mirror of
https://github.com/wassname/TTS.git
synced 2026-09-10 11:50:20 +08:00
adding more tests and refactoring
This commit is contained in:
erogol
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{
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"model": "glow_tts",
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"run_name": "glow-tts-gatedconv",
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"run_description": "glow-tts model training with gated conv.",
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// AUDIO PARAMETERS
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"audio":{
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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. If different than the original data, it is resampled.
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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": 0, // reference level db, theoretically 20db is the sound of air.
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// Griffin-Lim
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"power": 1.1, // 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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// Silence trimming
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"do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), 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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// 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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"spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.
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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": 1.0, // 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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"add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model.
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// DISTRIBUTED TRAINING
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"mixed_precision": false,
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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54323"
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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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// MODEL PARAMETERS
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"use_mas": false, // use Monotonic Alignment Search if true. Otherwise use pre-computed attention alignments.
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// TRAINING
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"batch_size": 2, // 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":1,
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"r": 1, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": 0, //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": true, // use noam warmup and lr schedule.
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"grad_clip": 5.0, // upper limit for gradients for clipping.
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"epochs": 1, // total number of epochs to train.
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"lr": 1e-3, // 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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"encoder_type": "gatedconv",
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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": 5000, // 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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"apex_amp_level": null,
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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": 3, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 500, // DATASET-RELATED: maximum text length
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"compute_f0": false, // compute f0 values in data-loader
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// PATHS
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"output_path": "tests/train_outputs/",
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// PHONEMES
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"phoneme_cache_path": "tests/outputs/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_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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// 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": "tests/data/ljspeech/",
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"meta_file_train": "metadata.csv",
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"meta_file_val": "metadata.csv"
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}
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]
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}
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{
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"run_name": "wavegrad-ljspeech",
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"run_description": "wavegrad ljspeech",
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"audio":{
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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. If different than the original data, it is resampled.
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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": 0, // 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 (false), 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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// 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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"spec_gain": 1.0, // scaler value appplied after log transform of spectrogram.
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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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// DISTRIBUTED TRAINING
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"mixed_precision": false,
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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54322"
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},
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"target_loss": "avg_wavegrad_loss", // loss value to pick the best model to save after each epoch
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// MODEL PARAMETERS
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"generator_model": "wavegrad",
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"model_params":{
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"y_conv_channels":32,
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"x_conv_channels":768,
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"ublock_out_channels": [512, 512, 256, 128, 128],
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"dblock_out_channels": [128, 128, 256, 512],
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"upsample_factors": [4, 4, 4, 2, 2],
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"upsample_dilations": [
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[1, 2, 1, 2],
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[1, 2, 1, 2],
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[1, 2, 4, 8],
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[1, 2, 4, 8],
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[1, 2, 4, 8]]
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},
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// DATASET
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"data_path": "tests/data/ljspeech/wavs/", // root data path. It finds all wav files recursively from there.
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"feature_path": null, // if you use precomputed features
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"seq_len": 6144, // 24 * hop_length
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"pad_short": 0, // additional padding for short wavs
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"conv_pad": 0, // additional padding against convolutions applied to spectrograms
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"use_noise_augment": false, // add noise to the audio signal for augmentation
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"use_cache": true, // use in memory cache to keep the computed features. This might cause OOM.
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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": 1, // Batch size for training.
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"train_noise_schedule":{
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"min_val": 1e-6,
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"max_val": 1e-2,
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"num_steps": 1000
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},
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"test_noise_schedule":{
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"min_val": 1e-6,
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"max_val": 1e-2,
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"num_steps": 2
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},
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// VALIDATION
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"run_eval": true, // enable/disable evaluation run
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// OPTIMIZER
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"epochs": 1, // total number of epochs to train.
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"clip_grad": 1.0, // Generator gradient clipping threshold. Apply gradient clipping if > 0
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"lr_scheduler": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
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"lr_scheduler_params": {
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"gamma": 0.5,
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"milestones": [100000, 200000, 300000, 400000, 500000, 600000]
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},
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"lr": 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate.
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// TENSORBOARD and LOGGING
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"print_step": 250, // Number of steps to log traning on console.
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"print_eval": false, // If True, it prints loss values for each step in eval run.
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"save_step": 10000, // Number of training steps expected to plot training stats on TB and save model checkpoints.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"tb_model_param_stats": true, // 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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"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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"eval_split_size": 4,
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// PATHS
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"output_path": "tests/train_outputs/"
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}
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Executable
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#!/usr/bin/env bash
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BASEDIR=$(dirname "$0")
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echo "$BASEDIR"
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# run training
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CUDA_VISIBLE_DEVICES="" python TTS/bin/train_glow_tts.py --config_path $BASEDIR/inputs/test_glow_tts.json
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# find the training folder
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LATEST_FOLDER=$(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 TTS/bin/train_glow_tts.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
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# remove all the outputs
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rm -rf $BASEDIR/train_outputs/
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Executable
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#!/usr/bin/env bash
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BASEDIR=$(dirname "$0")
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echo "$BASEDIR"
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# run training
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CUDA_VISIBLE_DEVICES="" python TTS/bin/train_tacotron.py --config_path $BASEDIR/inputs/test_train_config.json
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# find the training folder
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LATEST_FOLDER=$(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 TTS/bin/train_tacotron.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
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# remove all the outputs
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rm -rf $BASEDIR/train_outputs/
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Executable
+15
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#!/usr/bin/env bash
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BASEDIR=$(dirname "$0")
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echo "$BASEDIR"
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# create run dir
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mkdir $BASEDIR/train_outputs
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# run training
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CUDA_VISIBLE_DEVICES="" python TTS/bin/train_vocoder_wavegrad.py --config_path $BASEDIR/inputs/test_vocoder_wavegrad.json
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# find the training folder
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LATEST_FOLDER=$(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 TTS/bin/train_vocoder_wavegrad.py --continue_path $BASEDIR/train_outputs/$LATEST_FOLDER
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# remove all the outputs
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rm -rf $BASEDIR/train_outputs/$LATEST_FOLDER
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