File size: 11,255 Bytes
46a75d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
{
    "model": "Tacotron",
    "run_name": "test_sample_dataset_run",
    "run_description": "sample dataset test run",

    // 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": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
        "spec_gain": 20.0,

        // 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": 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
    },

    // 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": 1,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
    "eval_batch_size":1,
    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
    "gradual_training": [[0, 7, 4]], //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.
    "loss_masking": true,         // enable / disable loss masking against the sequence padding.
    "ga_alpha": 10.0,        // weight for guided attention loss. If > 0, guided attention is enabled.
    "mixed_precision": false,

    // VALIDATION
    "run_eval": true,
    "test_delay_epochs": 0,  //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.

    // 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.

    // OPTIMIZER
    "noam_schedule": false,        // use noam warmup and lr schedule.
    "grad_clip": 1.0,              // upper limit for gradients for clipping.
    "epochs": 1,                // 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": "bn",            // "original" or "bn".
    "prenet_dropout": false,        // 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": true,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
    "double_decoder_consistency": false,  // 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": 1,       // 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": true,  // 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": 0,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
    "num_eval_loader_workers": 0,    // number of evaluation data loader processes.
    "batch_group_size": 0,  //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": true,

    // PATHS
    "output_path": "tests/train_outputs/",

    // PHONEMES
    "phoneme_cache_path": "tests/train_outputs/phoneme_cache/",  // phoneme computation is slow, therefore, it caches results in the given folder.
    "use_phonemes": false,           // 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_d_vector_file": false,
    "d_vector_file": null,
    "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
    "use_gst": true,       			    // use global style tokens
    "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_style_tokens).
        "gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
        "gst_embedding_dim": 512,
        "gst_num_heads": 4,
        "gst_style_tokens": 10
    },

    // DATASETS
    "train_portion": 0.1,  // dataset portion used for training. It is mainly for internal experiments.
    "eval_portion": 0.1,   // dataset portion used for training. It is mainly for internal experiments.
    "datasets":   // List of datasets. They all merged and they get different speaker_ids.
        [
            {
                "formatter": "ljspeech",
                "path": "tests/data/ljspeech/",
                "meta_file_train": "metadata.csv",
                "meta_file_val": "metadata.csv"
            }
        ]

}