Hey all,
I am attempting to continue fine-tuning an existing model. This is a first attempt into training voice models. My hope was to take a working model, fine-tune it and see the ongoing, improving results after continued updated epochs.
I understand this may be a premature evaluation but from a working Tacotron2 model to just after the first epoch it sounds unrecognizable as human speech. I expected a very fine, gradual improvement baselining somewhere near where the original model ended up. Instead, it’s quite a huge drop to the point you can’t understand it. So I am trying to understand where I misconfigured / setup my training. Below are my configs and starting points:
Model : Multi-Speaker-Tacotron2 DDC using source code based on commit 6cc464e. (I tried the advertised 2136433 commit but there were issues getting it to the model to align to the code so I reviewed the GIT log and used a downstream commit where fixes appeared to be merged. This seemed like a safe commit to attempt and it did not provide any model-based warnings or errors when run.
I pulled the model and configurations from the associated Colab Notebook.
The configuration I ran with is:
{
"github_branch":"* origin2_dev",
"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",
"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": 48000, // 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": 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
"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!'(),-.:;? ",
//"characters": "ABCDEFGHIJKLMçãàáâêéíóôõúûabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
"characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ",
"punctuations":"!'(),-.:;? ",
"phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ'̃' "
},
// DISTRIBUTED TRAINING
"distributed":{
"backend": "nccl",
"url": "tcp:\/\/localhost:54322"
},
"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": 2, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
"gradual_training": [[0, 7, 32], [1, 5, 32], [100000, 3, 16], [250000, 2, 16], [500000, 1, 16]], //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": 25, // Number of steps to log traning 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": 250, // 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": true, // 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": "/data/output/LJSpeech/",
// PHONEMES
"phoneme_cache_path": "/workspace/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-us", // 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": "/workspace/train/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
}
]
}
As stated above, this configuration runs as expected without warnings or errors. But when I review the test audio via the Tensorboard, it’s indistinguishable from straight noise. (Attached)
test_audio.zip (528.5 KB) (rename to test_audio.wav without unzipping)
Further, the supporting images seem to indicate a “start over” scenario.
Finally, like I said, I didn’t see any warnings or errors on the application startup. I include the first portion of the application log here:
python /workspace/TTS/TTS/bin/distribute.py --script /workspace/TTS/TTS/bin/train_tacotron.py --config_path=/workspace/train/config.json --continue_path=/workspace/output/ --restore_path=/workspace/train/tts_model.pth.tar'
2021-02-18 16:14:28.576959: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-02-18 16:14:28.592577: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
> Using CUDA: True
> Number of GPUs: 2
> Training continues for /workspace/output/
> Setting up Audio Processor...
| > sample_rate:48000
| > 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:None
| > hop_length:256
| > win_length:1024
| > Found 39490 files in /data/VCTK/VCTK-Corpus
Training with 96 speakers: VCTK_p226, VCTK_p227, VCTK_p228, VCTK_p229, VCTK_p230, VCTK_p231, VCTK_p232, VCTK_p233, VCTK_p236, VCTK_p237, VCTK_p239, VCTK_p240, VCTK_p241, VCTK_p243, VCTK_p244, VCTK_p246, VCTK_p247, VCTK_p249, VCTK_p250, VCTK_p251, VCTK_p252, VCTK_p253, VCTK_p254, VCTK_p255, VCTK_p256, VCTK_p257, VCTK_p258, VCTK_p259, VCTK_p260, VCTK_p262, VCTK_p263, VCTK_p264, VCTK_p265, VCTK_p266, VCTK_p267, VCTK_p268, VCTK_p269, VCTK_p270, VCTK_p271, VCTK_p272, VCTK_p273, VCTK_p274, VCTK_p275, VCTK_p276, VCTK_p277, VCTK_p278, VCTK_p279, VCTK_p280, VCTK_p281, VCTK_p282, VCTK_p283, VCTK_p284, VCTK_p285, VCTK_p286, VCTK_p287, VCTK_p288, VCTK_p292, VCTK_p293, VCTK_p295, VCTK_p297, VCTK_p298, VCTK_p299, VCTK_p300, VCTK_p301, VCTK_p303, VCTK_p304, VCTK_p305, VCTK_p307, VCTK_p308, VCTK_p310, VCTK_p311, VCTK_p312, VCTK_p313, VCTK_p314, VCTK_p316, VCTK_p317, VCTK_p318, VCTK_p323, VCTK_p329, VCTK_p330, VCTK_p333, VCTK_p334, VCTK_p336, VCTK_p339, VCTK_p340, VCTK_p341, VCTK_p343, VCTK_p345, VCTK_p351, VCTK_p360, VCTK_p361, VCTK_p362, VCTK_p363, VCTK_p364, VCTK_p374, VCTK_p376
> Using model: Tacotron2
> Model restored from step 220000
> Model has 51314996 parameters
> EPOCH: 0/1000
> Number of output frames: 2
> DataLoader initialization
| > Use phonemes: True
| > phoneme language: en-us
| > Number of instances : 39096
| > Max length sequence: 181
| > Min length sequence: 9
| > Avg length sequence: 39.60463986085533
| > Num. instances discarded by max-min (max=153, min=2) seq limits: 184
| > Batch group size: 0.
> TRAINING (2021-02-18 16:16:17)
--> STEP: 24/1216 -- GLOBAL_STEP: 220025
| > decoder_loss: 4.02636 (4.76018)
| > postnet_loss: 3.88876 (4.63819)
| > stopnet_loss: 0.36340 (0.33130)
| > decoder_coarse_loss: 5.08758 (5.75460)
| > decoder_ddc_loss: 0.01690 (0.01789)
| > decoder_diff_spec_loss: 0.05701 (0.08949)
| > postnet_diff_spec_loss: 0.04774 (0.07405)
| > decoder_ssim_loss: 0.49228 (0.51219)
| > postnet_ssim_loss: 0.49113 (0.51146)
| > loss: 5.76943 (6.75253)
| > align_error: 0.62557 (0.60317)
| > avg_spec_length: 281.0
| > avg_text_length: 15.2
| > step_time: 3.8240
| > loader_time: 0.01
| > current_lr: 0.0001
Any help or insight into what I’m misunderstanding would be greatly appreciated. Thanks much for the library and the hard work!