forked from camenduru/ai-voice-cloning
added loading/saving of voice latents by model hash, so no more needing to manually regenerate every time you change models
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parent
5a41db978e
commit
534a761e49
99
src/utils.py
99
src/utils.py
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@ -97,6 +97,10 @@ def generate(
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voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
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else:
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progress(0, desc="Loading voice...")
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# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
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if hasattr(tts, 'autoregressive_model_hash'):
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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else:
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voice_samples, conditioning_latents = load_voice(voice)
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if voice_samples and len(voice_samples) > 0:
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@ -107,6 +111,9 @@ def generate(
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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if voice != "microphone":
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if hasattr(tts, 'autoregressive_model_hash'):
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
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else:
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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voice_samples = None
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else:
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@ -413,6 +420,32 @@ def cancel_generate():
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import tortoise.api
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tortoise.api.STOP_SIGNAL = True
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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 compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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global tts
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global args
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@ -435,6 +468,9 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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if hasattr(tts, 'autoregressive_model_hash'):
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
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else:
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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return voice
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@ -487,6 +523,7 @@ class TrainingState():
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self.eta = "?"
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self.eta_hhmmss = "?"
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self.last_info_check_at = 0
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self.losses = []
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self.load_losses()
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@ -497,7 +534,7 @@ class TrainingState():
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print("Spawning process: ", " ".join(self.cmd))
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self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
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def load_losses(self):
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def load_losses(self, update=False):
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if not os.path.isdir(f'{self.dataset_dir}/tb_logger/'):
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return
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try:
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@ -506,18 +543,26 @@ class TrainingState():
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except Exception as e:
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use_tensorboard = False
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keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
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infos = {}
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highest_step = self.last_info_check_at
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if use_tensorboard:
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logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
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infos = {}
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if update:
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logs = [logs[-1]]
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for log in logs:
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try:
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ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
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ea.Reload()
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keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
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for key in keys:
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scalar = ea.Scalars(key)
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for s in scalar:
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if update and s.step <= self.last_info_check_at:
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continue
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highest_step = max( highest_step, s.step )
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self.losses.append( { "step": s.step, "value": s.value, "type": key } )
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except Exception as e:
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print("Failed to parse event log:", log)
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@ -525,7 +570,9 @@ class TrainingState():
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else:
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logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
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infos = {}
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if update:
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logs = [logs[-1]]
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for log in logs:
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with open(log, 'r', encoding="utf-8") as f:
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lines = f.readlines()
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@ -546,9 +593,13 @@ class TrainingState():
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for k in infos:
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if 'loss_gpt_total' in infos[k]:
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self.losses.append({ "step": int(k), "value": infos[k]['loss_text_ce'], "type": "loss_text_ce" })
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self.losses.append({ "step": int(k), "value": infos[k]['loss_mel_ce'], "type": "loss_mel_ce" })
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self.losses.append({ "step": int(k), "value": infos[k]['loss_gpt_total'], "type": "loss_gpt_total" })
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for key in keys:
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if update and int(k) <= self.last_info_check_at:
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continue
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highest_step = max( highest_step, s.step )
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self.losses.append({ "step": int(k), "value": infos[k][key], "type": key })
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self.last_info_check_at = highest_step
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def cleanup_old(self, keep=2):
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if keep <= 0:
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@ -581,6 +632,7 @@ class TrainingState():
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if line.find('Start training from epoch') >= 0:
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self.epoch_time_start = time.time()
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self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
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should_return = True
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match = re.findall(r'epoch: ([\d,]+)', line)
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if match and len(match) > 0:
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@ -662,12 +714,15 @@ class TrainingState():
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if 'loss_gpt_total' in self.info:
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self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
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"""
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self.losses.append({ "step": self.it, "value": self.info['loss_text_ce'], "type": "loss_text_ce" })
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self.losses.append({ "step": self.it, "value": self.info['loss_mel_ce'], "type": "loss_mel_ce" })
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self.losses.append({ "step": self.it, "value": self.info['loss_gpt_total'], "type": "loss_gpt_total" })
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"""
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should_return = True
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self.load_losses(update=True)
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elif line.find('Saving models and training states') >= 0:
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self.checkpoint = self.checkpoint + 1
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@ -1035,32 +1090,6 @@ 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 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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