forked from camenduru/ai-voice-cloning
added to embedded metadata: datetime, model path, model hash
This commit is contained in:
parent
81eb58f0d6
commit
787b44807a
64
src/utils.py
64
src/utils.py
|
|
@ -307,6 +307,10 @@ def generate(
|
|||
'cond_free_k': cond_free_k,
|
||||
'experimentals': experimental_checkboxes,
|
||||
'time': time.time()-full_start_time,
|
||||
|
||||
'datetime': datetime.now().isoformat(),
|
||||
'model': tts.autoregressive_model_path,
|
||||
'model_hash': tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None,
|
||||
}
|
||||
|
||||
"""
|
||||
|
|
@ -324,6 +328,7 @@ def generate(
|
|||
|
||||
if args.voice_fixer:
|
||||
if not voicefixer:
|
||||
progress(0, "Loading voicefix...")
|
||||
load_voicefixer()
|
||||
|
||||
fixed_cache = {}
|
||||
|
|
@ -1006,7 +1011,33 @@ def get_voice_list(dir=get_voice_dir(), append_defaults=False):
|
|||
res = res + ["random", "microphone"]
|
||||
return res
|
||||
|
||||
def get_autoregressive_models(dir="./models/finetunes/"):
|
||||
def hash_file(path, algo="md5", buffer_size=0):
|
||||
import hashlib
|
||||
|
||||
hash = None
|
||||
if algo == "md5":
|
||||
hash = hashlib.md5()
|
||||
elif algo == "sha1":
|
||||
hash = hashlib.sha1()
|
||||
else:
|
||||
raise Exception(f'Unknown hash algorithm specified: {algo}')
|
||||
|
||||
if not os.path.exists(path):
|
||||
raise Exception(f'Path not found: {path}')
|
||||
|
||||
with open(path, 'rb') as f:
|
||||
if buffer_size > 0:
|
||||
while True:
|
||||
data = f.read(buffer_size)
|
||||
if not data:
|
||||
break
|
||||
hash.update(data)
|
||||
else:
|
||||
hash.update(f.read())
|
||||
|
||||
return "{0}".format(hash.hexdigest())
|
||||
|
||||
def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
|
||||
os.makedirs(dir, exist_ok=True)
|
||||
base = [get_model_path('autoregressive.pth')]
|
||||
halfp = get_halfp_model_path()
|
||||
|
|
@ -1018,12 +1049,20 @@ def get_autoregressive_models(dir="./models/finetunes/"):
|
|||
for training in os.listdir(f'./training/'):
|
||||
if not os.path.isdir(f'./training/{training}/') or not os.path.isdir(f'./training/{training}/models/'):
|
||||
continue
|
||||
#found = found + sorted([ f'./training/{training}/models/{d}' for d in os.listdir(f'./training/{training}/models/') if d[-8:] == "_gpt.pth" ])
|
||||
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 ]
|
||||
#found.append(f'./training/{training}/models/{models[-1]}_gpt.pth')
|
||||
|
||||
return base + additionals + found
|
||||
res = base + additionals + found
|
||||
|
||||
if prefixed:
|
||||
for i in range(len(res)):
|
||||
path = res[i]
|
||||
hash = hash_file(path)
|
||||
shorthash = hash[:8]
|
||||
|
||||
res[i] = f'[{shorthash}] {path}'
|
||||
|
||||
return res
|
||||
|
||||
def get_dataset_list(dir="./training/"):
|
||||
return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 and "train.txt" in os.listdir(os.path.join(dir, d)) ])
|
||||
|
|
@ -1256,8 +1295,6 @@ def save_args_settings():
|
|||
'training-default-bnb': args.training_default_bnb,
|
||||
}
|
||||
|
||||
print(settings)
|
||||
|
||||
os.makedirs('./config/', exist_ok=True)
|
||||
with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
|
||||
f.write(json.dumps(settings, indent='\t') )
|
||||
|
|
@ -1322,9 +1359,7 @@ def read_generate_settings(file, read_latents=True):
|
|||
except Exception as e:
|
||||
pass
|
||||
|
||||
if j is None:
|
||||
print("No metadata found in audio file to read")
|
||||
else:
|
||||
if j is not None:
|
||||
if 'latents' in j:
|
||||
if read_latents:
|
||||
latents = base64.b64decode(j['latents'])
|
||||
|
|
@ -1360,6 +1395,10 @@ def load_tts( restart=False, model=None ):
|
|||
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)
|
||||
|
||||
tts_loading = False
|
||||
|
||||
get_model_path('dvae.pth')
|
||||
|
|
@ -1381,6 +1420,10 @@ def reload_tts( model=None ):
|
|||
load_tts( restart=True, model=model )
|
||||
|
||||
def update_autoregressive_model(autoregressive_model_path):
|
||||
match = re.findall(r'^\[[a-fA-F0-9]{8}\] (.+?)$', autoregressive_model_path)
|
||||
if match:
|
||||
autoregressive_model_path = match[0]
|
||||
|
||||
if not autoregressive_model_path or not os.path.exists(autoregressive_model_path):
|
||||
print(f"Invalid model: {autoregressive_model_path}")
|
||||
return
|
||||
|
|
@ -1416,6 +1459,9 @@ def update_autoregressive_model(autoregressive_model_path):
|
|||
if tts.preloaded_tensors:
|
||||
tts.autoregressive = tts.autoregressive.to(tts.device)
|
||||
|
||||
if not hasattr(tts, 'autoregressive_model_hash'):
|
||||
tts.autoregressive_model_hash = hash_file(autoregressive_model_path)
|
||||
|
||||
print(f"Loaded model: {tts.autoregressive_model_path}")
|
||||
|
||||
do_gc()
|
||||
|
|
|
|||
|
|
@ -129,6 +129,9 @@ history_headers = {
|
|||
"Rep Pen": "repetition_penalty",
|
||||
"Cond-Free K": "cond_free_k",
|
||||
"Time": "time",
|
||||
"Datetime": "datetime",
|
||||
"Model": "model",
|
||||
"Model Hash": "model_hash",
|
||||
}
|
||||
|
||||
def history_view_results( voice ):
|
||||
|
|
@ -147,7 +150,7 @@ def history_view_results( voice ):
|
|||
for k in history_headers:
|
||||
v = file
|
||||
if k != "Name":
|
||||
v = metadata[history_headers[k]]
|
||||
v = metadata[history_headers[k]] if history_headers[k] in metadata else '?'
|
||||
values.append(v)
|
||||
|
||||
|
||||
|
|
@ -174,8 +177,6 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
|
|||
|
||||
latents = f'{outdir}/cond_latents.pth'
|
||||
|
||||
print(j, latents)
|
||||
|
||||
return (
|
||||
gr.update(value=j, visible=j is not None),
|
||||
gr.update(visible=j is not None),
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user