Keep getting the same results when training


I keep getting loss curves that looks like this.

I have about 15 hours of training data. I’ve tried different learning rates, however after a certain amount of steps it spikes like this. I’ve tried with different learning rates but seem to get the same result. Can anyone enlighten me :slight_smile: Thanks!

        "model": "Tacotron2",
        "run_name": "jens-non-ddc",
        "run_description": "tacotron2 with ddc",

            // 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. 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": 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 (false), 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": 1.0,         // 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": "/home/al/dev/TTS/jens/out.npy"    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by ''. If it is defined, mean-std based notmalization is used and other normalization params are ignored

        // if custom character set is not defined,
        // default set in is used
        // "characters":{
        //     "pad": "_",
        //     "eos": "~",
        //     "bos": "^",
        //     "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
        //     "punctuations":"!'(),-.:;? ",
        //     "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
        // },

            "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": 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], [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.
        "mixed_precision": true,     // 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,  // 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": 0.0,           // 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.
        // VALIDATION
        "run_eval": true,
        "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
        "test_sentences_file": "/home/al/dev/TTS/jens/sentences.txt",  // 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": 1000,                // 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.

        "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.

        "attention_type": "dynamic_convolution",  // 'original' or 'graves'
        "attention_heads": 4,          // number of attention heads (only for 'graves')
        "attention_norm": "softmax",   // 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 Use it, if attention does not work well with your dataset.
        "double_decoder_consistency": false,  // use DDC explained here
        "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.

        "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": 2000,      // 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": 3,       // DATASET-RELATED: minimum text length to use in training
        "max_seq_len": 260,     // DATASET-RELATED: maximum text length
        "compute_input_seq_cache": false,  // if true, text sequences are computed before starting training. If phonemes are enabled, they are also computed at this stage.

        // PATHS
        "output_path": "/home/al/dev/TTS/jens/model",

        // PHONEMES
        "phoneme_cache_path": "/home/al/dev/TTS/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": "sv",     // depending on your target language, pick one from

        // 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
        "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: /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/al/dev/TTS/jens/",
                    "meta_file_train": "metadata_train.csv",
                    "meta_file_val": "metadata_val.csv"