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| import time | |
| import soundfile as sf | |
| import wandb | |
| from Architectures.Vocoder.HiFiGAN_Dataset import HiFiGANDataset | |
| from Architectures.Vocoder.HiFiGAN_Discriminators import AvocodoHiFiGANJointDiscriminator | |
| from Architectures.Vocoder.HiFiGAN_Generator import HiFiGAN | |
| from Architectures.Vocoder.HiFiGAN_train_loop import train_loop | |
| from Utility.path_to_transcript_dicts import * | |
| from Utility.storage_config import MODELS_DIR | |
| def run(gpu_id, resume_checkpoint, finetune, resume, model_dir, use_wandb, wandb_resume_id, gpu_count): | |
| if gpu_id == "cpu": | |
| device = torch.device("cpu") | |
| else: | |
| device = torch.device("cuda") | |
| if gpu_count > 1: | |
| print("Multi GPU training not supported for HiFiGAN!") | |
| import sys | |
| sys.exit() | |
| print("Preparing") | |
| if model_dir is not None: | |
| model_save_dir = model_dir | |
| else: | |
| model_save_dir = os.path.join(MODELS_DIR, "HiFiGAN_clean_data_and_augmentation") | |
| os.makedirs(model_save_dir, exist_ok=True) | |
| print("Preparing new data...") | |
| take_all = False # use only files with a large enough samplerate or just use all of them | |
| file_lists_for_this_run_combined = list() | |
| fl = list(build_path_to_transcript_dict_mls_italian().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_mls_english().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_mls_french().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_mls_dutch().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_mls_polish().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_mls_spanish().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_mls_portuguese().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_karlsson().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_eva().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_bernd().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_friedrich().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_hokus().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_hui_others().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_elizabeth().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_nancy().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_vctk().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_libritts_all_clean().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_ljspeech().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10cmn().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_thorsten_emotional().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_thorsten_2022_10().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_thorsten_neutral().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10el().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10nl().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10fi().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10ru().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10hu().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10es().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_css10fr().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_nvidia_hifitts().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_spanish_blizzard_train().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_aishell3().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_VIVOS_viet().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_ESDS().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| fl = list(build_path_to_transcript_dict_CREMA_D().keys()) | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined += fl | |
| root = "/mount/arbeitsdaten61/studenten3/advanced-ml/2022/dhyanitr/projects/speech_datasets/Datasets/" | |
| for a in os.listdir(root): | |
| if os.path.isdir(a): | |
| for b in os.listdir(os.path.join(root, a)): | |
| if os.path.isdir(a): | |
| for c in os.listdir(os.path.join(root, a)): | |
| if os.path.isdir(a): | |
| print("If you can read this, the directories were more nested than I thought.") | |
| else: | |
| if c.endswith(".wav") or c.endswith(".flac"): | |
| _, sr = sf.read(os.path.join(root, a, b, c)) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined.append(os.path.join(root, a, b, c)) | |
| else: | |
| if b.endswith(".wav") or b.endswith(".flac"): | |
| _, sr = sf.read(os.path.join(root, a, b)) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined.append(os.path.join(root, a, b)) | |
| else: | |
| if a.endswith(".wav") or a.endswith(".flac"): | |
| _, sr = sf.read(os.path.join(root, a)) | |
| if sr >= 24000 or take_all: | |
| file_lists_for_this_run_combined.append(os.path.join(root, a)) | |
| print("filepaths collected") | |
| fisher_yates_shuffle(file_lists_for_this_run_combined) | |
| fisher_yates_shuffle(file_lists_for_this_run_combined) | |
| fisher_yates_shuffle(file_lists_for_this_run_combined) | |
| print("filepaths randomized") | |
| selection = file_lists_for_this_run_combined[:250000] # adjust the sample size until it fits into RAM | |
| fl = list(build_path_to_transcript_dict_RAVDESS().keys()) # these two datasets are kind of important to represent some out-of-distribution data for what we expect. | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| selection += fl | |
| fl = build_file_list_singing_voice_audio_database() # these two datasets are kind of important to represent some out-of-distribution data for what we expect. | |
| wav, sr = sf.read(fl[0]) | |
| if sr >= 24000 or take_all: | |
| selection += fl | |
| fisher_yates_shuffle(selection) | |
| fisher_yates_shuffle(selection) | |
| train_set = HiFiGANDataset(list_of_paths=selection, use_random_corruption=True) | |
| generator = HiFiGAN() | |
| discriminator = AvocodoHiFiGANJointDiscriminator() | |
| print("Training model") | |
| if use_wandb: | |
| wandb.init( | |
| name=f"{__name__.split('.')[-1]}_{time.strftime('%Y%m%d-%H%M%S')}" if wandb_resume_id is None else None, | |
| id=wandb_resume_id, # this is None if not specified in the command line arguments. | |
| resume="must" if wandb_resume_id is not None else None) | |
| train_loop(batch_size=64, | |
| epochs=180000, | |
| generator=generator, | |
| discriminator=discriminator, | |
| train_dataset=train_set, | |
| device=device, | |
| epochs_per_save=1, | |
| model_save_dir=model_save_dir, | |
| path_to_checkpoint=resume_checkpoint, | |
| resume=resume, | |
| use_wandb=use_wandb, | |
| finetune=finetune) | |
| if use_wandb: | |
| wandb.finish() | |
| def fisher_yates_shuffle(lst): | |
| for i in range(len(lst) - 1, 0, -1): | |
| j = random.randint(0, i) | |
| lst[i], lst[j] = lst[j], lst[i] | |