ai-voice-cloning/src/utils.py

import os
if 'XDG_CACHE_HOME' not in os.environ:
	os.environ['XDG_CACHE_HOME'] = os.path.realpath(os.path.join(os.getcwd(), './models/'))

if 'TORTOISE_MODELS_DIR' not in os.environ:
	os.environ['TORTOISE_MODELS_DIR'] = os.path.realpath(os.path.join(os.getcwd(), './models/tortoise/'))

if 'TRANSFORMERS_CACHE' not in os.environ:
	os.environ['TRANSFORMERS_CACHE'] = os.path.realpath(os.path.join(os.getcwd(), './models/transformers/'))

import argparse
import time
import json
import base64
import re
import urllib.request
import signal
import gc
import subprocess
import psutil
import yaml

import tqdm
import torch
import torchaudio
import music_tag
import gradio as gr
import gradio.utils
import pandas as pd

from datetime import datetime
from datetime import timedelta

from tortoise.api import TextToSpeech, MODELS, get_model_path, pad_or_truncate
from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
from tortoise.utils.text import split_and_recombine_text
from tortoise.utils.device import get_device_name, set_device_name, get_device_count, get_device_vram

MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"

WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v2"]
WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]

VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']

GENERATE_SETTINGS_ARGS = None

EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]

args = None
tts = None
tts_loading = False
webui = None
voicefixer = None
whisper_model = None
training_state = None

current_voice = None

def generate(**kwargs):
	parameters = {}
	parameters.update(kwargs)

	voice = parameters['voice']
	progress = parameters['progress'] if 'progress' in parameters else None
	if parameters['seed'] == 0:
		parameters['seed'] = None

	usedSeed = parameters['seed']

	global args
	global tts

	unload_whisper()
	unload_voicefixer()

	if not tts:
		# should check if it's loading or unloaded, and load it if it's unloaded
		if tts_loading:
			raise Exception("TTS is still initializing...")
		if progress is not None:
			progress(0, "Initializing TTS...")
		load_tts()
	if hasattr(tts, "loading") and tts.loading:
		raise Exception("TTS is still initializing...")

	do_gc()

	voice_samples = None
	conditioning_latents =None
	sample_voice = None

	voice_cache = {}
	def fetch_voice( voice ):
		print(f"Loading voice: {voice} with model {tts.autoregressive_model_hash[:8]}")
		cache_key = f'{voice}:{tts.autoregressive_model_hash[:8]}'
		if cache_key in voice_cache:
			return voice_cache[cache_key]

		sample_voice = None
		if voice == "microphone":
			if parameters['mic_audio'] is None:
				raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
			voice_samples, conditioning_latents = [load_audio(parameters['mic_audio'], tts.input_sample_rate)], None
		elif voice == "random":
			voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
		else:
			if progress is not None:
				progress(0, desc=f"Loading voice: {voice}")

			voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
			
		if voice_samples and len(voice_samples) > 0:
			if conditioning_latents is None:
				conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=parameters['voice_latents_chunks'])
				
			sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
			voice_samples = None

		voice_cache[cache_key] = (voice_samples, conditioning_latents, sample_voice)
		return voice_cache[cache_key]

	def get_settings( override=None ):
		settings = {
			'temperature': float(parameters['temperature']),

			'top_p': float(parameters['top_p']),
			'diffusion_temperature': float(parameters['diffusion_temperature']),
			'length_penalty': float(parameters['length_penalty']),
			'repetition_penalty': float(parameters['repetition_penalty']),
			'cond_free_k': float(parameters['cond_free_k']),

			'num_autoregressive_samples': parameters['num_autoregressive_samples'],
			'sample_batch_size': args.sample_batch_size,
			'diffusion_iterations': parameters['diffusion_iterations'],

			'voice_samples': None,
			'conditioning_latents': None,

			'use_deterministic_seed': parameters['seed'],
			'return_deterministic_state': True,
			'k': parameters['candidates'],
			'diffusion_sampler': parameters['diffusion_sampler'],
			'breathing_room': parameters['breathing_room'],
			'progress': parameters['progress'],
			'half_p': "Half Precision" in parameters['experimentals'],
			'cond_free': "Conditioning-Free" in parameters['experimentals'],
			'cvvp_amount': parameters['cvvp_weight'],
			'autoregressive_model': args.autoregressive_model,
		}

		# could be better to just do a ternary on everything above, but i am not a professional
		selected_voice = voice
		if override is not None:
			if 'voice' in override:
				selected_voice = override['voice']

			for k in override:
				if k not in settings:
					continue
				settings[k] = override[k]

		if settings['autoregressive_model'] is not None:
			if settings['autoregressive_model'] == "auto":
				settings['autoregressive_model'] = deduce_autoregressive_model(selected_voice)
			tts.load_autoregressive_model(settings['autoregressive_model'])

		settings['voice_samples'], settings['conditioning_latents'], _ = fetch_voice(voice=selected_voice)

		# clamp it down for the insane users who want this
		# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
		settings['sample_batch_size'] = args.sample_batch_size
		if not settings['sample_batch_size']:
			settings['sample_batch_size'] = tts.autoregressive_batch_size
		if settings['num_autoregressive_samples'] < settings['sample_batch_size']:
			settings['sample_batch_size'] = settings['num_autoregressive_samples']

		if settings['conditioning_latents'] is not None and len(settings['conditioning_latents']) == 2 and settings['cvvp_amount'] > 0:
			print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents with 'Slimmer voice latents' unchecked.")
			settings['cvvp_amount'] = 0
			
		return settings

	if not parameters['delimiter']:
		parameters['delimiter'] = "\n"
	elif parameters['delimiter'] == "\\n":
		parameters['delimiter'] = "\n"

	if parameters['delimiter'] and parameters['delimiter'] != "" and parameters['delimiter'] in parameters['text']:
		texts = parameters['text'].split(parameters['delimiter'])
	else:
		texts = split_and_recombine_text(parameters['text'])
 
	full_start_time = time.time()
 
	outdir = f"./results/{voice}/"
	os.makedirs(outdir, exist_ok=True)

	audio_cache = {}
	resample = None

	if tts.output_sample_rate != args.output_sample_rate:
		resampler = torchaudio.transforms.Resample(
			tts.output_sample_rate,
			args.output_sample_rate,
			lowpass_filter_width=16,
			rolloff=0.85,
			resampling_method="kaiser_window",
			beta=8.555504641634386,
		)

	volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None

	idx = 0
	idx_cache = {}
	for i, file in enumerate(os.listdir(outdir)):
		filename = os.path.basename(file)
		extension = os.path.splitext(filename)[1]
		if extension != ".json" and extension != ".wav":
			continue
		match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename)
		if match and len(match) > 0:
			key = int(match[0])
			idx_cache[key] = True

	if len(idx_cache) > 0:
		keys = sorted(list(idx_cache.keys()))
		idx = keys[-1] + 1

	idx = pad(idx, 4)

	def get_name(line=0, candidate=0, combined=False):
		name = f"{idx}"
		if combined:
			name = f"{name}_combined"
		elif len(texts) > 1:
			name = f"{name}_{line}"
		if parameters['candidates'] > 1:
			name = f"{name}_{candidate}"
		return name

	def get_info( voice, settings = None, latents = True ):
		info = {}
		info.update(parameters)
		info['time'] = time.time()-full_start_time

		info['datetime'] = datetime.now().isoformat()
		info['model'] = tts.autoregressive_model_path
		info['model_hash'] = tts.autoregressive_model_hash 
		info['progress'] = None
		del info['progress']

		if info['delimiter'] == "\n":
			info['delimiter'] = "\\n"

		if settings is not None:
			for k in settings:
				if k in info:
					info[k] = settings[k]

			if 'half_p' in settings and 'cond_free' in settings:
				info['experimentals'] = []
				if settings['half_p']:
					info['experimentals'].append("Half Precision")
				if settings['cond_free']:
					info['experimentals'].append("Conditioning-Free")

		if latents and "latents" not in info:
			voice = info['voice']
			model_hash = settings["model_hash"][:8] if settings is not None and "model_hash" in settings else tts.autoregressive_model_hash[:8]

			dir = f'{get_voice_dir()}/{voice}/'
			latents_path = f'{dir}/cond_latents_{model_hash}.pth'

			if voice == "random" or voice == "microphone":
				if latents and settings is not None and settings['conditioning_latents']:
					os.makedirs(dir, exist_ok=True)
					torch.save(conditioning_latents, latents_path)

			if latents_path and os.path.exists(latents_path):
				try:
					with open(latents_path, 'rb') as f:
						info['latents'] = base64.b64encode(f.read()).decode("ascii")
				except Exception as e:
					pass

		return info

	for line, cut_text in enumerate(texts):
		if parameters['emotion'] == "Custom":
			if parameters['prompt'] and parameters['prompt'].strip() != "":
				cut_text = f"[{parameters['prompt']},] {cut_text}"
		elif parameters['emotion'] != "None" and parameters['emotion']:
			cut_text = f"[I am really {parameters['emotion'].lower()},] {cut_text}"
		
		progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
		print(f"{progress.msg_prefix} Generating line: {cut_text}")
		start_time = time.time()

		# do setting editing
		match = re.findall(r'^(\{.+\}) (.+?)$', cut_text) 
		override = None
		if match and len(match) > 0:
			match = match[0]
			try:
				override = json.loads(match[0])
				cut_text = match[1].strip()
			except Exception as e:
				raise Exception("Prompt settings editing requested, but received invalid JSON")

		settings = get_settings( override=override )
		gen, additionals = tts.tts(cut_text, **settings )

		parameters['seed'] = additionals[0]
		run_time = time.time()-start_time
		print(f"Generating line took {run_time} seconds")

		if not isinstance(gen, list):
			gen = [gen]

		for j, g in enumerate(gen):
			audio = g.squeeze(0).cpu()
			name = get_name(line=line, candidate=j)

			settings['text'] = cut_text
			settings['time'] = run_time
			settings['datetime'] = datetime.now().isoformat(),
			settings['model'] = tts.autoregressive_model_path
			settings['model_hash'] = tts.autoregressive_model_hash

			audio_cache[name] = {
				'audio': audio,
				'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=settings)
			}
			# save here in case some error happens mid-batch
			torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, tts.output_sample_rate)

	del gen
	do_gc()

	for k in audio_cache:
		audio = audio_cache[k]['audio']

		if resampler is not None:
			audio = resampler(audio)
		if volume_adjust is not None:
			audio = volume_adjust(audio)

		audio_cache[k]['audio'] = audio
		torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate)

	output_voices = []
	for candidate in range(parameters['candidates']):
		if len(texts) > 1:
			audio_clips = []
			for line in range(len(texts)):
				name = get_name(line=line, candidate=candidate)
				audio = audio_cache[name]['audio']
				audio_clips.append(audio)
			
			name = get_name(candidate=candidate, combined=True)
			audio = torch.cat(audio_clips, dim=-1)
			torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)

			audio = audio.squeeze(0).cpu()
			audio_cache[name] = {
				'audio': audio,
				'settings': get_info(voice=voice),
				'output': True
			}
		else:
			name = get_name(candidate=candidate)
			audio_cache[name]['output'] = True


	if args.voice_fixer:
		if not voicefixer:
			progress(0, "Loading voicefix...")
			load_voicefixer()

		try:
			fixed_cache = {}
			for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
				del audio_cache[name]['audio']
				if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
					continue

				path = f'{outdir}/{voice}_{name}.wav'
				fixed = f'{outdir}/{voice}_{name}_fixed.wav'
				voicefixer.restore(
					input=path,
					output=fixed,
					cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
					#mode=mode,
				)
				
				fixed_cache[f'{name}_fixed'] = {
					'settings': audio_cache[name]['settings'],
					'output': True
				}
				audio_cache[name]['output'] = False
			
			for name in fixed_cache:
				audio_cache[name] = fixed_cache[name]
		except Exception as e:
			print(e)
			print("\nFailed to run Voicefixer")

	for name in audio_cache:
		if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
			if args.prune_nonfinal_outputs:
				audio_cache[name]['pruned'] = True
				os.remove(f'{outdir}/{voice}_{name}.wav')
			continue

		output_voices.append(f'{outdir}/{voice}_{name}.wav')

		if not args.embed_output_metadata:
			with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
				f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )

	if args.embed_output_metadata:
		for name in progress.tqdm(audio_cache, desc="Embedding metadata..."):
			if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
				continue

			metadata = music_tag.load_file(f"{outdir}/{voice}_{name}.wav")
			metadata['lyrics'] = json.dumps(audio_cache[name]['settings'])
			metadata.save()
 
	if sample_voice is not None:
		sample_voice = (tts.input_sample_rate, sample_voice.numpy())

	info = get_info(voice=voice, latents=False)
	print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")

	info['seed'] = usedSeed
	if 'latents' in info:
		del info['latents']

	os.makedirs('./config/', exist_ok=True)
	with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
		f.write(json.dumps(info, indent='\t') )

	stats = [
		[ parameters['seed'], "{:.3f}".format(info['time']) ]
	]

	return (
		sample_voice,
		output_voices,
		stats,
	)

def cancel_generate():
	import tortoise.api
	tortoise.api.STOP_SIGNAL = True

def hash_file(path, algo="md5", buffer_size=0):
	import hashlib

	hash = None
	if algo == "md5":
		hash = hashlib.md5()
	elif algo == "sha1":
		hash = hashlib.sha1()
	else:
		raise Exception(f'Unknown hash algorithm specified: {algo}')

	if not os.path.exists(path):
		raise Exception(f'Path not found: {path}')

	with open(path, 'rb') as f:
		if buffer_size > 0:
			while True:
				data = f.read(buffer_size)
				if not data:
					break
				hash.update(data)
		else:
			hash.update(f.read())

	return "{0}".format(hash.hexdigest())

def update_baseline_for_latents_chunks( voice ):
	global current_voice
	current_voice = voice

	path = f'{get_voice_dir()}/{voice}/'
	if not os.path.isdir(path):
		return 1

	dataset_file = f'./training/{voice}/train.txt'
	if os.path.exists(dataset_file):
		return 0 # 0 will leverage using the LJspeech dataset for computing latents

	files = os.listdir(path)
	
	total = 0
	total_duration = 0

	for file in files:
		if file[-4:] != ".wav":
			continue

		metadata = torchaudio.info(f'{path}/{file}')
		duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
		total_duration += duration
		total = total + 1


	# brain too fried to figure out a better way
	if args.autocalculate_voice_chunk_duration_size == 0:
		return int(total_duration / total) if total > 0 else 1
	return int(total_duration / args.autocalculate_voice_chunk_duration_size) if total_duration > 0 else 1

def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, progress=None):
	global tts
	global args
	
	unload_whisper()
	unload_voicefixer()

	if not tts:
		if tts_loading:
			raise Exception("TTS is still initializing...")
		load_tts()

	if hasattr(tts, "loading") and tts.loading:
		raise Exception("TTS is still initializing...")

	if args.autoregressive_model == "auto":
		tts.load_autoregressive_model(deduce_autoregressive_model(voice))

	if voice:
		load_from_dataset = voice_latents_chunks == 0

		if load_from_dataset:
			dataset_path = f'./training/{voice}/train.txt'
			if not os.path.exists(dataset_path):
				load_from_dataset = False
			else:
				with open(dataset_path, 'r', encoding="utf-8") as f:
					lines = f.readlines()

				print("Leveraging LJSpeech dataset for computing latents")

				voice_samples = []
				max_length = 0
				for line in lines:
					filename = f'./training/{voice}/{line.split("|")[0]}'
					
					waveform = load_audio(filename, 22050)
					max_length = max(max_length, waveform.shape[-1])
					voice_samples.append(waveform)

				for i in range(len(voice_samples)):
					voice_samples[i] = pad_or_truncate(voice_samples[i], max_length)

				voice_latents_chunks = len(voice_samples)
		if not load_from_dataset:
			voice_samples, _ = load_voice(voice, load_latents=False)

	if voice_samples is None:
		return

	conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents, progress=progress)

	if len(conditioning_latents) == 4:
		conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
	
	outfile = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
	torch.save(conditioning_latents, outfile)
	print(f'Saved voice latents: {outfile}')

	return conditioning_latents

# superfluous, but it cleans up some things
class TrainingState():
	def __init__(self, config_path, keep_x_past_checkpoints=0, start=True):
		# parse config to get its iteration
		with open(config_path, 'r') as file:
			self.config = yaml.safe_load(file)

		gpus = self.config["gpus"]

		self.killed = False

		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']
		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.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
		self.training_started = False

		self.info = {}

		self.epoch_rate = ""
		self.epoch_time_start = 0
		self.epoch_time_end = 0
		self.epoch_time_deltas = 0
		self.epoch_taken = 0
		
		self.it_rate = ""
		self.it_time_start = 0
		self.it_time_end = 0
		self.it_time_deltas = 0
		self.it_taken = 0
		self.last_step = 0

		self.eta = "?"
		self.eta_hhmmss = "?"

		self.nan_detected = False

		self.last_info_check_at = 0
		self.statistics = []
		self.losses = []
		self.metrics = {
			'step': "",
			'rate': "",
			'loss': "",
		}

		self.loss_milestones = [ 1.0, 0.15, 0.05 ]

		self.load_losses()
		if keep_x_past_checkpoints > 0:
			self.cleanup_old(keep=keep_x_past_checkpoints)
		if start:
			self.spawn_process(config_path=config_path, gpus=gpus)

	def spawn_process(self, config_path, gpus=1):
		self.cmd = ['train.bat', config_path] if os.name == "nt" else ['./train.sh', str(int(gpus)), config_path]

		print("Spawning process: ", " ".join(self.cmd))
		self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)

	def load_losses(self, update=False):
		if not os.path.isdir(f'{self.dataset_dir}/tb_logger/'):
			return
		try:
			from tensorboard.backend.event_processing import event_accumulator
			use_tensorboard = True
		except Exception as e:
			use_tensorboard = False

		keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total', 'val_loss_text_ce', 'val_loss_mel_ce']
		infos = {}
		highest_step = self.last_info_check_at

		if not update:
			self.statistics = []

		if use_tensorboard:
			logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
			if update:
				logs = [logs[-1]]

			for log in logs:
					ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
					ea.Reload()

					for key in keys:
						try:
							scalar = ea.Scalars(key)
							for s in scalar:
								if update and s.step <= self.last_info_check_at:
									continue
								highest_step = max( highest_step, s.step )
								self.statistics.append( { "step": s.step, "value": s.value, "type": key } )

								if key == 'loss_gpt_total':
									self.losses.append( { "step": s.step, "value": s.value, "type": key } )
						except Exception as e:
							pass

		else:
			logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
			if update:
				logs = [logs[-1]]

			for log in logs:
				with open(log, 'r', encoding="utf-8") as f:
					lines = f.readlines()
					for line in lines:
						if line.find('INFO: [epoch:') >= 0:
							# easily rip out our stats...
							match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', line)
							if not match or len(match) == 0:
								continue

							info = {}
							for k, v in match:
								info[k] = float(v.replace(",", ""))

							if 'iter' in info:
								it = info['iter']
								infos[it] = info

			for k in infos:
				if 'loss_gpt_total' in infos[k]:
					for key in keys:
						if update and int(k) <= self.last_info_check_at:
							continue
						highest_step = max( highest_step, s.step )
						self.statistics.append({ "step": int(k), "value": infos[k][key], "type": key })

						if key == "loss_gpt_total":
							self.losses.append({ "step": int(k), "value": infos[k][key], "type": key })

		self.last_info_check_at = highest_step

	def cleanup_old(self, keep=2):
		if keep <= 0:
			return

		if not os.path.isdir(self.dataset_dir):
			return
			
		models = sorted([ int(d[:-8]) for d in os.listdir(f'{self.dataset_dir}/models/') if d[-8:] == "_gpt.pth" ])
		states = sorted([ int(d[:-6]) for d in os.listdir(f'{self.dataset_dir}/training_state/') if d[-6:] == ".state" ])
		remove_models = models[:-2]
		remove_states = states[:-2]

		for d in remove_models:
			path = f'{self.dataset_dir}/models/{d}_gpt.pth'
			print("Removing", path)
			os.remove(path)
		for d in remove_states:
			path = f'{self.dataset_dir}/training_state/{d}.state'
			print("Removing", path)
			os.remove(path)

	def parse(self, line, verbose=False, keep_x_past_checkpoints=0, buffer_size=8, progress=None ):
		self.buffer.append(f'{line}')

		should_return = False
		percent = 0
		message = None

		# rip out iteration info
		if not self.training_started:
			if line.find('Start training from epoch') >= 0:
				self.it_time_start = time.time()
				self.epoch_time_start = time.time()
				self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
				should_return = True

				match = re.findall(r'epoch: ([\d,]+)', line)
				if match and len(match) > 0:
					self.epoch = int(match[0].replace(",", ""))
				match = re.findall(r'iter: ([\d,]+)', line)
				if match and len(match) > 0:
					self.it = int(match[0].replace(",", ""))

				self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
		else:
			lapsed = False

			message = None
			if line.find('INFO: [epoch:') >= 0:
				info_line = line.split("INFO:")[-1]
				# to-do, actually validate this works, and probably kill training when it's found, the model's dead by this point
				if ': nan' in info_line and not self.nan_detected:
					self.nan_detected = self.it

				# easily rip out our stats...
				match = re.findall(r'\b([a-z_0-9]+?)\b: *?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', info_line)
				if match and len(match) > 0:
					for k, v in match:
						self.info[k] = float(v.replace(",", ""))

				self.load_losses(update=True)
				should_return = True

				if 'epoch' in self.info:
					self.epoch = int(self.info['epoch'])
				if 'iter' in self.info:
					self.it = int(self.info['iter'])

			elif line.find('Saving models and training states') >= 0:
				self.checkpoint = self.checkpoint + 1

				percent = self.checkpoint / float(self.checkpoints)
				message = f'[{self.checkpoint}/{self.checkpoints}] Saving checkpoint...'
				if progress is not None:
					progress(percent, message)

				print(f'{"{:.3f}".format(percent*100)}% {message}')
				self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')

				self.cleanup_old(keep=keep_x_past_checkpoints)

			if line.find('%|') > 0:
				match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
				if match and len(match) > 0:
					match = match[0]
					per_cent = int(match[0])/100.0
					progressbar = match[1]
					step = int(match[2])
					steps = int(match[3])
					elapsed = match[4]
					until = match[5]
					rate = match[6]

					last_step = self.last_step
					self.last_step = step
					if last_step < step:
						self.it = self.it + (step - last_step)

					if last_step == step and step == steps:
						lapsed = True

					self.it_time_end = time.time()
					self.it_time_delta = self.it_time_end-self.it_time_start
					self.it_time_start = time.time()
					self.it_taken = self.it_taken + 1
					if self.it_time_delta:
						try:
							rate = f'{"{:.3f}".format(self.it_time_delta)}s/it' if self.it_time_delta >= 1 or self.it_time_delta == 0 else f'{"{:.3f}".format(1/self.it_time_delta)}it/s'
							self.it_rate = rate
						except Exception as e:
							pass

					self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
					if self.epochs != self.its:
						self.metrics['step'].append(f"{self.it}/{self.its}")
					if steps > 1:
						self.metrics['step'].append(f"{step}/{steps}")
					self.metrics['step'] = ", ".join(self.metrics['step'])

			if lapsed:
				self.epoch = self.epoch + 1
				self.it = int(self.epoch * (self.dataset_size / self.batch_size))

				self.epoch_time_end = time.time()
				self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
				self.epoch_time_start = time.time()
				try:
					self.epoch_rate = f'{"{:.3f}".format(self.epoch_time_delta)}s/epoch' if self.epoch_time_delta >= 1 or self.epoch_time_delta == 0 else f'{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s' # I doubt anyone will have it/s rates, but its here
				except Exception as e:
					pass
				
				#self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
				self.epoch_time_deltas = self.epoch_time_deltas + self.epoch_time_delta
				self.epoch_taken = self.epoch_taken + 1
				self.eta = (self.epochs - self.epoch) * (self.epoch_time_deltas / self.epoch_taken)
				try:
					eta = str(timedelta(seconds=int(self.eta)))
					self.eta_hhmmss = eta
				except Exception as e:
					pass

			self.metrics['rate'] = []
			if self.epoch_rate:
				self.metrics['rate'].append(self.epoch_rate)
			if self.it_rate and self.epoch_rate != self.it_rate:
				self.metrics['rate'].append(self.it_rate)
			self.metrics['rate'] = ", ".join(self.metrics['rate'])

			eta_hhmmss = "?"
			if self.eta_hhmmss:
				eta_hhmmss = self.eta_hhmmss
			else:
				try:
					eta = (self.its - self.it) * (self.it_time_deltas / self.it_taken)
					eta = str(timedelta(seconds=int(eta)))
					eta_hhmmss = eta
				except Exception as e:
					pass
			
			self.metrics['loss'] = []

			if 'learning_rate_gpt_0' in self.info:
				self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["learning_rate_gpt_0"])}')

			if len(self.losses) > 0:
				self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')

			if len(self.losses) >= 2:
				# """riemann sum""" but not really as this is for derivatives and not integrals
				deriv = 0
				accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
				loss_value = self.losses[-1]["value"]

				for i in range(accum_length):
					d1_loss = self.losses[accum_length-i-1]["value"]
					d2_loss = self.losses[accum_length-i-2]["value"]
					dloss = (d2_loss - d1_loss)

					d1_step = self.losses[accum_length-i-1]["step"]
					d2_step = self.losses[accum_length-i-2]["step"]
					dstep = (d2_step - d1_step)

					if dstep == 0:
						continue
			
					inst_deriv = dloss / dstep
					deriv += inst_deriv

				deriv = deriv / accum_length

				if deriv != 0: # dloss < 0:
					next_milestone = None
					for milestone in self.loss_milestones:
						if loss_value > milestone:
							next_milestone = milestone
							break
							
					if next_milestone:
						# tfw can do simple calculus but not basic algebra in my head
						est_its = (next_milestone - loss_value) / deriv
						if est_its >= 0:
							self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
					else:
						est_loss = inst_deriv * (self.its - self.it) + loss_value
						if est_loss >= 0:
							self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')

			self.metrics['loss'] = ", ".join(self.metrics['loss'])

			message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
			if self.nan_detected:
				message = f"[!NaN DETECTED! {self.nan_detected}] {message}"

			if message:
				percent = self.it / float(self.its) # self.epoch / float(self.epochs)
				if progress is not None:
					progress(percent, message)

				self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')

		if verbose and not self.training_started:
			should_return = True

		self.buffer = self.buffer[-buffer_size:]
		
		result = None
		if should_return:
			result = "".join(self.buffer) if not self.training_started else message

		return (
			result,
			percent,
			message,
		)

try:
	import altair as alt
	alt.data_transformers.enable('default', max_rows=None)
except Exception as e:
	print(e)
	pass

def run_training(config_path, verbose=False, keep_x_past_checkpoints=0, progress=gr.Progress(track_tqdm=True)):
	global training_state
	if training_state and training_state.process:
		return "Training already in progress"


	# ensure we have the dvae.pth
	get_model_path('dvae.pth')
	
	# I don't know if this is still necessary, as it was bitching at me for not doing this, despite it being in a separate process
	torch.multiprocessing.freeze_support()

	unload_tts()
	unload_whisper()
	unload_voicefixer()

	training_state = TrainingState(config_path=config_path, keep_x_past_checkpoints=keep_x_past_checkpoints)

	for line in iter(training_state.process.stdout.readline, ""):
		if training_state.killed:
			return

		result, percent, message = training_state.parse( line=line, verbose=verbose, keep_x_past_checkpoints=keep_x_past_checkpoints, progress=progress )
		print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
		if result:
			yield result

			if progress is not None and message:
				progress(percent, message)

	if training_state:
		training_state.process.stdout.close()
		return_code = training_state.process.wait()
		training_state = None

def update_training_dataplot(config_path=None):
	global training_state
	update = None

	if not training_state:
		if config_path:
			training_state = TrainingState(config_path=config_path, start=False)
			if training_state.statistics:
				update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=600, height=350,)
			del training_state
			training_state = None
	elif training_state.statistics:
		training_state.load_losses()
		update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=600, height=350,)

	return update

def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
	global training_state
	if not training_state or not training_state.process:
		return "Training not in progress"

	for line in iter(training_state.process.stdout.readline, ""):
		result, percent, message = training_state.parse( line=line, verbose=verbose, progress=progress )
		print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
		if result:
			yield result

			if progress is not None and message:
				progress(percent, message)

def stop_training():
	global training_state
	if training_state is None:
		return "No training in progress"
	print("Killing training process...")
	training_state.killed = True

	children = []
	# wrapped in a try/catch in case for some reason this fails outside of Linux
	try:
		children = [p.info for p in psutil.process_iter(attrs=['pid', 'name', 'cmdline']) if './src/train.py' in p.info['cmdline']]
	except Exception as e:
		pass

	training_state.process.stdout.close()
	training_state.process.terminate()
	training_state.process.kill()
	return_code = training_state.process.wait()

	for p in children:
		os.kill( p['pid'], signal.SIGKILL )

	training_state = None
	print("Killed training process.")
	return f"Training cancelled: {return_code}"

def get_halfp_model_path():
	autoregressive_model_path = get_model_path('autoregressive.pth')
	return autoregressive_model_path.replace(".pth", "_half.pth")

def convert_to_halfp():
	autoregressive_model_path = get_model_path('autoregressive.pth')
	print(f'Converting model to half precision: {autoregressive_model_path}')
	model = torch.load(autoregressive_model_path)
	for k in model:
		model[k] = model[k].half()

	outfile = get_halfp_model_path()
	torch.save(model, outfile)
	print(f'Converted model to half precision: {outfile}')

def whisper_transcribe( file, language=None ):
	# shouldn't happen, but it's for safety
	if not whisper_model:
		load_whisper_model(language=language)

	if args.whisper_backend == "openai/whisper":
		if not language:
			language = None

		return whisper_model.transcribe(file, language=language)

	elif args.whisper_backend == "lightmare/whispercpp":
		res = whisper_model.transcribe(file)
		segments = whisper_model.extract_text_and_timestamps( res )

		result = {
			'segments': []
		}
		for segment in segments:
			reparsed = {
				'start': segment[0] / 100.0,
				'end': segment[1] / 100.0,
				'text': segment[2],
			}
			result['segments'].append(reparsed)

		return result

	# credit to https://git.ecker.tech/yqxtqymn for the busywork of getting this added
	elif args.whisper_backend == "m-bain/whisperx":
		import whisperx
		device = "cuda" if get_device_name() == "cuda" else "cpu"
		result = whisper_model.transcribe(file)
		model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
		result_aligned = whisperx.align(result["segments"], model_a, metadata, file, device)

		for i in range(len(result_aligned['segments'])):
			del result_aligned['segments'][i]['word-segments']
			del result_aligned['segments'][i]['char-segments']

		result['segments'] = result_aligned['segments']

		return result

def prepare_dataset( files, outdir, language=None, skip_existings=False, progress=None ):
	unload_tts()

	global whisper_model
	if whisper_model is None:
		load_whisper_model(language=language)

	os.makedirs(f'{outdir}/audio/', exist_ok=True)

	results = {}
	transcription = []
	files = sorted(files)

	previous_list = []
	if skip_existings and os.path.exists(f'{outdir}/train.txt'):
		parsed_list = []
		with open(f'{outdir}/train.txt', 'r', encoding="utf-8") as f:
			parsed_list = f.readlines()

		for line in parsed_list:
			match = re.findall(r"^(.+?)_\d+\.wav$", line.split("|")[0])

			if match is None or len(match) == 0:
				continue
			
			if match[0] not in previous_list:
				previous_list.append(f'{match[0].split("/")[-1]}.wav')

	for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
		basename = os.path.basename(file)

		if basename in previous_list:
			print(f"Skipping already parsed file: {basename}")
			continue

		result = whisper_transcribe(file, language=language)
		results[basename] = result
		print(f"Transcribed file: {file}, {len(result['segments'])} found.")

		waveform, sampling_rate = torchaudio.load(file)
		num_channels, num_frames = waveform.shape

		idx = 0
		for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
			start = int(segment['start'] * sampling_rate)
			end = int(segment['end'] * sampling_rate)

			sliced_waveform = waveform[:, start:end]
			sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")

			if not torch.any(sliced_waveform < 0):
				print(f"Error with {sliced_name}, skipping...")
				continue

			torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)

			idx = idx + 1
			line = f"audio/{sliced_name}|{segment['text'].strip()}"
			transcription.append(line)
			with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
				f.write(f'\n{line}')

		do_gc()
	
	with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
		f.write(json.dumps(results, indent='\t'))

	unload_whisper()

	joined = "\n".join(transcription)
	if not skip_existings:
		with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
			f.write(joined)

	return f"Processed dataset to: {outdir}\n{joined}"

def prepare_validation_dataset( voice, text_length ):
	indir = f'./training/{voice}/'
	infile = f'{indir}/dataset.txt'
	if not os.path.exists(infile):
		infile = f'{indir}/train.txt'
		with open(f'{indir}/train.txt', 'r', encoding="utf-8") as src:
			with open(f'{indir}/dataset.txt', 'w', encoding="utf-8") as dst:
				dst.write(src.read())

	if not os.path.exists(infile):
		raise Exception(f"Missing dataset: {infile}")

	with open(infile, 'r', encoding="utf-8") as f:
		lines = f.readlines()

	validation = []
	training = []

	for line in lines:
		split = line.split("|")
		filename = split[0]
		text = split[1]

		if len(text) < text_length:
			validation.append(line.strip())
		else:
			training.append(line.strip())

	with open(f'{indir}/train.txt', 'w', encoding="utf-8") as f:
		f.write("\n".join(training))

	with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
		f.write("\n".join(validation))

	msg = f"Culled {len(validation)} lines"
	print(msg)
	return msg

def calc_iterations( epochs, lines, batch_size ):
	iterations = int(epochs * lines / float(batch_size))
	return iterations

def schedule_learning_rate( iterations, schedule=EPOCH_SCHEDULE ):
	return [int(iterations * d) for d in schedule]

def optimize_training_settings( **kwargs ):
	messages = []
	settings = {}
	settings.update(kwargs)

	dataset_path = f"./training/{settings['voice']}/train.txt"
	with open(dataset_path, 'r', encoding="utf-8") as f:
		lines = len(f.readlines())

	if settings['batch_size'] > lines:
		settings['batch_size'] = lines
		messages.append(f"Batch size is larger than your dataset, clamping batch size to: {settings['batch_size']}")	

	"""
	if lines % settings['batch_size'] != 0:
		settings['batch_size'] = int(lines / settings['batch_size'])
		if settings['batch_size'] == 0:
			settings['batch_size'] = 1
		messages.append(f"Batch size not neatly divisible by dataset size, adjusting batch size to: {settings['batch_size']}")
	"""
	if settings['gradient_accumulation_size'] == 0:
		settings['gradient_accumulation_size'] = 1
	
	if settings['batch_size'] / settings['gradient_accumulation_size'] < 2:
		settings['gradient_accumulation_size'] = int(settings['batch_size'] / 2)
		if settings['gradient_accumulation_size'] == 0:
			settings['gradient_accumulation_size'] = 1

		messages.append(f"Gradient accumulation size is too large for a given batch size, clamping gradient accumulation size to: {settings['gradient_accumulation_size']}")
	"""
	elif settings['batch_size'] % settings['gradient_accumulation_size'] != 0:
		settings['gradient_accumulation_size'] = int(settings['batch_size'] / settings['gradient_accumulation_size'])
		if settings['gradient_accumulation_size'] == 0:
			settings['gradient_accumulation_size'] = 1

		messages.append(f"Batch size is not evenly divisible by the gradient accumulation size, adjusting gradient accumulation size to: {settings['gradient_accumulation_size']}")

	if settings['batch_size'] % settings['gpus'] != 0:
		settings['batch_size'] = int(settings['batch_size'] / settings['gpus'])
		if settings['batch_size'] == 0:
			settings['batch_size'] = 1
		messages.append(f"Batch size not neatly divisible by GPU count, adjusting batch size to: {settings['batch_size']}")
	"""


	def get_device_batch_size( vram ):
		DEVICE_BATCH_SIZE_MAP = [
			(32, 64), # based on my two 6800XTs, I can only really safely get a ratio of 156:2 = 78
			(16, 8), # based on an A4000, I can do a ratio of 512:64 = 8:1
			(8, 4), # interpolated
			(6, 2), # based on my 2060, it only really lets me have a batch ratio of 2:1
		]
		for k, v in DEVICE_BATCH_SIZE_MAP:
			if vram > (k-1):
				return v
		return 1
	
	# assuming you have equal GPUs
	vram = get_device_vram() * settings['gpus']
	batch_ratio = int(settings['batch_size'] / settings['gradient_accumulation_size'])
	batch_cap = get_device_batch_size(vram)

	if batch_ratio > batch_cap:
		settings['gradient_accumulation_size'] = int(settings['batch_size'] / batch_cap)
		messages.append(f"Batch ratio ({batch_ratio}) is expected to exceed your VRAM capacity ({'{:.3f}'.format(vram)}GB, suggested {batch_cap} batch size cap), adjusting gradient accumulation size to: {settings['gradient_accumulation_size']}")

	iterations = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])

	if settings['epochs'] < settings['print_rate']:
		settings['print_rate'] = settings['epochs']
		messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {settings['print_rate']}")
	
	if settings['epochs'] < settings['save_rate']:
		settings['save_rate'] = settings['epochs']
		messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {settings['save_rate']}")

	if settings['epochs'] < settings['validation_rate']:
		settings['validation_rate'] = settings['epochs']
		messages.append(f"Validation rate is too small for the given iteration step, clamping validation rate to: {settings['validation_rate']}")

	if settings['resume_state'] and not os.path.exists(settings['resume_state']):
		settings['resume_state'] = None
		messages.append("Resume path specified, but does not exist. Disabling...")

	if settings['bitsandbytes']:
		messages.append("BitsAndBytes requested. Please note this is ! EXPERIMENTAL !")

	if settings['half_p']:
		if settings['bitsandbytes']:
			settings['half_p'] = False
			messages.append("Half Precision requested, but BitsAndBytes is also requested. Due to redundancies, disabling half precision...")
		else:
			messages.append("Half Precision requested. Please note this is ! EXPERIMENTAL !")
			if not os.path.exists(get_halfp_model_path()):
				convert_to_halfp()	

	messages.append(f"For {settings['epochs']} epochs with {lines} lines in batches of {settings['batch_size']}, iterating for {iterations} steps ({int(iterations / settings['epochs'])} steps per epoch)")

	return settings, messages

def save_training_settings( **kwargs ):
	messages = []
	settings = {}
	settings.update(kwargs)

	outjson = f'./training/{settings["voice"]}/train.json'
	with open(outjson, 'w', encoding="utf-8") as f:
		f.write(json.dumps(settings, indent='\t') )

	settings['dataset_path'] = f"./training/{settings['voice']}/train.txt"
	settings['validation_path'] = f"./training/{settings['voice']}/validation.txt"

	with open(settings['dataset_path'], 'r', encoding="utf-8") as f:
		lines = len(f.readlines())

	if not settings['source_model'] or settings['source_model'] == "auto":
		settings['source_model'] = f"./models/tortoise/autoregressive{'_half' if settings['half_p'] else ''}.pth"

	if settings['half_p']:
		if not os.path.exists(get_halfp_model_path()):
			convert_to_halfp()

	settings['iterations'] = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
	messages.append(f"For {settings['epochs']} epochs with {lines} lines, iterating for {settings['iterations']} steps")

	settings['print_rate'] = int(settings['print_rate'] * settings['iterations'] / settings['epochs'])
	settings['save_rate'] = int(settings['save_rate'] * settings['iterations'] / settings['epochs'])
	settings['validation_rate'] = int(settings['validation_rate'] * settings['iterations'] / settings['epochs'])

	settings['validation_batch_size'] = int(settings['batch_size'] / settings['gradient_accumulation_size'])

	settings['iterations'] = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
	if settings['iterations'] % settings['save_rate'] != 0:
		adjustment = int(settings['iterations'] / settings['save_rate']) * settings['save_rate']
		messages.append(f"Iteration rate is not evenly divisible by save rate, adjusting: {settings['iterations']} => {adjustment}")
		settings['iterations'] = adjustment

	if not os.path.exists(settings['validation_path']):
		settings['validation_enabled'] = False
		messages.append("Validation not found, disabling validation...")
	elif settings['validation_batch_size'] == 0:
		settings['validation_enabled'] = False
		messages.append("Validation batch size == 0, disabling validation...")
	else:
		settings['validation_enabled'] = True
		with open(settings['validation_path'], 'r', encoding="utf-8") as f:
			validation_lines = len(f.readlines())

		if validation_lines < settings['validation_batch_size']:
			settings['validation_batch_size'] = validation_lines
			messages.append(f"Batch size exceeds validation dataset size, clamping validation batch size to {validation_lines}")


	if settings['gpus'] > get_device_count():
		settings['gpus'] = get_device_count()

	settings['optimizer'] = 'adamw' if settings['gpus'] == 1 else 'adamw_zero'

	LEARNING_RATE_SCHEMES = ["MultiStepLR", "CosineAnnealingLR_Restart"]
	if 'learning_rate_scheme' not in settings or settings['learning_rate_scheme'] not in LEARNING_RATE_SCHEMES:
		settings['learning_rate_scheme'] = LEARNING_RATE_SCHEMES[0]

	learning_rate_schema = [f"default_lr_scheme: {settings['learning_rate_scheme']}"]
	if settings['learning_rate_scheme'] == "MultiStepLR":
		if not settings['learning_rate_schedule']:
			settings['learning_rate_schedule'] = EPOCH_SCHEDULE
		elif isinstance(settings['learning_rate_schedule'],str):
			settings['learning_rate_schedule'] = json.loads(settings['learning_rate_schedule'])

		settings['learning_rate_schedule'] = schedule_learning_rate( settings['iterations'] / settings['epochs'], settings['learning_rate_schedule'] )

		learning_rate_schema.append(f"  gen_lr_steps: {settings['learning_rate_schedule']}")
		learning_rate_schema.append(f"  lr_gamma: 0.5")
	elif settings['learning_rate_scheme'] == "CosineAnnealingLR_Restart":
		learning_rate_schema.append(f"  T_period: [120000, 120000, 120000]")
		learning_rate_schema.append(f"  warmup: 10000")
		learning_rate_schema.append(f"  eta_min: .01")
		learning_rate_schema.append(f"  restarts: [140000, 280000]")
		learning_rate_schema.append(f"  restart_weights: [.5, .25]")
	settings['learning_rate_scheme'] = "\n".join(learning_rate_schema)

	"""
	if resume_state:
		settings['pretrain_model_gpt'] = f"# {settings['pretrain_model_gpt']}"
	else:
		settings['resume_state'] = f"# resume_state: './training/{voice}/training_state/#.state'"

	# also disable validation if it doesn't make sense to do it
	if settings['dataset_path'] == settings['validation_path'] or not os.path.exists(settings['validation_path']):
		settings['validation_enabled'] = 'false'
	"""

	if settings['resume_state']:
		settings['source_model'] = f"# pretrain_model_gpt: {settings['source_model']}"
		settings['resume_state'] = f"resume_state: {settings['resume_state']}'"
	else:
		settings['source_model'] = f"pretrain_model_gpt: {settings['source_model']}"
		settings['resume_state'] = f"# resume_state: {settings['resume_state']}'"

	with open(f'./models/.template.yaml', 'r', encoding="utf-8") as f:
		yaml = f.read()

	# i could just load and edit the YAML directly, but this is easier, as I don't need to bother with path traversals
	for k in settings:
		if settings[k] is None:
			continue
		yaml = yaml.replace(f"${{{k}}}", str(settings[k]))

	outyaml = f'./training/{settings["voice"]}/train.yaml'
	with open(outyaml, 'w', encoding="utf-8") as f:
		f.write(yaml)
	

	messages.append(f"Saved training output to: {outyaml}")
	return settings, messages

def import_voices(files, saveAs=None, progress=None):
	global args

	if not isinstance(files, list):
		files = [files]

	for file in enumerate_progress(files, desc="Importing voice files", progress=progress):
		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:
			print(f"Importing latents to {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)}"
			print(f"Importing voice to {path}")

			waveform, sampling_rate = torchaudio.load(filename)

			if args.voice_fixer:
				if not voicefixer:
					load_voicefixer()

				# 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 get_voice_list(dir=get_voice_dir(), append_defaults=False):
	defaults = [ "random", "microphone" ]
	os.makedirs(dir, exist_ok=True)
	res = sorted([d for d in os.listdir(dir) if d is not in defaults and os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ])
	if append_defaults:
		res = res + defaults
	return res

def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
	os.makedirs(dir, exist_ok=True)
	base = [get_model_path('autoregressive.pth')]
	halfp = get_halfp_model_path()
	if os.path.exists(halfp):
		base.append(halfp)

	additionals = sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ])
	found = []
	for training in os.listdir(f'./training/'):
		if not os.path.isdir(f'./training/{training}/') or not os.path.isdir(f'./training/{training}/finetune/') or not os.path.isdir(f'./training/{training}/finetune/models/'):
			continue
		models = sorted([ int(d[:-8]) for d in os.listdir(f'./training/{training}/finetune/models/') if d[-8:] == "_gpt.pth" ])
		found = found + [ f'./training/{training}/finetune/models/{d}_gpt.pth' for d in models ]

	if len(found) > 0 or len(additionals) > 0:
		base = ["auto"] + base

	res = base + additionals + found
	
	if prefixed:
		for i in range(len(res)):
			path = res[i]
			hash = hash_file(path)
			shorthash = hash[:8]

			res[i] = f'[{shorthash}] {path}'

	return res

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/"):
	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)) ])

def do_gc():
	gc.collect()
	try:
		torch.cuda.empty_cache()
	except Exception as e:
		pass

def pad(num, zeroes):
	return str(num).zfill(zeroes+1)

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( dir = None ):
	if dir is None:
		check_for_updates("./.git/")
		check_for_updates("./.git/modules/dlas/")
		check_for_updates("./.git/modules/tortoise-tts/")
		return

	git_dir = dir
	if not os.path.isfile(f'{git_dir}/FETCH_HEAD'):
		print(f"Cannot check for updates for {dir}: not from a git repo")
		return False

	with open(f'{git_dir}/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(f"Cannot check for updates for {dir}: 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(f"Cannot check for updates for {dir}: cannot fetch from remote")
		return False

	remote = res[0]["commit"]["id"]

	if remote != local:
		print(f"New version found for {dir}: {local[:8]} => {remote[:8]}")
		return True

	return False

def enumerate_progress(iterable, desc=None, progress=None, verbose=None):
	if verbose and desc is not None:
		print(desc)

	if progress is None:
		return tqdm(iterable, disable=not verbose)
	return progress.tqdm(iterable, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc, track_tqdm=True)

def notify_progress(message, progress=None, verbose=True):
	if verbose:
		print(message)

	if progress is None:
		return

	progress(0, desc=message)

def get_args():
	global args
	return args

def setup_args():
	global args

	default_arguments = {
		'share': False,
		'listen': None,
		'check-for-updates': False,
		'models-from-local-only': False,
		'low-vram': False,
		'sample-batch-size': None,
		'embed-output-metadata': True,
		'latents-lean-and-mean': True,
		'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
		'voice-fixer-use-cuda': True,
		'force-cpu-for-conditioning-latents': False,
		'defer-tts-load': False,
		'device-override': None,
		'prune-nonfinal-outputs': True,
		'vocoder-model': VOCODERS[-1],
		'concurrency-count': 2,
		'autocalculate-voice-chunk-duration-size': 0,
		'output-sample-rate': 44100,
		'output-volume': 1,
		
		'autoregressive-model': None,
		'whisper-backend': 'openai/whisper',
		'whisper-model': "base",

		'training-default-halfp': False,
		'training-default-bnb': True,
	}

	if os.path.isfile('./config/exec.json'):
		with open(f'./config/exec.json', 'r', encoding="utf-8") as f:
			try:
				overrides = json.load(f)
				for k in overrides:
					default_arguments[k] = overrides[k]
			except Exception as e:
				print(e)
				pass

	parser = argparse.ArgumentParser()
	parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere")
	parser.add_argument("--listen", default=default_arguments['listen'], help="Path for Gradio to listen on")
	parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup")
	parser.add_argument("--models-from-local-only", action='store_true', default=default_arguments['models-from-local-only'], help="Only loads models from disk, does not check for updates for models")
	parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
	parser.add_argument("--no-embed-output-metadata", action='store_false', default=not default_arguments['embed-output-metadata'], help="Disables embedding output metadata into resulting WAV files for easily fetching its settings used with the web UI (data is stored in the lyrics metadata tag)")
	parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
	parser.add_argument("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.")
	parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.")
	parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
	parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
	parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
	parser.add_argument("--vocoder-model", default=default_arguments['vocoder-model'], action='store_true', help="Specifies with vocoder to use")
	parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
	parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
	parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
	parser.add_argument("--autocalculate-voice-chunk-duration-size", type=float, default=default_arguments['autocalculate-voice-chunk-duration-size'], help="Number of seconds to suggest voice chunk size for (for example, 100 seconds of audio at 10 seconds per chunk will suggest 10 chunks)")
	parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
	parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
	
	parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
	parser.add_argument("--whisper-backend", default=default_arguments['whisper-backend'], action='store_true', help="Picks which whisper backend to use (openai/whisper, lightmare/whispercpp, m-bain/whisperx)")
	parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
	
	parser.add_argument("--training-default-halfp", action='store_true', default=default_arguments['training-default-halfp'], help="Training default: halfp")
	parser.add_argument("--training-default-bnb", action='store_true', default=default_arguments['training-default-bnb'], help="Training default: bnb")
	
	parser.add_argument("--os", default="unix", help="Specifies which OS, easily")
	args = parser.parse_args()

	args.embed_output_metadata = not args.no_embed_output_metadata

	if not args.device_override:
		set_device_name(args.device_override)


	args.listen_host = None
	args.listen_port = None
	args.listen_path = None
	if args.listen:
		try:
			match = re.findall(r"^(?:(.+?):(\d+))?(\/.*?)?$", args.listen)[0]

			args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
			args.listen_port = match[1] if match[1] != "" else None
			args.listen_path = match[2] if match[2] != "" else "/"
		except Exception as e:
			pass

	if args.listen_port is not None:
		args.listen_port = int(args.listen_port)
		if args.listen_port == 0:
			args.listen_port = None
	
	return args

def update_args( **kwargs ):
	global args

	settings = {}
	settings.update(kwargs)

	args.listen = settings['listen']
	args.share = settings['share']
	args.check_for_updates = settings['check_for_updates']
	args.models_from_local_only = settings['models_from_local_only']
	args.low_vram = settings['low_vram']
	args.force_cpu_for_conditioning_latents = settings['force_cpu_for_conditioning_latents']
	args.defer_tts_load = settings['defer_tts_load']
	args.prune_nonfinal_outputs = settings['prune_nonfinal_outputs']
	args.device_override = settings['device_override']
	args.sample_batch_size = settings['sample_batch_size']
	args.embed_output_metadata = settings['embed_output_metadata']
	args.latents_lean_and_mean = settings['latents_lean_and_mean']
	args.voice_fixer = settings['voice_fixer']
	args.voice_fixer_use_cuda = settings['voice_fixer_use_cuda']
	args.concurrency_count = settings['concurrency_count']
	args.output_sample_rate = 44000
	args.autocalculate_voice_chunk_duration_size = settings['autocalculate_voice_chunk_duration_size']
	args.output_volume = settings['output_volume']
	
	args.autoregressive_model = settings['autoregressive_model']
	args.vocoder_model = settings['vocoder_model']
	args.whisper_backend = settings['whisper_backend']
	args.whisper_model = settings['whisper_model']

	args.training_default_halfp = settings['training_default_halfp']
	args.training_default_bnb = settings['training_default_bnb']

	save_args_settings()

def save_args_settings():
	global args
	settings = {
		'listen': None if not 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,
		'defer-tts-load': args.defer_tts_load,
		'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
		'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,
		'autocalculate-voice-chunk-duration-size': args.autocalculate_voice_chunk_duration_size,
		'output-volume': args.output_volume,
		
		'autoregressive-model': args.autoregressive_model,
		'vocoder-model': args.vocoder_model,
		'whisper-backend': args.whisper_backend,
		'whisper-model': args.whisper_model,

		'training-default-halfp': args.training_default_halfp,
		'training-default-bnb': args.training_default_bnb,
	}

	os.makedirs('./config/', exist_ok=True)
	with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
		f.write(json.dumps(settings, indent='\t') )

# super kludgy )`;
def set_generate_settings_arg_order(args):
	global GENERATE_SETTINGS_ARGS
	GENERATE_SETTINGS_ARGS = args

def import_generate_settings(file="./config/generate.json"):
	global GENERATE_SETTINGS_ARGS

	defaults = {
		'text': None,
		'delimiter': None,
		'emotion': None,
		'prompt': None,
		'voice': None,
		'mic_audio': None,
		'voice_latents_chunks': None,
		'candidates': None,
		'seed': None,
		'num_autoregressive_samples': 16,
		'diffusion_iterations': 30,
		'temperature': 0.8,
		'diffusion_sampler': "DDIM",
		'breathing_room': 8  ,
		'cvvp_weight': 0.0,
		'top_p': 0.8,
		'diffusion_temperature': 1.0,
		'length_penalty': 1.0,
		'repetition_penalty': 2.0,
		'cond_free_k': 2.0,
		'experimentals': None,
	}

	settings, _ = read_generate_settings(file, read_latents=False)
	
	res = []
	if GENERATE_SETTINGS_ARGS is not None:
		for k in GENERATE_SETTINGS_ARGS:
			if k not in defaults:
				continue
			res.append(defaults[k] if not settings or k not in settings or not settings[k] is None else settings[k])
	else:
		for k in defaults:
			res.append(defaults[k] if not settings or k not in settings or not settings[k] is None else settings[k])

	return tuple(res)


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 read_generate_settings(file, read_latents=True):
	j = None
	latents = None

	if isinstance(file, list) and len(file) == 1:
		file = file[0]

	try:
		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)
	except Exception as e:
		pass

	if j is not None:
		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 version_check_tts( min_version ):
	global tts
	if not tts:
		raise Exception("TTS is not initialized")

	if not hasattr(tts, 'version'):
		return False

	if min_version[0] > tts.version[0]:
		return True
	if min_version[1] > tts.version[1]:
		return True
	if min_version[2] >= tts.version[2]:
		return True
	return False

def load_tts( restart=False, autoregressive_model=None ):
	global args
	global tts

	if restart:
		unload_tts()

	if autoregressive_model:
		args.autoregressive_model = autoregressive_model
	else:
		autoregressive_model = args.autoregressive_model

	if autoregressive_model == "auto":
		autoregressive_model = deduce_autoregressive_model()

	print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {args.vocoder_model})")

	tts_loading = True
	try:
		tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, vocoder_model=args.vocoder_model)
	except Exception as e:
		tts = TextToSpeech(minor_optimizations=not args.low_vram)
		load_autoregressive_model(autoregressive_model)

	tts_loading = False

	get_model_path('dvae.pth')
	print("Loaded TorToiSe, ready for generation.")
	return tts

setup_tortoise = load_tts

def unload_tts():
	global tts

	if tts:
		del tts
		tts = None
		print("Unloaded TTS")
	do_gc()

def reload_tts( model=None ):
	load_tts( restart=True, model=model )

def get_current_voice():
	global current_voice
	if current_voice:
		return current_voice

	settings, _ = read_generate_settings("./config/generate.json", read_latents=False)
	
	if settings and "voice" in settings['voice']:
		return settings["voice"]
	
	return None

def deduce_autoregressive_model(voice=None):
	if not voice:
		voice = get_current_voice()

	if voice:
		if os.path.exists(f'./models/finetunes/{voice}.pth'):
			return f'./models/finetunes/{voice}.pth'
		
		dir = f'./training/{voice}/finetune/models/'
		if os.path.isdir(dir):
			counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
			names = [ f'{dir}/{d}_gpt.pth' for d in counts ]
			return names[-1]

	if args.autoregressive_model != "auto":
		return args.autoregressive_model

	return get_model_path('autoregressive.pth')

def update_autoregressive_model(autoregressive_model_path):
	match = re.findall(r'^\[[a-fA-F0-9]{8}\] (.+?)$', autoregressive_model_path)
	if match:
		autoregressive_model_path = match[0]

	if not autoregressive_model_path or not os.path.exists(autoregressive_model_path):
		print(f"Invalid model: {autoregressive_model_path}")
		return

	args.autoregressive_model = autoregressive_model_path
	save_args_settings()
	print(f'Stored autoregressive model to settings: {autoregressive_model_path}')

	global tts
	if not tts:
		if tts_loading:
			raise Exception("TTS is still initializing...")
		return
	
	if hasattr(tts, "loading") and tts.loading:
		raise Exception("TTS is still initializing...")

	if autoregressive_model_path == "auto":
		autoregressive_model_path = deduce_autoregressive_model()

	if autoregressive_model_path == tts.autoregressive_model_path:
		return

	tts.load_autoregressive_model(autoregressive_model_path)

	do_gc()
	
	return autoregressive_model_path

def update_vocoder_model(vocoder_model):
	args.vocoder_model = vocoder_model
	save_args_settings()
	print(f'Stored vocoder model to settings: {vocoder_model}')

	global tts
	if not tts:
		if tts_loading:
			raise Exception("TTS is still initializing...")
		return

	if hasattr(tts, "loading") and tts.loading:
		raise Exception("TTS is still initializing...")

	print(f"Loading model: {vocoder_model}")
	tts.load_vocoder_model(vocoder_model)
	print(f"Loaded model: {tts.vocoder_model}")

	do_gc()
	
	return vocoder_model

def load_voicefixer(restart=False):
	global voicefixer

	if restart:
		unload_voicefixer()

	try:
		print("Loading Voicefixer")
		from voicefixer import VoiceFixer
		voicefixer = VoiceFixer()
		print("Loaded Voicefixer")
	except Exception as e:
		print(f"Error occurred while tring to initialize voicefixer: {e}")
		if voicefixer:
			del voicefixer
		voicefixer = None

def unload_voicefixer():
	global voicefixer

	if voicefixer:
		del voicefixer
		voicefixer = None
		print("Unloaded Voicefixer")

	do_gc()

def load_whisper_model(language=None, model_name=None, progress=None):
	global whisper_model

	if args.whisper_backend not in WHISPER_BACKENDS:
		raise Exception(f"unavailable backend: {args.whisper_backend}")

	if args.whisper_backend != "m-bain/whisperx" and model_name == "large-v2":
		raise Exception("large-v2 is only available for m-bain/whisperx backend")
	
	if not model_name:
		model_name = args.whisper_model
	else:
		args.whisper_model = model_name
		save_args_settings()

	if language and f'{model_name}.{language}' in WHISPER_SPECIALIZED_MODELS:
		model_name = f'{model_name}.{language}'
		print(f"Loading specialized model for language: {language}")

	notify_progress(f"Loading Whisper model: {model_name}", progress)

	if args.whisper_backend == "openai/whisper":
		import whisper
		whisper_model = whisper.load_model(model_name)
	elif args.whisper_backend == "lightmare/whispercpp":
		from whispercpp import Whisper
		if not language:
			language = 'auto'

		b_lang = language.encode('ascii')
		whisper_model = Whisper(model_name, models_dir='./models/', language=b_lang)
	elif args.whisper_backend == "m-bain/whisperx":
		import whisperx
		device = "cuda" if get_device_name() == "cuda" else "cpu"
		whisper_model = whisperx.load_model(model_name, device)

	print("Loaded Whisper model")

def unload_whisper():
	global whisper_model

	if whisper_model:
		del whisper_model
		whisper_model = None
		print("Unloaded Whisper")

	do_gc()