@ -42,6 +42,8 @@ WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "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']
EPOCH_SCHEDULE = [ 9 , 18 , 25 , 33 ]
args = None
@ -1539,7 +1541,7 @@ def setup_args():
' defer-tts-load ' : False ,
' device-override ' : None ,
' prune-nonfinal-outputs ' : True ,
' use-bigvgan-vocoder' : True ,
' vocoder-model' : VOCODERS [ - 1 ] ,
' concurrency-count ' : 2 ,
' autocalculate-voice-chunk-duration-size ' : 0 ,
' output-sample-rate ' : 44100 ,
@ -1576,7 +1578,7 @@ def setup_args():
parser . add_argument ( " --force-cpu-for-conditioning-latents " , default = default_arguments [ ' force-cpu-for-conditioning-latents ' ] , action = ' store_true ' , help = " Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts) " )
parser . add_argument ( " --defer-tts-load " , default = default_arguments [ ' defer-tts-load ' ] , action = ' store_true ' , help = " Defers loading TTS model " )
parser . add_argument ( " --prune-nonfinal-outputs " , default = default_arguments [ ' prune-nonfinal-outputs ' ] , action = ' store_true ' , help = " Deletes non-final output files on completing a generation " )
parser . add_argument ( " -- use-bigvgan- vocoder" , default = default_arguments [ ' use-bigvgan- vocoder' ] , action = ' store_true ' , help = " Uses BigVGAN in place of the default vocoder " )
parser . add_argument ( " -- vocoder-model " , default = default_arguments [ ' -model ' ] , action = ' store_true ' , help = " Specifies with vocoder to use " )
parser . add_argument ( " --device-override " , default = default_arguments [ ' device-override ' ] , help = " A device string to override pass through Torch " )
parser . add_argument ( " --sample-batch-size " , default = default_arguments [ ' sample-batch-size ' ] , type = int , help = " Sets how many batches to use during the autoregressive samples pass " )
parser . add_argument ( " --concurrency-count " , type = int , default = default_arguments [ ' concurrency-count ' ] , help = " How many Gradio events to process at once " )
@ -1620,7 +1622,7 @@ def setup_args():
return args
def update_args ( listen , share , check_for_updates , models_from_local_only , low_vram , embed_output_metadata , latents_lean_and_mean , voice_fixer , voice_fixer_use_cuda , force_cpu_for_conditioning_latents , defer_tts_load , prune_nonfinal_outputs , use_bigvgan_vocoder, device_override, sample_batch_size , concurrency_count , autocalculate_voice_chunk_duration_size , output_volume , autoregressive _model, whisper_backend , whisper_model , training_default_halfp , training_default_bnb ) :
def update_args ( listen , share , check_for_updates , models_from_local_only , low_vram , embed_output_metadata , latents_lean_and_mean , voice_fixer , voice_fixer_use_cuda , force_cpu_for_conditioning_latents , defer_tts_load , prune_nonfinal_outputs , , sample_batch_size , concurrency_count , autocalculate_voice_chunk_duration_size , output_volume , autoregressive _model, vocoder _model, whisper_backend , whisper_model , training_default_halfp , training_default_bnb ) :
global args
args . listen = listen
@ -1631,7 +1633,6 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
args . force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
args . defer_tts_load = defer_tts_load
args . prune_nonfinal_outputs = prune_nonfinal_outputs
args . use_bigvgan_vocoder = use_bigvgan_vocoder
args . device_override = device_override
args . sample_batch_size = sample_batch_size
args . embed_output_metadata = embed_output_metadata
@ -1644,6 +1645,7 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
args . output_volume = output_volume
args . autoregressive_model = autoregressive_model
args . vocoder_model = vocoder_model
args . whisper_backend = whisper_backend
args . whisper_model = whisper_model
@ -1663,7 +1665,6 @@ def save_args_settings():
' force-cpu-for-conditioning-latents ' : args . force_cpu_for_conditioning_latents ,
' defer-tts-load ' : args . defer_tts_load ,
' prune-nonfinal-outputs ' : args . prune_nonfinal_outputs ,
' use-bigvgan-vocoder ' : args . use_bigvgan_vocoder ,
' device-override ' : args . device_override ,
' sample-batch-size ' : args . sample_batch_size ,
' embed-output-metadata ' : args . embed_output_metadata ,
@ -1676,6 +1677,7 @@ def save_args_settings():
' output-volume ' : args . output_volume ,
' autoregressive-model ' : args . autoregressive_model ,
' vocoder-model ' : args . vocoder_model ,
' whisper-backend ' : args . whisper_backend ,
' whisper-model ' : args . whisper_model ,
@ -1791,11 +1793,11 @@ def load_tts( restart=False, model=None ):
if model :
args . autoregressive_model = model
print ( f " Loading TorToiSe... ( using model : { args . autoregressive _model} ) " )
print ( f " Loading TorToiSe... ( AR : { args . autoregressive _model} , vocoder: { args . vocoder _model} ) " )
tts_loading = True
try :
tts = TextToSpeech ( minor_optimizations = not args . low_vram , autoregressive_model_path = args . autoregressive_model
tts = TextToSpeech ( minor_optimizations = not args . low_vram , autoregressive_model_path = args . autoregressive_model , vocoder_model = args . vocoder_model )
except Exception as e :
tts = TextToSpeech ( minor_optimizations = not args . low_vram )
load_autoregressive_model ( args . autoregressive_model )
@ -1843,34 +1845,31 @@ def update_autoregressive_model(autoregressive_model_path):
return
print ( f " Loading model: { autoregressive_model_path } " )
tts . load_autoregressive_model ( autoregressive_model_path )
print ( f " Loaded model: { tts . autoregressive_model_path } " )
if hasattr ( tts , ' load_autoregressive_model ' ) and tts . load_autoregressive_model ( autoregressive_model_path ) :
tts . load_autoregressive_model ( autoregressive_model_path )
# polyfill in case a user did NOT update the packages
# this shouldn't happen anymore, as I just clone mrq/tortoise-tts, and inject it into sys.path
else :
from tortoise . models . autoregressive import UnifiedVoice
tts . autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os . path . exists ( autoregressive_model_path ) else get_model_path ( ' autoregressive.pth ' , tts . models_dir )
do_gc ( )
return autoregressive_model_path
del tts . autoregressive
tts . autoregressive = UnifiedVoice ( max_mel_tokens = 604 , max_text_tokens = 402 , max_conditioning_inputs = 2 , layers = 30 ,
model_dim = 1024 ,
heads = 16 , number_text_tokens = 255 , start_text_token = 255 , checkpointing = False ,
train_solo_embeddings = False ) . cpu ( ) . eval ( )
tts . autoregressive . load_state_dict ( torch . load ( tts . autoregressive_model_path ) )
tts . autoregressive . post_init_gpt2_config ( kv_cache = tts . use_kv_cache )
if tts . preloaded_tensors :
tts . autoregressive = tts . autoregressive . to ( tts . device )
def update_vocoder_model ( vocoder_model ) :
args . vocoder_model = vocoder_model
save_args_settings ( )
print ( f ' Stored vocoder model to settings: { vocoder_model } ' )
if not hasattr ( tts , ' autoregressive_model_hash ' ) :
tts . autoregressive_model_hash = hash_file ( autoregressive_model_path )
global tts
if not tts :
if tts_loading :
raise Exception ( " TTS is still initializing... " )
return
print ( f " Loaded model: { tts . autoregressive_model_path } " )
print ( f " Loading model: { vocoder_model } " )
tts . load_vocoder_model ( vocoder_model )
print ( f " Loaded model: { tts . vocoder_model } " )
do_gc ( )
return autoregressive_model_path
return vocoder_model
def load_voicefixer ( restart = False ) :
global voicefixer
