forked from camenduru/ai-voice-cloning
big cleanup to make my life easier when i add more parameters
This commit is contained in:
parent
0ab091e7ff
commit
3f321fe664
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@ -1,16 +1,18 @@
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name: ${name}
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name: ${voice}
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model: extensibletrainer
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scale: 1
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gpu_ids: [0] # Superfluous, redundant, unnecessary, the way you launch the training script will set this
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start_step: 0
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checkpointing_enabled: true
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fp16: ${float16}
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fp16: ${half_p}
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bitsandbytes: ${bitsandbytes}
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gpus: ${gpus}
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wandb: false
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use_tb_logger: true
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datasets:
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train:
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name: ${dataset_name}
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name: training
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n_workers: ${workers}
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batch_size: ${batch_size}
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mode: paired_voice_audio
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@ -27,7 +29,7 @@ datasets:
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tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
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load_aligned_codes: False
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val: # I really do not care about validation right now
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name: ${validation_name}
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name: validation
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n_workers: ${workers}
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batch_size: ${validation_batch_size}
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mode: paired_voice_audio
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@ -114,8 +116,8 @@ networks:
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#only_alignment_head: False # uv3/4
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path:
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${pretrain_model_gpt}
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strict_load: true
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${source_model}
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${resume_state}
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train:
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@ -1,35 +0,0 @@
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import os
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import sys
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indir = f'./training/{sys.argv[1]}/'
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cap = int(sys.argv[2])
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if not os.path.isdir(indir):
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raise Exception(f"Invalid directory: {indir}")
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if not os.path.exists(f'{indir}/train.txt'):
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raise Exception(f"Missing dataset: {indir}/train.txt")
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with open(f'{indir}/train.txt', 'r', encoding="utf-8") as f:
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lines = f.readlines()
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validation = []
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training = []
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for line in lines:
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split = line.split("|")
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filename = split[0]
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text = split[1]
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if len(text) < cap:
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validation.append(line.strip())
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else:
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training.append(line.strip())
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with open(f'{indir}/train_culled.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(training))
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with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(validation))
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print(f"Culled {len(validation)} lines")
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@ -46,6 +46,7 @@ sys.path.insert(0, './dlas/')
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# without kludge, it'll have to be accessible as `codes` and not `dlas`
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import torch
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import datetime
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from codes import train as tr
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from utils import util, options as option
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@ -71,7 +72,7 @@ def train(yaml, launcher='none'):
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print('Disabled distributed training.')
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else:
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opt['dist'] = True
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tr.init_dist('nccl')
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tr.init_dist('nccl', timeout=datetime.timedelta(seconds=5*60))
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trainer.world_size = torch.distributed.get_world_size()
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trainer.rank = torch.distributed.get_rank()
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torch.cuda.set_device(torch.distributed.get_rank())
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546
src/utils.py
546
src/utils.py
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@ -34,7 +34,7 @@ from datetime import timedelta
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from tortoise.api import TextToSpeech, MODELS, get_model_path, pad_or_truncate
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from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
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from tortoise.utils.text import split_and_recombine_text
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from tortoise.utils.device import get_device_name, set_device_name
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from tortoise.utils.device import get_device_name, set_device_name, get_device_count
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MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
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@ -44,6 +44,8 @@ WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
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GENERATE_SETTINGS_ARGS = None
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EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
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args = None
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@ -56,30 +58,17 @@ training_state = None
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current_voice = None
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def generate(
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text,
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delimiter,
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emotion,
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prompt,
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voice,
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mic_audio,
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voice_latents_chunks,
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seed,
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candidates,
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num_autoregressive_samples,
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diffusion_iterations,
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temperature,
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diffusion_sampler,
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breathing_room,
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cvvp_weight,
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top_p,
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diffusion_temperature,
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length_penalty,
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repetition_penalty,
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cond_free_k,
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experimental_checkboxes,
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progress=None
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):
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def generate(**kwargs):
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parameters = {}
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parameters.update(kwargs)
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voice = parameters['voice']
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progress = parameters['progress'] if 'progress' in parameters else None
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if parameters['seed'] == 0:
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parameters['seed'] = None
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usedSeed = parameters['seed']
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global args
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global tts
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@ -90,6 +79,8 @@ def generate(
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# should check if it's loading or unloaded, and load it if it's unloaded
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if tts_loading:
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raise Exception("TTS is still initializing...")
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if progress is not None:
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progress(0, "Initializing TTS...")
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load_tts()
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if hasattr(tts, "loading") and tts.loading:
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raise Exception("TTS is still initializing...")
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@ -100,9 +91,6 @@ def generate(
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conditioning_latents =None
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sample_voice = None
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if seed == 0:
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seed = None
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voice_cache = {}
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def fetch_voice( voice ):
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print(f"Loading voice: {voice} with model {tts.autoregressive_model_hash[:8]}")
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@ -112,9 +100,9 @@ def generate(
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sample_voice = None
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if voice == "microphone":
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if mic_audio is None:
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if parameters['mic_audio'] is None:
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raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
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voice_samples, conditioning_latents = [load_audio(mic_audio, tts.input_sample_rate)], None
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voice_samples, conditioning_latents = [load_audio(parameters['mic_audio'], tts.input_sample_rate)], None
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elif voice == "random":
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voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
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else:
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@ -125,7 +113,7 @@ def generate(
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if voice_samples and len(voice_samples) > 0:
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if conditioning_latents is None:
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conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=voice_latents_chunks)
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conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=parameters['voice_latents_chunks'])
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sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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voice_samples = None
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@ -135,30 +123,30 @@ def generate(
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def get_settings( override=None ):
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settings = {
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'temperature': float(temperature),
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'temperature': float(parameters['temperature']),
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'top_p': float(top_p),
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'diffusion_temperature': float(diffusion_temperature),
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'length_penalty': float(length_penalty),
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'repetition_penalty': float(repetition_penalty),
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'cond_free_k': float(cond_free_k),
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'top_p': float(parameters['top_p']),
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'diffusion_temperature': float(parameters['diffusion_temperature']),
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'length_penalty': float(parameters['length_penalty']),
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'repetition_penalty': float(parameters['repetition_penalty']),
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'cond_free_k': float(parameters['cond_free_k']),
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'num_autoregressive_samples': num_autoregressive_samples,
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'num_autoregressive_samples': parameters['num_autoregressive_samples'],
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'sample_batch_size': args.sample_batch_size,
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'diffusion_iterations': diffusion_iterations,
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'diffusion_iterations': parameters['diffusion_iterations'],
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'voice_samples': None,
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'conditioning_latents': None,
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'use_deterministic_seed': seed,
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'use_deterministic_seed': parameters['seed'],
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'return_deterministic_state': True,
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'k': candidates,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'progress': progress,
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'half_p': "Half Precision" in experimental_checkboxes,
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'cond_free': "Conditioning-Free" in experimental_checkboxes,
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'cvvp_amount': cvvp_weight,
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'k': parameters['candidates'],
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'diffusion_sampler': parameters['diffusion_sampler'],
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'breathing_room': parameters['breathing_room'],
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'progress': parameters['progress'],
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'half_p': "Half Precision" in parameters['experimentals'],
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'cond_free': "Conditioning-Free" in parameters['experimentals'],
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'cvvp_amount': parameters['cvvp_weight'],
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'autoregressive_model': args.autoregressive_model,
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}
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@ -182,11 +170,11 @@ def generate(
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# clamp it down for the insane users who want this
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# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
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sample_batch_size = args.sample_batch_size
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if not sample_batch_size:
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sample_batch_size = tts.autoregressive_batch_size
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if num_autoregressive_samples < sample_batch_size:
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settings['sample_batch_size'] = num_autoregressive_samples
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settings['sample_batch_size'] = args.sample_batch_size
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if not settings['sample_batch_size']:
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settings['sample_batch_size'] = tts.autoregressive_batch_size
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if settings['num_autoregressive_samples'] < settings['sample_batch_size']:
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settings['sample_batch_size'] = settings['num_autoregressive_samples']
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if settings['conditioning_latents'] is not None and len(settings['conditioning_latents']) == 2 and settings['cvvp_amount'] > 0:
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print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents with 'Slimmer voice latents' unchecked.")
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@ -194,15 +182,15 @@ def generate(
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return settings
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if not delimiter:
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delimiter = "\n"
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elif delimiter == "\\n":
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delimiter = "\n"
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if not parameters['delimiter']:
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parameters['delimiter'] = "\n"
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elif parameters['delimiter'] == "\\n":
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parameters['delimiter'] = "\n"
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if delimiter and delimiter != "" and delimiter in text:
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texts = text.split(delimiter)
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if parameters['delimiter'] and parameters['delimiter'] != "" and parameters['delimiter'] in parameters['text']:
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texts = parameters['text'].split(parameters['delimiter'])
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else:
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texts = split_and_recombine_text(text)
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texts = split_and_recombine_text(parameters['text'])
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full_start_time = time.time()
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@ -248,37 +236,23 @@ def generate(
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name = f"{name}_combined"
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elif len(texts) > 1:
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name = f"{name}_{line}"
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if candidates > 1:
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if parameters['candidates'] > 1:
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name = f"{name}_{candidate}"
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return name
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def get_info( voice, settings = None, latents = True ):
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info = {
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'text': text,
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'delimiter': '\\n' if delimiter and delimiter == "\n" else delimiter,
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'emotion': emotion,
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'prompt': prompt,
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'voice': voice,
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'seed': seed,
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'candidates': candidates,
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'num_autoregressive_samples': num_autoregressive_samples,
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'diffusion_iterations': diffusion_iterations,
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'temperature': temperature,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'cvvp_weight': cvvp_weight,
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'top_p': top_p,
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'diffusion_temperature': diffusion_temperature,
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'length_penalty': length_penalty,
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'repetition_penalty': repetition_penalty,
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'cond_free_k': cond_free_k,
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'experimentals': experimental_checkboxes,
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'time': time.time()-full_start_time,
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info = {}
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info.update(parameters)
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info['time'] = time.time()-full_start_time,
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'datetime': datetime.now().isoformat(),
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'model': tts.autoregressive_model_path,
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'model_hash': tts.autoregressive_model_hash
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}
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info['datetime'] = datetime.now().isoformat(),
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info['model'] = tts.autoregressive_model_path,
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info['model_hash'] = tts.autoregressive_model_hash
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info['progress'] = None
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del info['progress']
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if info['delimiter'] == "\n":
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info['delimiter'] = "\\n"
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if settings is not None:
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for k in settings:
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@ -319,11 +293,11 @@ def generate(
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return info
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for line, cut_text in enumerate(texts):
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if emotion == "Custom":
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if prompt and prompt.strip() != "":
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cut_text = f"[{prompt},] {cut_text}"
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elif emotion != "None" and emotion:
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cut_text = f"[I am really {emotion.lower()},] {cut_text}"
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if parameters['emotion'] == "Custom":
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if parameters['prompt'] and parameters['prompt'].strip() != "":
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cut_text = f"[{parameters['prompt']},] {cut_text}"
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elif parameters['emotion'] != "None" and parameters['emotion']:
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cut_text = f"[I am really {parameters['emotion'].lower()},] {cut_text}"
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{progress.msg_prefix} Generating line: {cut_text}")
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@ -343,10 +317,10 @@ def generate(
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settings = get_settings( override=override )
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gen, additionals = tts.tts(cut_text, **settings )
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seed = additionals[0]
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parameters['seed'] = additionals[0]
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run_time = time.time()-start_time
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print(f"Generating line took {run_time} seconds")
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if not isinstance(gen, list):
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gen = [gen]
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@ -382,7 +356,7 @@ def generate(
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torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate)
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output_voices = []
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for candidate in range(candidates):
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for candidate in range(parameters['candidates']):
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if len(texts) > 1:
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audio_clips = []
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for line in range(len(texts)):
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@ -466,7 +440,7 @@ def generate(
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info = get_info(voice=voice, latents=False)
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print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")
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info['seed'] = seed
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info['seed'] = usedSeed
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if 'latents' in info:
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del info['latents']
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@ -475,7 +449,7 @@ def generate(
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f.write(json.dumps(info, indent='\t') )
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stats = [
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[ seed, "{:.3f}".format(info['time']) ]
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[ parameters['seed'], "{:.3f}".format(info['time']) ]
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]
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return (
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@ -609,14 +583,16 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
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# superfluous, but it cleans up some things
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class TrainingState():
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def __init__(self, config_path, keep_x_past_checkpoints=0, start=True, gpus=1):
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def __init__(self, config_path, keep_x_past_checkpoints=0, start=True):
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# parse config to get its iteration
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with open(config_path, 'r') as file:
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self.config = yaml.safe_load(file)
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gpus = self.config["gpus"]
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self.killed = False
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self.dataset_dir = f"./training/{self.config['name']}/"
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self.dataset_dir = f"./training/{self.config['name']}/finetune/"
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self.batch_size = self.config['datasets']['train']['batch_size']
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self.dataset_path = self.config['datasets']['train']['path']
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with open(self.dataset_path, 'r', encoding="utf-8") as f:
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@ -996,7 +972,7 @@ except Exception as e:
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print(e)
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pass
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def run_training(config_path, verbose=False, gpus=1, keep_x_past_checkpoints=0, progress=gr.Progress(track_tqdm=True)):
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def run_training(config_path, verbose=False, keep_x_past_checkpoints=0, progress=gr.Progress(track_tqdm=True)):
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global training_state
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if training_state and training_state.process:
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return "Training already in progress"
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@ -1008,26 +984,11 @@ def run_training(config_path, verbose=False, gpus=1, keep_x_past_checkpoints=0,
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# 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
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torch.multiprocessing.freeze_support()
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# edit any gpu-count-specific variables
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with open(config_path, 'r', encoding="utf-8") as f:
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yaml_string = f.read()
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edited = False
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if gpus > 1:
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yaml_string = yaml_string.replace(" adamw ", " adamw_zero ")
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edited = True
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else:
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yaml_string = yaml_string.replace(" adamw_zero ", " adamw ")
|
||||
edited = True
|
||||
if edited:
|
||||
print(f'Modified YAML config')
|
||||
with open(config_path, 'w', encoding="utf-8") as f:
|
||||
f.write(yaml_string)
|
||||
|
||||
unload_tts()
|
||||
unload_whisper()
|
||||
unload_voicefixer()
|
||||
|
||||
training_state = TrainingState(config_path=config_path, keep_x_past_checkpoints=keep_x_past_checkpoints, gpus=gpus)
|
||||
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:
|
||||
|
|
@ -1169,7 +1130,7 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
|
|||
if whisper_model is None:
|
||||
load_whisper_model(language=language)
|
||||
|
||||
os.makedirs(outdir, exist_ok=True)
|
||||
os.makedirs(f'{outdir}/audio/', exist_ok=True)
|
||||
|
||||
results = {}
|
||||
transcription = []
|
||||
|
|
@ -1216,10 +1177,10 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
|
|||
print(f"Error with {sliced_name}, skipping...")
|
||||
continue
|
||||
|
||||
torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate)
|
||||
torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)
|
||||
|
||||
idx = idx + 1
|
||||
line = f"{sliced_name}|{segment['text'].strip()}"
|
||||
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}')
|
||||
|
|
@ -1283,125 +1244,142 @@ def calc_iterations( epochs, lines, batch_size ):
|
|||
def schedule_learning_rate( iterations, schedule=EPOCH_SCHEDULE ):
|
||||
return [int(iterations * d) for d in schedule]
|
||||
|
||||
def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, validation_rate, resume_path, half_p, bnb, workers, source_model, voice ):
|
||||
name = f"{voice}-finetune"
|
||||
dataset_path = f"./training/{voice}/train.txt"
|
||||
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())
|
||||
|
||||
messages = []
|
||||
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 batch_size > lines:
|
||||
batch_size = lines
|
||||
messages.append(f"Batch size is larger than your dataset, clamping batch size to: {batch_size}")
|
||||
|
||||
if batch_size % lines != 0:
|
||||
nearest_slice = int(lines / batch_size) + 1
|
||||
batch_size = int(lines / nearest_slice)
|
||||
messages.append(f"Batch size not neatly divisible by dataset size, adjusting batch size to: {batch_size} ({nearest_slice} steps per epoch)")
|
||||
if settings['batch_size'] % lines != 0:
|
||||
nearest_slice = int(lines / settings['batch_size']) + 1
|
||||
settings['batch_size'] = int(lines / nearest_slice)
|
||||
messages.append(f"Batch size not neatly divisible by dataset size, adjusting batch size to: {settings['batch_size']} ({nearest_slice} steps per epoch)")
|
||||
|
||||
if gradient_accumulation_size == 0:
|
||||
gradient_accumulation_size = 1
|
||||
if settings['gradient_accumulation_size'] == 0:
|
||||
settings['gradient_accumulation_size'] = 1
|
||||
|
||||
if batch_size / gradient_accumulation_size < 2:
|
||||
gradient_accumulation_size = int(batch_size / 2)
|
||||
if gradient_accumulation_size == 0:
|
||||
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: {gradient_accumulation_size}")
|
||||
elif batch_size % gradient_accumulation_size != 0:
|
||||
gradient_accumulation_size = int(batch_size / gradient_accumulation_size)
|
||||
if gradient_accumulation_size == 0:
|
||||
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: {gradient_accumulation_size}")
|
||||
messages.append(f"Batch size is not evenly divisible by the gradient accumulation size, adjusting gradient accumulation size to: {settings['gradient_accumulation_size']}")
|
||||
|
||||
iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
|
||||
iterations = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
|
||||
|
||||
if epochs < print_rate:
|
||||
print_rate = epochs
|
||||
messages.append(f"Print rate is too small for the given iteration step, clamping print rate to: {print_rate}")
|
||||
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 epochs < save_rate:
|
||||
save_rate = epochs
|
||||
messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {save_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 epochs < validation_rate:
|
||||
validation_rate = epochs
|
||||
messages.append(f"Validation rate is too small for the given iteration step, clamping validation rate to: {validation_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 resume_path and not os.path.exists(resume_path):
|
||||
resume_path = None
|
||||
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 bnb:
|
||||
if settings['bitsandbytes']:
|
||||
messages.append("BitsAndBytes requested. Please note this is ! EXPERIMENTAL !")
|
||||
|
||||
if half_p:
|
||||
if bnb:
|
||||
half_p = False
|
||||
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 {epochs} epochs with {lines} lines in batches of {batch_size}, iterating for {iterations} steps ({int(iterations / epochs)} steps per epoch)")
|
||||
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 (
|
||||
learning_rate,
|
||||
text_ce_lr_weight,
|
||||
learning_rate_schedule,
|
||||
batch_size,
|
||||
gradient_accumulation_size,
|
||||
print_rate,
|
||||
save_rate,
|
||||
validation_rate,
|
||||
resume_path,
|
||||
messages
|
||||
)
|
||||
return settings, messages
|
||||
|
||||
def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weight=None, learning_rate_scheme=None, learning_rate_schedule=None, batch_size=None, gradient_accumulation_size=None, print_rate=None, save_rate=None, validation_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, validation_batch_size=None, output_name=None, resume_path=None, half_p=None, bnb=None, workers=None, source_model=None ):
|
||||
if not source_model:
|
||||
source_model = f"./models/tortoise/autoregressive{'_half' if half_p else ''}.pth"
|
||||
def save_training_settings( **kwargs ):
|
||||
messages = []
|
||||
settings = {}
|
||||
settings.update(kwargs)
|
||||
|
||||
settings = {
|
||||
"iterations": iterations if iterations else 500,
|
||||
"batch_size": batch_size if batch_size else 64,
|
||||
"learning_rate": learning_rate if learning_rate else 1e-5,
|
||||
"gradient_accumulation_size": gradient_accumulation_size if gradient_accumulation_size else 4,
|
||||
"print_rate": print_rate if print_rate else 1,
|
||||
"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 "./training/finetune/train.txt",
|
||||
"validation_name": validation_name if validation_name else "finetune",
|
||||
"validation_path": validation_path if validation_path else "./training/finetune/train.txt",
|
||||
'validation_rate': validation_rate if validation_rate else iterations,
|
||||
"validation_batch_size": validation_batch_size if validation_batch_size else batch_size,
|
||||
'validation_enabled': "true",
|
||||
settings['dataset_path'] = f"./training/{settings['voice']}/train.txt"
|
||||
settings['validation_path'] = f"./training/{settings['voice']}/validation.txt"
|
||||
|
||||
"text_ce_lr_weight": text_ce_lr_weight if text_ce_lr_weight else 0.01,
|
||||
with open(settings['dataset_path'], 'r', encoding="utf-8") as f:
|
||||
lines = len(f.readlines())
|
||||
|
||||
'resume_state': f"resume_state: '{resume_path}'",
|
||||
'pretrain_model_gpt': f"pretrain_model_gpt: '{source_model}'",
|
||||
if not settings['source_model'] or settings['source_model'] == "auto":
|
||||
settings['source_model'] = f"./models/tortoise/autoregressive{'_half' if settings['half_p'] else ''}.pth"
|
||||
|
||||
'float16': 'true' if half_p else 'false',
|
||||
'bitsandbytes': 'true' if bnb else 'false',
|
||||
if settings['half_p']:
|
||||
if not os.path.exists(get_halfp_model_path()):
|
||||
convert_to_halfp()
|
||||
|
||||
'workers': workers if workers else 2,
|
||||
}
|
||||
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()
|
||||
|
||||
LEARNING_RATE_SCHEMES = ["MultiStepLR", "CosineAnnealingLR_Restart"]
|
||||
if learning_rate_scheme not in LEARNING_RATE_SCHEMES:
|
||||
learning_rate_scheme = LEARNING_RATE_SCHEMES[0]
|
||||
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: {learning_rate_scheme}"]
|
||||
if learning_rate_scheme == "MultiStepLR":
|
||||
learning_rate_schema.append(f" gen_lr_steps: {learning_rate_schedule if learning_rate_schedule else EPOCH_SCHEDULE}")
|
||||
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 learning_rate_scheme == "CosineAnnealingLR_Restart":
|
||||
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")
|
||||
|
|
@ -1409,23 +1387,26 @@ def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weig
|
|||
learning_rate_schema.append(f" restart_weights: [.5, .25]")
|
||||
settings['learning_rate_scheme'] = "\n".join(learning_rate_schema)
|
||||
|
||||
if resume_path:
|
||||
"""
|
||||
if resume_state:
|
||||
settings['pretrain_model_gpt'] = f"# {settings['pretrain_model_gpt']}"
|
||||
else:
|
||||
settings['resume_state'] = f"# resume_state: './training/{name if name else 'finetune'}/training_state/#.state'"
|
||||
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'
|
||||
"""
|
||||
outjson = f'./training/{settings["voice"]}/train.json'
|
||||
with open(outjson, 'w', encoding="utf-8") as f:
|
||||
f.write(json.dumps(settings, indent='\t') )
|
||||
|
||||
|
||||
if half_p:
|
||||
if not os.path.exists(get_halfp_model_path()):
|
||||
convert_to_halfp()
|
||||
|
||||
if not output_name:
|
||||
output_name = f'{settings["name"]}.yaml'
|
||||
|
||||
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()
|
||||
|
|
@ -1436,11 +1417,13 @@ def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weig
|
|||
continue
|
||||
yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
|
||||
|
||||
outfile = f'./training/{output_name}'
|
||||
with open(outfile, 'w', encoding="utf-8") as f:
|
||||
outyaml = f'./training/{settings["voice"]}/train.yaml'
|
||||
with open(outyaml, 'w', encoding="utf-8") as f:
|
||||
f.write(yaml)
|
||||
|
||||
|
||||
return f"Training settings saved to: {outfile}"
|
||||
messages.append(f"Saved training output to: {outyaml}")
|
||||
return settings, messages
|
||||
|
||||
def import_voices(files, saveAs=None, progress=None):
|
||||
global args
|
||||
|
|
@ -1524,10 +1507,10 @@ def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
|
|||
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}/models/'):
|
||||
if not os.path.isdir(f'./training/{training}/') or not os.path.isdir(f'./training/{training}/finetunes/') or not os.path.isdir(f'./training/{training}/finetunes/models/'):
|
||||
continue
|
||||
models = sorted([ int(d[:-8]) for d in os.listdir(f'./training/{training}/models/') if d[-8:] == "_gpt.pth" ])
|
||||
found = found + [ f'./training/{training}/models/{d}_gpt.pth' for d in models ]
|
||||
models = sorted([ int(d[:-8]) for d in os.listdir(f'./training/{training}/finetunes/models/') if d[-8:] == "_gpt.pth" ])
|
||||
found = found + [ f'./training/{training}/finetunes/models/{d}_gpt.pth' for d in models ]
|
||||
|
||||
if len(found) > 0 or len(additionals) > 0:
|
||||
base = ["auto"] + base
|
||||
|
|
@ -1545,10 +1528,10 @@ def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
|
|||
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 len(os.listdir(os.path.join(dir, d))) > 0 and "train.txt" in os.listdir(os.path.join(dir, d)) ])
|
||||
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 len(os.listdir(os.path.join(dir, d))) > 0 and "train.yaml" in os.listdir(os.path.join(dir, d)) ])
|
||||
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()
|
||||
|
|
@ -1734,35 +1717,38 @@ def setup_args():
|
|||
|
||||
return args
|
||||
|
||||
def update_args( 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, defer_tts_load, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, autocalculate_voice_chunk_duration_size, output_volume, autoregressive_model, vocoder_model, whisper_backend, whisper_model, training_default_halfp, training_default_bnb ):
|
||||
def update_args( **kwargs ):
|
||||
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.defer_tts_load = defer_tts_load
|
||||
args.prune_nonfinal_outputs = prune_nonfinal_outputs
|
||||
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 = 44000
|
||||
args.autocalculate_voice_chunk_duration_size = autocalculate_voice_chunk_duration_size
|
||||
args.output_volume = output_volume
|
||||
|
||||
args.autoregressive_model = autoregressive_model
|
||||
args.vocoder_model = vocoder_model
|
||||
args.whisper_backend = whisper_backend
|
||||
args.whisper_model = whisper_model
|
||||
settings = {}
|
||||
settings.update(kwargs)
|
||||
|
||||
args.training_default_halfp = training_default_halfp
|
||||
args.training_default_bnb = training_default_bnb
|
||||
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()
|
||||
|
||||
|
|
@ -1801,37 +1787,49 @@ def save_args_settings():
|
|||
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)
|
||||
|
||||
if settings is None:
|
||||
return None
|
||||
res = []
|
||||
if GENERATE_SETTINGS_ARGS is not None:
|
||||
for k in GENERATE_SETTINGS_ARGS:
|
||||
res.append(defaults[k] if not settings or settings[k] is None else settings[k])
|
||||
else:
|
||||
for k in defaults:
|
||||
res.append(defaults[k] if not settings or settings[k] is None else settings[k])
|
||||
|
||||
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'],
|
||||
)
|
||||
return tuple(res)
|
||||
|
||||
|
||||
def reset_generation_settings():
|
||||
|
|
@ -1955,10 +1953,10 @@ def deduce_autoregressive_model(voice=None):
|
|||
voice = get_current_voice()
|
||||
|
||||
if voice:
|
||||
dir = f'./training/{voice}-finetune/models/'
|
||||
if os.path.exists(f'./training/finetunes/{voice}.pth'):
|
||||
return f'./training/finetunes/{voice}.pth'
|
||||
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 ]
|
||||
|
|
|
|||
588
src/webui.py
588
src/webui.py
|
|
@ -4,6 +4,7 @@ import time
|
|||
import json
|
||||
import base64
|
||||
import re
|
||||
import inspect
|
||||
import urllib.request
|
||||
|
||||
import torch
|
||||
|
|
@ -22,7 +23,38 @@ from utils import *
|
|||
|
||||
args = setup_args()
|
||||
|
||||
def run_generation(
|
||||
GENERATE_SETTINGS = {}
|
||||
TRANSCRIBE_SETTINGS = {}
|
||||
EXEC_SETTINGS = {}
|
||||
TRAINING_SETTINGS = {}
|
||||
|
||||
PRESETS = {
|
||||
'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
|
||||
'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
|
||||
'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
|
||||
'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
|
||||
}
|
||||
|
||||
HISTORY_HEADERS = {
|
||||
"Name": "",
|
||||
"Samples": "num_autoregressive_samples",
|
||||
"Iterations": "diffusion_iterations",
|
||||
"Temp.": "temperature",
|
||||
"Sampler": "diffusion_sampler",
|
||||
"CVVP": "cvvp_weight",
|
||||
"Top P": "top_p",
|
||||
"Diff. Temp.": "diffusion_temperature",
|
||||
"Len Pen": "length_penalty",
|
||||
"Rep Pen": "repetition_penalty",
|
||||
"Cond-Free K": "cond_free_k",
|
||||
"Time": "time",
|
||||
"Datetime": "datetime",
|
||||
"Model": "model",
|
||||
"Model Hash": "model_hash",
|
||||
}
|
||||
|
||||
# can't use *args OR **kwargs if I want to retain the ability to use progress
|
||||
def generate_proxy(
|
||||
text,
|
||||
delimiter,
|
||||
emotion,
|
||||
|
|
@ -30,8 +62,8 @@ def run_generation(
|
|||
voice,
|
||||
mic_audio,
|
||||
voice_latents_chunks,
|
||||
seed,
|
||||
candidates,
|
||||
seed,
|
||||
num_autoregressive_samples,
|
||||
diffusion_iterations,
|
||||
temperature,
|
||||
|
|
@ -43,47 +75,20 @@ def run_generation(
|
|||
length_penalty,
|
||||
repetition_penalty,
|
||||
cond_free_k,
|
||||
experimental_checkboxes,
|
||||
experimentals,
|
||||
progress=gr.Progress(track_tqdm=True)
|
||||
):
|
||||
if not text:
|
||||
raise gr.Error("Please provide text.")
|
||||
if not voice:
|
||||
raise gr.Error("Please provide a voice.")
|
||||
kwargs = locals()
|
||||
|
||||
try:
|
||||
sample, outputs, stats = generate(
|
||||
text=text,
|
||||
delimiter=delimiter,
|
||||
emotion=emotion,
|
||||
prompt=prompt,
|
||||
voice=voice,
|
||||
mic_audio=mic_audio,
|
||||
voice_latents_chunks=voice_latents_chunks,
|
||||
seed=seed,
|
||||
candidates=candidates,
|
||||
num_autoregressive_samples=num_autoregressive_samples,
|
||||
diffusion_iterations=diffusion_iterations,
|
||||
temperature=temperature,
|
||||
diffusion_sampler=diffusion_sampler,
|
||||
breathing_room=breathing_room,
|
||||
cvvp_weight=cvvp_weight,
|
||||
top_p=top_p,
|
||||
diffusion_temperature=diffusion_temperature,
|
||||
length_penalty=length_penalty,
|
||||
repetition_penalty=repetition_penalty,
|
||||
cond_free_k=cond_free_k,
|
||||
experimental_checkboxes=experimental_checkboxes,
|
||||
progress=progress
|
||||
)
|
||||
sample, outputs, stats = generate(**kwargs)
|
||||
except Exception as e:
|
||||
message = str(e)
|
||||
if message == "Kill signal detected":
|
||||
unload_tts()
|
||||
|
||||
raise gr.Error(message)
|
||||
raise e
|
||||
|
||||
|
||||
return (
|
||||
outputs[0],
|
||||
gr.update(value=sample, visible=sample is not None),
|
||||
|
|
@ -91,14 +96,8 @@ def run_generation(
|
|||
gr.update(value=stats, visible=True),
|
||||
)
|
||||
|
||||
|
||||
def update_presets(value):
|
||||
PRESETS = {
|
||||
'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
|
||||
'Fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
|
||||
'Standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
|
||||
'High Quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
|
||||
}
|
||||
|
||||
if value in PRESETS:
|
||||
preset = PRESETS[value]
|
||||
return (gr.update(value=preset['num_autoregressive_samples']), gr.update(value=preset['diffusion_iterations']))
|
||||
|
|
@ -117,24 +116,6 @@ def get_training_configs():
|
|||
def update_training_configs():
|
||||
return gr.update(choices=get_training_list())
|
||||
|
||||
history_headers = {
|
||||
"Name": "",
|
||||
"Samples": "num_autoregressive_samples",
|
||||
"Iterations": "diffusion_iterations",
|
||||
"Temp.": "temperature",
|
||||
"Sampler": "diffusion_sampler",
|
||||
"CVVP": "cvvp_weight",
|
||||
"Top P": "top_p",
|
||||
"Diff. Temp.": "diffusion_temperature",
|
||||
"Len Pen": "length_penalty",
|
||||
"Rep Pen": "repetition_penalty",
|
||||
"Cond-Free K": "cond_free_k",
|
||||
"Time": "time",
|
||||
"Datetime": "datetime",
|
||||
"Model": "model",
|
||||
"Model Hash": "model_hash",
|
||||
}
|
||||
|
||||
def history_view_results( voice ):
|
||||
results = []
|
||||
files = []
|
||||
|
|
@ -148,10 +129,10 @@ def history_view_results( voice ):
|
|||
continue
|
||||
|
||||
values = []
|
||||
for k in history_headers:
|
||||
for k in HISTORY_HEADERS:
|
||||
v = file
|
||||
if k != "Name":
|
||||
v = metadata[history_headers[k]] if history_headers[k] in metadata else '?'
|
||||
v = metadata[HISTORY_HEADERS[k]] if HISTORY_HEADERS[k] in metadata else '?'
|
||||
values.append(v)
|
||||
|
||||
|
||||
|
|
@ -193,181 +174,55 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
|
|||
def prepare_dataset_proxy( voice, language, skip_existings, progress=gr.Progress(track_tqdm=True) ):
|
||||
return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, progress=progress )
|
||||
|
||||
def optimize_training_settings_proxy( *args, **kwargs ):
|
||||
tup = optimize_training_settings(*args, **kwargs)
|
||||
def update_args_proxy( *args ):
|
||||
kwargs = {}
|
||||
keys = list(EXEC_SETTINGS.keys())
|
||||
for i in range(len(args)):
|
||||
k = keys[i]
|
||||
v = args[i]
|
||||
kwargs[k] = v
|
||||
|
||||
return (
|
||||
gr.update(value=tup[0]),
|
||||
gr.update(value=tup[1]),
|
||||
gr.update(value=tup[2]),
|
||||
gr.update(value=tup[3]),
|
||||
gr.update(value=tup[4]),
|
||||
gr.update(value=tup[5]),
|
||||
gr.update(value=tup[6]),
|
||||
gr.update(value=tup[7]),
|
||||
gr.update(value=tup[8]),
|
||||
"\n".join(tup[9])
|
||||
)
|
||||
update_args(**kwargs)
|
||||
def optimize_training_settings_proxy( *args ):
|
||||
kwargs = {}
|
||||
keys = list(TRAINING_SETTINGS.keys())
|
||||
for i in range(len(args)):
|
||||
k = keys[i]
|
||||
v = args[i]
|
||||
kwargs[k] = v
|
||||
|
||||
settings, messages = optimize_training_settings(**kwargs)
|
||||
output = list(settings.values())
|
||||
return output[:-1] + ["\n".join(messages)]
|
||||
|
||||
def import_training_settings_proxy( voice ):
|
||||
indir = f'./training/{voice}/'
|
||||
outdir = f'./training/{voice}-finetune/'
|
||||
|
||||
in_config_path = f"{indir}/train.yaml"
|
||||
out_config_path = None
|
||||
out_configs = []
|
||||
if os.path.isdir(outdir):
|
||||
out_configs = sorted([d[:-5] for d in os.listdir(outdir) if d[-5:] == ".yaml" ])
|
||||
if len(out_configs) > 0:
|
||||
out_config_path = f'{outdir}/{out_configs[-1]}.yaml'
|
||||
|
||||
config_path = out_config_path if out_config_path else in_config_path
|
||||
|
||||
messages = []
|
||||
with open(config_path, 'r') as file:
|
||||
config = yaml.safe_load(file)
|
||||
messages.append(f"Importing from: {config_path}")
|
||||
|
||||
dataset_path = f"./training/{voice}/train.txt"
|
||||
with open(dataset_path, 'r', encoding="utf-8") as f:
|
||||
lines = len(f.readlines())
|
||||
messages.append(f"Basing epoch size to {lines} lines")
|
||||
|
||||
batch_size = config['datasets']['train']['batch_size']
|
||||
gradient_accumulation_size = config['train']['mega_batch_factor']
|
||||
|
||||
iterations = config['train']['niter']
|
||||
steps_per_iteration = int(lines / batch_size)
|
||||
epochs = int(iterations / steps_per_iteration)
|
||||
|
||||
|
||||
learning_rate = config['steps']['gpt_train']['optimizer_params']['lr']
|
||||
text_ce_lr_weight = config['steps']['gpt_train']['losses']['text_ce']['weight']
|
||||
learning_rate_schedule = [ int(x / steps_per_iteration) for x in config['train']['gen_lr_steps'] ]
|
||||
|
||||
|
||||
print_rate = int(config['logger']['print_freq'] / steps_per_iteration)
|
||||
save_rate = int(config['logger']['save_checkpoint_freq'] / steps_per_iteration)
|
||||
validation_rate = int(config['train']['val_freq'] / steps_per_iteration)
|
||||
|
||||
half_p = config['fp16']
|
||||
bnb = True
|
||||
|
||||
statedir = f'{outdir}/training_state/'
|
||||
resumes = []
|
||||
resume_path = None
|
||||
source_model = get_halfp_model_path() if half_p else get_model_path('autoregressive.pth')
|
||||
|
||||
if "pretrain_model_gpt" in config['path']:
|
||||
source_model = config['path']['pretrain_model_gpt']
|
||||
elif "resume_state" in config['path']:
|
||||
resume_path = config['path']['resume_state']
|
||||
injson = f'./training/{voice}/train.json'
|
||||
statedir = f'./training/{voice}/training_state/'
|
||||
|
||||
with open(injson, 'r', encoding="utf-8") as f:
|
||||
settings = json.loads(f.read())
|
||||
|
||||
if os.path.isdir(statedir):
|
||||
resumes = sorted([int(d[:-6]) for d in os.listdir(statedir) if d[-6:] == ".state" ])
|
||||
|
||||
if len(resumes) > 0:
|
||||
resume_path = f'{statedir}/{resumes[-1]}.state'
|
||||
messages.append(f"Latest resume found: {resume_path}")
|
||||
if len(resumes) > 0:
|
||||
settings['resume_state'] = f'{statedir}/{resumes[-1]}.state'
|
||||
messages.append(f"Found most recent training state: {settings['resume_state']}")
|
||||
|
||||
output = list(settings.values())
|
||||
messages.append(f"Imported training settings: {injson}")
|
||||
return output[:-1] + ["\n".join(messages)]
|
||||
|
||||
def save_training_settings_proxy( *args ):
|
||||
kwargs = {}
|
||||
keys = list(TRAINING_SETTINGS.keys())
|
||||
for i in range(len(args)):
|
||||
k = keys[i]
|
||||
v = args[i]
|
||||
kwargs[k] = v
|
||||
|
||||
if "ext" in config and "bitsandbytes" in config["ext"]:
|
||||
bnb = config["ext"]["bitsandbytes"]
|
||||
|
||||
workers = config['datasets']['train']['n_workers']
|
||||
|
||||
messages = "\n".join(messages)
|
||||
|
||||
return (
|
||||
epochs,
|
||||
learning_rate,
|
||||
text_ce_lr_weight,
|
||||
learning_rate_schedule,
|
||||
batch_size,
|
||||
gradient_accumulation_size,
|
||||
print_rate,
|
||||
save_rate,
|
||||
validation_rate,
|
||||
resume_path,
|
||||
half_p,
|
||||
bnb,
|
||||
workers,
|
||||
source_model,
|
||||
messages
|
||||
)
|
||||
|
||||
|
||||
def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, validation_rate, resume_path, half_p, bnb, workers, source_model, voice ):
|
||||
name = f"{voice}-finetune"
|
||||
dataset_name = f"{voice}-train"
|
||||
dataset_path = f"./training/{voice}/train.txt"
|
||||
validation_name = f"{voice}-val"
|
||||
validation_path = f"./training/{voice}/validation.txt"
|
||||
|
||||
with open(dataset_path, 'r', encoding="utf-8") as f:
|
||||
lines = len(f.readlines())
|
||||
|
||||
messages = []
|
||||
|
||||
iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
|
||||
messages.append(f"For {epochs} epochs with {lines} lines, iterating for {iterations} steps")
|
||||
|
||||
print_rate = int(print_rate * iterations / epochs)
|
||||
save_rate = int(save_rate * iterations / epochs)
|
||||
validation_rate = int(validation_rate * iterations / epochs)
|
||||
|
||||
validation_batch_size = int(batch_size / gradient_accumulation_size)
|
||||
|
||||
if iterations % save_rate != 0:
|
||||
adjustment = int(iterations / save_rate) * save_rate
|
||||
messages.append(f"Iteration rate is not evenly divisible by save rate, adjusting: {iterations} => {adjustment}")
|
||||
iterations = adjustment
|
||||
|
||||
if not os.path.exists(validation_path):
|
||||
validation_rate = iterations
|
||||
validation_path = dataset_path
|
||||
messages.append("Validation not found, disabling validation...")
|
||||
else:
|
||||
with open(validation_path, 'r', encoding="utf-8") as f:
|
||||
validation_lines = len(f.readlines())
|
||||
|
||||
|
||||
if validation_lines < validation_batch_size:
|
||||
validation_batch_size = validation_lines
|
||||
messages.append(f"Batch size exceeds validation dataset size, clamping validation batch size to {validation_lines}")
|
||||
|
||||
if not learning_rate_schedule:
|
||||
learning_rate_schedule = EPOCH_SCHEDULE
|
||||
elif isinstance(learning_rate_schedule,str):
|
||||
learning_rate_schedule = json.loads(learning_rate_schedule)
|
||||
|
||||
learning_rate_schedule = schedule_learning_rate( iterations / epochs, learning_rate_schedule )
|
||||
|
||||
messages.append(save_training_settings(
|
||||
iterations=iterations,
|
||||
batch_size=batch_size,
|
||||
learning_rate=learning_rate,
|
||||
text_ce_lr_weight=text_ce_lr_weight,
|
||||
learning_rate_schedule=learning_rate_schedule,
|
||||
gradient_accumulation_size=gradient_accumulation_size,
|
||||
print_rate=print_rate,
|
||||
save_rate=save_rate,
|
||||
validation_rate=validation_rate,
|
||||
name=name,
|
||||
dataset_name=dataset_name,
|
||||
dataset_path=dataset_path,
|
||||
validation_name=validation_name,
|
||||
validation_path=validation_path,
|
||||
validation_batch_size=validation_batch_size,
|
||||
output_name=f"{voice}/train.yaml",
|
||||
resume_path=resume_path,
|
||||
half_p=half_p,
|
||||
bnb=bnb,
|
||||
workers=workers,
|
||||
source_model=source_model,
|
||||
))
|
||||
settings, messages = save_training_settings(**kwargs)
|
||||
return "\n".join(messages)
|
||||
|
||||
def update_voices():
|
||||
|
|
@ -406,60 +261,68 @@ def setup_gradio():
|
|||
autoregressive_models = get_autoregressive_models()
|
||||
dataset_list = get_dataset_list()
|
||||
|
||||
GENERATE_SETTINGS_ARGS = list(inspect.signature(generate_proxy).parameters.keys())[:-1]
|
||||
for i in range(len(GENERATE_SETTINGS_ARGS)):
|
||||
arg = GENERATE_SETTINGS_ARGS[i]
|
||||
GENERATE_SETTINGS[arg] = None
|
||||
set_generate_settings_arg_order(GENERATE_SETTINGS_ARGS)
|
||||
|
||||
with gr.Blocks() as ui:
|
||||
with gr.Tab("Generate"):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
text = gr.Textbox(lines=4, label="Input Prompt")
|
||||
GENERATE_SETTINGS["text"] = gr.Textbox(lines=4, label="Input Prompt")
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
delimiter = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
|
||||
GENERATE_SETTINGS["delimiter"] = gr.Textbox(lines=1, label="Line Delimiter", placeholder="\\n")
|
||||
|
||||
emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
|
||||
prompt = gr.Textbox(lines=1, label="Custom Emotion")
|
||||
voice = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
|
||||
mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
|
||||
voice_latents_chunks = gr.Number(label="Voice Chunks", precision=0, value=0)
|
||||
GENERATE_SETTINGS["emotion"] = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom", "None"], value="None", label="Emotion", type="value", interactive=True )
|
||||
GENERATE_SETTINGS["prompt"] = gr.Textbox(lines=1, label="Custom Emotion", visible=False)
|
||||
GENERATE_SETTINGS["voice"] = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
|
||||
GENERATE_SETTINGS["mic_audio"] = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
|
||||
GENERATE_SETTINGS["voice_latents_chunks"] = gr.Number(label="Voice Chunks", precision=0, value=0)
|
||||
with gr.Row():
|
||||
refresh_voices = gr.Button(value="Refresh Voice List")
|
||||
recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
|
||||
|
||||
voice.change(
|
||||
GENERATE_SETTINGS["voice"].change(
|
||||
fn=update_baseline_for_latents_chunks,
|
||||
inputs=voice,
|
||||
outputs=voice_latents_chunks
|
||||
inputs=GENERATE_SETTINGS["voice"],
|
||||
outputs=GENERATE_SETTINGS["voice_latents_chunks"]
|
||||
)
|
||||
voice.change(
|
||||
GENERATE_SETTINGS["voice"].change(
|
||||
fn=lambda value: gr.update(visible=value == "microphone"),
|
||||
inputs=voice,
|
||||
outputs=mic_audio,
|
||||
inputs=GENERATE_SETTINGS["voice"],
|
||||
outputs=GENERATE_SETTINGS["mic_audio"],
|
||||
)
|
||||
with gr.Column():
|
||||
candidates = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
|
||||
seed = gr.Number(value=0, precision=0, label="Seed")
|
||||
GENERATE_SETTINGS["candidates"] = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates")
|
||||
GENERATE_SETTINGS["seed"] = gr.Number(value=0, precision=0, label="Seed")
|
||||
|
||||
preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value" )
|
||||
num_autoregressive_samples = gr.Slider(value=128, minimum=2, maximum=512, step=1, label="Samples")
|
||||
diffusion_iterations = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
|
||||
|
||||
temperature = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
|
||||
GENERATE_SETTINGS["num_autoregressive_samples"] = gr.Slider(value=128, minimum=2, maximum=512, step=1, label="Samples")
|
||||
GENERATE_SETTINGS["diffusion_iterations"] = gr.Slider(value=128, minimum=0, maximum=512, step=1, label="Iterations")
|
||||
|
||||
GENERATE_SETTINGS["temperature"] = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
|
||||
|
||||
show_experimental_settings = gr.Checkbox(label="Show Experimental Settings")
|
||||
reset_generation_settings_button = gr.Button(value="Reset to Default")
|
||||
with gr.Column(visible=False) as col:
|
||||
experimental_column = col
|
||||
|
||||
experimental_checkboxes = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
|
||||
breathing_room = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
|
||||
diffusion_sampler = gr.Radio(
|
||||
GENERATE_SETTINGS["experimentals"] = gr.CheckboxGroup(["Half Precision", "Conditioning-Free"], value=["Conditioning-Free"], label="Experimental Flags")
|
||||
GENERATE_SETTINGS["breathing_room"] = gr.Slider(value=8, minimum=1, maximum=32, step=1, label="Pause Size")
|
||||
GENERATE_SETTINGS["diffusion_sampler"] = gr.Radio(
|
||||
["P", "DDIM"], # + ["K_Euler_A", "DPM++2M"],
|
||||
value="DDIM", label="Diffusion Samplers", type="value"
|
||||
)
|
||||
cvvp_weight = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
|
||||
top_p = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
|
||||
diffusion_temperature = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
|
||||
length_penalty = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty")
|
||||
repetition_penalty = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty")
|
||||
cond_free_k = gr.Slider(value=2.0, minimum=0, maximum=4, label="Conditioning-Free K")
|
||||
GENERATE_SETTINGS["cvvp_weight"] = gr.Slider(value=0, minimum=0, maximum=1, label="CVVP Weight")
|
||||
GENERATE_SETTINGS["top_p"] = gr.Slider(value=0.8, minimum=0, maximum=1, label="Top P")
|
||||
GENERATE_SETTINGS["diffusion_temperature"] = gr.Slider(value=1.0, minimum=0, maximum=1, label="Diffusion Temperature")
|
||||
GENERATE_SETTINGS["length_penalty"] = gr.Slider(value=1.0, minimum=0, maximum=8, label="Length Penalty")
|
||||
GENERATE_SETTINGS["repetition_penalty"] = gr.Slider(value=2.0, minimum=0, maximum=8, label="Repetition Penalty")
|
||||
GENERATE_SETTINGS["cond_free_k"] = gr.Slider(value=2.0, minimum=0, maximum=4, label="Conditioning-Free K")
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
submit = gr.Button(value="Generate")
|
||||
|
|
@ -483,7 +346,7 @@ def setup_gradio():
|
|||
with gr.Tab("History"):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
history_info = gr.Dataframe(label="Results", headers=list(history_headers.keys()))
|
||||
history_info = gr.Dataframe(label="Results", headers=list(HISTORY_HEADERS.keys()))
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
history_voices = gr.Dropdown(choices=result_voices, label="Voice", type="value", value=result_voices[0] if len(result_voices) > 0 else "")
|
||||
|
|
@ -521,51 +384,40 @@ def setup_gradio():
|
|||
with gr.Tab("Generate Configuration"):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
training_settings = [
|
||||
gr.Number(label="Epochs", value=500, precision=0),
|
||||
]
|
||||
TRAINING_SETTINGS["epochs"] = gr.Number(label="Epochs", value=500, precision=0)
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
training_settings = training_settings + [
|
||||
gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6),
|
||||
gr.Slider(label="Text_CE LR Ratio", value=0.01, minimum=0, maximum=1),
|
||||
]
|
||||
training_settings = training_settings + [
|
||||
gr.Textbox(label="Learning Rate Schedule", placeholder=str(EPOCH_SCHEDULE)),
|
||||
]
|
||||
TRAINING_SETTINGS["learning_rate"] = gr.Slider(label="Learning Rate", value=1e-5, minimum=0, maximum=1e-4, step=1e-6)
|
||||
TRAINING_SETTINGS["text_ce_lr_weight"] = gr.Slider(label="Text_CE LR Ratio", value=0.01, minimum=0, maximum=1)
|
||||
|
||||
TRAINING_SETTINGS["learning_rate_schedule"] = gr.Textbox(label="Learning Rate Schedule", placeholder=str(EPOCH_SCHEDULE))
|
||||
with gr.Row():
|
||||
training_settings = training_settings + [
|
||||
gr.Number(label="Batch Size", value=128, precision=0),
|
||||
gr.Number(label="Gradient Accumulation Size", value=4, precision=0),
|
||||
]
|
||||
TRAINING_SETTINGS["batch_size"] = gr.Number(label="Batch Size", value=128, precision=0)
|
||||
TRAINING_SETTINGS["gradient_accumulation_size"] = gr.Number(label="Gradient Accumulation Size", value=4, precision=0)
|
||||
with gr.Row():
|
||||
training_settings = training_settings + [
|
||||
gr.Number(label="Print Frequency (in epochs)", value=5, precision=0),
|
||||
gr.Number(label="Save Frequency (in epochs)", value=5, precision=0),
|
||||
gr.Number(label="Validation Frequency (in epochs)", value=5, precision=0),
|
||||
]
|
||||
training_settings = training_settings + [
|
||||
gr.Textbox(label="Resume State Path", placeholder="./training/${voice}-finetune/training_state/${last_state}.state"),
|
||||
]
|
||||
TRAINING_SETTINGS["print_rate"] = gr.Number(label="Print Frequency (in epochs)", value=5, precision=0)
|
||||
TRAINING_SETTINGS["save_rate"] = gr.Number(label="Save Frequency (in epochs)", value=5, precision=0)
|
||||
TRAINING_SETTINGS["validation_rate"] = gr.Number(label="Validation Frequency (in epochs)", value=5, precision=0)
|
||||
|
||||
with gr.Row():
|
||||
training_halfp = gr.Checkbox(label="Half Precision", value=args.training_default_halfp)
|
||||
training_bnb = gr.Checkbox(label="BitsAndBytes", value=args.training_default_bnb)
|
||||
TRAINING_SETTINGS["half_p"] = gr.Checkbox(label="Half Precision", value=args.training_default_halfp)
|
||||
TRAINING_SETTINGS["bitsandbytes"] = gr.Checkbox(label="BitsAndBytes", value=args.training_default_bnb)
|
||||
|
||||
training_workers = gr.Number(label="Worker Processes", value=2, precision=0)
|
||||
|
||||
source_model = gr.Dropdown( choices=autoregressive_models, label="Source Model", type="value", value=autoregressive_models[0] )
|
||||
dataset_list_dropdown = gr.Dropdown( choices=dataset_list, label="Dataset", type="value", value=dataset_list[0] if len(dataset_list) else "" )
|
||||
training_settings = training_settings + [ training_halfp, training_bnb, training_workers, source_model, dataset_list_dropdown ]
|
||||
TRAINING_SETTINGS["workers"] = gr.Number(label="Worker Processes", value=2, precision=0)
|
||||
TRAINING_SETTINGS["gpus"] = gr.Number(label="GPUs", value=get_device_count(), precision=0)
|
||||
TRAINING_SETTINGS["source_model"] = gr.Dropdown( choices=autoregressive_models, label="Source Model", type="value", value=autoregressive_models[0] )
|
||||
TRAINING_SETTINGS["resume_state"] = gr.Textbox(label="Resume State Path", placeholder="./training/${voice}/training_state/${last_state}.state")
|
||||
|
||||
TRAINING_SETTINGS["voice"] = gr.Dropdown( choices=dataset_list, label="Dataset", type="value", value=dataset_list[0] if len(dataset_list) else "" )
|
||||
|
||||
with gr.Row():
|
||||
refresh_dataset_list = gr.Button(value="Refresh Dataset List")
|
||||
import_dataset_button = gr.Button(value="Reuse/Import Dataset")
|
||||
training_refresh_dataset = gr.Button(value="Refresh Dataset List")
|
||||
training_import_settings = gr.Button(value="Reuse/Import Dataset")
|
||||
with gr.Column():
|
||||
save_yaml_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
|
||||
training_configuration_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
|
||||
with gr.Row():
|
||||
optimize_yaml_button = gr.Button(value="Validate Training Configuration")
|
||||
save_yaml_button = gr.Button(value="Save Training Configuration")
|
||||
training_optimize_configuration = gr.Button(value="Validate Training Configuration")
|
||||
training_save_configuration = gr.Button(value="Save Training Configuration")
|
||||
with gr.Tab("Run Training"):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
|
|
@ -588,9 +440,7 @@ def setup_gradio():
|
|||
training_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
|
||||
verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
|
||||
|
||||
with gr.Row():
|
||||
training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
|
||||
training_gpu_count = gr.Number(label="GPUs", value=get_device_count())
|
||||
training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
|
||||
with gr.Row():
|
||||
start_training_button = gr.Button(value="Train")
|
||||
stop_training_button = gr.Button(value="Stop")
|
||||
|
|
@ -599,43 +449,40 @@ def setup_gradio():
|
|||
with gr.Row():
|
||||
exec_inputs = []
|
||||
with gr.Column():
|
||||
exec_inputs = exec_inputs + [
|
||||
gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/"),
|
||||
gr.Checkbox(label="Public Share Gradio", value=args.share),
|
||||
gr.Checkbox(label="Check For Updates", value=args.check_for_updates),
|
||||
gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only),
|
||||
gr.Checkbox(label="Low VRAM", value=args.low_vram),
|
||||
gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata),
|
||||
gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean),
|
||||
gr.Checkbox(label="Use Voice Fixer on Generated Output", value=args.voice_fixer),
|
||||
gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda),
|
||||
gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents),
|
||||
gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load),
|
||||
gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs),
|
||||
gr.Textbox(label="Device Override", value=args.device_override),
|
||||
]
|
||||
EXEC_SETTINGS['listen'] = gr.Textbox(label="Listen", value=args.listen, placeholder="127.0.0.1:7860/")
|
||||
EXEC_SETTINGS['share'] = gr.Checkbox(label="Public Share Gradio", value=args.share)
|
||||
EXEC_SETTINGS['check_for_updates'] = gr.Checkbox(label="Check For Updates", value=args.check_for_updates)
|
||||
EXEC_SETTINGS['models_from_local_only'] = gr.Checkbox(label="Only Load Models Locally", value=args.models_from_local_only)
|
||||
EXEC_SETTINGS['low_vram'] = gr.Checkbox(label="Low VRAM", value=args.low_vram)
|
||||
EXEC_SETTINGS['embed_output_metadata'] = gr.Checkbox(label="Embed Output Metadata", value=args.embed_output_metadata)
|
||||
EXEC_SETTINGS['latents_lean_and_mean'] = gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean)
|
||||
EXEC_SETTINGS['voice_fixer'] = gr.Checkbox(label="Use Voice Fixer on Generated Output", value=args.voice_fixer)
|
||||
EXEC_SETTINGS['voice_fixer_use_cuda'] = gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda)
|
||||
EXEC_SETTINGS['force_cpu_for_conditioning_latents'] = gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents)
|
||||
EXEC_SETTINGS['defer_tts_load'] = gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load)
|
||||
EXEC_SETTINGS['prune_nonfinal_outputs'] = gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs)
|
||||
EXEC_SETTINGS['device_override'] = gr.Textbox(label="Device Override", value=args.device_override)
|
||||
with gr.Column():
|
||||
exec_inputs = exec_inputs + [
|
||||
gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
|
||||
gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count),
|
||||
gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size),
|
||||
gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
|
||||
]
|
||||
EXEC_SETTINGS['sample_batch_size'] = gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size)
|
||||
EXEC_SETTINGS['concurrency_count'] = gr.Number(label="Gradio Concurrency Count", precision=0, value=args.concurrency_count)
|
||||
EXEC_SETTINGS['autocalculate_voice_chunk_duration_size'] = gr.Number(label="Auto-Calculate Voice Chunk Duration (in seconds)", precision=0, value=args.autocalculate_voice_chunk_duration_size)
|
||||
EXEC_SETTINGS['output_volume'] = gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume)
|
||||
|
||||
autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
|
||||
EXEC_SETTINGS['autoregressive_model'] = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
|
||||
|
||||
vocoder_models = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])
|
||||
whisper_backend = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
|
||||
whisper_model_dropdown = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
|
||||
EXEC_SETTINGS['vocoder_model'] = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])
|
||||
EXEC_SETTINGS['whisper_backend'] = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
|
||||
EXEC_SETTINGS['whisper_model'] = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
|
||||
|
||||
exec_inputs = exec_inputs + [ autoregressive_model_dropdown, vocoder_models, whisper_backend, whisper_model_dropdown, training_halfp, training_bnb ]
|
||||
EXEC_SETTINGS['training_default_halfp'] = TRAINING_SETTINGS['half_p']
|
||||
EXEC_SETTINGS['training_default_bnb'] = TRAINING_SETTINGS['bitsandbytes']
|
||||
|
||||
with gr.Row():
|
||||
autoregressive_models_update_button = gr.Button(value="Refresh Model List")
|
||||
gr.Button(value="Check for Updates").click(check_for_updates)
|
||||
gr.Button(value="(Re)Load TTS").click(
|
||||
reload_tts,
|
||||
inputs=autoregressive_model_dropdown,
|
||||
inputs=EXEC_SETTINGS['autoregressive_model'],
|
||||
outputs=None
|
||||
)
|
||||
# kill_button = gr.Button(value="Close UI")
|
||||
|
|
@ -648,49 +495,26 @@ def setup_gradio():
|
|||
|
||||
autoregressive_models_update_button.click(
|
||||
update_model_list_proxy,
|
||||
inputs=autoregressive_model_dropdown,
|
||||
outputs=autoregressive_model_dropdown,
|
||||
inputs=EXEC_SETTINGS['autoregressive_model'],
|
||||
outputs=EXEC_SETTINGS['autoregressive_model'],
|
||||
)
|
||||
|
||||
for i in exec_inputs:
|
||||
i.change( fn=update_args, inputs=exec_inputs )
|
||||
exec_inputs = list(EXEC_SETTINGS.values())
|
||||
for k in EXEC_SETTINGS:
|
||||
EXEC_SETTINGS[k].change( fn=update_args_proxy, inputs=exec_inputs )
|
||||
|
||||
autoregressive_model_dropdown.change(
|
||||
EXEC_SETTINGS['autoregressive_model'].change(
|
||||
fn=update_autoregressive_model,
|
||||
inputs=autoregressive_model_dropdown,
|
||||
inputs=EXEC_SETTINGS['autoregressive_model'],
|
||||
outputs=None
|
||||
)
|
||||
|
||||
vocoder_models.change(
|
||||
EXEC_SETTINGS['vocoder_model'].change(
|
||||
fn=update_vocoder_model,
|
||||
inputs=vocoder_models,
|
||||
inputs=EXEC_SETTINGS['vocoder_model'],
|
||||
outputs=None
|
||||
)
|
||||
|
||||
input_settings = [
|
||||
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,
|
||||
]
|
||||
|
||||
history_voices.change(
|
||||
fn=history_view_results,
|
||||
inputs=history_voices,
|
||||
|
|
@ -734,45 +558,46 @@ def setup_gradio():
|
|||
preset.change(fn=update_presets,
|
||||
inputs=preset,
|
||||
outputs=[
|
||||
num_autoregressive_samples,
|
||||
diffusion_iterations,
|
||||
GENERATE_SETTINGS['num_autoregressive_samples'],
|
||||
GENERATE_SETTINGS['diffusion_iterations'],
|
||||
],
|
||||
)
|
||||
|
||||
recompute_voice_latents.click(compute_latents_proxy,
|
||||
inputs=[
|
||||
voice,
|
||||
voice_latents_chunks,
|
||||
GENERATE_SETTINGS['voice'],
|
||||
GENERATE_SETTINGS['voice_latents_chunks'],
|
||||
],
|
||||
outputs=voice,
|
||||
outputs=GENERATE_SETTINGS['voice'],
|
||||
)
|
||||
|
||||
emotion.change(
|
||||
GENERATE_SETTINGS['emotion'].change(
|
||||
fn=lambda value: gr.update(visible=value == "Custom"),
|
||||
inputs=emotion,
|
||||
outputs=prompt
|
||||
inputs=GENERATE_SETTINGS['emotion'],
|
||||
outputs=GENERATE_SETTINGS['prompt']
|
||||
)
|
||||
mic_audio.change(fn=lambda value: gr.update(value="microphone"),
|
||||
inputs=mic_audio,
|
||||
outputs=voice
|
||||
GENERATE_SETTINGS['mic_audio'].change(fn=lambda value: gr.update(value="microphone"),
|
||||
inputs=GENERATE_SETTINGS['mic_audio'],
|
||||
outputs=GENERATE_SETTINGS['voice']
|
||||
)
|
||||
|
||||
refresh_voices.click(update_voices,
|
||||
inputs=None,
|
||||
outputs=[
|
||||
voice,
|
||||
GENERATE_SETTINGS['voice'],
|
||||
dataset_settings[0],
|
||||
history_voices
|
||||
]
|
||||
)
|
||||
|
||||
generate_settings = list(GENERATE_SETTINGS.values())
|
||||
submit.click(
|
||||
lambda: (gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)),
|
||||
outputs=[source_sample, candidates_list, generation_results],
|
||||
)
|
||||
|
||||
submit_event = submit.click(run_generation,
|
||||
inputs=input_settings,
|
||||
submit_event = submit.click(generate_proxy,
|
||||
inputs=generate_settings,
|
||||
outputs=[output_audio, source_sample, candidates_list, generation_results],
|
||||
api_name="generate",
|
||||
)
|
||||
|
|
@ -780,13 +605,13 @@ def setup_gradio():
|
|||
|
||||
copy_button.click(import_generate_settings,
|
||||
inputs=audio_in, # JSON elements cannot be used as inputs
|
||||
outputs=input_settings
|
||||
outputs=generate_settings
|
||||
)
|
||||
|
||||
reset_generation_settings_button.click(
|
||||
fn=reset_generation_settings,
|
||||
inputs=None,
|
||||
outputs=input_settings
|
||||
outputs=generate_settings
|
||||
)
|
||||
|
||||
history_copy_settings_button.click(history_copy_settings,
|
||||
|
|
@ -794,7 +619,7 @@ def setup_gradio():
|
|||
history_voices,
|
||||
history_results_list,
|
||||
],
|
||||
outputs=input_settings
|
||||
outputs=generate_settings
|
||||
)
|
||||
|
||||
refresh_configs.click(
|
||||
|
|
@ -806,7 +631,6 @@ def setup_gradio():
|
|||
inputs=[
|
||||
training_configs,
|
||||
verbose_training,
|
||||
training_gpu_count,
|
||||
training_keep_x_past_datasets,
|
||||
],
|
||||
outputs=[
|
||||
|
|
@ -855,38 +679,28 @@ def setup_gradio():
|
|||
],
|
||||
outputs=prepare_dataset_output #console_output
|
||||
)
|
||||
refresh_dataset_list.click(
|
||||
|
||||
training_refresh_dataset.click(
|
||||
lambda: gr.update(choices=get_dataset_list()),
|
||||
inputs=None,
|
||||
outputs=dataset_list_dropdown,
|
||||
outputs=TRAINING_SETTINGS["voice"],
|
||||
)
|
||||
optimize_yaml_button.click(optimize_training_settings_proxy,
|
||||
training_settings = list(TRAINING_SETTINGS.values())
|
||||
training_optimize_configuration.click(optimize_training_settings_proxy,
|
||||
inputs=training_settings,
|
||||
outputs=training_settings[1:10] + [save_yaml_output] #console_output
|
||||
outputs=training_settings[:-1] + [training_configuration_output] #console_output
|
||||
)
|
||||
import_dataset_button.click(import_training_settings_proxy,
|
||||
inputs=dataset_list_dropdown,
|
||||
outputs=training_settings[:14] + [save_yaml_output] #console_output
|
||||
training_import_settings.click(import_training_settings_proxy,
|
||||
inputs=TRAINING_SETTINGS['voice'],
|
||||
outputs=training_settings[:-1] + [training_configuration_output] #console_output
|
||||
)
|
||||
save_yaml_button.click(save_training_settings_proxy,
|
||||
training_save_configuration.click(save_training_settings_proxy,
|
||||
inputs=training_settings,
|
||||
outputs=save_yaml_output #console_output
|
||||
outputs=training_configuration_output #console_output
|
||||
)
|
||||
|
||||
"""
|
||||
def kill_process():
|
||||
ui.close()
|
||||
exit()
|
||||
|
||||
kill_button.click(
|
||||
kill_process,
|
||||
inputs=None,
|
||||
outputs=None
|
||||
)
|
||||
"""
|
||||
|
||||
if os.path.isfile('./config/generate.json'):
|
||||
ui.load(import_generate_settings, inputs=None, outputs=input_settings)
|
||||
ui.load(import_generate_settings, inputs=None, outputs=generate_settings)
|
||||
|
||||
if args.check_for_updates:
|
||||
ui.load(check_for_updates)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user