;)

models/.template.valle.yaml

@@ -8,6 +8,9 @@ spkr_name_getter: "lambda p: p.parts[-3]"
 model: ${model_name}
 batch_size: ${batch_size}
 eval_batch_size: ${validation_batch_size}
+
+max_iter: ${iterations}
+save_ckpt_every: ${save_rate}
 eval_every: ${validation_rate}
 
 sampling_temperature: 1.0

src/train.py

@@ -2,68 +2,48 @@ import os
 import sys
 import argparse
 import yaml
+import datetime
 
-"""
+from torch.distributed.run import main as torchrun
-if 'BITSANDBYTES_OVERRIDE_LINEAR' not in os.environ:
+
-    os.environ['BITSANDBYTES_OVERRIDE_LINEAR'] = '0'
+# I don't want this invoked from an import
-if 'BITSANDBYTES_OVERRIDE_EMBEDDING' not in os.environ:
+if __name__ != "__main__":
-    os.environ['BITSANDBYTES_OVERRIDE_EMBEDDING'] = '1'
+    raise Exception("Do not invoke this from an import")
-if 'BITSANDBYTES_OVERRIDE_ADAM' not in os.environ:
-    os.environ['BITSANDBYTES_OVERRIDE_ADAM'] = '1'
-if 'BITSANDBYTES_OVERRIDE_ADAMW' not in os.environ:
-    os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '1'
-"""
 
-if __name__ == "__main__":
 parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
+parser.add_argument('--yaml', type=str, help='Path to training configuration file.', default='./training/voice/train.yml', nargs='+') # ugh
-    parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
+parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='Job launcher')
-    parser.add_argument('--mode', type=str, default='none', help='mode')
 args = parser.parse_args()
-    args.opt = " ".join(args.opt) # absolutely disgusting
+args.yaml = " ".join(args.yaml) # absolutely disgusting
+config_path = args.yaml
 
-    with open(args.opt, 'r') as file:
+with open(config_path, 'r') as file:
     opt_config = yaml.safe_load(file)
 
-    if "ext" in opt_config and "bitsandbytes" in opt_config["ext"] and not opt_config["ext"]["bitsandbytes"]:
+# it'd be downright sugoi if I was able to install DLAS as a pip package
+sys.path.insert(0, './modules/dlas/codes/')
+sys.path.insert(0, './modules/dlas/')
+
+# yucky override
+if "bitsandbytes" in opt_config and not opt_config["bitsandbytes"]:
     os.environ['BITSANDBYTES_OVERRIDE_LINEAR'] = '0'
     os.environ['BITSANDBYTES_OVERRIDE_EMBEDDING'] = '0'
     os.environ['BITSANDBYTES_OVERRIDE_ADAM'] = '0'
     os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '0'
 
-# this is some massive kludge that only works if it's called from a shell and not an import/PIP package
-# it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell
-
-sys.path.insert(0, './modules/dlas/codes/')
-# this is also because DLAS is not written as a package in mind
-# it'll gripe when it wants to import from train.py
-sys.path.insert(0, './modules/dlas/')
-
-# for PIP, replace it with:
-# sys.path.insert(0, os.path.dirname(os.path.realpath(dlas.__file__)))
-# sys.path.insert(0, f"{os.path.dirname(os.path.realpath(dlas.__file__))}/../")
-
-# don't even really bother trying to get DLAS PIP'd
-# without kludge, it'll have to be accessible as `codes` and not `dlas`
-
 import torch
-import datetime
 from codes import train as tr
 from utils import util, options as option
-from torch.distributed.run import main
 
 # this is effectively just copy pasted and cleaned up from the __main__ section of training.py
-# I'll clean it up better
+def train(config_path, launcher='none'):
-
+    opt = option.parse(config_path, is_train=True)
-def train(yaml, launcher='none'):
-    opt = option.parse(yaml, is_train=True)
 
     if launcher == 'none' and opt['gpus'] > 1:
-        return main([f"--nproc_per_node={opt['gpus']}", "--master_port=1234", "./src/train.py", "-opt", yaml, "--launcher=pytorch"])
+        return torchrun([f"--nproc_per_node={opt['gpus']}", "./src/train.py", "--yaml", config_path, "--launcher=pytorch"])
 
     trainer = tr.Trainer()
-    #### distributed training settings
+    if launcher == 'none':
-    if launcher == 'none':  # disabled distributed training
         opt['dist'] = False
         trainer.rank = -1
         if len(opt['gpu_ids']) == 1:
@@ -76,10 +56,9 @@ def train(yaml, launcher='none'):
         trainer.rank = torch.distributed.get_rank()
         torch.cuda.set_device(torch.distributed.get_rank())
 
-    trainer.init(yaml, opt, launcher, '')
+    trainer.init(config_path, opt, launcher, '')
     trainer.do_training()
 
-if __name__ == "__main__":
 try:
     import torch_intermediary
     if torch_intermediary.OVERRIDE_ADAM:
@@ -89,4 +68,4 @@ if __name__ == "__main__":
 except Exception as e:
     pass
 
-    train(args.opt, args.launcher)
+train(config_path, args.launcher)

src/utils.py

@@ -47,7 +47,7 @@ WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
 WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
 WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp"]
 VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
-TTSES = ['tortoise'] # + ['vall-e']
+TTSES = ['tortoise']
 
 GENERATE_SETTINGS_ARGS = None
 
@@ -69,6 +69,9 @@ try:
 except Exception as e:
 	pass
 
+if VALLE_ENABLED:
+	TTSES.append('vall-e')
+
 args = None
 tts = None
 tts_loading = False
@@ -613,28 +616,41 @@ class TrainingState():
 		with open(config_path, 'r') as file:
 			self.config = yaml.safe_load(file)
 
-		gpus = self.config["gpus"]
 
 		self.killed = False
 		
+		self.it = 0
+		self.step = 0
+		self.epoch = 0
+		self.checkpoint = 0
+
+		if args.tts_backend == "tortoise":
+			gpus = self.config["gpus"]
+
 			self.dataset_dir = f"./training/{self.config['name']}/finetune/"
 			self.batch_size = self.config['datasets']['train']['batch_size']
 			self.dataset_path = self.config['datasets']['train']['path']
+			
+			self.its = self.config['train']['niter']
+			self.steps = 1
+			self.epochs = 1 # int(self.its*self.batch_size/self.dataset_size)
+			self.checkpoints = int(self.its / self.config['logger']['save_checkpoint_freq'])
+		elif args.tts_backend == "vall-e":
+			self.batch_size = self.config['batch_size']
+			self.dataset_dir = f".{self.config['data_root']}/finetune/"
+			self.dataset_path = f"{self.config['data_root']}/train.txt"
+
+			self.its = 1
+			self.steps = 1
+			self.epochs = 1
+			self.checkpoints = 1
+
+			self.json_config = json.load(open(f"{self.config['data_root']}/train.json", 'r', encoding="utf-8"))
+			gpus = self.json_config['gpus']
+
 		with open(self.dataset_path, 'r', encoding="utf-8") as f:
 			self.dataset_size = len(f.readlines())
 
-		self.it = 0
-		self.its = self.config['train']['niter']
-		
-		self.step = 0
-		self.steps = 1
-
-		self.epoch = 0
-		self.epochs = int(self.its*self.batch_size/self.dataset_size)
-
-		self.checkpoint = 0
-		self.checkpoints = int(self.its / self.config['logger']['save_checkpoint_freq'])
-
 		self.buffer = []
 
 		self.open_state = False
@@ -672,6 +688,9 @@ class TrainingState():
 			self.spawn_process(config_path=config_path, gpus=gpus)
 
 	def spawn_process(self, config_path, gpus=1):
+		if args.tts_backend == "vall-e":
+			self.cmd = ['torchrun', '--nproc_per_node', f'{gpus}', '-m', 'vall_e.train', f'yaml="{config_path}"']
+		else:
 			self.cmd = ['train.bat', config_path] if os.name == "nt" else ['./train.sh', config_path]
 
 		print("Spawning process: ", " ".join(self.cmd))
@@ -1221,6 +1240,8 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 	lines = {
 		'training': [],
 		'validation': [],
+		'recordings': [],
+		'supervisions': [],
 	}
 
 	normalizer = EnglishTextNormalizer() if normalize else None
@@ -1310,13 +1331,11 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 
 			lines['training' if not culled else 'validation'].append(line) 
 
-			if culled or not VALLE_ENABLED:
+			if culled or args.tts_backend != "vall-e":
 				continue
 			
-			# VALL-E dataset
 			os.makedirs(f'{indir}/valle/', exist_ok=True)
 
-			try:
 			from vall_e.emb.qnt import encode as quantize
 			from vall_e.emb.g2p import encode as phonemize
 			
@@ -1329,10 +1348,6 @@ def prepare_dataset( voice, use_segments, text_length, audio_length, normalize=T
 			phonemes = phonemize(normalized_text)
 			open(f'{indir}/valle/{file.replace(".wav",".phn.txt")}', 'w', encoding='utf-8').write(" ".join(phonemes))
 
-			except Exception as e:
-				print(e)
-				pass
-
 	training_joined = "\n".join(lines['training'])
 	validation_joined = "\n".join(lines['validation'])
 
@@ -1588,8 +1603,9 @@ def save_training_settings( **kwargs ):
 		with open(out, 'w', encoding="utf-8") as f:
 			f.write(yaml)
 	
+	if args.tts_backend == "tortoise":
 		use_template(f'./models/.template.dlas.yaml', f'./training/{settings["voice"]}/train.yaml')
-
+	elif args.tts_backend == "vall-e":
 		settings['model_name'] = "ar"
 		use_template(f'./models/.template.valle.yaml', f'./training/{settings["voice"]}/ar.yaml')
 		settings['model_name'] = "nar"
@@ -1692,8 +1708,13 @@ 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 "train.txt" in os.listdir(os.path.join(dir, d)) ])
 
 def get_training_list(dir="./training/"):
+	if args.tts_backend == "tortoise":
 		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)) ])
 	
+	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)) ])
+	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)) ])
+	return ars + nars
+
 def pad(num, zeroes):
 	return str(num).zfill(zeroes+1)
 

train.bat

@@ -1,5 +1,5 @@
 call .\venv\Scripts\activate.bat
 set PYTHONUTF8=1
-python ./src/train.py -opt "%1"
+python ./src/train.py --yaml "%1"
 pause
 deactivate

train.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 source ./venv/bin/activate
-python3 ./src/train.py -opt "$1"
+python3 ./src/train.py --yaml "$1"
 deactivate