forked from mrq/ai-voice-cloning
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@ -8,6 +8,9 @@ spkr_name_getter: "lambda p: p.parts[-3]"
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model: ${model_name}
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batch_size: ${batch_size}
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eval_batch_size: ${validation_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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sampling_temperature: 1.0
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67
src/train.py
67
src/train.py
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@ -2,68 +2,48 @@ import os
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import sys
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import argparse
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import yaml
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import datetime
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"""
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if 'BITSANDBYTES_OVERRIDE_LINEAR' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_LINEAR'] = '0'
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if 'BITSANDBYTES_OVERRIDE_EMBEDDING' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_EMBEDDING'] = '1'
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if 'BITSANDBYTES_OVERRIDE_ADAM' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_ADAM'] = '1'
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if 'BITSANDBYTES_OVERRIDE_ADAMW' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '1'
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"""
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from torch.distributed.run import main as torchrun
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# I don't want this invoked from an import
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if __name__ != "__main__":
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raise Exception("Do not invoke this from an import")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--mode', type=str, default='none', help='mode')
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parser.add_argument('--yaml', type=str, help='Path to training configuration file.', default='./training/voice/train.yml', nargs='+') # ugh
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='Job launcher')
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args = parser.parse_args()
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args.opt = " ".join(args.opt) # absolutely disgusting
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args.yaml = " ".join(args.yaml) # absolutely disgusting
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config_path = args.yaml
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with open(args.opt, 'r') as file:
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with open(config_path, 'r') as file:
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opt_config = yaml.safe_load(file)
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if "ext" in opt_config and "bitsandbytes" in opt_config["ext"] and not opt_config["ext"]["bitsandbytes"]:
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# it'd be downright sugoi if I was able to install DLAS as a pip package
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sys.path.insert(0, './modules/dlas/codes/')
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sys.path.insert(0, './modules/dlas/')
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# yucky override
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if "bitsandbytes" in opt_config and not opt_config["bitsandbytes"]:
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os.environ['BITSANDBYTES_OVERRIDE_LINEAR'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_EMBEDDING'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_ADAM'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '0'
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# this is some massive kludge that only works if it's called from a shell and not an import/PIP package
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# it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell
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sys.path.insert(0, './modules/dlas/codes/')
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# this is also because DLAS is not written as a package in mind
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# it'll gripe when it wants to import from train.py
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sys.path.insert(0, './modules/dlas/')
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# for PIP, replace it with:
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# sys.path.insert(0, os.path.dirname(os.path.realpath(dlas.__file__)))
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# sys.path.insert(0, f"{os.path.dirname(os.path.realpath(dlas.__file__))}/../")
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# don't even really bother trying to get DLAS PIP'd
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# without kludge, it'll have to be accessible as `codes` and not `dlas`
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import torch
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import datetime
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from codes import train as tr
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from utils import util, options as option
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from torch.distributed.run import main
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# this is effectively just copy pasted and cleaned up from the __main__ section of training.py
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# I'll clean it up better
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def train(yaml, launcher='none'):
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opt = option.parse(yaml, is_train=True)
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def train(config_path, launcher='none'):
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opt = option.parse(config_path, is_train=True)
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if launcher == 'none' and opt['gpus'] > 1:
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return main([f"--nproc_per_node={opt['gpus']}", "--master_port=1234", "./src/train.py", "-opt", yaml, "--launcher=pytorch"])
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return torchrun([f"--nproc_per_node={opt['gpus']}", "./src/train.py", "--yaml", config_path, "--launcher=pytorch"])
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trainer = tr.Trainer()
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#### distributed training settings
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if launcher == 'none': # disabled distributed training
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if launcher == 'none':
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opt['dist'] = False
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trainer.rank = -1
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if len(opt['gpu_ids']) == 1:
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@ -76,10 +56,9 @@ def train(yaml, launcher='none'):
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trainer.rank = torch.distributed.get_rank()
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torch.cuda.set_device(torch.distributed.get_rank())
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trainer.init(yaml, opt, launcher, '')
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trainer.init(config_path, opt, launcher, '')
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trainer.do_training()
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if __name__ == "__main__":
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try:
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import torch_intermediary
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if torch_intermediary.OVERRIDE_ADAM:
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@ -89,4 +68,4 @@ if __name__ == "__main__":
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except Exception as e:
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pass
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train(args.opt, args.launcher)
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train(config_path, args.launcher)
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65
src/utils.py
65
src/utils.py
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@ -47,7 +47,7 @@ WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
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WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
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WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp"]
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
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TTSES = ['tortoise'] # + ['vall-e']
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TTSES = ['tortoise']
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GENERATE_SETTINGS_ARGS = None
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@ -69,6 +69,9 @@ try:
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except Exception as e:
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pass
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if VALLE_ENABLED:
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TTSES.append('vall-e')
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args = None
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tts = None
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tts_loading = False
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@ -613,28 +616,41 @@ class TrainingState():
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with open(config_path, 'r') as file:
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self.config = yaml.safe_load(file)
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gpus = self.config["gpus"]
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self.killed = False
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self.it = 0
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self.step = 0
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self.epoch = 0
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self.checkpoint = 0
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if args.tts_backend == "tortoise":
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gpus = self.config["gpus"]
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self.dataset_dir = f"./training/{self.config['name']}/finetune/"
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self.batch_size = self.config['datasets']['train']['batch_size']
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self.dataset_path = self.config['datasets']['train']['path']
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self.its = self.config['train']['niter']
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self.steps = 1
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self.epochs = 1 # int(self.its*self.batch_size/self.dataset_size)
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self.checkpoints = int(self.its / self.config['logger']['save_checkpoint_freq'])
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elif args.tts_backend == "vall-e":
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self.batch_size = self.config['batch_size']
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self.dataset_dir = f".{self.config['data_root']}/finetune/"
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self.dataset_path = f"{self.config['data_root']}/train.txt"
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self.its = 1
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self.steps = 1
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self.epochs = 1
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self.checkpoints = 1
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self.json_config = json.load(open(f"{self.config['data_root']}/train.json", 'r', encoding="utf-8"))
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gpus = self.json_config['gpus']
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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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self.it = 0
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self.its = self.config['train']['niter']
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self.step = 0
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self.steps = 1
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self.epoch = 0
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self.epochs = int(self.its*self.batch_size/self.dataset_size)
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self.checkpoint = 0
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self.checkpoints = int(self.its / self.config['logger']['save_checkpoint_freq'])
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self.buffer = []
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self.open_state = False
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@ -672,6 +688,9 @@ class TrainingState():
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self.spawn_process(config_path=config_path, gpus=gpus)
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def spawn_process(self, config_path, gpus=1):
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if args.tts_backend == "vall-e":
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self.cmd = ['torchrun', '--nproc_per_node', f'{gpus}', '-m', '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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print("Spawning process: ", " ".join(self.cmd))
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@ -1221,6 +1240,8 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
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lines = {
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'training': [],
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'validation': [],
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'recordings': [],
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'supervisions': [],
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}
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normalizer = EnglishTextNormalizer() if normalize else None
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@ -1310,13 +1331,11 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
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lines['training' if not culled else 'validation'].append(line)
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if culled or not VALLE_ENABLED:
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if culled or args.tts_backend != "vall-e":
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continue
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# VALL-E dataset
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os.makedirs(f'{indir}/valle/', exist_ok=True)
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try:
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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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@ -1329,10 +1348,6 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
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phonemes = phonemize(normalized_text)
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open(f'{indir}/valle/{file.replace(".wav",".phn.txt")}', 'w', encoding='utf-8').write(" ".join(phonemes))
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except Exception as e:
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print(e)
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pass
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training_joined = "\n".join(lines['training'])
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validation_joined = "\n".join(lines['validation'])
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@ -1588,8 +1603,9 @@ def save_training_settings( **kwargs ):
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with open(out, 'w', encoding="utf-8") as f:
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f.write(yaml)
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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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@ -1692,8 +1708,13 @@ 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 "train.txt" in os.listdir(os.path.join(dir, d)) ])
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def get_training_list(dir="./training/"):
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if args.tts_backend == "tortoise":
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return sorted([f'./training/{d}/train.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "train.yaml" in os.listdir(os.path.join(dir, d)) ])
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ars = sorted([f'./training/{d}/ar.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "ar.yaml" in os.listdir(os.path.join(dir, d)) ])
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nars = sorted([f'./training/{d}/nar.yaml' for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and "nar.yaml" in os.listdir(os.path.join(dir, d)) ])
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return ars + nars
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def pad(num, zeroes):
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return str(num).zfill(zeroes+1)
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@ -1,5 +1,5 @@
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call .\venv\Scripts\activate.bat
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set PYTHONUTF8=1
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python ./src/train.py -opt "%1"
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python ./src/train.py --yaml "%1"
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pause
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deactivate
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