forked from mrq/ai-voice-cloning
we do a little garbage collection
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58c981d714
commit
fc5b303319
66
src/utils.py
66
src/utils.py
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@ -15,6 +15,7 @@ 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 tqdm
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import torch
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@ -40,6 +41,9 @@ webui = None
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voicefixer = None
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whisper_model = None
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def do_gc():
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gc.collect()
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def get_args():
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global args
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return args
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@ -152,6 +156,8 @@ def generate(
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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do_gc()
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if voice != "microphone":
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voices = [voice]
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else:
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@ -307,6 +313,9 @@ def generate(
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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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@ -480,19 +489,44 @@ def setup_tortoise(restart=False):
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global args
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global tts
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if args.voice_fixer and not restart:
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do_gc()
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if args.voice_fixer:
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setup_voicefixer(restart=restart)
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if restart:
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del tts
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tts = None
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print("Initializating TorToiSe...")
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print(f"Initializating TorToiSe... (using model: {args.autoregressive_model})")
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
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get_model_path('dvae.pth')
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print("TorToiSe initialized, ready for generation.")
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return tts
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def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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global tts
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global args
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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do_gc()
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voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
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if voice_samples is None:
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return
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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return voice
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def save_training_settings( iterations=None, batch_size=None, learning_rate=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None ):
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settings = {
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"iterations": iterations if iterations else 500,
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@ -737,31 +771,11 @@ def update_autoregressive_model(path_name):
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raise Exception("TTS is uninitialized or still initializing...")
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print(f"Loading model: {path_name}")
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if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(path_name):
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args.autoregressive_model = path_name
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save_args_settings()
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# polyfill in case a user did NOT update the packages
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else:
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from tortoise.models.autoregressive import UnifiedVoice
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previous_path = tts.autoregressive_model_path
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tts.autoregressive_model_path = path_name if path_name and os.path.exists(path_name) else get_model_path('autoregressive.pth')
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del tts.autoregressive
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tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
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model_dim=1024,
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heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
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train_solo_embeddings=False).cpu().eval()
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tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path))
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tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache)
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if tts.preloaded_tensors:
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tts.autoregressive = tts.autoregressive.to(tts.device)
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if previous_path != tts.autoregressive_model_path:
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args.autoregressive_model = path_name
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save_args_settings()
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tts.load_autoregressive_model(path_name)
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print(f"Loaded model: {tts.autoregressive_model_path}")
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args.autoregressive_model = path_name
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save_args_settings()
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return path_name
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21
src/webui.py
21
src/webui.py
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@ -86,27 +86,6 @@ def run_generation(
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gr.update(value=stats, visible=True),
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)
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def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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global tts
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global args
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if not tts:
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raise Exception("TTS is uninitialized or still initializing...")
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voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
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if voice_samples is None:
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return
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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return voice
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def update_presets(value):
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PRESETS = {
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'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
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