tab to generate the training YAML

README.md

@@ -221,6 +221,27 @@ If you want to reuse its generation settings, simply click `Copy Settings`.
 
 To import a voice, click `Import Voice`. Remember to click `Refresh Voice List` in the `Generate` panel afterwards, if it's a new voice.
 
+### Training
+
+This tab will contain a collection of sub-tabs pertaining to training.
+
+#### Configuration
+
+This will generate the YAML necessary to feed into training. For now, you can set:
+* `Batch Size`: size of batches for training, more batches = faster training, at the cost of higher VRAM. setting this to 1 will lead to problems
+* `Learning Rate`: how large changes to training will be made, lower values = better over the long term, while higher values will fry a model fast. For fine-tuning, the default *should* be fine, but in the future, a learning rate scheduler would be better (have a higher learning rate initially, then step it down over enough steps/epochs)
+* `Print Frequency`: how often to print (I assume)
+* `Save Frequency`: how often to save checkpoints
+* `Training Name`: name to save the configuration as, as well as the training script to create the folder under
+* `Dataset Name`: **!**TODO**!**: fill
+* `Dataset Path`: path to the input training text file. For LJSpeech-esque datasets, this is to a textfile formatted like:
+```
+wavs/LJ001-0001.wav|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition
+wavs/LJ001-0002.wav|in being comparatively modern.|in being comparatively modern.
+```
+* `Validation Name`: **!**TODO**!**: fill
+* `Validation Path`: path for the validation set, similar to the dataset. I'm not necessarily sure what to really use for this, so explicitly for testing, I just copied the training dataset text
+
 ### Settings
 
 This tab (should) hold a bunch of other settings, from tunables that shouldn't be tampered with, to settings pertaining to the web UI itself.

src/train.py

@@ -0,0 +1,35 @@
+import torch
+import argparse
+
+from ..dlas.codes import *
+from ..dlas.codes.utils import util, options as option
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml')
+parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
+args = parser.parse_args()
+opt = option.parse(args.opt, is_train=True)
+if args.launcher != 'none':
+    # export CUDA_VISIBLE_DEVICES for running in distributed mode.
+    if 'gpu_ids' in opt.keys():
+        gpu_list = ','.join(str(x) for x in opt['gpu_ids'])
+        os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list
+        print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
+trainer = Trainer()
+
+#### distributed training settings
+if args.launcher == 'none':  # disabled distributed training
+    opt['dist'] = False
+    trainer.rank = -1
+    if len(opt['gpu_ids']) == 1:
+        torch.cuda.set_device(opt['gpu_ids'][0])
+    print('Disabled distributed training.')
+else:
+    opt['dist'] = True
+    init_dist('nccl')
+    trainer.world_size = torch.distributed.get_world_size()
+    trainer.rank = torch.distributed.get_rank()
+    torch.cuda.set_device(torch.distributed.get_rank())
+
+trainer.init(args.opt, opt, args.launcher)
+trainer.do_training()

src/utils.py

@@ -138,7 +138,7 @@ def generate(
 	try:
 		tts
 	except NameError:
-		raise gr.Error("TTS is still initializing...")
+		raise Exception("TTS is still initializing...")
 
 	if voice != "microphone":
 		voices = [voice]
@@ -147,7 +147,7 @@ def generate(
 
 	if voice == "microphone":
 		if mic_audio is None:
-			raise gr.Error("Please provide audio from mic when choosing `microphone` as a voice input")
+			raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
 		mic = load_audio(mic_audio, tts.input_sample_rate)
 		voice_samples, conditioning_latents = [mic], None
 	elif voice == "random":
@@ -431,4 +431,250 @@ def setup_tortoise(restart=False):
 	print("Initializating TorToiSe...")
 	tts = TextToSpeech(minor_optimizations=not args.low_vram)
 	print("TorToiSe initialized, ready for generation.")
-	return tts
+	return tts
+
+def save_training_settings( batch_size=None, learning_rate=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None ):
+	settings = {
+		"batch_size": batch_size if batch_size else 128,
+		"learning_rate": learning_rate if learning_rate else 1e-5,
+		"print_rate": print_rate if print_rate else 50,
+		"save_rate": save_rate if save_rate else 50,
+		"name": name if name else "finetune",
+		"dataset_name": dataset_name if dataset_name else "finetune",
+		"dataset_path": dataset_path if dataset_path else "./experiments/finetune/train.txt",
+		"validation_name": validation_name if validation_name else "finetune",
+		"validation_path": validation_path if validation_path else "./experiments/finetune/val.txt",
+	}
+
+	with open(f'./training/.template.yaml', 'r', encoding="utf-8") as f:
+		yaml = f.read()
+
+	for k in settings:
+		print(f"${{{k}}} => {settings[k]}")
+		yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
+	
+	with open(f'./training/{settings["name"]}.yaml', 'w', encoding="utf-8") as f:
+		f.write(yaml)
+
+def reset_generation_settings():
+	with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
+		f.write(json.dumps({}, indent='\t') )
+	return import_generate_settings()
+
+def import_voice(file, saveAs = None):
+	global args
+
+	j, latents = read_generate_settings(file, read_latents=True)
+	
+	if j is not None and saveAs is None:
+		saveAs = j['voice']
+	if saveAs is None or saveAs == "":
+		raise Exception("Specify a voice name")
+
+	outdir = f'{get_voice_dir()}/{saveAs}/'
+	os.makedirs(outdir, exist_ok=True)
+	if latents:
+		with open(f'{outdir}/cond_latents.pth', 'wb') as f:
+			f.write(latents)
+		latents = f'{outdir}/cond_latents.pth'
+		print(f"Imported latents to {latents}")
+	else:
+		filename = file.name
+		if filename[-4:] != ".wav":
+			raise Exception("Please convert to a WAV first")
+
+		path = f"{outdir}/{os.path.basename(filename)}"
+		waveform, sampling_rate = torchaudio.load(filename)
+
+		if args.voice_fixer:
+			# resample to best bandwidth since voicefixer will do it anyways through librosa
+			if sampling_rate != 44100:
+				print(f"Resampling imported voice sample: {path}")
+				resampler = torchaudio.transforms.Resample(
+					sampling_rate,
+					44100,
+					lowpass_filter_width=16,
+					rolloff=0.85,
+					resampling_method="kaiser_window",
+					beta=8.555504641634386,
+				)
+				waveform = resampler(waveform)
+				sampling_rate = 44100
+
+			torchaudio.save(path, waveform, sampling_rate)
+
+			print(f"Running 'voicefixer' on voice sample: {path}")
+			voicefixer.restore(
+				input = path,
+				output = path,
+				cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
+				#mode=mode,
+			)
+		else:
+			torchaudio.save(path, waveform, sampling_rate)
+
+
+		print(f"Imported voice to {path}")
+
+
+def import_generate_settings(file="./config/generate.json"):
+	settings, _ = read_generate_settings(file, read_latents=False)
+	
+	if settings is None:
+		return None
+
+	return (
+		None if 'text' not in settings else settings['text'],
+		None if 'delimiter' not in settings else settings['delimiter'],
+		None if 'emotion' not in settings else settings['emotion'],
+		None if 'prompt' not in settings else settings['prompt'],
+		None if 'voice' not in settings else settings['voice'],
+		None,
+		None,
+		None if 'seed' not in settings else settings['seed'],
+		None if 'candidates' not in settings else settings['candidates'],
+		None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'],
+		None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'],
+		0.8 if 'temperature' not in settings else settings['temperature'],
+		"DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'],
+		8   if 'breathing_room' not in settings else settings['breathing_room'],
+		0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'],
+		0.8 if 'top_p' not in settings else settings['top_p'],
+		1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'],
+		1.0 if 'length_penalty' not in settings else settings['length_penalty'],
+		2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'],
+		2.0 if 'cond_free_k' not in settings else settings['cond_free_k'],
+		None if 'experimentals' not in settings else settings['experimentals'],
+	)
+
+def curl(url):
+	try:
+		req = urllib.request.Request(url, headers={'User-Agent': 'Python'})
+		conn = urllib.request.urlopen(req)
+		data = conn.read()
+		data = data.decode()
+		data = json.loads(data)
+		conn.close()
+		return data
+	except Exception as e:
+		print(e)
+		return None
+
+def check_for_updates():
+	if not os.path.isfile('./.git/FETCH_HEAD'):
+		print("Cannot check for updates: not from a git repo")
+		return False
+
+	with open(f'./.git/FETCH_HEAD', 'r', encoding="utf-8") as f:
+		head = f.read()
+	
+	match = re.findall(r"^([a-f0-9]+).+?https:\/\/(.+?)\/(.+?)\/(.+?)\n", head)
+	if match is None or len(match) == 0:
+		print("Cannot check for updates: cannot parse FETCH_HEAD")
+		return False
+
+	match = match[0]
+
+	local = match[0]
+	host = match[1]
+	owner = match[2]
+	repo = match[3]
+
+	res = curl(f"https://{host}/api/v1/repos/{owner}/{repo}/branches/") #this only works for gitea instances
+
+	if res is None or len(res) == 0:
+		print("Cannot check for updates: cannot fetch from remote")
+		return False
+
+	remote = res[0]["commit"]["id"]
+
+	if remote != local:
+		print(f"New version found: {local[:8]} => {remote[:8]}")
+		return True
+
+	return False
+
+def reload_tts():
+	global tts
+	del tts
+	tts = setup_tortoise(restart=True)
+
+def cancel_generate():
+	tortoise.api.STOP_SIGNAL = True
+
+def get_voice_list(dir=get_voice_dir()):
+	os.makedirs(dir, exist_ok=True)
+	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 ]) + ["microphone", "random"]
+
+def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
+	global args
+
+	args.listen = listen
+	args.share = share
+	args.check_for_updates = check_for_updates
+	args.models_from_local_only = models_from_local_only
+	args.low_vram = low_vram
+	args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
+	args.device_override = device_override
+	args.sample_batch_size = sample_batch_size
+	args.embed_output_metadata = embed_output_metadata
+	args.latents_lean_and_mean = latents_lean_and_mean
+	args.voice_fixer = voice_fixer
+	args.voice_fixer_use_cuda = voice_fixer_use_cuda
+	args.concurrency_count = concurrency_count
+	args.output_sample_rate = output_sample_rate
+	args.output_volume = output_volume
+
+	settings = {
+		'listen': None if args.listen else args.listen,
+		'share': args.share,
+		'low-vram':args.low_vram,
+		'check-for-updates':args.check_for_updates,
+		'models-from-local-only':args.models_from_local_only,
+		'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
+		'device-override': args.device_override,
+		'sample-batch-size': args.sample_batch_size,
+		'embed-output-metadata': args.embed_output_metadata,
+		'latents-lean-and-mean': args.latents_lean_and_mean,
+		'voice-fixer': args.voice_fixer,
+		'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
+		'concurrency-count': args.concurrency_count,
+		'output-sample-rate': args.output_sample_rate,
+		'output-volume': args.output_volume,
+	}
+
+	with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
+		f.write(json.dumps(settings, indent='\t') )
+
+def read_generate_settings(file, read_latents=True, read_json=True):
+	j = None
+	latents = None
+
+	if file is not None:
+		if hasattr(file, 'name'):
+			file = file.name
+
+		if file[-4:] == ".wav":
+			metadata = music_tag.load_file(file)
+			if 'lyrics' in metadata:
+				j = json.loads(str(metadata['lyrics']))
+		elif file[-5:] == ".json":
+			with open(file, 'r') as f:
+				j = json.load(f)
+
+	if j is None:
+		print("No metadata found in audio file to read")
+	else:
+		if 'latents' in j:
+			if read_latents:
+				latents = base64.b64decode(j['latents'])
+			del j['latents']
+		
+
+		if "time" in j:
+			j["time"] = "{:.3f}".format(j["time"])
+
+	return (
+		j,
+		latents,
+	)

src/webui.py

@@ -21,6 +21,71 @@ from utils import *
 
 args = setup_args()
 
+def run_generation(
+	text,
+	delimiter,
+	emotion,
+	prompt,
+	voice,
+	mic_audio,
+	voice_latents_chunks,
+	seed,
+	candidates,
+	num_autoregressive_samples,
+	diffusion_iterations,
+	temperature,
+	diffusion_sampler,
+	breathing_room,
+	cvvp_weight,
+	top_p,
+	diffusion_temperature,
+	length_penalty,
+	repetition_penalty,
+	cond_free_k,
+	experimental_checkboxes,
+	progress=gr.Progress(track_tqdm=True)
+):
+	try:
+		sample, outputs, stats = generate(
+			text,
+			delimiter,
+			emotion,
+			prompt,
+			voice,
+			mic_audio,
+			voice_latents_chunks,
+			seed,
+			candidates,
+			num_autoregressive_samples,
+			diffusion_iterations,
+			temperature,
+			diffusion_sampler,
+			breathing_room,
+			cvvp_weight,
+			top_p,
+			diffusion_temperature,
+			length_penalty,
+			repetition_penalty,
+			cond_free_k,
+			experimental_checkboxes,
+			progress
+		)
+	except Exception as e:
+		message = str(e)
+		if message == "Kill signal detected":
+			reload_tts()
+
+		raise gr.Error(message)
+	
+
+	return (
+		outputs[0],
+		gr.update(value=sample, visible=sample is not None),
+		gr.update(choices=outputs, value=outputs[0], visible=len(outputs) > 1, interactive=True),
+		gr.update(visible=len(outputs) > 1),
+		gr.update(value=stats, visible=True),
+	)
+
 def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
 	global tts
 	global args
@@ -58,230 +123,6 @@ def update_presets(value):
 	else:
 		return (gr.update(), gr.update())
 
-def read_generate_settings(file, read_latents=True, read_json=True):
-	j = None
-	latents = None
-
-	if file is not None:
-		if hasattr(file, 'name'):
-			file = file.name
-
-		if file[-4:] == ".wav":
-			metadata = music_tag.load_file(file)
-			if 'lyrics' in metadata:
-				j = json.loads(str(metadata['lyrics']))
-		elif file[-5:] == ".json":
-			with open(file, 'r') as f:
-				j = json.load(f)
-
-	if j is None:
-		gr.Error("No metadata found in audio file to read")
-	else:
-		if 'latents' in j:
-			if read_latents:
-				latents = base64.b64decode(j['latents'])
-			del j['latents']
-		
-
-		if "time" in j:
-			j["time"] = "{:.3f}".format(j["time"])
-
-	return (
-		j,
-		latents,
-	)
-
-def import_voice(file, saveAs = None):
-	global args
-
-	j, latents = read_generate_settings(file, read_latents=True)
-	
-	if j is not None and saveAs is None:
-		saveAs = j['voice']
-	if saveAs is None or saveAs == "":
-		raise gr.Error("Specify a voice name")
-
-	outdir = f'{get_voice_dir()}/{saveAs}/'
-	os.makedirs(outdir, exist_ok=True)
-	if latents:
-		with open(f'{outdir}/cond_latents.pth', 'wb') as f:
-			f.write(latents)
-		latents = f'{outdir}/cond_latents.pth'
-		print(f"Imported latents to {latents}")
-	else:
-		filename = file.name
-		if filename[-4:] != ".wav":
-			raise gr.Error("Please convert to a WAV first")
-
-		path = f"{outdir}/{os.path.basename(filename)}"
-		waveform, sampling_rate = torchaudio.load(filename)
-
-		if args.voice_fixer:
-			# resample to best bandwidth since voicefixer will do it anyways through librosa
-			if sampling_rate != 44100:
-				print(f"Resampling imported voice sample: {path}")
-				resampler = torchaudio.transforms.Resample(
-					sampling_rate,
-					44100,
-					lowpass_filter_width=16,
-					rolloff=0.85,
-					resampling_method="kaiser_window",
-					beta=8.555504641634386,
-				)
-				waveform = resampler(waveform)
-				sampling_rate = 44100
-
-			torchaudio.save(path, waveform, sampling_rate)
-
-			print(f"Running 'voicefixer' on voice sample: {path}")
-			voicefixer.restore(
-				input = path,
-				output = path,
-				cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
-				#mode=mode,
-			)
-		else:
-			torchaudio.save(path, waveform, sampling_rate)
-
-
-		print(f"Imported voice to {path}")
-
-
-def import_generate_settings(file="./config/generate.json"):
-	settings, _ = read_generate_settings(file, read_latents=False)
-	
-	if settings is None:
-		return None
-
-	return (
-		None if 'text' not in settings else settings['text'],
-		None if 'delimiter' not in settings else settings['delimiter'],
-		None if 'emotion' not in settings else settings['emotion'],
-		None if 'prompt' not in settings else settings['prompt'],
-		None if 'voice' not in settings else settings['voice'],
-		None,
-		None,
-		None if 'seed' not in settings else settings['seed'],
-		None if 'candidates' not in settings else settings['candidates'],
-		None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'],
-		None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'],
-		0.8 if 'temperature' not in settings else settings['temperature'],
-		"DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'],
-		8   if 'breathing_room' not in settings else settings['breathing_room'],
-		0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'],
-		0.8 if 'top_p' not in settings else settings['top_p'],
-		1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'],
-		1.0 if 'length_penalty' not in settings else settings['length_penalty'],
-		2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'],
-		2.0 if 'cond_free_k' not in settings else settings['cond_free_k'],
-		None if 'experimentals' not in settings else settings['experimentals'],
-	)
-
-def curl(url):
-	try:
-		req = urllib.request.Request(url, headers={'User-Agent': 'Python'})
-		conn = urllib.request.urlopen(req)
-		data = conn.read()
-		data = data.decode()
-		data = json.loads(data)
-		conn.close()
-		return data
-	except Exception as e:
-		print(e)
-		return None
-
-def check_for_updates():
-	if not os.path.isfile('./.git/FETCH_HEAD'):
-		print("Cannot check for updates: not from a git repo")
-		return False
-
-	with open(f'./.git/FETCH_HEAD', 'r', encoding="utf-8") as f:
-		head = f.read()
-	
-	match = re.findall(r"^([a-f0-9]+).+?https:\/\/(.+?)\/(.+?)\/(.+?)\n", head)
-	if match is None or len(match) == 0:
-		print("Cannot check for updates: cannot parse FETCH_HEAD")
-		return False
-
-	match = match[0]
-
-	local = match[0]
-	host = match[1]
-	owner = match[2]
-	repo = match[3]
-
-	res = curl(f"https://{host}/api/v1/repos/{owner}/{repo}/branches/") #this only works for gitea instances
-
-	if res is None or len(res) == 0:
-		print("Cannot check for updates: cannot fetch from remote")
-		return False
-
-	remote = res[0]["commit"]["id"]
-
-	if remote != local:
-		print(f"New version found: {local[:8]} => {remote[:8]}")
-		return True
-
-	return False
-
-def reload_tts():
-	global tts
-	del tts
-	tts = setup_tortoise(restart=True)
-
-def cancel_generate():
-	tortoise.api.STOP_SIGNAL = True
-
-def get_voice_list(dir=get_voice_dir()):
-	os.makedirs(dir, exist_ok=True)
-	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 ]) + ["microphone", "random"]
-
-def update_voices():
-	return (
-		gr.Dropdown.update(choices=get_voice_list()),
-		gr.Dropdown.update(choices=get_voice_list("./results/")),
-	)
-
-def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
-	global args
-
-	args.listen = listen
-	args.share = share
-	args.check_for_updates = check_for_updates
-	args.models_from_local_only = models_from_local_only
-	args.low_vram = low_vram
-	args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
-	args.device_override = device_override
-	args.sample_batch_size = sample_batch_size
-	args.embed_output_metadata = embed_output_metadata
-	args.latents_lean_and_mean = latents_lean_and_mean
-	args.voice_fixer = voice_fixer
-	args.voice_fixer_use_cuda = voice_fixer_use_cuda
-	args.concurrency_count = concurrency_count
-	args.output_sample_rate = output_sample_rate
-	args.output_volume = output_volume
-
-	settings = {
-		'listen': None if args.listen else args.listen,
-		'share': args.share,
-		'low-vram':args.low_vram,
-		'check-for-updates':args.check_for_updates,
-		'models-from-local-only':args.models_from_local_only,
-		'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
-		'device-override': args.device_override,
-		'sample-batch-size': args.sample_batch_size,
-		'embed-output-metadata': args.embed_output_metadata,
-		'latents-lean-and-mean': args.latents_lean_and_mean,
-		'voice-fixer': args.voice_fixer,
-		'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
-		'concurrency-count': args.concurrency_count,
-		'output-sample-rate': args.output_sample_rate,
-		'output-volume': args.output_volume,
-	}
-
-	with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
-		f.write(json.dumps(settings, indent='\t') )
-
 def setup_gradio():
 	global args
 	global ui
@@ -528,6 +369,29 @@ def setup_gradio():
 						import_voice_name,
 					]
 				)
+		with gr.Tab("Training"):
+			with gr.Tab("Configuration"):
+				with gr.Row():
+					with gr.Column():
+						training_settings = [
+							gr.Slider(label="Batch Size", value=128),
+							gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
+							gr.Number(label="Print Frequency", value=50),
+							gr.Number(label="Save Frequency", value=50),
+						]
+					with gr.Column():
+						training_settings = training_settings + [
+							gr.Textbox(label="Training Name", placeholder="finetune"),
+							gr.Textbox(label="Dataset Name", placeholder="finetune"),
+							gr.Textbox(label="Dataset Path", placeholder="./experiments/finetune/train.txt"),
+							gr.Textbox(label="Validation Name", placeholder="finetune"),
+							gr.Textbox(label="Validation Path", placeholder="./experiments/finetune/val.txt"),
+						]
+						save_yaml_button = gr.Button(value="Save Training Configuration")
+						save_yaml_button.click(save_training_settings,
+							inputs=training_settings,
+							outputs=None
+						)
 		with gr.Tab("Settings"):
 			with gr.Row():
 				exec_inputs = []
@@ -586,71 +450,15 @@ def setup_gradio():
 		]
 
 		# YUCK
-		def run_generation(
+		def update_voices():
-			text,
-			delimiter,
-			emotion,
-			prompt,
-			voice,
-			mic_audio,
-			voice_latents_chunks,
-			seed,
-			candidates,
-			num_autoregressive_samples,
-			diffusion_iterations,
-			temperature,
-			diffusion_sampler,
-			breathing_room,
-			cvvp_weight,
-			top_p,
-			diffusion_temperature,
-			length_penalty,
-			repetition_penalty,
-			cond_free_k,
-			experimental_checkboxes,
-			progress=gr.Progress(track_tqdm=True)
-		):
-			try:
-				sample, outputs, stats = generate(
-					text,
-					delimiter,
-					emotion,
-					prompt,
-					voice,
-					mic_audio,
-					voice_latents_chunks,
-					seed,
-					candidates,
-					num_autoregressive_samples,
-					diffusion_iterations,
-					temperature,
-					diffusion_sampler,
-					breathing_room,
-					cvvp_weight,
-					top_p,
-					diffusion_temperature,
-					length_penalty,
-					repetition_penalty,
-					cond_free_k,
-					experimental_checkboxes,
-					progress
-				)
-			except Exception as e:
-				message = str(e)
-				if message == "Kill signal detected":
-					reload_tts()
-
-				raise gr.Error(message)
-			
-
 			return (
-				outputs[0],
+				gr.Dropdown.update(choices=get_voice_list()),
-				gr.update(value=sample, visible=sample is not None),
+				gr.Dropdown.update(choices=get_voice_list("./results/")),
-				gr.update(choices=outputs, value=outputs[0], visible=len(outputs) > 1, interactive=True),
-				gr.update(visible=len(outputs) > 1),
-				gr.update(value=stats, visible=True),
 			)
 
+		def history_copy_settings( voice, file ):
+			return import_generate_settings( f"./results/{voice}/{file}" )
+
 		refresh_voices.click(update_voices,
 			inputs=None,
 			outputs=[
@@ -681,21 +489,12 @@ def setup_gradio():
 			outputs=input_settings
 		)
 
-		def reset_generation_settings():
-			with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
-				f.write(json.dumps({}, indent='\t') )
-			return import_generate_settings()
-
 		reset_generation_settings_button.click(
 			fn=reset_generation_settings,
 			inputs=None,
 			outputs=input_settings
 		)
 
-		def history_copy_settings( voice, file ):
-			settings = import_generate_settings( f"./results/{voice}/{file}" )
-			return settings
-
 		history_copy_settings_button.click(history_copy_settings,
 			inputs=[
 				history_voices,

training/.template.yaml

@@ -0,0 +1,144 @@
+name: ${name}
+model: extensibletrainer
+scale: 1
+gpu_ids: [0] # <-- unless you have multiple gpus, use this
+start_step: -1
+checkpointing_enabled: true  # <-- Gradient checkpointing. Enable for huge GPU memory savings. Disable for distributed training.
+fp16: false # might want to check this out
+wandb: false  # <-- enable to log to wandb. tensorboard logging is always enabled.
+use_tb_logger: true
+
+datasets:
+  train:
+    name: ${dataset_name}
+    n_workers: 8 # idk what this does
+    batch_size: ${batch_size} # This leads to ~16GB of vram usage on my 3090.
+    mode: paired_voice_audio
+    path: ${dataset_path}
+    fetcher_mode: ['lj'] # CHANGEME if your dataset isn't in LJSpeech format
+    phase: train
+    max_wav_length: 255995
+    max_text_length: 200
+    sample_rate: 22050
+    load_conditioning: True
+    num_conditioning_candidates: 2
+    conditioning_length: 44000
+    use_bpe_tokenizer: True
+    load_aligned_codes: False
+  val:
+    name: ${validation_name}
+    n_workers: 1
+    batch_size: 32 # this could be higher probably
+    mode: paired_voice_audio
+    path: ${validation_path}
+    fetcher_mode: ['lj']
+    phase: val # might be broken idk
+    max_wav_length: 255995
+    max_text_length: 200
+    sample_rate: 22050
+    load_conditioning: True
+    num_conditioning_candidates: 2
+    conditioning_length: 44000
+    use_bpe_tokenizer: True
+    load_aligned_codes: False
+
+steps:        
+  gpt_train:
+    training: gpt
+    loss_log_buffer: 500 # no idea what this does
+
+    # Generally follows the recipe from the DALLE paper.
+    optimizer: adamw # this should be adamw_zero if you're using distributed training
+    optimizer_params:
+      lr: !!float ${learning_rate} # CHANGEME: this was originally 1e-4; I reduced it to 1e-5 because it's fine-tuning, but **you should experiment with this value**
+      weight_decay: !!float 1e-2
+      beta1: 0.9
+      beta2: 0.96
+    clip_grad_eps: 4
+
+    injectors:  # TODO: replace this entire sequence with the GptVoiceLatentInjector
+      paired_to_mel:
+        type: torch_mel_spectrogram
+        mel_norm_file: ./experiments/clips_mel_norms.pth
+        in: wav
+        out: paired_mel
+      paired_cond_to_mel:
+        type: for_each
+        subtype: torch_mel_spectrogram
+        mel_norm_file: ./experiments/clips_mel_norms.pth
+        in: conditioning
+        out: paired_conditioning_mel
+      to_codes:
+        type: discrete_token
+        in: paired_mel
+        out: paired_mel_codes
+        dvae_config: "./experiments/train_diffusion_vocoder_22k_level.yml" # EXTREMELY IMPORTANT
+      paired_fwd_text:
+        type: generator
+        generator: gpt
+        in: [paired_conditioning_mel, padded_text, text_lengths, paired_mel_codes, wav_lengths]
+        out: [loss_text_ce, loss_mel_ce, logits]      
+    losses:
+      text_ce:
+        type: direct
+        weight: .01
+        key: loss_text_ce
+      mel_ce:
+        type: direct
+        weight: 1
+        key: loss_mel_ce
+
+networks:
+  gpt:
+    type: generator 
+    which_model_G: unified_voice2 # none of the unified_voice*.py files actually match the tortoise inference code... 4 and 3 have "alignment_head" (wtf is that?), 2 lacks the types=1 parameter.
+    kwargs:
+      layers: 30 # WAS 8
+      model_dim: 1024 # WAS 512
+      heads: 16 # WAS 8
+      max_text_tokens: 402 # WAS 120
+      max_mel_tokens: 604 # WAS 250
+      max_conditioning_inputs: 2 # WAS 1
+      mel_length_compression: 1024
+      number_text_tokens: 256 # supposed to be 255 for newer unified_voice files 
+      number_mel_codes: 8194
+      start_mel_token: 8192
+      stop_mel_token: 8193
+      start_text_token: 255
+      train_solo_embeddings: False # missing in uv3/4
+      use_mel_codes_as_input: True # ditto
+      checkpointing: True
+      #types: 1 # this is MISSING, but in my analysis 1 is equivalent to not having it.
+      #only_alignment_head: False  # uv3/4
+
+path:
+  pretrain_model_gpt: './experiments/autoregressive.pth' # CHANGEME: copy this from tortoise cache
+  strict_load: true
+  #resume_state: ./experiments/train_imgnet_vqvae_stage1/training_state/0.state   # <-- Set this to resume from a previous training state.
+
+# afaik all units here are measured in **steps** (i.e. one batch of batch_size is 1 unit)
+train: # CHANGEME: ALL OF THESE PARAMETERS SHOULD BE EXPERIMENTED WITH
+  niter: 50000
+  warmup_iter: -1
+  mega_batch_factor: 4    # <-- Gradient accumulation factor. If you are running OOM, increase this to [2,4,8].
+  val_freq: 500
+
+  default_lr_scheme: MultiStepLR
+  gen_lr_steps: [500, 1000, 1400, 1800] #[50000, 100000, 140000, 180000]
+  lr_gamma: 0.5
+
+eval:
+  output_state: gen
+  injectors:
+    gen_inj_eval:
+      type: generator
+      generator: generator
+      in: hq
+      out: [gen, codebook_commitment_loss]
+
+logger: 
+  print_freq: 100
+  save_checkpoint_freq: 500 # CHANGEME: especially you should increase this it's really slow
+  visuals: [gen, mel]
+  visual_debug_rate: 500
+  is_mel_spectrogram: true