Stop loss spikes

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 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.

// if custom character set is not defined,
// default set in is used
    "pad": "_",
    "eos": "~",
    "bos": "^",
    "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzÁÀÂÃÉÊÍÓÔÕÚáàâãéêíóôõúç!'(),-.:;? ",
    "punctuations":"!'(),-.:;? ",

"reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.

"batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
"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.

"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.

"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_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 Use it, if attention does not work well with your dataset.

"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.

"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

"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

"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!

I had the same problem with ‘master’ and I am not getting it in ‘dev’.

try trimming silences in your dataset.

Thanks. Running it now to see how it goes.

You mean the silences at the beginning and end of the audio file, right?
Are there any guidelines on how long these silences can be (at most)? I’ve checked some audio files from the LJ dataset using audacity, and it seems the silences can go up to .1 seconds.

.1 is good enough. That should be fine

Tried both suggestions, but the stop loss spikes keep happening. A small jump at r=2, and then a big jump r=1.

@maneeshkyadav What was the dev branch commit you tried? I’ve tried master branch commit fab74dd and dev branch f7b1ca

@erogol I indeed found some clips in my dataset with silences at the beginning and at the end which were over .1seconds. After cleaning all those clips the problem still persists. Any other idea of what may be causing this?

I cloned from dev branch commit: 2a15e391669f9073ba10ef7ff20bb54ec5246977

Things seems to have been working ok across a bunch of runs. I can post graphs for successful runs if needed (spike graphs are in another thread).