Hi, I am training a Tacotron2 model on a proprietary dataset with around
1000 clips and a total duration of approximately 90 minutes. I’ve been
able to achieve okay’ish results, with decent generalization to new
test sentences. I am puzzled, however, by the stop loss
performance. Specifically, it increases a lot once the gradual r
training gets to r=2 (15k steps) and r=1 (27k steps).
Any idea of what may be causing this?
The config file used is as follows:
{
“model”: “Tacotron2”, // one of the model in models/
“run_name”: “ljspeech-stft_params”,
“run_description”: “tacotron2 cosntant stf parameters”,// AUDIO PARAMETERS "audio":{ // Audio processing parameters "num_mels": 80, // size of the mel spec frame. "num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame. "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled. "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. "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis. "min_level_db": -100, // normalization range "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air. "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. // Normalization parameters "signal_norm": true, // normalize the spec values in range [0, 1] "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. "mel_fmin": 50.0, // 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!! "do_trim_silence": false, // 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. }, // VOCABULARY PARAMETERS // if custom character set is not defined, // default set in symbols.py is used "characters":{ "pad": "_", "eos": "~", "bos": "^", "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzÁÀÂÃÉÊÍÓÔÕÚáàâãéêíóôõúç!'(),-.:;? ", "punctuations":"!'(),-.:;? ", "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ" }, "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], [20, 5, 64], [5000, 3, 32], [15000, 2, 32], [27000, 1, 16]], "loss_masking": true, // enable / disable loss masking against the sequence padding. // OPTIMIZER "noam_schedule": false, // use noam warmup and lr schedule. "grad_clip": 1.0, // upper limit for gradients for clipping. "epochs": 10000, // 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": true, // 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": true, // enable/disable dropout at prenet. // ATTENTION "attention_type": "original", // 'original' or 'graves' "attention_heads": 4, // number of attention heads (only for 'graves') "attention_norm": "softmax", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron. "windowing": false, // Enables attention windowing. Used only in eval mode. "use_forward_attn": true, // 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. // 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. // DATA LOADING "text_cleaner": "en_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": 0, //Number of batches to shuffle after bucketing. "min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training "max_seq_len": 300, // DATASET-RELATED: maximum text length // PHONEMES "phoneme_cache_path": "mozilla_us_phonemes_2_1", // 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_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning. "style_wav_for_test": null, // path to style wav file to be used in TacotronGST inference. "use_gst": false, // TACOTRON ONLY: use global style tokens}
Thanks in advance!