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>>> Dias |
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>>> othiele |
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>>> Dias |
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>>> sanjaesc |
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>>> Dias |
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>>> othiele |
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>>> sanjaesc |
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>>> Dias
[November 4, 2020, 8:09am]
I try to train LjSpeech dataset with TTS and multiband-melgan, but in
the output I get only noise and no voice at all. Here are the configs
that I use:
TTS config:
{ slash
'model': 'Tacotron2', slash
'run_name': 'ljspeech-ddc', slash
'run_description': 'tacotron2 with DDC and differential spectral loss.',
// AUDIO PARAMETERS
'audio':{
// stft parameters
'fft_size': 1024, // number of stft frequency levels. Size of the linear spectogram frame.
'win_length': 1024, // stft window length in ms.
'hop_length': 256, // stft window hop-lengh in ms.
'frame_length_ms': null, // stft window length in ms.If null, 'win_length' is used.
'frame_shift_ms': null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
// Audio processing parameters
'sample_rate': 22050, // DATASET-RELATED: wav sample-rate.
'preemphasis': 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
'ref_level_db': 20, // reference level db, theoretically 20db is the sound of air.
// Silence trimming
'do_trim_silence': true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
'trim_db': 60, // threshold for timming silence. Set this according to your dataset.
// Griffin-Lim
'power': 1.5, // value to sharpen wav signals after GL algorithm.
'griffin_lim_iters': 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
// MelSpectrogram parameters
'num_mels': 80, // size of the mel spec frame.
'mel_fmin': 50.0, // minimum freq level for mel-spec. ~50 for male and
95 for female voices. Tune for dataset!!
','mel_fmax': 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
'spec_gain': 1,
// Normalization parameters
'signal_norm': true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
'min_level_db': -100, // lower bound for normalization
'symmetric_norm': true, // move normalization to range [-1, 1]
'max_norm': 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
'clip_norm': true, // clip normalized values into the range.
'stats_path': 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: slash / slash /localhost:54321'
},
'reinit_layers': [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
// TRAINING
'batch_size': 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
'eval_batch_size':16,
'r': 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
'gradual_training': [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
'apex_amp_level': null, // level of optimization with NVIDIA's apex feature for automatic mixed FP16/FP32 precision (AMP), NOTE: currently only O1 is supported, and use 'O1' to activate.
// LOSS SETTINGS
'loss_masking': true, // enable / disable loss masking against the sequence padding.
'decoder_loss_alpha': 0.5, // decoder loss weight. If > 0, it is enabled
'postnet_loss_alpha': 0.25, // postnet loss weight. If > 0, it is enabled
'ga_alpha': 5.0, // weight for guided attention loss. If > 0, guided attention is enabled.
'diff_spec_alpha': 0.25, // differential spectral loss weight. If > 0, it is enabled
// VALIDATION
'run_eval': true,
'test_delay_epochs': 10, //Until attention is aligned, testing only wastes computation time.
'test_sentences_file': null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
// OPTIMIZER
'noam_schedule': false, // use noam warmup and lr schedule.
'grad_clip': 1.0, // upper limit for gradients for clipping.
'epochs': 100, // total number of epochs to train.
'lr': 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
'wd': 0.000001, // Weight decay weight.
'warmup_steps': 4000, // Noam decay steps to increase the learning rate from 0 to 'lr'
'seq_len_norm': false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
// TACOTRON PRENET
'memory_size': -1, // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
'prenet_type': 'original', // 'original' or 'bn'.
'prenet_dropout': false, // enable/disable dropout at prenet.
// TACOTRON ATTENTION
'attention_type': 'original', // 'original' or 'graves'
'attention_heads': 4, // number of attention heads (only for 'graves')
'attention_norm': 'sigmoid', // softmax or sigmoid.
'windowing': false, // Enables attention windowing. Used only in eval mode.
'use_forward_attn': false, // if it uses forward attention. In general, it aligns faster.
'forward_attn_mask': false, // Additional masking forcing monotonicity only in eval mode.
'transition_agent': false, // enable/disable transition agent of forward attention.
'location_attn': true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
'bidirectional_decoder': false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
'double_decoder_consistency': true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
'ddc_r': 7, // reduction rate for coarse decoder.
// STOPNET
'stopnet': true, // Train stopnet predicting the end of synthesis.
'separate_stopnet': true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
// TENSORBOARD and LOGGING
'print_step': 25, // Number of steps to log training on console.
'tb_plot_step': 100, // Number of steps to plot TB training figures.
'print_eval': false, // If True, it prints intermediate loss values in evalulation.
'save_step': 10000, // Number of training steps expected to save traninpg stats and checkpoints.
'checkpoint': true, // If true, it saves checkpoints per 'save_step'
'tb_model_param_stats': false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
// DATA LOADING
'text_cleaner': 'phoneme_cleaners',
'enable_eos_bos_chars': false, // enable/disable beginning of sentence and end of sentence chars.
'num_loader_workers': 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
'num_val_loader_workers': 4, // number of evaluation data loader processes.
'batch_group_size': 4, //Number of batches to shuffle after bucketing.
'min_seq_len': 6, // DATASET-RELATED: minimum text length to use in training
'max_seq_len': 153, // DATASET-RELATED: maximum text length
// PATHS
'output_path': '/home/dias/Downloads/Models/LJSpeech/',
// PHONEMES
'phoneme_cache_path': '/home/dias/Downloads/Models/phoneme_cache/', // phoneme computation is slow, therefore, it caches results in the given folder.
'use_phonemes': true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
'phoneme_language': 'en-us', // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
// MULTI-SPEAKER and GST
'use_speaker_embedding': false, // use speaker embedding to enable multi-speaker learning.
'use_gst': false, // use global style tokens
'use_external_speaker_embedding_file': false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
'external_speaker_embedding_file': '../../speakers-vctk-en.json', // if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
'gst': { // gst parameter if gst is enabled
'gst_style_input': null, // Condition the style input either on a
// -> wave file [path to wave] or
// -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {'0': 0.15, '1': 0.15, '5': -0.15}
// with the dictionary being len(dict) <= len(gst_style_tokens).
'gst_embedding_dim': 512,
'gst_num_heads': 4,
'gst_style_tokens': 10,
'gst_use_speaker_embedding': false
},
// DATASETS
'datasets': // List of datasets. They all merged and they get different speaker_ids.
[
{
'name': 'ljspeech',
'path': '/home/dias/Downloads/LJSpeech-1.1/',
'meta_file_train': 'metadata.csv', // for vtck if list, ignore speakers id in list for train, its useful for test cloning with new speakers
'meta_file_val': null
}
]
}
Vocoder config:
{ slash
'run_name': 'multiband-melgan', slash
'run_description': 'multiband melgan mean-var scaling',
// AUDIO PARAMETERS
'audio':{
'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. If different than the original data, it is resampled.
'preemphasis': 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
'ref_level_db': 0, // 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 (false), TWEB (false), Nancy (true)
'trim_db': 60, // threshold for timming silence. Set this according to your dataset.
// MelSpectrogram parameters
'num_mels': 80, // size of the mel spec frame.
'mel_fmin': 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
'mel_fmax': 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
'spec_gain': 1.0, // scaler value appplied after log transform of spectrogram.
// 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
},
// DISTRIBUTED TRAINING
// 'distributed':{
// 'backend': 'nccl',
// 'url': 'tcp: slash / slash /localhost:54321'
// },
// LOSS PARAMETERS
'use_stft_loss': true,
'use_subband_stft_loss': true, // use only with multi-band models.
'use_mse_gan_loss': true,
'use_hinge_gan_loss': false,
'use_feat_match_loss': false, // use only with melgan discriminators
// loss weights
'stft_loss_weight': 0.5,
'subband_stft_loss_weight': 0.5,
'mse_G_loss_weight': 2.5,
'hinge_G_loss_weight': 2.5,
'feat_match_loss_weight': 25,
// multiscale stft loss parameters
'stft_loss_params': {
'n_ffts': [1024, 2048, 512],
'hop_lengths': [120, 240, 50],
'win_lengths': [600, 1200, 240]
},
// subband multiscale stft loss parameters
'subband_stft_loss_params':{
'n_ffts': [384, 683, 171],
'hop_lengths': [30, 60, 10],
'win_lengths': [150, 300, 60]
},
'target_loss': 'avg_G_loss', // loss value to pick the best model to save after each epoch
// DISCRIMINATOR
'discriminator_model': 'melgan_multiscale_discriminator',
'discriminator_model_params':{
'base_channels': 16,
'max_channels':512,
'downsample_factors':[4, 4, 4]
},
'steps_to_start_discriminator': 200000, // steps required to start GAN trainining.1
// GENERATOR
'generator_model': 'multiband_melgan_generator',
'generator_model_params': {
'upsample_factors':[8, 4, 2],
'num_res_blocks': 4
},
// DATASET
'data_path': '/home/dias/Downloads/LJSpeech-1.1/wavs/',
'feature_path': null,
'seq_len': 16384,
'pad_short': 2000,
'conv_pad': 0,
'use_noise_augment': false,
'use_cache': true,
'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': 64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
// VALIDATION
'run_eval': true,
'test_delay_epochs': 10, //Until attention is aligned, testing only wastes computation time.
'test_sentences_file': null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
// OPTIMIZER
'epochs': 100, // total number of epochs to train.
'wd': 0.0, // Weight decay weight.
'gen_clip_grad': -1, // Generator gradient clipping threshold. Apply gradient clipping if > 0
'disc_clip_grad': -1, // Discriminator gradient clipping threshold.
#how-to-adjust-learning-rate
'lr_scheduler_gen_params': {
'gamma': 0.5,
'milestones': [100000, 200000, 300000, 400000, 500000, 600000]
},
#how-to-adjust-learning-rate
'lr_scheduler_disc_params': {
'gamma': 0.5,
'milestones': [100000, 200000, 300000, 400000, 500000, 600000]
},
'lr_gen': 1e-4, // Initial learning rate. If Noam decay is active, maximum learning rate.
'lr_disc': 1e-4,
// TENSORBOARD and LOGGING
'print_step': 25, // Number of steps to log traning on console.
'print_eval': false, // If True, it prints loss values for each step in eval run.
'save_step': 25000, // Number of training steps expected to plot training stats on TB and save model checkpoints.
'checkpoint': true, // If true, it saves checkpoints per 'save_step'
'tb_model_param_stats': false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
// DATA LOADING
'num_loader_workers': 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
'num_val_loader_workers': 4, // number of evaluation data loader processes.
'eval_split_size': 10,
// PATHS
'output_path': '/home/dias/Downloads/Models/LJSpeech/'
} slash
Any ideas what I did wrong?
[This is an archived TTS discussion thread from discourse.mozilla.org/t/no-output-on-generating-voice]
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