Hey all,
I’m sure this is me just missing a piece of the puzzle but I’m stumped.
If I run the following:
CUDA_VISIBLE_DEVICES="1" python TTS/bin/distribute.py --script train_tacotron.py --config_path=/workspace/tacotron/config.json --continue_path=/workspace/tacotron/
Then I get a normally running instance. (I see files found and then processing begins)
Alternatively, if I run:
CUDA_VISIBLE_DEVICES="1,2" python TTS/bin/distribute.py --script train_tacotron.py --config_path=/workspace/tacotron/config.json --continue_path=/workspace/tacotron/
Then I only get the following output (it then hangs indefinitely):
['/workspace/TTS/TTS/bin/train_tacotron.py', '--continue_path=/workspace/tacotron/', '--restore_path=', '--config_path=/workspace/tacotron/config.json', '--group_id=group_2021_01_25-154310', '--rank=0']
['/workspace/TTS/TTS/bin/train_tacotron.py', '--continue_path=/workspace/tacotron/', '--restore_path=', '--config_path=/workspace/tacotron/config.json', '--group_id=group_2021_01_25-154310', '--rank=1']
2021-01-25 15:43:11.260445: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-01-25 15:43:11.308820: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
> Using CUDA: True
> Number of GPUs: 2
> Training continues for /workspace/tacotron/
> Setting up Audio Processor...
| > sample_rate:48000
| > resample:False
| > num_mels:80
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:0
| > fft_size:1024
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:True
| > symmetric_norm:True
| > mel_fmin:50.0
| > mel_fmax:7600.0
| > spec_gain:1.0
| > stft_pad_mode:reflect
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:True
| > trim_db:25
| > do_sound_norm:False
| > stats_path:/workspace/scale_stats.npy
| > hop_length:256
| > win_length:1024
| > Found 39490 files in /data/VCTK/VCTK-Corpus
Netstat shows the processes coming up and listening:
tcp 0 0 0.0.0.0:55555 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:55555 127.0.0.1:56320 ESTABLISHED
tcp 0 0 127.0.0.1:56320 127.0.0.1:55555 ESTABLISHED
tcp 0 0 127.0.0.1:55555 127.0.0.1:56316 ESTABLISHED
tcp 0 0 127.0.0.1:55555 127.0.0.1:56318 ESTABLISHED
tcp 0 0 127.0.0.1:56318 127.0.0.1:55555 ESTABLISHED
tcp 0 0 127.0.0.1:56316 127.0.0.1:55555 ESTABLISHED
I use the same config for both:
(Note, I experience the same behavior if I’m pointed at port 54321 or 55555 as reported here. (Used an alternative port to rule out any issues with initial 54321 port))
{
“github_branch”:"* master",
“model”: “Tacotron2”, // one of the model in models/
“run_name”: “vctk-r=2-ddc”,
“run_description”: “tacotron2 on vctl r=2 with ddc only without guided attention”,
“compute_input_seq_cache”: false,
“mixed_precision”: false,
// AUDIO PARAMETERS
"audio":{
"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.
"sample_rate": 48000,
"preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"ref_level_db": 0, // 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": 25, // 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": "/workspace/scale_stats.npy" // 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!'(),-.:;? ",
//"characters": "ABCDEFGHIJKLMçãàáâêéíóôõúûabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
"characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ",
"punctuations":"!'(),-.:;? ",
"phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ'̃' "
},
** // DISTRIBUTED TRAINING**
** “distributed”:{**
** “backend”: “nccl”,**
** “url”: “tcp://localhost:55555”**
** },**
"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": 16, // 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": 2, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
"gradual_training": null, //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 SETTINGS
"loss_masking": false, // enable / disable loss masking against the sequence padding.
"decoder_loss_alpha": 0.5, // decoder loss weight. If > 0, it is enabled
"postnet_loss_alpha": 0.25, // postnet loss weight. If > 0, it is enabled
"ga_alpha": 0.0, // weight for guided attention loss. If > 0, guided attention is enabled.
"decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
"postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
"decoder_ssim_alpha": 0.5, // differential spectral loss weight. If > 0, it is enabled
"postnet_ssim_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
// VALIDATION
"run_eval": true,
"test_delay_epochs": 1, //Until attention is aligned, testing only wastes computation time.
"test_sentences_file": null,//"../../../datasets/BRSpeech-3-Speakers-Paper/BRSpeech-3-Speakers-Paper/TTS-Portuguese_Corpus/test_setences.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": 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": 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.
"double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
"ddc_r": 4, // reduction rate for coarse decoder.
"apex_amp_level": null, // 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.
// 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": 50, // Number of steps to log traning on console.
"tb_plot_step": 50, // Number of steps to plot TB training figures.
"print_eval": false, // If True, it prints intermediate loss values in evalulation.
"save_step": 500, // 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": "english_cleaners",
"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"num_val_loader_workers": 8, // number of evaluation data loader processes.
"batch_group_size": 0, //Number of batches to shuffle after bucketing.
"min_seq_len": 2, // DATASET-RELATED: minimum text length to use in training
"max_seq_len": 153, // DATASET-RELATED: maximum text length
// PATHS
// "output_path": "/data5/rw/pit/keep/", // DATASET-RELATED: output path for all training outputs.
"output_path": "/workspace/output/",
// PHONEMES
"phoneme_cache_path": "/workspace/checkpoints/mozilla_en_phonemes/", // 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", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
// MULTI-SPEAKER and GST
// "speaker_encoder_config_path":"/root/speaker_encoder/LibriTTS-common-voice-voxceleb_angle_proto/config.json", // config.json for the speaker encoder
// "speaker_encoder_checkpoint_path": "/root/speaker_encoder/LibriTTS-common-voice-voxceleb_angle_proto/320k.pth.tar", // Speaker Encoder Checkpoint full path
"use_speaker_embedding": true, // use speaker embedding to enable multi-speaker learning.
"use_external_speaker_embedding_file": true, // 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: https://arxiv.org/abs /1806.04558
"external_speaker_embedding_file": "/root/speakers.json", // if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
"use_gst": false, // TACOTRON ONLY: use global style tokens
"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).
"disable_gst": false, // if true its force GST predict zero matrix.
"gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
"gst_embedding_dim": 128,
"gst_num_heads": 4,
"gst_style_tokens": 5
},
// DATASETS
"datasets": // List of datasets. They all merged and they get different s$
[
{
"name": "vctk",
"path": "/data/VCTK/VCTK-Corpus",
// para o teste no vctk eu escolhi 1 locutor de cada ACCEPTS,
"meta_file_train": ["p225", "p234", "p238", "p245", "p248", "p261", "p294", "p302", "p326", "p335", "p347"], // for vtck if list, ignore speakers id in list for train, its useful for test cloning with new speakers
"meta_file_val": null
}
]
}
I have been unable to determine what add’l configurations are necessary to get nccl online. Any thoughts would be welcome. Note that these are running in docker instances on a CUDA-powered quad-GPU server. Not attempting to run across multiple machines at this point.