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>>> maneeshkyadav |
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>>> erogol |
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>>> rui |
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>>> rui |
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>>> erogol |
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>>> rui |
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>>> maneeshkyadav |
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>>> rui
[April 6, 2020, 7:34pm]
Hi, I am training a Tacotron2 model on a proprietary dataset with
around slash
1000 clips and a total duration of approximately 90 minutes. I've been slash
able to achieve okay'ish results, with decent generalization to new slash
test sentences. I am puzzled, however, by the stop loss slash
performance. Specifically, it increases a lot once the gradual r slash
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:
> { slash
> 'model': 'Tacotron2', // one of the model in models/ slash
> 'run_name': 'ljspeech-stft_params', slash
> '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!
[This is an archived TTS discussion thread from discourse.mozilla.org/t/stop-loss-spikes]
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