From 97bd5f9734e16abea5474180a0f7527532b38708 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Tue, 30 Mar 2021 14:18:35 +0200 Subject: [PATCH] [ci skip] config update #3 WIP --- TTS/bin/train_tacotron.py | 1 + TTS/tts/configs/config.json | 287 +++++++++++++++--------------------- TTS/utils/arguments.py | 43 ++---- TTS/utils/io.py | 47 +++--- 4 files changed, 158 insertions(+), 220 deletions(-) diff --git a/TTS/bin/train_tacotron.py b/TTS/bin/train_tacotron.py index e5e956b5..b864d303 100755 --- a/TTS/bin/train_tacotron.py +++ b/TTS/bin/train_tacotron.py @@ -14,6 +14,7 @@ from torch.utils.data import DataLoader from TTS.tts.datasets.preprocess import load_meta_data from TTS.tts.datasets.TTSDataset import MyDataset from TTS.tts.layers.losses import TacotronLoss +from TTS.tts.configs.tacotron_config import TacotronConfig from TTS.tts.utils.generic_utils import setup_model from TTS.tts.utils.io import save_best_model, save_checkpoint from TTS.tts.utils.measures import alignment_diagonal_score diff --git a/TTS/tts/configs/config.json b/TTS/tts/configs/config.json index 91b2134e..95e787e0 100644 --- a/TTS/tts/configs/config.json +++ b/TTS/tts/configs/config.json @@ -1,173 +1,126 @@ { - "model": "Tacotron2", - "run_name": "ljspeech-ddc", - "run_description": "tacotron2 with DDC and differential spectral loss.", - - // AUDIO PARAMETERS - "audio":{ - // stft parameters - "fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame. - "win_length": 1024, // stft window length in ms. - "hop_length": 256, // stft window hop-lengh in ms. - "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used. - "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used. - - // Audio processing parameters - "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. - "preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis. - "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air. - - // Silence trimming - "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true) - "trim_db": 60, // threshold for timming silence. Set this according to your dataset. - - // Griffin-Lim - "power": 1.5, // value to sharpen wav signals after GL algorithm. - "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation. - - // MelSpectrogram parameters - "num_mels": 80, // size of the mel spec frame. - "mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! - "mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!! + "attention_heads": 4, + "attention_norm": "sigmoid", + "attention_type": "original", + "audio_config": { + "clip_norm": true, + "do_trim_silence": true, + "fft_size": 1024, + "frame_length_ms": null, + "frame_shift_ms": null, + "griffin_lim_iters": 60, + "hop_length": 256, + "max_norm": 4, + "mel_fmax": 7600, + "mel_fmin": 50, + "min_level_db": -100, + "num_mels": 80, + "power": 1.5, + "preemphasis": 0, + "ref_level_db": 20, + "sample_rate": 22050, + "signal_norm": true, "spec_gain": 1, - - // Normalization parameters - "signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params. - "min_level_db": -100, // lower bound for normalization - "symmetric_norm": true, // move normalization to range [-1, 1] - "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm] - "clip_norm": true, // clip normalized values into the range. - "stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy" // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored + "stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy", + "symmetric_norm": true, + "trim_db": 60, + "win_length": 1024 }, - - // VOCABULARY PARAMETERS - // if custom character set is not defined, - // default set in symbols.py is used - // "characters":{ - // "pad": "_", - // "eos": "~", - // "bos": "^", - // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ", - // "punctuations":"!'(),-.:;? ", - // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ" - // }, - - // DISTRIBUTED TRAINING - "distributed":{ - "backend": "nccl", - "url": "tcp:\/\/localhost:54321" - }, - - "reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers. - - // TRAINING - "batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'. - "eval_batch_size":16, - "r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled. - "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed. - "mixed_precision": true, // level of optimization with NVIDIA's apex feature for automatic mixed FP16/FP32 precision (AMP), NOTE: currently only O1 is supported, and use "O1" to activate. - - // LOSS SETTINGS - "loss_masking": true, // enable / disable loss masking against the sequence padding. - "decoder_loss_alpha": 0.5, // original decoder loss weight. If > 0, it is enabled - "postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled - "postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled - "decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled - "decoder_ssim_alpha": 0.5, // decoder ssim loss weight. If > 0, it is enabled - "postnet_ssim_alpha": 0.25, // postnet ssim loss weight. If > 0, it is enabled - "ga_alpha": 5.0, // weight for guided attention loss. If > 0, guided attention is enabled. - "stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples. - - - // VALIDATION - "run_eval": true, - "test_delay_epochs": 10, //Until attention is aligned, testing only wastes computation time. - "test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences. - - // OPTIMIZER - "noam_schedule": false, // use noam warmup and lr schedule. - "grad_clip": 1.0, // upper limit for gradients for clipping. - "epochs": 1000, // total number of epochs to train. - "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate. - "wd": 0.000001, // Weight decay weight. - "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr" - "seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths. - - // TACOTRON PRENET - "memory_size": -1, // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame. - "prenet_type": "original", // "original" or "bn". - "prenet_dropout": true, // enable/disable dropout at prenet. - - // TACOTRON ATTENTION - "attention_type": "original", // 'original' , 'graves', 'dynamic_convolution' - "attention_heads": 4, // number of attention heads (only for 'graves') - "attention_norm": "sigmoid", // softmax or sigmoid. - "windowing": false, // Enables attention windowing. Used only in eval mode. - "use_forward_attn": false, // if it uses forward attention. In general, it aligns faster. - "forward_attn_mask": false, // Additional masking forcing monotonicity only in eval mode. - "transition_agent": false, // enable/disable transition agent of forward attention. - "location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default. - "bidirectional_decoder": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset. - "double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/ - "ddc_r": 7, // reduction rate for coarse decoder. - - // STOPNET - "stopnet": true, // Train stopnet predicting the end of synthesis. - "separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER. - - // TENSORBOARD and LOGGING - "print_step": 25, // Number of steps to log training on console. - "tb_plot_step": 100, // Number of steps to plot TB training figures. - "print_eval": false, // If True, it prints intermediate loss values in evalulation. - "save_step": 10000, // Number of training steps expected to save traninpg stats and checkpoints. - "checkpoint": true, // If true, it saves checkpoints per "save_step" - "keep_all_best": false, // If true, keeps all best_models after keep_after steps - "keep_after": 10000, // Global step after which to keep best models if keep_all_best is true - "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. - - // DATA LOADING - "text_cleaner": "phoneme_cleaners", - "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars. - "num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values. - "num_val_loader_workers": 4, // number of evaluation data loader processes. - "batch_group_size": 4, //Number of batches to shuffle after bucketing. - "min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training - "max_seq_len": 153, // DATASET-RELATED: maximum text length - "compute_input_seq_cache": false, // if true, text sequences are computed before starting training. If phonemes are enabled, they are also computed at this stage. - "use_noise_augment": true, - - // PATHS - "output_path": "/home/erogol/Models/LJSpeech/", - - // PHONEMES - "phoneme_cache_path": "/home/erogol/Models/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder. - "use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation. - "phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages - - // MULTI-SPEAKER and GST - "use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning. - "use_gst": false, // use global style tokens - "use_external_speaker_embedding_file": false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558 - "external_speaker_embedding_file": "../../speakers-vctk-en.json", // if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558 - "gst": { // gst parameter if gst is enabled - "gst_style_input": null, // Condition the style input either on a - // -> wave file [path to wave] or - // -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15} - // with the dictionary being len(dict) <= len(gst_num_style_tokens). - "gst_embedding_dim": 512, - "gst_num_heads": 4, - "gst_num_style_tokens": 10, - "gst_use_speaker_embedding": false - }, - - // DATASETS - "datasets": // List of datasets. They all merged and they get different speaker_ids. + "bidirectional_decoder": false, + "compute_input_seq_cache": false, + "ddc_r": 7, + "decoder_diff_spec_alpha": 0.25, + "decoder_loss_alpha": 0.5, + "decoder_ssim_alpha": 0.5, + "double_decoder_consistency": true, + "enable_eos_bos_chars": false, + "forward_attn_mask": false, + "ga_alpha": 5, + "grad_clip": 1, + "gradual_training": [ [ - { - "name": "ljspeech", - "path": "/home/erogol/Data/LJSpeech-1.1/", - "meta_file_train": "metadata.csv", // for vtck if list, ignore speakers id in list for train, its useful for test cloning with new speakers - "meta_file_val": null - } + 0, + 7, + 64 + ], + [ + 1, + 5, + 64 + ], + [ + 50000, + 3, + 32 + ], + [ + 130000, + 2, + 32 + ], + [ + 290000, + 1, + 32 ] + ], + "location_attn": true, + "lr": 0.0001, + "memory_size": -1, + "noam_schedule": false, + "phoneme_cache_path": "/home/erogol/Models/phoneme_cache/", + "phoneme_language": "en-us", + "postnet_diff_spec_alpha": 0.25, + "postnet_loss_alpha": 0.25, + "postnet_ssim_alpha": 0.25, + "prenet_dropout": false, + "prenet_type": "original", + "r": 7, + "separate_stopnet": true, + "seq_len_norm": false, + "stopnet": true, + "stopnet_pos_weight": 15, + "test_sentences_file": null, + "text_cleaner": "phoneme_cleaners", + "training_config": { + "batch_group_size": 4, + "batch_size": 32, + "checkpoint": true, + "datasets": [ + { + "meta_file_train": "metadata.csv", + "meta_file_val": null, + "name": "ljspeech", + "path": "/home/erogol/Data/LJSpeech-1.1/" + } + ], + "epochs": 1000, + "eval_batch_size": 16, + "keep_after": 10000, + "keep_all_best": false, + "loss_masking": true, + "max_seq_len": 153, + "min_seq_len": 6, + "mixed_precision": true, + "model": "Tacotron2", + "num_loader_workers": 4, + "num_val_loader_workers": 4, + "output_path": "/home/erogol/Models/LJSpeech/", + "print_eval": false, + "print_step": 25, + "run_description": "tacotron2 with DDC and differential spectral loss.", + "run_eval": true, + "run_name": "ljspeech-ddc", + "save_step": 10000, + "tb_model_param_stats": false, + "tb_plot_step": 100, + "test_delay_epochs": 10, + "use_noise_augment": true + }, + "transition_agent": false, + "use_forward_attn": false, + "use_phonemes": true, + "warmup_steps": 4000, + "wd": 0.000001, + "windowing": false } diff --git a/TTS/utils/arguments.py b/TTS/utils/arguments.py index af0a1598..4f6e2317 100644 --- a/TTS/utils/arguments.py +++ b/TTS/utils/arguments.py @@ -117,16 +117,11 @@ def get_last_checkpoint(path): return last_models["checkpoint"], last_models["best_model"] -def process_args(args, model_class): - """Process parsed comand line arguments based on model class (tts or vocoder). +def process_args(args, config, tb_prefix): + """Process parsed comand line arguments. Args: args (argparse.Namespace or dict like): Parsed input arguments. - model_type (str): Model type used to check config parameters and setup - the TensorBoard logger. One of ['tts', 'vocoder']. - - Raises: - ValueError: If `model_type` is not one of implemented choices. Returns: c (TTS.utils.io.AttrDict): Config paramaters. @@ -138,28 +133,21 @@ def process_args(args, model_class): the TensorBoard loggind. """ if args.continue_path: + # continue a previous training from its output folder args.output_path = args.continue_path args.config_path = os.path.join(args.continue_path, "config.json") args.restore_path, best_model = get_last_checkpoint(args.continue_path) if not args.best_path: args.best_path = best_model - # setup output paths and read configs - c = load_config(args.config_path) - _ = os.path.dirname(os.path.realpath(__file__)) - - if "mixed_precision" in c and c.mixed_precision: + c = config.load_json(args.config_path) + if c.mixed_precision: print(" > Mixed precision mode is ON") - - out_path = args.continue_path - if not out_path: - out_path = create_experiment_folder(c.output_path, c.run_name, args.debug) - + if not os.path.exists(c.output_path): + out_path = create_experiment_folder(c.output_path, c.run_name, + args.debug) audio_path = os.path.join(out_path, "test_audios") - - c_logger = ConsoleLogger() - tb_logger = None - + # setup rank 0 process in distributed training if args.rank == 0: os.makedirs(audio_path, exist_ok=True) new_fields = {} @@ -169,18 +157,15 @@ def process_args(args, model_class): # if model characters are not set in the config file # save the default set to the config file for future # compatibility. - if model_class == "tts" and "characters" not in c: + if c.has('characters_config'): used_characters = parse_symbols() new_fields["characters"] = used_characters copy_model_files(c, args.config_path, out_path, new_fields) os.chmod(audio_path, 0o775) os.chmod(out_path, 0o775) - log_path = out_path - - tb_logger = TensorboardLogger(log_path, model_name=model_class.upper()) - - # write model config to tensorboard - tb_logger.tb_add_text("model-config", f"
{json.dumps(c, indent=4)}
", 0) - + tb_logger = TensorboardLogger(log_path, model_name=tb_prefix) + # write model desc to tensorboard + tb_logger.tb_add_text("model-description", c["run_description"], 0) + c_logger = ConsoleLogger() return c, out_path, audio_path, c_logger, tb_logger diff --git a/TTS/utils/io.py b/TTS/utils/io.py index 84493e07..12745459 100644 --- a/TTS/utils/io.py +++ b/TTS/utils/io.py @@ -23,33 +23,32 @@ class AttrDict(dict): self.__dict__ = self -def read_json_with_comments(json_path): - # fallback to json - with open(json_path, "r", encoding="utf-8") as f: - input_str = f.read() - # handle comments - input_str = re.sub(r"\\\n", "", input_str) - input_str = re.sub(r"//.*\n", "\n", input_str) - data = json.loads(input_str) - return data +# def read_json_with_comments(json_path): +# # fallback to json +# with open(json_path, "r", encoding="utf-8") as f: +# input_str = f.read() +# # handle comments +# input_str = re.sub(r'\\\n', '', input_str) +# input_str = re.sub(r'//.*\n', '\n', input_str) +# data = json.loads(input_str) +# return data +# def load_config(config_path: str) -> AttrDict: +# """Load config files and discard comments -def load_config(config_path: str) -> AttrDict: - """Load config files and discard comments +# Args: +# config_path (str): path to config file. +# """ +# config = AttrDict() - Args: - config_path (str): path to config file. - """ - config = AttrDict() - - ext = os.path.splitext(config_path)[1] - if ext in (".yml", ".yaml"): - with open(config_path, "r", encoding="utf-8") as f: - data = yaml.safe_load(f) - else: - data = read_json_with_comments(config_path) - config.update(data) - return config +# ext = os.path.splitext(config_path)[1] +# # if ext in (".yml", ".yaml"): +# # with open(config_path, "r", encoding="utf-8") as f: +# # data = yaml.safe_load(f) +# # else: +# data = read_json_with_comments(config_path) +# config.update(data) +# return config def copy_model_files(c, config_file, out_path, new_fields):