mirror of https://github.com/coqui-ai/TTS.git
linter changes and train_tts_test and train_vocoder_test fixes
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erogol
parent
3131308baa
commit
d4319fe42d
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@ -670,12 +670,12 @@ if __name__ == '__main__':
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args = parser.parse_args()
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if args.continue_path != '':
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print(f" > Training continues for {args.continue_path}")
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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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list_of_files = glob.glob(args.continue_path + "/*.pth.tar") # * means all if need specific format then *.csv
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latest_model_file = max(list_of_files, key=os.path.getctime)
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args.restore_path = latest_model_file
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print(f" > Training continues for {args.restore_path}")
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# setup output paths and read configs
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c = load_config(args.config_path)
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@ -7,7 +7,7 @@ import pyworld as pw
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from mozilla_voice_tts.tts.utils.data import StandardScaler
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#pylint: disable=too-many-public-methods
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class AudioProcessor(object):
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def __init__(self,
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sample_rate=None,
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@ -1,6 +1,5 @@
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import re
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import json
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from shutil import copyfile
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class AttrDict(dict):
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"""A custom dict which converts dict keys
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@ -1,4 +1,3 @@
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<<<<<<< HEAD:tests/inputs/test_train_config.json
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{
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"model": "Tacotron2",
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"run_name": "test_sample_dataset_run",
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@ -132,9 +131,19 @@
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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_external_speaker_embedding_file": false,
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"external_speaker_embedding_file": null,
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"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
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"style_wav_for_test": null, // path to style wav file to be used in TacotronGST inference.
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"use_gst": false, // TACOTRON ONLY: use global style tokens
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"use_gst": true, // use global style tokens
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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_style_tokens).
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"gst_embedding_dim": 512,
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"gst_num_heads": 4,
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"gst_style_tokens": 10
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},
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// DATASETS
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"train_portion": 0.1, // dataset portion used for training. It is mainly for internal experiments.
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@ -151,161 +160,3 @@
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}
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=======
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{
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"model": "Tacotron2",
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"run_name": "ljspeech-ddc-bn",
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"run_description": "tacotron2 with ddc and batch-normalization",
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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": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"spec_gain": 20,
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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": null // 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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},
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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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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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"ga_alpha": 10.0, // weight for guided attention loss. If > 0, guided attention is enabled.
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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": "bn", // "original" or "bn".
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"prenet_dropout": false, // enable/disable dropout at prenet.
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// TACOTRON ATTENTION
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"attention_type": "original", // 'original' or 'graves'
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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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"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": 0, //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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// PATHS
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"output_path": "/home/erogol/Models/LJSpeech/",
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// PHONEMES
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"phoneme_cache_path": "/media/erogol/data_ssd2/mozilla_us_phonemes_3", // 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": true, // use global style tokens
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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_style_tokens).
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"gst_embedding_dim": 512,
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"gst_num_heads": 4,
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"gst_style_tokens": 10
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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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[
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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",
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"meta_file_val": null
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}
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]
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}
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>>>>>>> Added support for Tacotron2 GST + abbility to condition style input with wav or tokens:config.json
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@ -139,6 +139,6 @@
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"eval_split_size": 10,
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// PATHS
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"output_path": "tests/outputs/train_outputs/"
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"output_path": "tests/train_outputs/"
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}
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@ -7,9 +7,9 @@ mkdir $BASEDIR/train_outputs
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# run training
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CUDA_VISIBLE_DEVICES="" python mozilla_voice_tts/bin/train_vocoder.py --config_path $BASEDIR/inputs/test_vocoder_multiband_melgan_config.json
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# find the training folder
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LATEST_FOLDER=$(ls $BASEDIR/outputs/train_outputs/| sort | tail -1)
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LATEST_FOLDER=$(ls $BASEDIR/train_outputs/| sort | tail -1)
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echo $LATEST_FOLDER
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# continue the previous training
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CUDA_VISIBLE_DEVICES="" python mozilla_voice_tts/bin/train_vocoder.py --continue_path $BASEDIR/outputs/train_outputs/$LATEST_FOLDER
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CUDA_VISIBLE_DEVICES="" python mozilla_voice_tts/bin/train_vocoder.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
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# remove all the outputs
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rm -rf $BASEDIR/train_outputs/
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rm -rf $BASEDIR/train_outputs/$LATEST_FOLDER
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