diff --git a/src/utils.py b/src/utils.py index aa92f12..859167b 100755 --- a/src/utils.py +++ b/src/utils.py @@ -16,6 +16,7 @@ import re import urllib.request import signal import gc +import subprocess import tqdm import torch @@ -40,91 +41,7 @@ tts = None webui = None voicefixer = None whisper_model = None - -def do_gc(): - gc.collect() - -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, - 'whisper-model': "base", - 'autoregressive-model': None, - 'concurrency-count': 2, - 'output-sample-rate': 44100, - 'output-volume': 1, - } - - if os.path.isfile('./config/exec.json'): - with open(f'./config/exec.json', 'r', encoding="utf-8") as f: - overrides = json.load(f) - for k in overrides: - default_arguments[k] = overrides[k] - - 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("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch") - parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.") - parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.") - 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("--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("--os", default="unix", help="Specifies which OS, easily") - args = parser.parse_args() - - args.embed_output_metadata = not args.no_embed_output_metadata - - 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) - - return args - -def pad(num, zeroes): - return str(num).zfill(zeroes+1) +training_process = None def generate( text, @@ -154,10 +71,14 @@ def generate( global tts if not tts: + # should check if it's loading or unloaded, and load it if it's unloaded raise Exception("TTS is uninitialized or still initializing...") do_gc() + unload_whisper() + unload_voicefixer() + if voice != "microphone": voices = [voice] else: @@ -244,7 +165,7 @@ def generate( audio_cache = {} resample = None - # not a ternary in the event for some reason I want to rely on librosa's upsampling interpolator rather than torchaudio's, for some reason + if tts.output_sample_rate != args.output_sample_rate: resampler = torchaudio.transforms.Resample( tts.output_sample_rate, @@ -385,7 +306,10 @@ def generate( with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f: f.write(json.dumps(info, indent='\t') ) - if args.voice_fixer and voicefixer is not None: + if args.voice_fixer: + if not voicefixer: + load_voicefixer() + fixed_output_voices = [] for path in progress.tqdm(output_voices, desc="Running voicefix..."): fixed = path.replace(".wav", "_fixed.wav") @@ -434,23 +358,43 @@ def generate( stats, ) -import subprocess +def cancel_generate(): + from tortoise.api import STOP_SIGNAL + STOP_SIGNAL = True -training_process = None -def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)): - try: - print("Unloading TTS to save VRAM.") - global tts - del tts - tts = None - trytorch.cuda.empty_cache() - except Exception as e: - pass +def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)): + global tts + global args + + if not tts: + raise Exception("TTS is uninitialized or still initializing...") + + unload_whisper() + unload_voicefixer() + + voice_samples, conditioning_latents = 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, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents) + + if len(conditioning_latents) == 4: + conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None) + + torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth') + + return voice +def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)): global training_process - torch.multiprocessing.freeze_support() - do_gc() + # 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() cmd = ['train.bat', config_path] if os.name == "nt" else ['bash', './train.sh', config_path] print("Spawning process: ", " ".join(cmd)) @@ -510,7 +454,6 @@ def run_training(config_path, verbose=False, buffer_size=8, progress=gr.Progress return "".join(buffer[-buffer_size:]) - def stop_training(): global training_process if training_process is None: @@ -519,66 +462,51 @@ def stop_training(): training_process = None return "Training cancelled" -def setup_voicefixer(restart=False): - global voicefixer - if restart: - del voicefixer - voicefixer = None - - try: - print("Initializating voice-fixer") - from voicefixer import VoiceFixer - voicefixer = VoiceFixer() - print("initialized voice-fixer") - except Exception as e: - print(f"Error occurred while tring to initialize voicefixer: {e}") - -def setup_tortoise(restart=False): - global args - global tts - - do_gc() +def prepare_dataset( files, outdir, language=None, progress=None ): + unload_tts() - if args.voice_fixer: - setup_voicefixer(restart=restart) + global whisper_model + if whisper_model is None: + load_whisper_model() - if restart: - del tts - tts = None + os.makedirs(outdir, exist_ok=True) - print(f"Initializating TorToiSe... (using model: {args.autoregressive_model})") - try: - tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model) - except Exception as e: - tts = TextToSpeech(minor_optimizations=not args.low_vram) - load_autoregressive_model(args.autoregressive_model) + idx = 0 + results = {} + transcription = [] - get_model_path('dvae.pth') - print("TorToiSe initialized, ready for generation.") - return tts + for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress): + print(f"Transcribing file: {file}") + + result = whisper_model.transcribe(file, language=language if language else "English") + results[os.path.basename(file)] = result -def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)): - global tts - global args + print(f"Transcribed file: {file}, {len(result['segments'])} found.") - if not tts: - raise Exception("TTS is uninitialized or still initializing...") + waveform, sampling_rate = torchaudio.load(file) + num_channels, num_frames = waveform.shape - do_gc() + 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) - voice_samples, conditioning_latents = load_voice(voice, load_latents=False) + sliced_waveform = waveform[:, start:end] + sliced_name = f"{pad(idx, 4)}.wav" - if voice_samples is None: - return + torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate) - 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) + transcription.append(f"{sliced_name}|{segment['text'].strip()}") + idx = idx + 1 + + with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f: + f.write(json.dumps(results, indent='\t')) + + with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f: + f.write("\n".join(transcription)) - if len(conditioning_latents) == 4: - conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None) - - torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth') + unload_whisper() - return voice + return f"Processed dataset to: {outdir}" def calc_iterations( epochs, lines, batch_size ): iterations = int(epochs * lines / float(batch_size)) @@ -679,54 +607,6 @@ def save_training_settings( iterations=None, batch_size=None, learning_rate=None return f"Training settings saved to: {outfile}" -def prepare_dataset( files, outdir, language=None, progress=None ): - global whisper_model - if whisper_model is None: - notify_progress(f"Loading Whisper model: {args.whisper_model}", progress) - whisper_model = whisper.load_model(args.whisper_model) - - os.makedirs(outdir, exist_ok=True) - - idx = 0 - results = {} - transcription = [] - - for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress): - print(f"Transcribing file: {file}") - - result = whisper_model.transcribe(file, language=language if language else "English") - results[os.path.basename(file)] = result - - print(f"Transcribed file: {file}, {len(result['segments'])} found.") - - waveform, sampling_rate = torchaudio.load(file) - num_channels, num_frames = waveform.shape - - 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 = f"{pad(idx, 4)}.wav" - - torchaudio.save(f"{outdir}/{sliced_name}", sliced_waveform, sampling_rate) - - transcription.append(f"{sliced_name}|{segment['text'].strip()}") - idx = idx + 1 - - with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f: - f.write(json.dumps(results, indent='\t')) - - with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f: - f.write("\n".join(transcription)) - - return f"Processed dataset to: {outdir}" - -def reset_generation_settings(): - with open(f'./config/generate.json', 'w', encoding="utf-8") as f: - f.write(json.dumps({}, indent='\t') ) - return import_generate_settings() - def import_voices(files, saveAs=None, progress=None): global args @@ -760,7 +640,10 @@ def import_voices(files, saveAs=None, progress=None): waveform, sampling_rate = torchaudio.load(filename) - if args.voice_fixer and voicefixer is not None: + 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}") @@ -789,35 +672,29 @@ def import_voices(files, saveAs=None, progress=None): print(f"Imported voice to {path}") -def import_generate_settings(file="./config/generate.json"): - settings, _ = read_generate_settings(file, read_latents=False) - - if settings is None: - return None +def get_voice_list(dir=get_voice_dir()): + os.makedirs(dir, exist_ok=True) + return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"] - 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 get_autoregressive_models(dir="./models/finetunes/"): + os.makedirs(dir, exist_ok=True) + return [get_model_path('autoregressive.pth')] + sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ]) + +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: + trytorch.cuda.empty_cache() + except Exception as e: + pass + +def pad(num, zeroes): + return str(num).zfill(zeroes+1) def curl(url): try: @@ -866,71 +743,102 @@ def check_for_updates(): return False -def reload_tts(): - setup_tortoise(restart=True) +def enumerate_progress(iterable, desc=None, progress=None, verbose=None): + if verbose and desc is not None: + print(desc) -def cancel_generate(): - from tortoise.api import STOP_SIGNAL - STOP_SIGNAL = True + 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 get_voice_list(dir=get_voice_dir()): - os.makedirs(dir, exist_ok=True) - return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"] +def notify_progress(message, progress=None, verbose=True): + if verbose: + print(message) -def get_autoregressive_models(dir="./models/finetunes/"): - os.makedirs(dir, exist_ok=True) - return [get_model_path('autoregressive.pth')] + sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ]) + if progress is None: + return -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)) ]) + progress(0, desc=message) -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 get_args(): + global args + return args -def update_whisper_model(name): - global whisper_model - if whisper_model: - del whisper_model - whisper_model = None - - args.whisper_model = name +def setup_args(): + global args - print(f"Loading Whisper model: {args.whisper_model}") - whisper_model = whisper.load_model(args.whisper_model) + 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, + 'whisper-model': "base", + 'autoregressive-model': None, + 'concurrency-count': 2, + 'output-sample-rate': 44100, + 'output-volume': 1, + } -def update_autoregressive_model(autoregressive_model_path): - args.autoregressive_model = autoregressive_model_path - save_args_settings() - print(f'Stored autoregressive model to settings: {autoregressive_model_path}') + if os.path.isfile('./config/exec.json'): + with open(f'./config/exec.json', 'r', encoding="utf-8") as f: + overrides = json.load(f) + for k in overrides: + default_arguments[k] = overrides[k] - global tts - if not tts: - raise Exception("TTS is uninitialized or still initializing...") + 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("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch") + parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.") + parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.") + 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("--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("--os", default="unix", help="Specifies which OS, easily") + args = parser.parse_args() - print(f"Loading model: {autoregressive_model_path}") + args.embed_output_metadata = not args.no_embed_output_metadata - if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(autoregressive_model_path): - tts.load_autoregressive_model(autoregressive_model_path) - # polyfill in case a user did NOT update the packages - # this shouldn't happen anymore, as I just clone mrq/tortoise-tts, and inject it into sys.path - else: - from tortoise.models.autoregressive import UnifiedVoice + if not args.device_override: + set_device_name(args.device_override) - tts.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', tts.models_dir) + args.listen_host = None + args.listen_port = None + args.listen_path = None + if args.listen: + try: + match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0] - del tts.autoregressive - tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30, - model_dim=1024, - heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False, - train_solo_embeddings=False).cpu().eval() - tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path)) - tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache) - if tts.preloaded_tensors: - tts.autoregressive = tts.autoregressive.to(tts.device) + 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 - print(f"Loaded model: {tts.autoregressive_model_path}") + if args.listen_port is not None: + args.listen_port = int(args.listen_port) - return autoregressive_model_path + 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, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ): global args @@ -980,6 +888,44 @@ def save_args_settings(): 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, read_json=True): j = None latents = None @@ -1013,19 +959,119 @@ def read_generate_settings(file, read_latents=True, read_json=True): latents, ) -def enumerate_progress(iterable, desc=None, progress=None, verbose=None): - if verbose and desc is not None: - print(desc) +def load_tts(restart=False): + global args + global tts - 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) + if restart: + unload_tts() -def notify_progress(message, progress=None, verbose=True): - if verbose: - print(message) + print(f"Loading TorToiSe... (using model: {args.autoregressive_model})") + try: + tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model) + except Exception as e: + tts = TextToSpeech(minor_optimizations=not args.low_vram) + load_autoregressive_model(args.autoregressive_model) - if progress is None: - return + get_model_path('dvae.pth') + print("Loaded TorToiSe, ready for generation.") + return tts + +setup_tortoise = load_tts + +def unload_tts(): + global tts - progress(0, desc=message) \ No newline at end of file + if tts: + print("Unloading TTS") + del tts + tts = None + do_gc() + +def reload_tts(): + setup_tortoise(restart=True) + +def update_autoregressive_model(autoregressive_model_path): + args.autoregressive_model = autoregressive_model_path + save_args_settings() + print(f'Stored autoregressive model to settings: {autoregressive_model_path}') + + global tts + if not tts: + raise Exception("TTS is uninitialized or still initializing...") + + print(f"Loading model: {autoregressive_model_path}") + + if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(autoregressive_model_path): + tts.load_autoregressive_model(autoregressive_model_path) + # polyfill in case a user did NOT update the packages + # this shouldn't happen anymore, as I just clone mrq/tortoise-tts, and inject it into sys.path + else: + from tortoise.models.autoregressive import UnifiedVoice + + tts.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', tts.models_dir) + + del tts.autoregressive + tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30, + model_dim=1024, + heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False, + train_solo_embeddings=False).cpu().eval() + tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path)) + tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache) + if tts.preloaded_tensors: + tts.autoregressive = tts.autoregressive.to(tts.device) + + print(f"Loaded model: {tts.autoregressive_model_path}") + + do_gc() + + return autoregressive_model_path + +def load_voicefixer(restart=False): + global voicefixer + + if restart: + unload_voicefixer() + + try: + print("Loading Voicefixer") + from voicefixer import VoiceFixer + voicefixer = VoiceFixer() + except Exception as e: + print(f"Error occurred while tring to initialize voicefixer: {e}") + +def unload_voicefixer(): + global voicefixer + + if voicefixer: + print("Unloading Voicefixer") + del voicefixer + voicefixer = None + + do_gc() + +def load_whisper_model(name=None, progress=None): + if not name: + name = args.whisper_model + else: + args.whisper_model = name + + notify_progress(f"Loading Whisper model: {args.whisper_model}", progress) + whisper_model = whisper.load_model(args.whisper_model) + +def unload_whisper(): + global whisper_model + + if whisper_model: + print("Unloading Whisper") + del whisper_model + whisper_model = None + + do_gc() + +def update_whisper_model(name, progress=None): + global whisper_model + if whisper_model: + unload_whisper() + + load_whisper_model(name) \ No newline at end of file