forked from mrq/ai-voice-cloning
cleanups, realigning vall-e training
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909325bb5a
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f822c87344
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@ -5,7 +5,9 @@ log_root: ./training/${voice}/finetune/logs/
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data_dirs: [./training/${voice}/valle/]
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spkr_name_getter: "lambda p: p.parts[-3]" # "lambda p: p.parts[-1].split('-')[0]"
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model: ${model_name}
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max_phones: 72
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models: '${models}'
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batch_size: ${batch_size}
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gradient_accumulation_steps: ${gradient_accumulation_size}
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eval_batch_size: ${batch_size}
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@ -13,7 +15,3 @@ eval_batch_size: ${batch_size}
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max_iter: ${iterations}
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save_ckpt_every: ${save_rate}
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eval_every: ${validation_rate}
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max_phones: 256
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sampling_temperature: 1.0
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130
src/utils.py
130
src/utils.py
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@ -642,7 +642,6 @@ class TrainingState():
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self.yaml_config = yaml.safe_load(file)
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self.json_config = json.load(open(f"{self.training_dir}/train.json", 'r', encoding="utf-8"))
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self.dataset_dir = f"{self.training_dir}/finetune/"
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self.dataset_path = f"{self.training_dir}/train.txt"
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with open(self.dataset_path, 'r', encoding="utf-8") as f:
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self.dataset_size = len(f.readlines())
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@ -690,9 +689,6 @@ class TrainingState():
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'loss': "",
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}
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self.buffer_json = None
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self.json_buffer = []
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self.loss_milestones = [ 1.0, 0.15, 0.05 ]
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if keep_x_past_checkpoints > 0:
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@ -704,18 +700,18 @@ class TrainingState():
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if args.tts_backend == "vall-e":
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self.cmd = ['deepspeed', f'--num_gpus={gpus}', '--module', 'vall_e.train', f'yaml="{config_path}"']
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else:
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self.cmd = ['train.bat', config_path] if os.name == "nt" else ['./train.sh', config_path]
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self.cmd = [f'train.{"bat" if os.name == "nt" else "sh"}', config_path]
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print("Spawning process: ", " ".join(self.cmd))
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self.process = subprocess.Popen(self.cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
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def parse_metrics(self, data):
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if isinstance(data, str):
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if line.find('INFO: Training Metrics:') >= 0:
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data = json.loads(line.split("INFO: Training Metrics:")[-1])
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if line.find('Training Metrics:') >= 0:
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data = json.loads(line.split("Training Metrics:")[-1])
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data['mode'] = "training"
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elif line.find('INFO: Validation Metrics:') >= 0:
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data = json.loads(line.split("INFO: Validation Metrics:")[-1])
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elif line.find('Validation Metrics:') >= 0:
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data = json.loads(line.split("Validation Metrics:")[-1])
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data['mode'] = "validation"
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else:
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return
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@ -755,21 +751,19 @@ class TrainingState():
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self.metrics['step'] = ", ".join(self.metrics['step'])
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epoch = self.epoch + (self.step / self.steps)
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if 'lr' in self.info:
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self.statistics['lr'].append({'epoch': epoch, 'it': self.it, 'value': self.info['lr'], 'type': 'learning_rate'})
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if args.tts_backend == "tortoise":
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for k in ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']:
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for k in ['lr'] if args.tts_backend == "tortoise" else ['ar.lr', 'nar.lr', 'aar-half.lr', 'nar-half.lr', 'ar-quarter.lr', 'nar-quarter.lr']:
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if k not in self.info:
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continue
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self.statistics['lr'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': k})
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for k in ['loss_text_ce', 'loss_mel_ce'] if args.tts_backend == "tortoise" else ['ar.loss', 'nar.loss', 'aar-half.loss', 'nar-half.loss', 'ar-quarter.loss', 'nar-quarter.loss']:
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if k not in self.info:
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continue
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if k == "loss_gpt_total":
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self.losses.append( self.statistics['loss'][-1] )
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else:
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self.statistics['loss'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': f'{"val_" if data["mode"] == "validation" else ""}{k}' })
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else:
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k = "loss"
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self.statistics['loss'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': f'{"val_" if data["mode"] == "validation" else ""}{k}' })
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self.losses.append( self.statistics['loss'][-1] )
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return data
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@ -846,9 +840,17 @@ class TrainingState():
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return message
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def load_statistics(self, update=False):
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if not os.path.isdir(f'{self.dataset_dir}/'):
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if not os.path.isdir(self.training_dir):
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return
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if args.tts_backend == "tortoise":
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logs = sorted([f'{self.training_dir}/finetune/{d}' for d in os.listdir(f'{self.training_dir}/finetune/') if d[-4:] == ".log" ])
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else:
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logs = sorted([f'{self.training_dir}/logs/{d}/log.txt' for d in os.listdir(f'{self.training_dir}/logs/') ])
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if update:
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logs = [logs[-1]]
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infos = {}
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highest_step = self.last_info_check_at
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@ -857,28 +859,28 @@ class TrainingState():
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self.statistics['lr'] = []
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self.it_rates = 0
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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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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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for line in lines:
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if line.find('INFO: Training Metrics:') >= 0:
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data = json.loads(line.split("INFO: Training Metrics:")[-1])
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if line.find('Training Metrics:') >= 0:
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data = json.loads(line.split("Training Metrics:")[-1])
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data['mode'] = "training"
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elif line.find('INFO: Validation Metrics:') >= 0:
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data = json.loads(line.split("INFO: Validation Metrics:")[-1])
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elif line.find('Validation Metrics:') >= 0:
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data = json.loads(line.split("Validation Metrics:")[-1])
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data['mode'] = "validation"
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else:
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continue
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if args.tts_backend == "tortoise":
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if "it" not in data:
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continue
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it = data['it']
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else:
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if "global_step" not in data:
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continue
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it = data['global_step']
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if update and it <= self.last_info_check_at:
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continue
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@ -891,20 +893,23 @@ class TrainingState():
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if keep <= 0:
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return
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if not os.path.isdir(self.dataset_dir):
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if args.tts_backend == "vall-e":
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return
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models = sorted([ int(d[:-8]) for d in os.listdir(f'{self.dataset_dir}/models/') if d[-8:] == "_gpt.pth" ])
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states = sorted([ int(d[:-6]) for d in os.listdir(f'{self.dataset_dir}/training_state/') if d[-6:] == ".state" ])
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if not os.path.isdir(f'{self.training_dir}/finetune/'):
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return
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models = sorted([ int(d[:-8]) for d in os.listdir(f'{self.training_dir}/finetune/models/') if d[-8:] == "_gpt.pth" ])
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states = sorted([ int(d[:-6]) for d in os.listdir(f'{self.training_dir}/finetune/training_state/') if d[-6:] == ".state" ])
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remove_models = models[:-keep]
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remove_states = states[:-keep]
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for d in remove_models:
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path = f'{self.dataset_dir}/models/{d}_gpt.pth'
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path = f'{self.training_dir}/finetune/models/{d}_gpt.pth'
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print("Removing", path)
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os.remove(path)
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for d in remove_states:
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path = f'{self.dataset_dir}/training_state/{d}.state'
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path = f'{self.training_dir}/finetune/training_state/{d}.state'
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print("Removing", path)
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os.remove(path)
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@ -930,34 +935,10 @@ class TrainingState():
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MESSAGE_START = 'Start training from epoch'
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MESSAGE_FINSIHED = 'Finished training'
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MESSAGE_SAVING = 'INFO: Saving models and training states.'
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MESSAGE_SAVING = 'Saving models and training states.'
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MESSAGE_METRICS_TRAINING = 'INFO: Training Metrics:'
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MESSAGE_METRICS_VALIDATION = 'INFO: Validation Metrics:'
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if args.tts_backend == "vall-e":
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if self.buffer_json:
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self.json_buffer.append(line)
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if line.find("{") == 0 and not self.buffer_json:
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self.buffer_json = True
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self.json_buffer = [line]
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if line.find("}") == 0 and self.buffer_json:
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try:
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data = json.loads("\n".join(self.json_buffer))
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except Exception as e:
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print(str(e))
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if data and 'model.loss' in data:
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self.training_started = True
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data = self.parse_valle_metrics( data )
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print("Training JSON:", data)
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else:
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data = None
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self.buffer_json = None
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self.json_buffer = []
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MESSAGE_METRICS_TRAINING = 'Training Metrics:'
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MESSAGE_METRICS_VALIDATION = 'Validation Metrics:'
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if line.find(MESSAGE_FINSIHED) >= 0:
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self.killed = True
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@ -1469,6 +1450,13 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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result = segments[file]
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path = f'{indir}/audio/{file}'
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if not os.path.exists(path):
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message = f"Missing segment, skipping... {file}"
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print(message)
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messages.append(message)
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errored += 1
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continue
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text = result['text']
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lang = result['lang']
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language = result['language']
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@ -1479,6 +1467,8 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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if phonemize:
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text = phonemes
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normalized = normalizer(text) if normalize else text
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if len(text) > 200:
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message = f"Text length too long (200 < {len(text)}), skipping... {file}"
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print(message)
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@ -1511,18 +1501,16 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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os.makedirs(f'{indir}/valle/', exist_ok=True)
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if not os.path.exists(f'{indir}/valle/{file.replace(".wav",".qnt.pt")}'):
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from vall_e.emb.qnt import encode as quantize
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# from vall_e.emb.g2p import encode as phonemize
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quantized = quantize( waveform, sample_rate ).cpu()
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torch.save(quantized, f'{indir}/valle/{file.replace(".wav",".qnt.pt")}')
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print("Quantized:", file)
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tokens = tokenize_text(text, config="./models/tokenizers/ipa.json", stringed=False, skip_specials=True)
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tokenized = " ".join( tokens )
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tokenized = tokenized.replace(" \u02C8", "\u02C8")
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tokenized = tokenized.replace(" \u02CC", "\u02CC")
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open(f'{indir}/valle/{file.replace(".wav",".phn.txt")}', 'w', encoding='utf-8').write(tokenized)
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if not os.path.exists(f'{indir}/valle/{file.replace(".wav",".phn.txt")}'):
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from vall_e.emb.g2p import encode as phonemize
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phonemized = phonemize( normalized )
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open(f'{indir}/valle/{file.replace(".wav",".phn.txt")}', 'w', encoding='utf-8').write(" ".join(phonemized))
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training_joined = "\n".join(lines['training'])
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validation_joined = "\n".join(lines['validation'])
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@ -1786,10 +1774,8 @@ def save_training_settings( **kwargs ):
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if args.tts_backend == "tortoise":
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use_template(f'./models/.template.dlas.yaml', f'./training/{settings["voice"]}/train.yaml')
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elif args.tts_backend == "vall-e":
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settings['model_name'] = "ar"
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use_template(f'./models/.template.valle.yaml', f'./training/{settings["voice"]}/ar.yaml')
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settings['model_name'] = "nar"
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use_template(f'./models/.template.valle.yaml', f'./training/{settings["voice"]}/nar.yaml')
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settings['model_name'] = "[ 'ar-quarter', 'nar-quarter' ]"
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use_template(f'./models/.template.valle.yaml', f'./training/{settings["voice"]}/config.yaml')
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messages.append(f"Saved training output")
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return settings, messages
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