mirror of https://github.com/coqui-ai/TTS.git
[ci skip] config update #3 WIP
Eren Gölge
parent
a21c0b5585
commit
97bd5f9734
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@ -14,6 +14,7 @@ from torch.utils.data import DataLoader
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.tts.datasets.TTSDataset import MyDataset
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from TTS.tts.layers.losses import TacotronLoss
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from TTS.tts.configs.tacotron_config import TacotronConfig
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from TTS.tts.utils.generic_utils import setup_model
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from TTS.tts.utils.io import save_best_model, save_checkpoint
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from TTS.tts.utils.measures import alignment_diagonal_score
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@ -1,173 +1,126 @@
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{
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"model": "Tacotron2",
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"run_name": "ljspeech-ddc",
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"run_description": "tacotron2 with DDC and differential spectral loss.",
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// AUDIO PARAMETERS
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"audio":{
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// stft parameters
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"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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// Audio processing parameters
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"sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
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"preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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// Silence trimming
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"do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
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"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
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// Griffin-Lim
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// MelSpectrogram parameters
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"num_mels": 80, // size of the mel spec frame.
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"mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
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"attention_heads": 4,
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"attention_norm": "sigmoid",
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"attention_type": "original",
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"audio_config": {
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"clip_norm": true,
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"do_trim_silence": true,
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"fft_size": 1024,
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"frame_length_ms": null,
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"frame_shift_ms": null,
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"griffin_lim_iters": 60,
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"hop_length": 256,
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"max_norm": 4,
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"mel_fmax": 7600,
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"mel_fmin": 50,
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"min_level_db": -100,
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"num_mels": 80,
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"power": 1.5,
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"preemphasis": 0,
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"ref_level_db": 20,
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"sample_rate": 22050,
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"signal_norm": true,
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"spec_gain": 1,
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// Normalization parameters
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"signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
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"min_level_db": -100, // lower bound for normalization
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"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
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"stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy",
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"symmetric_norm": true,
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"trim_db": 60,
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"win_length": 1024
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},
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// VOCABULARY PARAMETERS
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// if custom character set is not defined,
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// default set in symbols.py is used
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// "characters":{
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// "pad": "_",
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// "eos": "~",
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// "bos": "^",
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// "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
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// "punctuations":"!'(),-.:;? ",
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// "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
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// },
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// DISTRIBUTED TRAINING
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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54321"
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},
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"reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
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// TRAINING
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"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"eval_batch_size":16,
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"r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
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"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.
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"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.
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// LOSS SETTINGS
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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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"decoder_loss_alpha": 0.5, // original decoder loss weight. If > 0, it is enabled
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"postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled
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"postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
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"decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
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"decoder_ssim_alpha": 0.5, // decoder ssim loss weight. If > 0, it is enabled
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"postnet_ssim_alpha": 0.25, // postnet ssim loss weight. If > 0, it is enabled
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"ga_alpha": 5.0, // weight for guided attention loss. If > 0, guided attention is enabled.
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"stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples.
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": 10, //Until attention is aligned, testing only wastes computation time.
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"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.
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// OPTIMIZER
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"noam_schedule": false, // use noam warmup and lr schedule.
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"grad_clip": 1.0, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"wd": 0.000001, // Weight decay weight.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"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.
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// TACOTRON PRENET
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"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.
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"prenet_type": "original", // "original" or "bn".
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"prenet_dropout": true, // enable/disable dropout at prenet.
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// TACOTRON ATTENTION
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"attention_type": "original", // 'original' , 'graves', 'dynamic_convolution'
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"attention_heads": 4, // number of attention heads (only for 'graves')
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"attention_norm": "sigmoid", // softmax or sigmoid.
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"windowing": false, // Enables attention windowing. Used only in eval mode.
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"use_forward_attn": false, // if it uses forward attention. In general, it aligns faster.
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"forward_attn_mask": false, // Additional masking forcing monotonicity only in eval mode.
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"transition_agent": false, // enable/disable transition agent of forward attention.
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"location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
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"bidirectional_decoder": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
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"double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
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"ddc_r": 7, // reduction rate for coarse decoder.
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// STOPNET
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"stopnet": true, // Train stopnet predicting the end of synthesis.
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"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.
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// TENSORBOARD and LOGGING
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"print_step": 25, // Number of steps to log training on console.
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"tb_plot_step": 100, // Number of steps to plot TB training figures.
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"print_eval": false, // If True, it prints intermediate loss values in evalulation.
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"save_step": 10000, // Number of training steps expected to save traninpg stats and checkpoints.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"keep_all_best": false, // If true, keeps all best_models after keep_after steps
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"keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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// DATA LOADING
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"text_cleaner": "phoneme_cleaners",
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"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
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"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 4, // number of evaluation data loader processes.
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"batch_group_size": 4, //Number of batches to shuffle after bucketing.
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"min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 153, // DATASET-RELATED: maximum text length
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"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.
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"use_noise_augment": true,
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// PATHS
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"output_path": "/home/erogol/Models/LJSpeech/",
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// PHONEMES
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"phoneme_cache_path": "/home/erogol/Models/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
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"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
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"phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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// MULTI-SPEAKER and GST
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"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
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"use_gst": false, // use global style tokens
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"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
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"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
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"gst": { // gst parameter if gst is enabled
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"gst_style_input": null, // Condition the style input either on a
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// -> wave file [path to wave] or
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// -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
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// with the dictionary being len(dict) <= len(gst_num_style_tokens).
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"gst_embedding_dim": 512,
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"gst_num_heads": 4,
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"gst_num_style_tokens": 10,
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"gst_use_speaker_embedding": false
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},
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// DATASETS
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"datasets": // List of datasets. They all merged and they get different speaker_ids.
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"bidirectional_decoder": false,
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"compute_input_seq_cache": false,
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"ddc_r": 7,
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"decoder_diff_spec_alpha": 0.25,
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"decoder_loss_alpha": 0.5,
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"decoder_ssim_alpha": 0.5,
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"double_decoder_consistency": true,
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"enable_eos_bos_chars": false,
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"forward_attn_mask": false,
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"ga_alpha": 5,
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"grad_clip": 1,
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"gradual_training": [
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[
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{
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"name": "ljspeech",
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"path": "/home/erogol/Data/LJSpeech-1.1/",
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"meta_file_train": "metadata.csv", // for vtck if list, ignore speakers id in list for train, its useful for test cloning with new speakers
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"meta_file_val": null
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}
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0,
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7,
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64
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],
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[
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1,
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5,
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64
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],
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[
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50000,
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3,
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32
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],
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[
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130000,
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2,
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32
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],
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[
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290000,
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1,
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32
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]
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],
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"location_attn": true,
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"lr": 0.0001,
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"memory_size": -1,
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"noam_schedule": false,
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"phoneme_cache_path": "/home/erogol/Models/phoneme_cache/",
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"phoneme_language": "en-us",
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"postnet_diff_spec_alpha": 0.25,
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"postnet_loss_alpha": 0.25,
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"postnet_ssim_alpha": 0.25,
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"prenet_dropout": false,
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"prenet_type": "original",
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"r": 7,
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"separate_stopnet": true,
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"seq_len_norm": false,
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"stopnet": true,
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"stopnet_pos_weight": 15,
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"test_sentences_file": null,
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"text_cleaner": "phoneme_cleaners",
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"training_config": {
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"batch_group_size": 4,
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"batch_size": 32,
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"checkpoint": true,
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"datasets": [
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{
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"meta_file_train": "metadata.csv",
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"meta_file_val": null,
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"name": "ljspeech",
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"path": "/home/erogol/Data/LJSpeech-1.1/"
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}
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],
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"epochs": 1000,
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"eval_batch_size": 16,
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"keep_after": 10000,
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"keep_all_best": false,
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"loss_masking": true,
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"max_seq_len": 153,
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"min_seq_len": 6,
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"mixed_precision": true,
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"model": "Tacotron2",
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"num_loader_workers": 4,
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"num_val_loader_workers": 4,
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"output_path": "/home/erogol/Models/LJSpeech/",
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"print_eval": false,
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"print_step": 25,
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"run_description": "tacotron2 with DDC and differential spectral loss.",
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"run_eval": true,
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"run_name": "ljspeech-ddc",
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"save_step": 10000,
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"tb_model_param_stats": false,
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"tb_plot_step": 100,
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"test_delay_epochs": 10,
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"use_noise_augment": true
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},
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"transition_agent": false,
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"use_forward_attn": false,
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"use_phonemes": true,
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"warmup_steps": 4000,
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"wd": 0.000001,
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"windowing": false
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}
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@ -117,16 +117,11 @@ def get_last_checkpoint(path):
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return last_models["checkpoint"], last_models["best_model"]
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def process_args(args, model_class):
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"""Process parsed comand line arguments based on model class (tts or vocoder).
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def process_args(args, config, tb_prefix):
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"""Process parsed comand line arguments.
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Args:
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args (argparse.Namespace or dict like): Parsed input arguments.
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model_type (str): Model type used to check config parameters and setup
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the TensorBoard logger. One of ['tts', 'vocoder'].
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Raises:
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ValueError: If `model_type` is not one of implemented choices.
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Returns:
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c (TTS.utils.io.AttrDict): Config paramaters.
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@ -138,28 +133,21 @@ def process_args(args, model_class):
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the TensorBoard loggind.
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"""
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if args.continue_path:
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# continue a previous training from its output folder
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args.output_path = args.continue_path
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args.config_path = os.path.join(args.continue_path, "config.json")
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args.restore_path, best_model = get_last_checkpoint(args.continue_path)
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if not args.best_path:
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args.best_path = best_model
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# setup output paths and read configs
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c = load_config(args.config_path)
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_ = os.path.dirname(os.path.realpath(__file__))
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if "mixed_precision" in c and c.mixed_precision:
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c = config.load_json(args.config_path)
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if c.mixed_precision:
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print(" > Mixed precision mode is ON")
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out_path = args.continue_path
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if not out_path:
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out_path = create_experiment_folder(c.output_path, c.run_name, args.debug)
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if not os.path.exists(c.output_path):
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out_path = create_experiment_folder(c.output_path, c.run_name,
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args.debug)
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audio_path = os.path.join(out_path, "test_audios")
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c_logger = ConsoleLogger()
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tb_logger = None
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# setup rank 0 process in distributed training
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if args.rank == 0:
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os.makedirs(audio_path, exist_ok=True)
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new_fields = {}
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@ -169,18 +157,15 @@ def process_args(args, model_class):
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# if model characters are not set in the config file
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# save the default set to the config file for future
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# compatibility.
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if model_class == "tts" and "characters" not in c:
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if c.has('characters_config'):
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used_characters = parse_symbols()
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new_fields["characters"] = used_characters
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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"<pre>{json.dumps(c, indent=4)}</pre>", 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
|
||||
|
|
|
|||
|
|
@ -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):
|
||||
|
|
|
|||
Loading…
Reference in New Issue