2057 lines
68 KiB
Python
2057 lines
68 KiB
Python
import os
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if 'XDG_CACHE_HOME' not in os.environ:
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os.environ['XDG_CACHE_HOME'] = os.path.realpath(os.path.join(os.getcwd(), './models/'))
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if 'TORTOISE_MODELS_DIR' not in os.environ:
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os.environ['TORTOISE_MODELS_DIR'] = os.path.realpath(os.path.join(os.getcwd(), './models/tortoise/'))
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if 'TRANSFORMERS_CACHE' not in os.environ:
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os.environ['TRANSFORMERS_CACHE'] = os.path.realpath(os.path.join(os.getcwd(), './models/transformers/'))
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import argparse
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import time
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import json
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import base64
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import re
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import urllib.request
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import signal
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import gc
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import subprocess
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import psutil
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import yaml
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import tqdm
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import torch
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import torchaudio
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import music_tag
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import gradio as gr
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import gradio.utils
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import pandas as pd
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from datetime import datetime
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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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MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
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WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v2"]
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WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
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WHISPER_BACKENDS = ["openai/whisper", "lightmare/whispercpp", "m-bain/whisperx"]
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VOCODERS = ['univnet', 'bigvgan_base_24khz_100band', 'bigvgan_24khz_100band']
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EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
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args = None
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tts = None
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tts_loading = False
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webui = None
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voicefixer = None
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whisper_model = None
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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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global args
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global tts
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unload_whisper()
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unload_voicefixer()
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if not tts:
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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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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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do_gc()
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voice_samples = None
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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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cache_key = f'{voice}:{tts.autoregressive_model_hash[:8]}'
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if cache_key in voice_cache:
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return voice_cache[cache_key]
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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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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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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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if progress is not None:
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progress(0, desc=f"Loading voice: {voice}")
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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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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sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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voice_samples = None
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voice_cache[cache_key] = (voice_samples, conditioning_latents, sample_voice)
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return voice_cache[cache_key]
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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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'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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'num_autoregressive_samples': 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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'voice_samples': None,
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'conditioning_latents': None,
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'use_deterministic_seed': 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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'autoregressive_model': args.autoregressive_model,
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}
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# could be better to just do a ternary on everything above, but i am not a professional
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selected_voice = voice
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if override is not None:
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if 'voice' in override:
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selected_voice = override['voice']
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for k in override:
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if k not in settings:
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continue
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settings[k] = override[k]
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if settings['autoregressive_model'] is not None:
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if settings['autoregressive_model'] == "auto":
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settings['autoregressive_model'] = deduce_autoregressive_model(selected_voice)
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tts.load_autoregressive_model(settings['autoregressive_model'])
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settings['voice_samples'], settings['conditioning_latents'], _ = fetch_voice(voice=selected_voice)
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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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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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settings['cvvp_amount'] = 0
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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 delimiter and delimiter != "" and delimiter in text:
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texts = text.split(delimiter)
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else:
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texts = split_and_recombine_text(text)
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full_start_time = time.time()
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outdir = f"./results/{voice}/"
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os.makedirs(outdir, exist_ok=True)
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audio_cache = {}
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resample = None
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if tts.output_sample_rate != args.output_sample_rate:
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resampler = torchaudio.transforms.Resample(
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tts.output_sample_rate,
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args.output_sample_rate,
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lowpass_filter_width=16,
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rolloff=0.85,
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resampling_method="kaiser_window",
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beta=8.555504641634386,
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)
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volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None
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idx = 0
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idx_cache = {}
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for i, file in enumerate(os.listdir(outdir)):
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filename = os.path.basename(file)
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extension = os.path.splitext(filename)[1]
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if extension != ".json" and extension != ".wav":
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continue
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match = re.findall(rf"^{voice}_(\d+)(?:.+?)?{extension}$", filename)
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key = int(match[0])
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idx_cache[key] = True
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if len(idx_cache) > 0:
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keys = sorted(list(idx_cache.keys()))
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idx = keys[-1] + 1
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idx = pad(idx, 4)
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def get_name(line=0, candidate=0, combined=False):
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name = f"{idx}"
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if combined:
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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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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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'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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if settings is not None:
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for k in settings:
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if k in info:
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info[k] = settings[k]
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if 'half_p' in settings and 'cond_free' in settings:
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info['experimentals'] = []
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if settings['half_p']:
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info['experimentals'].append("Half Precision")
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if settings['cond_free']:
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info['experimentals'].append("Conditioning-Free")
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if latents and "latents" not in info:
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voice = info['voice']
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents.pth'
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if voice == "random" or voice == "microphone":
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if latents and settings['conditioning_latents']:
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dir = f'{get_voice_dir()}/{voice}/'
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if not os.path.isdir(dir):
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os.makedirs(dir, exist_ok=True)
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latents_path = f'{dir}/cond_latents.pth'
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torch.save(conditioning_latents, latents_path)
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else:
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if settings and "model_hash" in settings:
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{settings["model_hash"][:8]}.pth'
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else:
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
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if latents_path and os.path.exists(latents_path):
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try:
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with open(latents_path, 'rb') as f:
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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except Exception as e:
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pass
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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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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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start_time = time.time()
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# do setting editing
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match = re.findall(r'^(\{.+\}) (.+?)$', cut_text)
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override = None
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if match and len(match) > 0:
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match = match[0]
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try:
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override = json.loads(match[0])
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cut_text = match[1].strip()
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except Exception as e:
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raise Exception("Prompt settings editing requested, but received invalid JSON")
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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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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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for j, g in enumerate(gen):
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audio = g.squeeze(0).cpu()
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name = get_name(line=line, candidate=j)
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settings['text'] = cut_text
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settings['time'] = run_time
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settings['datetime'] = datetime.now().isoformat(),
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settings['model'] = tts.autoregressive_model_path
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settings['model_hash'] = tts.autoregressive_model_hash
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audio_cache[name] = {
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'audio': audio,
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'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=settings)
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}
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# save here in case some error happens mid-batch
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torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, tts.output_sample_rate)
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del gen
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do_gc()
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for k in audio_cache:
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audio = audio_cache[k]['audio']
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if resampler is not None:
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audio = resampler(audio)
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if volume_adjust is not None:
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audio = volume_adjust(audio)
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audio_cache[k]['audio'] = audio
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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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if len(texts) > 1:
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audio_clips = []
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for line in range(len(texts)):
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name = get_name(line=line, candidate=candidate)
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audio = audio_cache[name]['audio']
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audio_clips.append(audio)
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name = get_name(candidate=candidate, combined=True)
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audio = torch.cat(audio_clips, dim=-1)
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torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)
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audio = audio.squeeze(0).cpu()
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audio_cache[name] = {
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'audio': audio,
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'settings': get_info(voice=voice),
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'output': True
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}
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else:
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name = get_name(candidate=candidate)
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audio_cache[name]['output'] = True
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if args.voice_fixer:
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if not voicefixer:
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progress(0, "Loading voicefix...")
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load_voicefixer()
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fixed_cache = {}
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for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
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del audio_cache[name]['audio']
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if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
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continue
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path = f'{outdir}/{voice}_{name}.wav'
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fixed = f'{outdir}/{voice}_{name}_fixed.wav'
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voicefixer.restore(
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input=path,
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output=fixed,
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cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
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#mode=mode,
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)
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fixed_cache[f'{name}_fixed'] = {
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'settings': audio_cache[name]['settings'],
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'output': True
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}
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audio_cache[name]['output'] = False
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for name in fixed_cache:
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audio_cache[name] = fixed_cache[name]
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for name in audio_cache:
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if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
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if args.prune_nonfinal_outputs:
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audio_cache[name]['pruned'] = True
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os.remove(f'{outdir}/{voice}_{name}.wav')
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continue
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output_voices.append(f'{outdir}/{voice}_{name}.wav')
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if not args.embed_output_metadata:
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with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
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if args.embed_output_metadata:
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for name in progress.tqdm(audio_cache, desc="Embedding metadata..."):
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if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
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continue
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metadata = music_tag.load_file(f"{outdir}/{voice}_{name}.wav")
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metadata['lyrics'] = json.dumps(audio_cache[name]['settings'])
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metadata.save()
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if sample_voice is not None:
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sample_voice = (tts.input_sample_rate, sample_voice.numpy())
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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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if 'latents' in info:
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del info['latents']
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os.makedirs('./config/', exist_ok=True)
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with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
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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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]
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return (
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sample_voice,
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output_voices,
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stats,
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)
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|
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def cancel_generate():
|
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import tortoise.api
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tortoise.api.STOP_SIGNAL = True
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|
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def hash_file(path, algo="md5", buffer_size=0):
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import hashlib
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|
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hash = None
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if algo == "md5":
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hash = hashlib.md5()
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elif algo == "sha1":
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hash = hashlib.sha1()
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else:
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raise Exception(f'Unknown hash algorithm specified: {algo}')
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|
|
if not os.path.exists(path):
|
|
raise Exception(f'Path not found: {path}')
|
|
|
|
with open(path, 'rb') as f:
|
|
if buffer_size > 0:
|
|
while True:
|
|
data = f.read(buffer_size)
|
|
if not data:
|
|
break
|
|
hash.update(data)
|
|
else:
|
|
hash.update(f.read())
|
|
|
|
return "{0}".format(hash.hexdigest())
|
|
|
|
def update_baseline_for_latents_chunks( voice ):
|
|
global current_voice
|
|
current_voice = voice
|
|
|
|
path = f'{get_voice_dir()}/{voice}/'
|
|
if not os.path.isdir(path):
|
|
return 1
|
|
|
|
dataset_file = f'./training/{voice}/train.txt'
|
|
if os.path.exists(dataset_file):
|
|
return 0 # 0 will leverage using the LJspeech dataset for computing latents
|
|
|
|
files = os.listdir(path)
|
|
|
|
total = 0
|
|
total_duration = 0
|
|
|
|
for file in files:
|
|
if file[-4:] != ".wav":
|
|
continue
|
|
|
|
metadata = torchaudio.info(f'{path}/{file}')
|
|
duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
|
|
total_duration += duration
|
|
total = total + 1
|
|
|
|
|
|
# brain too fried to figure out a better way
|
|
if args.autocalculate_voice_chunk_duration_size == 0:
|
|
return int(total_duration / total) if total > 0 else 1
|
|
return int(total_duration / args.autocalculate_voice_chunk_duration_size) if total_duration > 0 else 1
|
|
|
|
def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, progress=None):
|
|
global tts
|
|
global args
|
|
|
|
unload_whisper()
|
|
unload_voicefixer()
|
|
|
|
if not tts:
|
|
if tts_loading:
|
|
raise Exception("TTS is still initializing...")
|
|
load_tts()
|
|
|
|
if hasattr(tts, "loading") and tts.loading:
|
|
raise Exception("TTS is still initializing...")
|
|
|
|
if args.autoregressive_model == "auto":
|
|
tts.load_autoregressive_model(deduce_autoregressive_model(voice))
|
|
|
|
if voice:
|
|
load_from_dataset = voice_latents_chunks == 0
|
|
|
|
if load_from_dataset:
|
|
dataset_path = f'./training/{voice}/train.txt'
|
|
if not os.path.exists(dataset_path):
|
|
load_from_dataset = False
|
|
else:
|
|
with open(dataset_path, 'r', encoding="utf-8") as f:
|
|
lines = f.readlines()
|
|
|
|
print("Leveraging LJSpeech dataset for computing latents")
|
|
|
|
voice_samples = []
|
|
max_length = 0
|
|
for line in lines:
|
|
filename = f'./training/{voice}/{line.split("|")[0]}'
|
|
|
|
waveform = load_audio(filename, 22050)
|
|
max_length = max(max_length, waveform.shape[-1])
|
|
voice_samples.append(waveform)
|
|
|
|
for i in range(len(voice_samples)):
|
|
voice_samples[i] = pad_or_truncate(voice_samples[i], max_length)
|
|
|
|
voice_latents_chunks = len(voice_samples)
|
|
if not load_from_dataset:
|
|
voice_samples, _ = load_voice(voice, load_latents=False)
|
|
|
|
if voice_samples is None:
|
|
return
|
|
|
|
conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents, progress=progress)
|
|
|
|
if len(conditioning_latents) == 4:
|
|
conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
|
|
|
|
outfile = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
|
|
torch.save(conditioning_latents, outfile)
|
|
print(f'Saved voice latents: {outfile}')
|
|
|
|
return conditioning_latents
|
|
|
|
# superfluous, but it cleans up some things
|
|
class TrainingState():
|
|
def __init__(self, config_path, keep_x_past_checkpoints=0, start=True, gpus=1):
|
|
# parse config to get its iteration
|
|
with open(config_path, 'r') as file:
|
|
self.config = yaml.safe_load(file)
|
|
|
|
self.killed = False
|
|
|
|
self.dataset_dir = f"./training/{self.config['name']}/"
|
|
self.batch_size = self.config['datasets']['train']['batch_size']
|
|
self.dataset_path = self.config['datasets']['train']['path']
|
|
with open(self.dataset_path, 'r', encoding="utf-8") as f:
|
|
self.dataset_size = len(f.readlines())
|
|
|
|
self.it = 0
|
|
self.its = self.config['train']['niter']
|
|
|
|
self.epoch = 0
|
|
self.epochs = int(self.its*self.batch_size/self.dataset_size)
|
|
|
|
self.checkpoint = 0
|
|
self.checkpoints = int(self.its / self.config['logger']['save_checkpoint_freq'])
|
|
|
|
self.buffer = []
|
|
|
|
self.open_state = False
|
|
self.training_started = False
|
|
|
|
self.info = {}
|
|
|
|
self.epoch_rate = ""
|
|
self.epoch_time_start = 0
|
|
self.epoch_time_end = 0
|
|
self.epoch_time_deltas = 0
|
|
self.epoch_taken = 0
|
|
|
|
self.it_rate = ""
|
|
self.it_time_start = 0
|
|
self.it_time_end = 0
|
|
self.it_time_deltas = 0
|
|
self.it_taken = 0
|
|
self.last_step = 0
|
|
|
|
self.eta = "?"
|
|
self.eta_hhmmss = "?"
|
|
|
|
self.nan_detected = False
|
|
|
|
self.last_info_check_at = 0
|
|
self.statistics = []
|
|
self.losses = []
|
|
self.metrics = {
|
|
'step': "",
|
|
'rate': "",
|
|
'loss': "",
|
|
}
|
|
|
|
self.loss_milestones = [ 1.0, 0.15, 0.05 ]
|
|
|
|
self.load_losses()
|
|
if keep_x_past_checkpoints > 0:
|
|
self.cleanup_old(keep=keep_x_past_checkpoints)
|
|
if start:
|
|
self.spawn_process(config_path=config_path, gpus=gpus)
|
|
|
|
def spawn_process(self, config_path, gpus=1):
|
|
self.cmd = ['train.bat', config_path] if os.name == "nt" else ['./train.sh', str(int(gpus)), config_path]
|
|
|
|
print("Spawning process: ", " ".join(self.cmd))
|
|
self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
|
|
|
|
def load_losses(self, update=False):
|
|
if not os.path.isdir(f'{self.dataset_dir}/tb_logger/'):
|
|
return
|
|
try:
|
|
from tensorboard.backend.event_processing import event_accumulator
|
|
use_tensorboard = True
|
|
except Exception as e:
|
|
use_tensorboard = False
|
|
|
|
keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total', 'val_loss_text_ce', 'val_loss_mel_ce']
|
|
infos = {}
|
|
highest_step = self.last_info_check_at
|
|
|
|
if not update:
|
|
self.statistics = []
|
|
|
|
if use_tensorboard:
|
|
logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
|
|
if update:
|
|
logs = [logs[-1]]
|
|
|
|
for log in logs:
|
|
ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
|
|
ea.Reload()
|
|
|
|
for key in keys:
|
|
try:
|
|
scalar = ea.Scalars(key)
|
|
for s in scalar:
|
|
if update and s.step <= self.last_info_check_at:
|
|
continue
|
|
highest_step = max( highest_step, s.step )
|
|
self.statistics.append( { "step": s.step, "value": s.value, "type": key } )
|
|
|
|
if key == 'loss_gpt_total':
|
|
self.losses.append( { "step": s.step, "value": s.value, "type": key } )
|
|
except Exception as e:
|
|
pass
|
|
|
|
else:
|
|
logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
|
|
if update:
|
|
logs = [logs[-1]]
|
|
|
|
for log in logs:
|
|
with open(log, 'r', encoding="utf-8") as f:
|
|
lines = f.readlines()
|
|
for line in lines:
|
|
if line.find('INFO: [epoch:') >= 0:
|
|
# easily rip out our stats...
|
|
match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', line)
|
|
if not match or len(match) == 0:
|
|
continue
|
|
|
|
info = {}
|
|
for k, v in match:
|
|
info[k] = float(v.replace(",", ""))
|
|
|
|
if 'iter' in info:
|
|
it = info['iter']
|
|
infos[it] = info
|
|
|
|
for k in infos:
|
|
if 'loss_gpt_total' in infos[k]:
|
|
for key in keys:
|
|
if update and int(k) <= self.last_info_check_at:
|
|
continue
|
|
highest_step = max( highest_step, s.step )
|
|
self.statistics.append({ "step": int(k), "value": infos[k][key], "type": key })
|
|
|
|
if key == "loss_gpt_total":
|
|
self.losses.append({ "step": int(k), "value": infos[k][key], "type": key })
|
|
|
|
self.last_info_check_at = highest_step
|
|
|
|
def cleanup_old(self, keep=2):
|
|
if keep <= 0:
|
|
return
|
|
|
|
if not os.path.isdir(self.dataset_dir):
|
|
return
|
|
|
|
models = sorted([ int(d[:-8]) for d in os.listdir(f'{self.dataset_dir}/models/') if d[-8:] == "_gpt.pth" ])
|
|
states = sorted([ int(d[:-6]) for d in os.listdir(f'{self.dataset_dir}/training_state/') if d[-6:] == ".state" ])
|
|
remove_models = models[:-2]
|
|
remove_states = states[:-2]
|
|
|
|
for d in remove_models:
|
|
path = f'{self.dataset_dir}/models/{d}_gpt.pth'
|
|
print("Removing", path)
|
|
os.remove(path)
|
|
for d in remove_states:
|
|
path = f'{self.dataset_dir}/training_state/{d}.state'
|
|
print("Removing", path)
|
|
os.remove(path)
|
|
|
|
def parse(self, line, verbose=False, keep_x_past_checkpoints=0, buffer_size=8, progress=None ):
|
|
self.buffer.append(f'{line}')
|
|
|
|
should_return = False
|
|
percent = 0
|
|
message = None
|
|
|
|
# rip out iteration info
|
|
if not self.training_started:
|
|
if line.find('Start training from epoch') >= 0:
|
|
self.it_time_start = time.time()
|
|
self.epoch_time_start = time.time()
|
|
self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
|
|
should_return = True
|
|
|
|
match = re.findall(r'epoch: ([\d,]+)', line)
|
|
if match and len(match) > 0:
|
|
self.epoch = int(match[0].replace(",", ""))
|
|
match = re.findall(r'iter: ([\d,]+)', line)
|
|
if match and len(match) > 0:
|
|
self.it = int(match[0].replace(",", ""))
|
|
|
|
self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
|
|
else:
|
|
lapsed = False
|
|
|
|
message = None
|
|
if line.find('INFO: [epoch:') >= 0:
|
|
info_line = line.split("INFO:")[-1]
|
|
# to-do, actually validate this works, and probably kill training when it's found, the model's dead by this point
|
|
if ': nan' in info_line and not self.nan_detected:
|
|
self.nan_detected = self.it
|
|
|
|
# easily rip out our stats...
|
|
match = re.findall(r'\b([a-z_0-9]+?)\b: *?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', info_line)
|
|
if match and len(match) > 0:
|
|
for k, v in match:
|
|
self.info[k] = float(v.replace(",", ""))
|
|
|
|
self.load_losses(update=True)
|
|
should_return = True
|
|
|
|
if 'epoch' in self.info:
|
|
self.epoch = int(self.info['epoch'])
|
|
if 'iter' in self.info:
|
|
self.it = int(self.info['iter'])
|
|
|
|
elif line.find('Saving models and training states') >= 0:
|
|
self.checkpoint = self.checkpoint + 1
|
|
|
|
percent = self.checkpoint / float(self.checkpoints)
|
|
message = f'[{self.checkpoint}/{self.checkpoints}] Saving checkpoint...'
|
|
if progress is not None:
|
|
progress(percent, message)
|
|
|
|
print(f'{"{:.3f}".format(percent*100)}% {message}')
|
|
self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
|
|
|
|
self.cleanup_old(keep=keep_x_past_checkpoints)
|
|
|
|
if line.find('%|') > 0:
|
|
match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
|
|
if match and len(match) > 0:
|
|
match = match[0]
|
|
per_cent = int(match[0])/100.0
|
|
progressbar = match[1]
|
|
step = int(match[2])
|
|
steps = int(match[3])
|
|
elapsed = match[4]
|
|
until = match[5]
|
|
rate = match[6]
|
|
|
|
last_step = self.last_step
|
|
self.last_step = step
|
|
if last_step < step:
|
|
self.it = self.it + (step - last_step)
|
|
|
|
if last_step == step and step == steps:
|
|
lapsed = True
|
|
|
|
self.it_time_end = time.time()
|
|
self.it_time_delta = self.it_time_end-self.it_time_start
|
|
self.it_time_start = time.time()
|
|
self.it_taken = self.it_taken + 1
|
|
if self.it_time_delta:
|
|
try:
|
|
rate = f'{"{:.3f}".format(self.it_time_delta)}s/it' if self.it_time_delta >= 1 or self.it_time_delta == 0 else f'{"{:.3f}".format(1/self.it_time_delta)}it/s'
|
|
self.it_rate = rate
|
|
except Exception as e:
|
|
pass
|
|
|
|
self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
|
|
if self.epochs != self.its:
|
|
self.metrics['step'].append(f"{self.it}/{self.its}")
|
|
if steps > 1:
|
|
self.metrics['step'].append(f"{step}/{steps}")
|
|
self.metrics['step'] = ", ".join(self.metrics['step'])
|
|
|
|
if lapsed:
|
|
self.epoch = self.epoch + 1
|
|
self.it = int(self.epoch * (self.dataset_size / self.batch_size))
|
|
|
|
self.epoch_time_end = time.time()
|
|
self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
|
|
self.epoch_time_start = time.time()
|
|
try:
|
|
self.epoch_rate = f'{"{:.3f}".format(self.epoch_time_delta)}s/epoch' if self.epoch_time_delta >= 1 or self.epoch_time_delta == 0 else f'{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s' # I doubt anyone will have it/s rates, but its here
|
|
except Exception as e:
|
|
pass
|
|
|
|
#self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
|
|
self.epoch_time_deltas = self.epoch_time_deltas + self.epoch_time_delta
|
|
self.epoch_taken = self.epoch_taken + 1
|
|
self.eta = (self.epochs - self.epoch) * (self.epoch_time_deltas / self.epoch_taken)
|
|
try:
|
|
eta = str(timedelta(seconds=int(self.eta)))
|
|
self.eta_hhmmss = eta
|
|
except Exception as e:
|
|
pass
|
|
|
|
self.metrics['rate'] = []
|
|
if self.epoch_rate:
|
|
self.metrics['rate'].append(self.epoch_rate)
|
|
if self.it_rate and self.epoch_rate != self.it_rate:
|
|
self.metrics['rate'].append(self.it_rate)
|
|
self.metrics['rate'] = ", ".join(self.metrics['rate'])
|
|
|
|
eta_hhmmss = "?"
|
|
if self.eta_hhmmss:
|
|
eta_hhmmss = self.eta_hhmmss
|
|
else:
|
|
try:
|
|
eta = (self.its - self.it) * (self.it_time_deltas / self.it_taken)
|
|
eta = str(timedelta(seconds=int(eta)))
|
|
eta_hhmmss = eta
|
|
except Exception as e:
|
|
pass
|
|
|
|
self.metrics['loss'] = []
|
|
|
|
if 'learning_rate_gpt_0' in self.info:
|
|
self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["learning_rate_gpt_0"])}')
|
|
|
|
if len(self.losses) > 0:
|
|
self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
|
|
|
|
if len(self.losses) >= 2:
|
|
# """riemann sum""" but not really as this is for derivatives and not integrals
|
|
deriv = 0
|
|
accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
|
|
loss_value = self.losses[-1]["value"]
|
|
|
|
for i in range(accum_length):
|
|
d1_loss = self.losses[accum_length-i-1]["value"]
|
|
d2_loss = self.losses[accum_length-i-2]["value"]
|
|
dloss = (d2_loss - d1_loss)
|
|
|
|
d1_step = self.losses[accum_length-i-1]["step"]
|
|
d2_step = self.losses[accum_length-i-2]["step"]
|
|
dstep = (d2_step - d1_step)
|
|
|
|
if dstep == 0:
|
|
continue
|
|
|
|
inst_deriv = dloss / dstep
|
|
deriv += inst_deriv
|
|
|
|
deriv = deriv / accum_length
|
|
|
|
if deriv != 0: # dloss < 0:
|
|
next_milestone = None
|
|
for milestone in self.loss_milestones:
|
|
if loss_value > milestone:
|
|
next_milestone = milestone
|
|
break
|
|
|
|
if next_milestone:
|
|
# tfw can do simple calculus but not basic algebra in my head
|
|
est_its = (next_milestone - loss_value) / deriv
|
|
if est_its >= 0:
|
|
self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
|
|
else:
|
|
est_loss = inst_deriv * (self.its - self.it) + loss_value
|
|
if est_loss >= 0:
|
|
self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
|
|
|
|
self.metrics['loss'] = ", ".join(self.metrics['loss'])
|
|
|
|
message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
|
|
if self.nan_detected:
|
|
message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
|
|
|
|
if message:
|
|
percent = self.it / float(self.its) # self.epoch / float(self.epochs)
|
|
if progress is not None:
|
|
progress(percent, message)
|
|
|
|
self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
|
|
|
|
if verbose and not self.training_started:
|
|
should_return = True
|
|
|
|
self.buffer = self.buffer[-buffer_size:]
|
|
|
|
result = None
|
|
if should_return:
|
|
result = "".join(self.buffer) if not self.training_started else message
|
|
|
|
return (
|
|
result,
|
|
percent,
|
|
message,
|
|
)
|
|
|
|
try:
|
|
import altair as alt
|
|
alt.data_transformers.enable('default', max_rows=None)
|
|
except Exception as e:
|
|
print(e)
|
|
pass
|
|
|
|
def run_training(config_path, verbose=False, gpus=1, keep_x_past_checkpoints=0, progress=gr.Progress(track_tqdm=True)):
|
|
global training_state
|
|
if training_state and training_state.process:
|
|
return "Training already in progress"
|
|
|
|
|
|
# ensure we have the dvae.pth
|
|
get_model_path('dvae.pth')
|
|
|
|
# I don't know if this is still necessary, as it was bitching at me for not doing this, despite it being in a separate process
|
|
torch.multiprocessing.freeze_support()
|
|
|
|
unload_tts()
|
|
unload_whisper()
|
|
unload_voicefixer()
|
|
|
|
training_state = TrainingState(config_path=config_path, keep_x_past_checkpoints=keep_x_past_checkpoints, gpus=gpus)
|
|
|
|
for line in iter(training_state.process.stdout.readline, ""):
|
|
if training_state.killed:
|
|
return
|
|
|
|
result, percent, message = training_state.parse( line=line, verbose=verbose, keep_x_past_checkpoints=keep_x_past_checkpoints, progress=progress )
|
|
print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
|
|
if result:
|
|
yield result
|
|
|
|
if progress is not None and message:
|
|
progress(percent, message)
|
|
|
|
if training_state:
|
|
training_state.process.stdout.close()
|
|
return_code = training_state.process.wait()
|
|
training_state = None
|
|
|
|
def update_training_dataplot(config_path=None):
|
|
global training_state
|
|
update = None
|
|
|
|
if not training_state:
|
|
if config_path:
|
|
training_state = TrainingState(config_path=config_path, start=False)
|
|
if training_state.statistics:
|
|
update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=600, height=350,)
|
|
del training_state
|
|
training_state = None
|
|
elif training_state.statistics:
|
|
training_state.load_losses()
|
|
update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics), x_lim=[0,training_state.its], x="step", y="value", title="Training Metrics", color="type", tooltip=['step', 'value', 'type'], width=600, height=350,)
|
|
|
|
return update
|
|
|
|
def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
|
|
global training_state
|
|
if not training_state or not training_state.process:
|
|
return "Training not in progress"
|
|
|
|
for line in iter(training_state.process.stdout.readline, ""):
|
|
result, percent, message = training_state.parse( line=line, verbose=verbose, progress=progress )
|
|
print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
|
|
if result:
|
|
yield result
|
|
|
|
if progress is not None and message:
|
|
progress(percent, message)
|
|
|
|
def stop_training():
|
|
global training_state
|
|
if training_state is None:
|
|
return "No training in progress"
|
|
print("Killing training process...")
|
|
training_state.killed = True
|
|
|
|
children = []
|
|
# wrapped in a try/catch in case for some reason this fails outside of Linux
|
|
try:
|
|
children = [p.info for p in psutil.process_iter(attrs=['pid', 'name', 'cmdline']) if './src/train.py' in p.info['cmdline']]
|
|
except Exception as e:
|
|
pass
|
|
|
|
training_state.process.stdout.close()
|
|
training_state.process.terminate()
|
|
training_state.process.kill()
|
|
return_code = training_state.process.wait()
|
|
|
|
for p in children:
|
|
os.kill( p['pid'], signal.SIGKILL )
|
|
|
|
training_state = None
|
|
print("Killed training process.")
|
|
return f"Training cancelled: {return_code}"
|
|
|
|
def get_halfp_model_path():
|
|
autoregressive_model_path = get_model_path('autoregressive.pth')
|
|
return autoregressive_model_path.replace(".pth", "_half.pth")
|
|
|
|
def convert_to_halfp():
|
|
autoregressive_model_path = get_model_path('autoregressive.pth')
|
|
print(f'Converting model to half precision: {autoregressive_model_path}')
|
|
model = torch.load(autoregressive_model_path)
|
|
for k in model:
|
|
model[k] = model[k].half()
|
|
|
|
outfile = get_halfp_model_path()
|
|
torch.save(model, outfile)
|
|
print(f'Converted model to half precision: {outfile}')
|
|
|
|
def whisper_transcribe( file, language=None ):
|
|
# shouldn't happen, but it's for safety
|
|
if not whisper_model:
|
|
load_whisper_model(language=language)
|
|
|
|
if args.whisper_backend == "openai/whisper":
|
|
if not language:
|
|
language = None
|
|
|
|
return whisper_model.transcribe(file, language=language)
|
|
|
|
elif args.whisper_backend == "lightmare/whispercpp":
|
|
res = whisper_model.transcribe(file)
|
|
segments = whisper_model.extract_text_and_timestamps( res )
|
|
|
|
result = {
|
|
'segments': []
|
|
}
|
|
for segment in segments:
|
|
reparsed = {
|
|
'start': segment[0] / 100.0,
|
|
'end': segment[1] / 100.0,
|
|
'text': segment[2],
|
|
}
|
|
result['segments'].append(reparsed)
|
|
|
|
return result
|
|
|
|
# credit to https://git.ecker.tech/yqxtqymn for the busywork of getting this added
|
|
elif args.whisper_backend == "m-bain/whisperx":
|
|
import whisperx
|
|
device = "cuda" if get_device_name() == "cuda" else "cpu"
|
|
result = whisper_model.transcribe(file)
|
|
model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
|
|
result_aligned = whisperx.align(result["segments"], model_a, metadata, file, device)
|
|
|
|
for i in range(len(result_aligned['segments'])):
|
|
del result_aligned['segments'][i]['word-segments']
|
|
del result_aligned['segments'][i]['char-segments']
|
|
|
|
result['segments'] = result_aligned['segments']
|
|
|
|
return result
|
|
|
|
def prepare_dataset( files, outdir, language=None, skip_existings=False, progress=None ):
|
|
unload_tts()
|
|
|
|
global whisper_model
|
|
if whisper_model is None:
|
|
load_whisper_model(language=language)
|
|
|
|
os.makedirs(outdir, exist_ok=True)
|
|
|
|
results = {}
|
|
transcription = []
|
|
files = sorted(files)
|
|
|
|
previous_list = []
|
|
if skip_existings and os.path.exists(f'{outdir}/train.txt'):
|
|
parsed_list = []
|
|
with open(f'{outdir}/train.txt', 'r', encoding="utf-8") as f:
|
|
parsed_list = f.readlines()
|
|
|
|
for line in parsed_list:
|
|
match = re.findall(r"^(.+?)_\d+\.wav$", line.split("|")[0])
|
|
|
|
if match is None or len(match) == 0:
|
|
continue
|
|
|
|
if match[0] not in previous_list:
|
|
previous_list.append(f'{match[0]}.wav')
|
|
|
|
for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
|
|
basename = os.path.basename(file)
|
|
|
|
if basename in previous_list:
|
|
print(f"Skipping already parsed file: {basename}")
|
|
continue
|
|
|
|
result = whisper_transcribe(file, language=language)
|
|
results[basename] = result
|
|
print(f"Transcribed file: {file}, {len(result['segments'])} found.")
|
|
|
|
waveform, sampling_rate = torchaudio.load(file)
|
|
num_channels, num_frames = waveform.shape
|
|
|
|
idx = 0
|
|
for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
|
|
start = int(segment['start'] * sampling_rate)
|
|
end = int(segment['end'] * sampling_rate)
|
|
|
|
sliced_waveform = waveform[:, start:end]
|
|
sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")
|
|
|
|
if not torch.any(sliced_waveform < 0):
|
|
print(f"Error with {sliced_name}, skipping...")
|
|
continue
|
|
|
|
torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate)
|
|
|
|
idx = idx + 1
|
|
line = f"{sliced_name}|{segment['text'].strip()}"
|
|
transcription.append(line)
|
|
with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
|
|
f.write(f'\n{line}')
|
|
|
|
do_gc()
|
|
|
|
with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
|
|
f.write(json.dumps(results, indent='\t'))
|
|
|
|
unload_whisper()
|
|
|
|
joined = "\n".join(transcription)
|
|
if not skip_existings:
|
|
with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
|
|
f.write(joined)
|
|
|
|
return f"Processed dataset to: {outdir}\n{joined}"
|
|
|
|
def prepare_validation_dataset( voice, text_length ):
|
|
indir = f'./training/{voice}/'
|
|
infile = f'{indir}/dataset.txt'
|
|
if not os.path.exists(infile):
|
|
infile = f'{indir}/train.txt'
|
|
with open(f'{indir}/train.txt', 'r', encoding="utf-8") as src:
|
|
with open(f'{indir}/dataset.txt', 'w', encoding="utf-8") as dst:
|
|
dst.write(src.read())
|
|
|
|
if not os.path.exists(infile):
|
|
raise Exception(f"Missing dataset: {infile}")
|
|
|
|
with open(infile, 'r', encoding="utf-8") as f:
|
|
lines = f.readlines()
|
|
|
|
validation = []
|
|
training = []
|
|
|
|
for line in lines:
|
|
split = line.split("|")
|
|
filename = split[0]
|
|
text = split[1]
|
|
|
|
if len(text) < text_length:
|
|
validation.append(line.strip())
|
|
else:
|
|
training.append(line.strip())
|
|
|
|
with open(f'{indir}/train.txt', 'w', encoding="utf-8") as f:
|
|
f.write("\n".join(training))
|
|
|
|
with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
|
|
f.write("\n".join(validation))
|
|
|
|
msg = f"Culled {len(validation)} lines"
|
|
print(msg)
|
|
return msg
|
|
|
|
def calc_iterations( epochs, lines, batch_size ):
|
|
iterations = int(epochs * lines / float(batch_size))
|
|
return iterations
|
|
|
|
def schedule_learning_rate( iterations, schedule=EPOCH_SCHEDULE ):
|
|
return [int(iterations * d) for d in schedule]
|
|
|
|
def optimize_training_settings( 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"
|
|
|
|
with open(dataset_path, 'r', encoding="utf-8") as f:
|
|
lines = len(f.readlines())
|
|
|
|
messages = []
|
|
|
|
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 gradient_accumulation_size == 0:
|
|
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
|
|
|
|
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"Batch size is not evenly divisible by the gradient accumulation size, adjusting gradient accumulation size to: {gradient_accumulation_size}")
|
|
|
|
iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=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 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 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 resume_path and not os.path.exists(resume_path):
|
|
resume_path = None
|
|
messages.append("Resume path specified, but does not exist. Disabling...")
|
|
|
|
if bnb:
|
|
messages.append("BitsAndBytes requested. Please note this is ! EXPERIMENTAL !")
|
|
|
|
if half_p:
|
|
if bnb:
|
|
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)")
|
|
|
|
return (
|
|
learning_rate,
|
|
text_ce_lr_weight,
|
|
learning_rate_schedule,
|
|
batch_size,
|
|
gradient_accumulation_size,
|
|
print_rate,
|
|
save_rate,
|
|
validation_rate,
|
|
resume_path,
|
|
messages
|
|
)
|
|
|
|
def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weight=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, 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"
|
|
|
|
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,
|
|
"gen_lr_steps": learning_rate_schedule if learning_rate_schedule else EPOCH_SCHEDULE,
|
|
"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,
|
|
|
|
"text_ce_lr_weight": text_ce_lr_weight if text_ce_lr_weight else 0.01,
|
|
|
|
'resume_state': f"resume_state: '{resume_path}'",
|
|
'pretrain_model_gpt': f"pretrain_model_gpt: '{source_model}'",
|
|
|
|
'float16': 'true' if half_p else 'false',
|
|
'bitsandbytes': 'true' if bnb else 'false',
|
|
|
|
'workers': workers if workers else 2,
|
|
}
|
|
|
|
if resume_path:
|
|
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'"
|
|
|
|
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'
|
|
|
|
|
|
with open(f'./models/.template.yaml', 'r', encoding="utf-8") as f:
|
|
yaml = f.read()
|
|
|
|
# i could just load and edit the YAML directly, but this is easier, as I don't need to bother with path traversals
|
|
for k in settings:
|
|
if settings[k] is None:
|
|
continue
|
|
yaml = yaml.replace(f"${{{k}}}", str(settings[k]))
|
|
|
|
outfile = f'./training/{output_name}'
|
|
with open(outfile, 'w', encoding="utf-8") as f:
|
|
f.write(yaml)
|
|
|
|
return f"Training settings saved to: {outfile}"
|
|
|
|
def import_voices(files, saveAs=None, progress=None):
|
|
global args
|
|
|
|
if not isinstance(files, list):
|
|
files = [files]
|
|
|
|
for file in enumerate_progress(files, desc="Importing voice files", progress=progress):
|
|
j, latents = read_generate_settings(file, read_latents=True)
|
|
|
|
if j is not None and saveAs is None:
|
|
saveAs = j['voice']
|
|
if saveAs is None or saveAs == "":
|
|
raise Exception("Specify a voice name")
|
|
|
|
outdir = f'{get_voice_dir()}/{saveAs}/'
|
|
os.makedirs(outdir, exist_ok=True)
|
|
|
|
if latents:
|
|
print(f"Importing latents to {latents}")
|
|
with open(f'{outdir}/cond_latents.pth', 'wb') as f:
|
|
f.write(latents)
|
|
latents = f'{outdir}/cond_latents.pth'
|
|
print(f"Imported latents to {latents}")
|
|
else:
|
|
filename = file.name
|
|
if filename[-4:] != ".wav":
|
|
raise Exception("Please convert to a WAV first")
|
|
|
|
path = f"{outdir}/{os.path.basename(filename)}"
|
|
print(f"Importing voice to {path}")
|
|
|
|
waveform, sampling_rate = torchaudio.load(filename)
|
|
|
|
if args.voice_fixer:
|
|
if not voicefixer:
|
|
load_voicefixer()
|
|
|
|
# resample to best bandwidth since voicefixer will do it anyways through librosa
|
|
if sampling_rate != 44100:
|
|
print(f"Resampling imported voice sample: {path}")
|
|
resampler = torchaudio.transforms.Resample(
|
|
sampling_rate,
|
|
44100,
|
|
lowpass_filter_width=16,
|
|
rolloff=0.85,
|
|
resampling_method="kaiser_window",
|
|
beta=8.555504641634386,
|
|
)
|
|
waveform = resampler(waveform)
|
|
sampling_rate = 44100
|
|
|
|
torchaudio.save(path, waveform, sampling_rate)
|
|
|
|
print(f"Running 'voicefixer' on voice sample: {path}")
|
|
voicefixer.restore(
|
|
input = path,
|
|
output = path,
|
|
cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
|
|
#mode=mode,
|
|
)
|
|
else:
|
|
torchaudio.save(path, waveform, sampling_rate)
|
|
|
|
print(f"Imported voice to {path}")
|
|
|
|
def get_voice_list(dir=get_voice_dir(), append_defaults=False):
|
|
os.makedirs(dir, exist_ok=True)
|
|
res = 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 ])
|
|
if append_defaults:
|
|
res = res + ["random", "microphone"]
|
|
return res
|
|
|
|
def get_autoregressive_models(dir="./models/finetunes/", prefixed=False):
|
|
os.makedirs(dir, exist_ok=True)
|
|
base = [get_model_path('autoregressive.pth')]
|
|
halfp = get_halfp_model_path()
|
|
if os.path.exists(halfp):
|
|
base.append(halfp)
|
|
|
|
additionals = sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ])
|
|
found = []
|
|
for training in os.listdir(f'./training/'):
|
|
if not os.path.isdir(f'./training/{training}/') or not os.path.isdir(f'./training/{training}/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 ]
|
|
|
|
if len(found) > 0 or len(additionals) > 0:
|
|
base = ["auto"] + base
|
|
|
|
res = base + additionals + found
|
|
|
|
if prefixed:
|
|
for i in range(len(res)):
|
|
path = res[i]
|
|
hash = hash_file(path)
|
|
shorthash = hash[:8]
|
|
|
|
res[i] = f'[{shorthash}] {path}'
|
|
|
|
return res
|
|
|
|
def get_dataset_list(dir="./training/"):
|
|
return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 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)) ])
|
|
|
|
def do_gc():
|
|
gc.collect()
|
|
try:
|
|
torch.cuda.empty_cache()
|
|
except Exception as e:
|
|
pass
|
|
|
|
def pad(num, zeroes):
|
|
return str(num).zfill(zeroes+1)
|
|
|
|
def curl(url):
|
|
try:
|
|
req = urllib.request.Request(url, headers={'User-Agent': 'Python'})
|
|
conn = urllib.request.urlopen(req)
|
|
data = conn.read()
|
|
data = data.decode()
|
|
data = json.loads(data)
|
|
conn.close()
|
|
return data
|
|
except Exception as e:
|
|
print(e)
|
|
return None
|
|
|
|
def check_for_updates( dir = None ):
|
|
if dir is None:
|
|
check_for_updates("./.git/")
|
|
check_for_updates("./.git/modules/dlas/")
|
|
check_for_updates("./.git/modules/tortoise-tts/")
|
|
return
|
|
|
|
git_dir = dir
|
|
if not os.path.isfile(f'{git_dir}/FETCH_HEAD'):
|
|
print(f"Cannot check for updates for {dir}: not from a git repo")
|
|
return False
|
|
|
|
with open(f'{git_dir}/FETCH_HEAD', 'r', encoding="utf-8") as f:
|
|
head = f.read()
|
|
|
|
match = re.findall(r"^([a-f0-9]+).+?https:\/\/(.+?)\/(.+?)\/(.+?)\n", head)
|
|
if match is None or len(match) == 0:
|
|
print(f"Cannot check for updates for {dir}: cannot parse FETCH_HEAD")
|
|
return False
|
|
|
|
match = match[0]
|
|
|
|
local = match[0]
|
|
host = match[1]
|
|
owner = match[2]
|
|
repo = match[3]
|
|
|
|
res = curl(f"https://{host}/api/v1/repos/{owner}/{repo}/branches/") #this only works for gitea instances
|
|
|
|
if res is None or len(res) == 0:
|
|
print(f"Cannot check for updates for {dir}: cannot fetch from remote")
|
|
return False
|
|
|
|
remote = res[0]["commit"]["id"]
|
|
|
|
if remote != local:
|
|
print(f"New version found for {dir}: {local[:8]} => {remote[:8]}")
|
|
return True
|
|
|
|
return False
|
|
|
|
def enumerate_progress(iterable, desc=None, progress=None, verbose=None):
|
|
if verbose and desc is not None:
|
|
print(desc)
|
|
|
|
if progress is None:
|
|
return tqdm(iterable, disable=not verbose)
|
|
return progress.tqdm(iterable, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc, track_tqdm=True)
|
|
|
|
def notify_progress(message, progress=None, verbose=True):
|
|
if verbose:
|
|
print(message)
|
|
|
|
if progress is None:
|
|
return
|
|
|
|
progress(0, desc=message)
|
|
|
|
def get_args():
|
|
global args
|
|
return args
|
|
|
|
def setup_args():
|
|
global args
|
|
|
|
default_arguments = {
|
|
'share': False,
|
|
'listen': None,
|
|
'check-for-updates': False,
|
|
'models-from-local-only': False,
|
|
'low-vram': False,
|
|
'sample-batch-size': None,
|
|
'embed-output-metadata': True,
|
|
'latents-lean-and-mean': True,
|
|
'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
|
|
'voice-fixer-use-cuda': True,
|
|
'force-cpu-for-conditioning-latents': False,
|
|
'defer-tts-load': False,
|
|
'device-override': None,
|
|
'prune-nonfinal-outputs': True,
|
|
'vocoder-model': VOCODERS[-1],
|
|
'concurrency-count': 2,
|
|
'autocalculate-voice-chunk-duration-size': 0,
|
|
'output-sample-rate': 44100,
|
|
'output-volume': 1,
|
|
|
|
'autoregressive-model': None,
|
|
'whisper-backend': 'openai/whisper',
|
|
'whisper-model': "base",
|
|
|
|
'training-default-halfp': False,
|
|
'training-default-bnb': True,
|
|
}
|
|
|
|
if os.path.isfile('./config/exec.json'):
|
|
with open(f'./config/exec.json', 'r', encoding="utf-8") as f:
|
|
try:
|
|
overrides = json.load(f)
|
|
for k in overrides:
|
|
default_arguments[k] = overrides[k]
|
|
except Exception as e:
|
|
print(e)
|
|
pass
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--share", action='store_true', default=default_arguments['share'], help="Lets Gradio return a public URL to use anywhere")
|
|
parser.add_argument("--listen", default=default_arguments['listen'], help="Path for Gradio to listen on")
|
|
parser.add_argument("--check-for-updates", action='store_true', default=default_arguments['check-for-updates'], help="Checks for update on startup")
|
|
parser.add_argument("--models-from-local-only", action='store_true', default=default_arguments['models-from-local-only'], help="Only loads models from disk, does not check for updates for models")
|
|
parser.add_argument("--low-vram", action='store_true', default=default_arguments['low-vram'], help="Disables some optimizations that increases VRAM usage")
|
|
parser.add_argument("--no-embed-output-metadata", action='store_false', default=not default_arguments['embed-output-metadata'], help="Disables embedding output metadata into resulting WAV files for easily fetching its settings used with the web UI (data is stored in the lyrics metadata tag)")
|
|
parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
|
|
parser.add_argument("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.")
|
|
parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.")
|
|
parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
|
|
parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
|
|
parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
|
|
parser.add_argument("--vocoder-model", default=default_arguments['vocoder-model'], action='store_true', help="Specifies with vocoder to use")
|
|
parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
|
|
parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
|
|
parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
|
|
parser.add_argument("--autocalculate-voice-chunk-duration-size", type=float, default=default_arguments['autocalculate-voice-chunk-duration-size'], help="Number of seconds to suggest voice chunk size for (for example, 100 seconds of audio at 10 seconds per chunk will suggest 10 chunks)")
|
|
parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
|
|
parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
|
|
|
|
parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
|
|
parser.add_argument("--whisper-backend", default=default_arguments['whisper-backend'], action='store_true', help="Picks which whisper backend to use (openai/whisper, lightmare/whispercpp, m-bain/whisperx)")
|
|
parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
|
|
|
|
parser.add_argument("--training-default-halfp", action='store_true', default=default_arguments['training-default-halfp'], help="Training default: halfp")
|
|
parser.add_argument("--training-default-bnb", action='store_true', default=default_arguments['training-default-bnb'], help="Training default: bnb")
|
|
|
|
parser.add_argument("--os", default="unix", help="Specifies which OS, easily")
|
|
args = parser.parse_args()
|
|
|
|
args.embed_output_metadata = not args.no_embed_output_metadata
|
|
|
|
if not args.device_override:
|
|
set_device_name(args.device_override)
|
|
|
|
|
|
args.listen_host = None
|
|
args.listen_port = None
|
|
args.listen_path = None
|
|
if args.listen:
|
|
try:
|
|
match = re.findall(r"^(?:(.+?):(\d+))?(\/.*?)?$", args.listen)[0]
|
|
|
|
args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
|
|
args.listen_port = match[1] if match[1] != "" else None
|
|
args.listen_path = match[2] if match[2] != "" else "/"
|
|
except Exception as e:
|
|
pass
|
|
|
|
if args.listen_port is not None:
|
|
args.listen_port = int(args.listen_port)
|
|
if args.listen_port == 0:
|
|
args.listen_port = None
|
|
|
|
return args
|
|
|
|
def update_args( 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 ):
|
|
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
|
|
|
|
args.training_default_halfp = training_default_halfp
|
|
args.training_default_bnb = training_default_bnb
|
|
|
|
save_args_settings()
|
|
|
|
def save_args_settings():
|
|
global args
|
|
settings = {
|
|
'listen': None if not args.listen else args.listen,
|
|
'share': args.share,
|
|
'low-vram':args.low_vram,
|
|
'check-for-updates':args.check_for_updates,
|
|
'models-from-local-only':args.models_from_local_only,
|
|
'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
|
|
'defer-tts-load': args.defer_tts_load,
|
|
'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
|
|
'device-override': args.device_override,
|
|
'sample-batch-size': args.sample_batch_size,
|
|
'embed-output-metadata': args.embed_output_metadata,
|
|
'latents-lean-and-mean': args.latents_lean_and_mean,
|
|
'voice-fixer': args.voice_fixer,
|
|
'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
|
|
'concurrency-count': args.concurrency_count,
|
|
'output-sample-rate': args.output_sample_rate,
|
|
'autocalculate-voice-chunk-duration-size': args.autocalculate_voice_chunk_duration_size,
|
|
'output-volume': args.output_volume,
|
|
|
|
'autoregressive-model': args.autoregressive_model,
|
|
'vocoder-model': args.vocoder_model,
|
|
'whisper-backend': args.whisper_backend,
|
|
'whisper-model': args.whisper_model,
|
|
|
|
'training-default-halfp': args.training_default_halfp,
|
|
'training-default-bnb': args.training_default_bnb,
|
|
}
|
|
|
|
os.makedirs('./config/', exist_ok=True)
|
|
with open(f'./config/exec.json', 'w', encoding="utf-8") as f:
|
|
f.write(json.dumps(settings, indent='\t') )
|
|
|
|
|
|
|
|
def import_generate_settings(file="./config/generate.json"):
|
|
settings, _ = read_generate_settings(file, read_latents=False)
|
|
|
|
if settings is None:
|
|
return None
|
|
|
|
return (
|
|
None if 'text' not in settings else settings['text'],
|
|
None if 'delimiter' not in settings else settings['delimiter'],
|
|
None if 'emotion' not in settings else settings['emotion'],
|
|
None if 'prompt' not in settings else settings['prompt'],
|
|
None if 'voice' not in settings else settings['voice'],
|
|
None,
|
|
None,
|
|
None if 'seed' not in settings else settings['seed'],
|
|
None if 'candidates' not in settings else settings['candidates'],
|
|
None if 'num_autoregressive_samples' not in settings else settings['num_autoregressive_samples'],
|
|
None if 'diffusion_iterations' not in settings else settings['diffusion_iterations'],
|
|
0.8 if 'temperature' not in settings else settings['temperature'],
|
|
"DDIM" if 'diffusion_sampler' not in settings else settings['diffusion_sampler'],
|
|
8 if 'breathing_room' not in settings else settings['breathing_room'],
|
|
0.0 if 'cvvp_weight' not in settings else settings['cvvp_weight'],
|
|
0.8 if 'top_p' not in settings else settings['top_p'],
|
|
1.0 if 'diffusion_temperature' not in settings else settings['diffusion_temperature'],
|
|
1.0 if 'length_penalty' not in settings else settings['length_penalty'],
|
|
2.0 if 'repetition_penalty' not in settings else settings['repetition_penalty'],
|
|
2.0 if 'cond_free_k' not in settings else settings['cond_free_k'],
|
|
None if 'experimentals' not in settings else settings['experimentals'],
|
|
)
|
|
|
|
|
|
def reset_generation_settings():
|
|
with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
|
|
f.write(json.dumps({}, indent='\t') )
|
|
return import_generate_settings()
|
|
|
|
def read_generate_settings(file, read_latents=True):
|
|
j = None
|
|
latents = None
|
|
|
|
if isinstance(file, list) and len(file) == 1:
|
|
file = file[0]
|
|
|
|
try:
|
|
if file is not None:
|
|
if hasattr(file, 'name'):
|
|
file = file.name
|
|
|
|
if file[-4:] == ".wav":
|
|
metadata = music_tag.load_file(file)
|
|
if 'lyrics' in metadata:
|
|
j = json.loads(str(metadata['lyrics']))
|
|
elif file[-5:] == ".json":
|
|
with open(file, 'r') as f:
|
|
j = json.load(f)
|
|
except Exception as e:
|
|
pass
|
|
|
|
if j is not None:
|
|
if 'latents' in j:
|
|
if read_latents:
|
|
latents = base64.b64decode(j['latents'])
|
|
del j['latents']
|
|
|
|
|
|
if "time" in j:
|
|
j["time"] = "{:.3f}".format(j["time"])
|
|
|
|
|
|
|
|
return (
|
|
j,
|
|
latents,
|
|
)
|
|
|
|
def version_check_tts( min_version ):
|
|
global tts
|
|
if not tts:
|
|
raise Exception("TTS is not initialized")
|
|
|
|
if not hasattr(tts, 'version'):
|
|
return False
|
|
|
|
if min_version[0] > tts.version[0]:
|
|
return True
|
|
if min_version[1] > tts.version[1]:
|
|
return True
|
|
if min_version[2] >= tts.version[2]:
|
|
return True
|
|
return False
|
|
|
|
def load_tts( restart=False, autoregressive_model=None ):
|
|
global args
|
|
global tts
|
|
|
|
if restart:
|
|
unload_tts()
|
|
|
|
if autoregressive_model:
|
|
args.autoregressive_model = autoregressive_model
|
|
else:
|
|
autoregressive_model = args.autoregressive_model
|
|
|
|
if autoregressive_model == "auto":
|
|
autoregressive_model = deduce_autoregressive_model()
|
|
|
|
print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {args.vocoder_model})")
|
|
|
|
tts_loading = True
|
|
try:
|
|
tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, vocoder_model=args.vocoder_model)
|
|
except Exception as e:
|
|
tts = TextToSpeech(minor_optimizations=not args.low_vram)
|
|
load_autoregressive_model(autoregressive_model)
|
|
|
|
tts_loading = False
|
|
|
|
get_model_path('dvae.pth')
|
|
print("Loaded TorToiSe, ready for generation.")
|
|
return tts
|
|
|
|
setup_tortoise = load_tts
|
|
|
|
def unload_tts():
|
|
global tts
|
|
|
|
if tts:
|
|
del tts
|
|
tts = None
|
|
print("Unloaded TTS")
|
|
do_gc()
|
|
|
|
def reload_tts( model=None ):
|
|
load_tts( restart=True, model=model )
|
|
|
|
def get_current_voice():
|
|
global current_voice
|
|
if current_voice:
|
|
return current_voice
|
|
|
|
settings, _ = read_generate_settings("./config/generate.json", read_latents=False)
|
|
|
|
if settings and "voice" in settings['voice']:
|
|
return settings["voice"]
|
|
|
|
return None
|
|
|
|
def deduce_autoregressive_model(voice=None):
|
|
if not voice:
|
|
voice = get_current_voice()
|
|
|
|
if voice:
|
|
dir = f'./training/{voice}-finetune/models/'
|
|
if os.path.exists(f'./training/finetunes/{voice}.pth'):
|
|
return f'./training/finetunes/{voice}.pth'
|
|
|
|
if os.path.isdir(dir):
|
|
counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
|
|
names = [ f'{dir}/{d}_gpt.pth' for d in counts ]
|
|
return names[-1]
|
|
|
|
if args.autoregressive_model != "auto":
|
|
return args.autoregressive_model
|
|
|
|
return get_model_path('autoregressive.pth')
|
|
|
|
def update_autoregressive_model(autoregressive_model_path):
|
|
match = re.findall(r'^\[[a-fA-F0-9]{8}\] (.+?)$', autoregressive_model_path)
|
|
if match:
|
|
autoregressive_model_path = match[0]
|
|
|
|
if not autoregressive_model_path or not os.path.exists(autoregressive_model_path):
|
|
print(f"Invalid model: {autoregressive_model_path}")
|
|
return
|
|
|
|
args.autoregressive_model = autoregressive_model_path
|
|
save_args_settings()
|
|
print(f'Stored autoregressive model to settings: {autoregressive_model_path}')
|
|
|
|
global tts
|
|
if not tts:
|
|
if tts_loading:
|
|
raise Exception("TTS is still initializing...")
|
|
return
|
|
|
|
if hasattr(tts, "loading") and tts.loading:
|
|
raise Exception("TTS is still initializing...")
|
|
|
|
if autoregressive_model_path == "auto":
|
|
autoregressive_model_path = deduce_autoregressive_model()
|
|
|
|
if autoregressive_model_path == tts.autoregressive_model_path:
|
|
return
|
|
|
|
tts.load_autoregressive_model(autoregressive_model_path)
|
|
|
|
do_gc()
|
|
|
|
return autoregressive_model_path
|
|
|
|
def update_vocoder_model(vocoder_model):
|
|
args.vocoder_model = vocoder_model
|
|
save_args_settings()
|
|
print(f'Stored vocoder model to settings: {vocoder_model}')
|
|
|
|
global tts
|
|
if not tts:
|
|
if tts_loading:
|
|
raise Exception("TTS is still initializing...")
|
|
return
|
|
|
|
if hasattr(tts, "loading") and tts.loading:
|
|
raise Exception("TTS is still initializing...")
|
|
|
|
print(f"Loading model: {vocoder_model}")
|
|
tts.load_vocoder_model(vocoder_model)
|
|
print(f"Loaded model: {tts.vocoder_model}")
|
|
|
|
do_gc()
|
|
|
|
return vocoder_model
|
|
|
|
def load_voicefixer(restart=False):
|
|
global voicefixer
|
|
|
|
if restart:
|
|
unload_voicefixer()
|
|
|
|
try:
|
|
print("Loading Voicefixer")
|
|
from voicefixer import VoiceFixer
|
|
voicefixer = VoiceFixer()
|
|
print("Loaded Voicefixer")
|
|
except Exception as e:
|
|
print(f"Error occurred while tring to initialize voicefixer: {e}")
|
|
|
|
def unload_voicefixer():
|
|
global voicefixer
|
|
|
|
if voicefixer:
|
|
del voicefixer
|
|
voicefixer = None
|
|
print("Unloaded Voicefixer")
|
|
|
|
do_gc()
|
|
|
|
def load_whisper_model(language=None, model_name=None, progress=None):
|
|
global whisper_model
|
|
|
|
if args.whisper_backend not in WHISPER_BACKENDS:
|
|
raise Exception(f"unavailable backend: {args.whisper_backend}")
|
|
|
|
if args.whisper_backend != "m-bain/whisperx" and model_name == "large-v2":
|
|
raise Exception("large-v2 is only available for m-bain/whisperx backend")
|
|
|
|
if not model_name:
|
|
model_name = args.whisper_model
|
|
else:
|
|
args.whisper_model = model_name
|
|
save_args_settings()
|
|
|
|
if language and f'{model_name}.{language}' in WHISPER_SPECIALIZED_MODELS:
|
|
model_name = f'{model_name}.{language}'
|
|
print(f"Loading specialized model for language: {language}")
|
|
|
|
notify_progress(f"Loading Whisper model: {model_name}", progress)
|
|
|
|
if args.whisper_backend == "openai/whisper":
|
|
import whisper
|
|
whisper_model = whisper.load_model(model_name)
|
|
elif args.whisper_backend == "lightmare/whispercpp":
|
|
from whispercpp import Whisper
|
|
if not language:
|
|
language = 'auto'
|
|
|
|
b_lang = language.encode('ascii')
|
|
whisper_model = Whisper(model_name, models_dir='./models/', language=b_lang)
|
|
elif args.whisper_backend == "m-bain/whisperx":
|
|
import whisperx
|
|
device = "cuda" if get_device_name() == "cuda" else "cpu"
|
|
whisper_model = whisperx.load_model(model_name, device)
|
|
|
|
print("Loaded Whisper model")
|
|
|
|
def unload_whisper():
|
|
global whisper_model
|
|
|
|
if whisper_model:
|
|
del whisper_model
|
|
whisper_model = None
|
|
print("Unloaded Whisper")
|
|
|
|
do_gc() |