + = 1
continue
if not os . path . exists ( qnt_file ) :
waveform = None
if ' waveform ' in result :
waveform , sample_rate = result [ ' waveform ' ]
elif os . path . exists ( path ) :
waveform , sample_rate = torchaudio . load ( path )
error = validate_waveform ( waveform , sample_rate )
if error :
message = f " { error } , skipping... { file } "
print ( message )
messages . append ( message )
errored + = 1
continue
if waveform is not None :
jobs [ ' quantize ' ] [ 0 ] . append ( qnt_file )
jobs [ ' quantize ' ] [ 1 ] . append ( ( waveform , sample_rate ) )
"""
quantized = valle_quantize ( waveform , sample_rate ) . cpu ( )
torch . save ( quantized , f ' { indir } /valle/ { file . replace ( " .wav " , " .qnt.pt " ) } ' )
print ( " Quantized: " , file )
"""
for i in tqdm ( range ( len ( jobs [ ' quantize ' ] [ 0 ] ) ) , desc = " Quantizing " ) :
qnt_file = jobs [ ' quantize ' ] [ 0 ] [ i ]
waveform , sample_rate = jobs [ ' quantize ' ] [ 1 ] [ i ]
quantized = valle_quantize ( waveform , sample_rate ) . cpu ( )
torch . save ( quantized , qnt_file )
print ( " Quantized: " , qnt_file )
#print("Quantized:", qnt_file
for i in tqdm ( range ( len ( jobs [ ' phonemize ' ] [ 0 ] ) ) , desc = " Phonemizing " ) :
phn_file = jobs [ ' phonemize ' ] [ 0 ] [ i ]
@ -2640,7 +2722,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
try :
phonemized = valle_phonemize ( normalized )
open ( phn_file , ' w ' , encoding = ' utf-8 ' ) . write ( " " . join ( phonemized ) )
print ( " Phonemized: " , phn_file
#print("Phonemized:", phn_file
except Exception as e :
message = f " Failed to phonemize: { phn_file } : { normalized } "
messages . append ( message )
@ -2980,7 +3062,7 @@ def get_voice( name, dir=get_voice_dir(), load_latents=True ):
voice = voice + list ( glob ( f ' { subj } /*.pth ' ) )
return sorted ( voice )
def get_voice_list ( dir = get_voice_dir ( ) , append_defaults = False :
def get_voice_list ( dir = get_voice_dir ( ) , append_defaults = False , extensions = [ " wav " , " mp3 " , " flac " , " pth " ] ):
defaults = [ " random " , " microphone " ]
os . makedirs ( dir , exist_ok = True )
#res = sorted([d for d in os.listdir(dir) if d not in defaults and os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ])
@ -2993,7 +3075,7 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
continue
if len ( os . listdir ( os . path . join ( dir , name ) ) ) == 0 :
continue
files = get_voice ( name , dir = dir )
files = get_voice ( name , dir = dir , extensions = extensions )
if len ( files ) > 0 :
res . append ( name )
@ -3001,7 +3083,7 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
for subdir in os . listdir ( f ' { dir } / { name } ' ) :
if not os . path . isdir ( f ' { dir } / { name } / { subdir } ' ) :
continue
files = get_voice ( f ' { name } / { subdir } ' , dir = dir )
files = get_voice ( f ' { name } / { subdir } ' , dir = dir , extensions = extensions )
if len ( files ) == 0 :
continue
res . append ( f ' { name } / { subdir } ' )
