From d7a5ad9fd9f1c728e5866bd515362bb297edab1b Mon Sep 17 00:00:00 2001
From: mrq <barry.quiggles@protonmail.com>
Date: Tue, 7 Mar 2023 04:34:39 +0000
Subject: [PATCH] cleaned up some model loading logic, added 'auto' mode for AR
model (deduced by current voice)
---
src/utils.py | 107 ++++++++++++++++++++++++++++++++++-----------------
tortoise-tts | 2 +-
2 files changed, 72 insertions(+), 37 deletions(-)
diff --git a/src/utils.py b/src/utils.py
index 309b72f..0aec5a8 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -54,6 +54,8 @@ voicefixer = None
whisper_model = None
training_state = None
+current_voice = None
+
def generate(
text,
delimiter,
@@ -117,10 +119,7 @@ def generate(
else:
progress(0, desc=f"Loading voice: {voice}")
# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
- if hasattr(tts, 'autoregressive_model_hash'):
- voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
- else:
- voice_samples, conditioning_latents = load_voice(voice)
+ voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
if voice_samples and len(voice_samples) > 0:
conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=voice_latents_chunks)
@@ -146,6 +145,10 @@ def generate(
print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
cvvp_weight = 0
+ autoregressive_model = args.autoregressive_model
+ if autoregressive_model == "auto":
+ autoregressive_model = deduce_autoregressive_model(voice)
+
def get_settings( override=None ):
settings = {
'temperature': float(temperature),
@@ -172,7 +175,7 @@ def generate(
'half_p': "Half Precision" in experimental_checkboxes,
'cond_free': "Conditioning-Free" in experimental_checkboxes,
'cvvp_amount': cvvp_weight,
- 'autoregressive_model': args.autoregressive_model,
+ 'autoregressive_model': autoregressive_model,
}
# could be better to just do a ternary on everything above, but i am not a professional
@@ -180,18 +183,10 @@ def generate(
if 'voice' in override:
voice = override['voice']
- if "autoregressive_model" in override and override["autoregressive_model"] == "auto":
- dir = f'./training/{voice}-finetune/models/'
- if os.path.exists(f'./training/finetunes/{voice}.pth'):
- override["autoregressive_model"] = f'./training/finetunes/{voice}.pth'
- elif os.path.isdir(dir):
- counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
- names = [ f'./{dir}/{d}_gpt.pth' for d in counts ]
- override["autoregressive_model"] = names[-1]
- else:
- override["autoregressive_model"] = None
+ if "autoregressive_model" in override:
+ if override["autoregressive_model"] == "auto":
+ override["autoregressive_model"] = deduce_autoregressive_model(voice)
- # necessary to ensure the right model gets loaded for the latents
tts.load_autoregressive_model( override["autoregressive_model"] )
fetched = fetch_voice(voice)
@@ -204,8 +199,7 @@ def generate(
continue
settings[k] = override[k]
- if hasattr(tts, 'autoregressive_model_path') and tts.autoregressive_model_path != settings["autoregressive_model"]:
- tts.load_autoregressive_model( settings["autoregressive_model"] )
+ tts.load_autoregressive_model( settings["autoregressive_model"] )
# clamp it down for the insane users who want this
# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
@@ -302,7 +296,7 @@ def generate(
'datetime': datetime.now().isoformat(),
'model': tts.autoregressive_model_path,
- 'model_hash': tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None,
+ 'model_hash': tts.autoregressive_model_hash
}
if settings is not None:
@@ -331,7 +325,7 @@ def generate(
else:
if settings and "model_hash" in settings:
latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{settings["model_hash"][:8]}.pth'
- elif hasattr(tts, "autoregressive_model_hash"):
+ else:
latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
if latents_path and os.path.exists(latents_path):
@@ -387,7 +381,7 @@ def generate(
used_settings['time'] = run_time
used_settings['datetime'] = datetime.now().isoformat(),
used_settings['model'] = tts.autoregressive_model_path
- used_settings['model_hash'] = tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None
+ used_settings['model_hash'] = tts.autoregressive_model_hash
audio_cache[name] = {
'audio': audio,
@@ -540,6 +534,9 @@ def hash_file(path, algo="md5", buffer_size=0):
return "{0}".format(hash.hexdigest())
def update_baseline_for_latents_chunks( voice ):
+ global current_voice
+ current_voice = voice
+
path = f'{get_voice_dir()}/{voice}/'
if not os.path.isdir(path):
return 1
@@ -583,6 +580,9 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
if hasattr(tts, "loading") and tts.loading:
raise Exception("TTS is still initializing...")
+ if args.autoregressive_model == "auto":
+ tts.load_autoregressive_model(deduce_autoregressive_model(voice))
+
if voice:
load_from_dataset = voice_latents_chunks == 0
@@ -620,10 +620,7 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
if len(conditioning_latents) == 4:
conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
- if hasattr(tts, 'autoregressive_model_hash'):
- torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
- else:
- torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
+ torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
return conditioning_latents
@@ -1460,6 +1457,9 @@ def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
models = sorted([ int(d[:-8]) for d in os.listdir(f'./training/{training}/models/') if d[-8:] == "_gpt.pth" ])
found = found + [ f'./training/{training}/models/{d}_gpt.pth' for d in models ]
+ if len(found) > 0 or len(additionals) > 0:
+ base = ["auto"] + base
+
res = base + additionals + found
if prefixed:
@@ -1815,28 +1815,29 @@ def version_check_tts( min_version ):
return True
return False
-def load_tts( restart=False, model=None ):
+def load_tts( restart=False, autoregressive_model=None ):
global args
global tts
if restart:
unload_tts()
+ if autoregressive_model:
+ args.autoregressive_model = autoregressive_model
+ else:
+ autoregressive_model = args.autoregressive_model
- if model:
- args.autoregressive_model = model
+ if autoregressive_model == "auto":
+ autoregressive_model = deduce_autoregressive_model()
- print(f"Loading TorToiSe... (AR: {args.autoregressive_model}, vocoder: {args.vocoder_model})")
+ print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {args.vocoder_model})")
tts_loading = True
try:
- tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model, vocoder_model=args.vocoder_model)
+ tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=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)
-
- if not hasattr(tts, 'autoregressive_model_hash'):
- tts.autoregressive_model_hash = hash_file(tts.autoregressive_model_path)
+ load_autoregressive_model(autoregressive_model)
tts_loading = False
@@ -1858,6 +1859,37 @@ def unload_tts():
def reload_tts( model=None ):
load_tts( restart=True, model=model )
+def get_current_voice():
+ global current_voice
+ if current_voice:
+ return current_voice
+
+ settings, _ = read_generate_settings("./config/generate.json", read_latents=False)
+
+ if settings and "voice" in settings['voice']:
+ return settings["voice"]
+
+ return None
+
+def deduce_autoregressive_model(voice=None):
+ if not voice:
+ voice = get_current_voice()
+
+ if voice:
+ dir = f'./training/{voice}-finetune/models/'
+ if os.path.exists(f'./training/finetunes/{voice}.pth'):
+ return f'./training/finetunes/{voice}.pth'
+
+ if os.path.isdir(dir):
+ counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
+ names = [ f'{dir}/{d}_gpt.pth' for d in counts ]
+ return names[-1]
+
+ if args.autoregressive_model != "auto":
+ return args.autoregressive_model
+
+ return get_model_path('autoregressive.pth')
+
def update_autoregressive_model(autoregressive_model_path):
match = re.findall(r'^\[[a-fA-F0-9]{8}\] (.+?)$', autoregressive_model_path)
if match:
@@ -1880,10 +1912,13 @@ def update_autoregressive_model(autoregressive_model_path):
if hasattr(tts, "loading") and tts.loading:
raise Exception("TTS is still initializing...")
+ if autoregressive_model_path == "auto":
+ autoregressive_model_path = deduce_autoregressive_model()
+
+ if autoregressive_model_path == tts.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}")
do_gc()
diff --git a/tortoise-tts b/tortoise-tts
index e2db36a..26133c2 160000
--- a/tortoise-tts
+++ b/tortoise-tts
@@ -1 +1 @@
-Subproject commit e2db36af602297501132f7f68331755f5904825a
+Subproject commit 26133c20314b77155e77be804b43909dab9809d6