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>>> erogol |
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[October 3, 2020, 4:59pm]
Hi,
I'm trying to fine tune this Tacotron 2
model using a voice from the
libri_tts dataset. However, whenever the training gets to the validation
phase, it raises the following error: slash
numpy.linalg.linalg.LinAlgError: SVD did not converge slash
I looked into it, and it appears that the basis for the Mel Spectrogram
being generated in audio.py's slash _build_mel_basis function is not properly
being inverted in slash _mel_to_linear. I tried isolating the variables and
creating the matrix in a standalone Python file, which pinv was able to
invert without any issues. I've successfully fine tuned one of the
Tacotron 1 models with this dataset, but I can't seem to manage to find
a fix for this one. I've reproduced the functions below, along with my
config file. The only major change I made was to the sample rate to
match up with the new data. I appreciate any and all advice.
def _mel_to_linear(self, mel_spec):
inv_mel_basis = np.linalg.pinv(self._build_mel_basis())
return np.maximum(1e-10, np.dot(inv_mel_basis, mel_spec))
def _build_mel_basis(self, ):
n_fft = (self.num_freq - 1) curl-run-all.sh discourse.mozilla.org html-to-markdown.sh ordered-posts ordered-posts~ TTS.cdx tts.commands tts-emails.txt TTS.pages tts-telegram.txt TTS.warc.gz 2
if self.mel_fmax is not None:
assert self.mel_fmax <= self.sample_rate // 2
return librosa.filters.mel(
self.sample_rate,
n_fft,
n_mels=self.num_mels,
fmin=self.mel_fmin,
fmax=self.mel_fmax)
{
'github_branch':'* dev',
'restore_path':'A: slash Other slash Installations slash chatbot slash Speech_Synthesis slash TTS_Final_Load_Test slash TTS slash models slash best_model.pth.tar',
'github_branch':'* dev',
'model': 'Tacotron2', // one of the model in models/
'run_name': 'ljspeech-bn',
'run_description': 'tacotron2 basline finetuned with BN prenet',
// 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': 24000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
'frame_length_ms': 50.0, // stft window length in ms.
'frame_shift_ms': 12.5, // stft window hop-lengh in ms.
'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': 0.0, // 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': true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
},
// 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': null, //[[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 16], [290000, 1, 32]], // ONLY TACOTRON - set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled.
'loss_masking': true, // enable / disable loss masking against the sequence padding.
// 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, // 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.
'lr_decay': false, // if true, Noam learning rate decaying is applied through training.
'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': 'bn', // 'original' or 'bn'.
'prenet_dropout': false, // enable/disable dropout at prenet.
// ATTENTION
'attention_type': 'original', // 'original' or 'graves'
'attention_heads': 5, // number of attention heads (only for 'graves')
'attention_norm': 'sigmoid', // 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': 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.
// 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 traning on console.
'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': 0, //Number of batches to shuffle after bucketing.
'min_seq_len': 6, // DATASET-RELATED: minimum text length to use in training
'max_seq_len': 150, // DATASET-RELATED: maximum text length
// PATHS
'output_path': 'A: slash Other slash Installations slash chatbot slash Speech_Synthesis slash TTS_Final_Load_Test slash TTS slash Outputs', // DATASET-RELATED: output path for all training outputs.
//'output_path': '/media/erogol/data_ssd/Models/runs/',
// PHONEMES
'phoneme_cache_path': 'ljspeech_ph_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.
'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
// DATASETS
'datasets': // List of datasets. They all merged and they get different speaker_ids.
[
{
'name': 'libri_tts',
'path': 'A: slash Other slash Installations slash chatbot slash Speech_Synthesis slash TTS_Old_2 slash TTS slash RC_Voice_Source',
//'path': '/home/erogol/Data/LJSpeech-1.1',
'meta_file_train': null,
'meta_file_val': null
}
]
}
[This is an archived TTS discussion thread from discourse.mozilla.org/t/svd-not-converging-during-validation-phase]
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