Hi, I started training with
python distribute.py --config_path config.json | tee training.log
After training for around 223 epochs, it breaks and gave this out in my training.log
EVALUATION
warning: audio amplitude out of range, auto clipped.
| > Synthesizing test sentences
warning: audio amplitude out of range, auto clipped.
warning: audio amplitude out of range, auto clipped.
warning: audio amplitude out of range, auto clipped.
–> EVAL PERFORMANCE
| > avg_postnet_loss: 0.17037 (+0.00013)
| > avg_decoder_loss: 0.25991 (-0.00214)
| > avg_stopnet_loss: 0.06618 (+0.00142)
| > avg_align_error: 0.35647 (-0.00567)
| > avg_ga_loss: 0.01053 (-0.00001)EPOCH: 223/1000
Number of output frames: 3
TRAINING (2020-06-24 11:14:35)
! Run is kept in …/LJSpeech/ljspeech-June-24-2020_01+02AM-8f8ba5e
[‘train.py’, ‘–continue_path=’, ‘–restore_path=’, ‘–config_path=config.json’, ‘–group_id=group_2020_06_24-010230’, ‘–rank=0’]
I thought maybe my VM might have rebooted so I ran again with checkpoints
python train.py --config_path config.json --restore_path /workspace/LJSpeech/ljspeech-June-24-2020_01+02AM-8f8ba5e/checkpoint_50000.pth.tar | tee training.log
But after 219 epochs again my training stopped and gave this error
EPOCH: 219/1000
Number of output frames: 2
TRAINING (2020-06-25 06:52:31)
! Run is kept in …/LJSpeech/ljspeech-June-24-2020_12+50PM-8f8ba5e
Traceback (most recent call last):
File “train.py”, line 676, in
main(args)
File “train.py”, line 591, in main
global_step, epoch)
File “train.py”, line 148, in train
for num_iter, data in enumerate(data_loader):
File “/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py”, line 279, in iter
return _MultiProcessingDataLoaderIter(self)
File “/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py”, line 719, in init
w.start()
File “/opt/conda/lib/python3.7/multiprocessing/process.py”, line 112, in start
self._popen = self._Popen(self)
File “/opt/conda/lib/python3.7/multiprocessing/context.py”, line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File “/opt/conda/lib/python3.7/multiprocessing/context.py”, line 277, in _Popen
return Popen(process_obj)
File “/opt/conda/lib/python3.7/multiprocessing/popen_fork.py”, line 20, in init
self._launch(process_obj)
File “/opt/conda/lib/python3.7/multiprocessing/popen_fork.py”, line 70, in _launch
self.pid = os.fork()
OSError: [Errno 12] Cannot allocate memory
Here is the output of my config.json file
{
“model”: “Tacotron2”,
“run_name”: “ljspeech”,
“run_description”: “tacotron2”,// AUDIO PARAMETERS "audio":{ // stft parameters "num_freq": 513, // 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": 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!! // 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": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored }, // VOCABULARY PARAMETERS // if custom character set is not defined, // default set in symbols.py is used // "characters":{ // "pad": "_", // "eos": "~", // "bos": "^", // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ", // "punctuations":"!'(),-.:;? ", // "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ" // }, // DISTRIBUTED TRAINING "distributed":{ "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'. "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], [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. "loss_masking": true, // enable / disable loss masking against the sequence padding. "ga_alpha": 10.0, // weight for guided attention loss. If > 0, guided attention is enabled. // 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": 1.0, // 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. // 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": "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. "print_eval": false, // If True, it prints intermediate loss values in evalulation. "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": 153, // DATASET-RELATED: maximum text length // PATHS "output_path": "../LJSpeech/", // PHONEMES "phoneme_cache_path": "../TTS/mozilla_us_phonemes_3", // 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": "it", // 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": "ljspeech", "path": "../LJSpeech-1.1/", "meta_file_train": "metadata.csv", "meta_file_val": null } ]}
I don’t know what happened, can you suggest what is wrong here?
@erogol
