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