avg_align_error not changing for Glow TTS [Bug]

lihaoyangML commented 3 years ago

Hi sir, I think issue 408 is still present in both v0.0.12 and v0.013. When I used glow_tts_ljspeech.json to run a training on LJSpeech, the avg_align_loss remained constant. Here is the Tensorboard output:

To Reproduce

Installation of the library

git clone https://github.com/coqui-ai/TTS.git git reset --hard f02f033 pip3 install -e . pip3 install packaging pip3 install numba==0.48 pip3 install umap-learn==0.4.6

updating the path information in glow_tts_ljspeech.json

{ "model": "glow_tts", "run_name": "glow-tts-residual_bn_conv", "run_description": "glow-tts model training with residual BN conv.",

    // 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.
        "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.

        // Griffin-Lim
        "power": 1.1,           // 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.

        // 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.

        // 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.00

        // Normalization parameters
        "signal_norm": false,   // 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": 1.0,        // 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"
    // },

    "add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model.

    // 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.

    // MODEL PARAMETERS
    // "use_mas": false,       // use Monotonic Alignment Search if true. Otherwise use pre-computed attention alignments.
    "hidden_channels_encoder": 192,
    "hidden_channels_decoder": 192,
    "hidden_channels_duration_predictor": 256,
    "use_encoder_prenet": true,
    "encoder_type": "rel_pos_transformer",
    "encoder_params": {
        "kernel_size":3,
        "dropout_p": 0.1,
        "num_layers": 6,
        "num_heads": 2,
        "hidden_channels_ffn": 768,
        "input_length": null
    },

    // 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": 1,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
    "loss_masking": true,   // enable / disable loss masking against the sequence padding.
    "mixed_precision": true,
    "data_dep_init_iter": 10,

    // VALIDATION
    "run_eval": true,
    "test_delay_epochs": 0,       //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": true,         // use noam warmup and lr schedule.
    "grad_clip": 5.0,              // upper limit for gradients for clipping.
    "epochs": 10000,               // total number of epochs to train.
    "lr": 1e-3,                    // 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.

    // TENSORBOARD and LOGGING
    "print_step": 25,       // Number of steps to log training 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": 5000,      // Number of training steps expected to save traninpg stats and checkpoints.
    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
    "keep_all_best": false,  // If true, keeps all best_models after keep_after steps
    "keep_after": 10000,    // Global step after which to keep best models if keep_all_best is true
    "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": 3,       // DATASET-RELATED: minimum text length to use in training
    "max_seq_len": 500,     // DATASET-RELATED: maximum text length
    "compute_f0": false,     // compute f0 values in data-loader
    "use_noise_augment": true,  //add a random noise to audio signal for augmentation at training .
    "compute_input_seq_cache": true,

    // PATHS
    "output_path": "trained_models/ljspeech/models",

    // PHONEMES
    "phoneme_cache_path": "trained_models/ljspeech/caches",  // 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.
    "use_external_speaker_embedding_file": false,
    "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": "/home/William/Desktop/mozilla_tts/LJSpeech-1.1/",
                "meta_file_train": "metadata.csv",
                "meta_file_val": null
                // "path_for_attn": "/home/erogol/Data/LJSpeech-1.1/alignments/"
            }
        ]
}

Running Glow TTS training

CUDA_VISIBLE_DEVICES="1" python TTS/bin/train_glow_tts.py --config_path TTS/tts/configs/glow_tts_ljspeech.json

Stopped training after 4k steps, as avg_align_error did not change.

My environment:

Ubuntu 20.04:
PyTorch 1.7.1+cu110:
Python 3.8.5:

I have tried both v0.0.12 and v0.0.13 and the issue persisted. Thank you and please let me know if you need more information from me.

david-eom commented 3 years ago

Hi Eren, I am encountering the same issue here as well. I have tried to train the Glow-TTS model with LJSpeech dataset with version 0.0.13, the avg_align_error in the tensorboard output likewise stays constant 5k steps into the training.

I have also experimented with different encoder_type, and the problem persists. All trainings are carried out with default configs from the current repo / config for released models.

residual_bn_conv avg_align_error: 0.05798
gated_conv avg_align_error: 0.1014
rel_pos_transformer avg_align_error: 0.1014
time_depth_separable avg_align_error: 0.1014

I do understand that this is an out-of-date version, but would still like to seek your kind advice and assistance if the issue could be resolved. Thank you in advance!

erogol commented 3 years ago

Why do you prefer to use the older version ?

lihaoyangML commented 3 years ago

Hi Eren, thanks for the reply. I am using the older version because I want to experiment fine tuning the released multi-speaker glow TTS models in the repo on my own multi-speaker dataset. However, I think the pretrained models, such as SC-GlowTTS-Trans are not compatible with the newer version.

erogol commented 3 years ago

nope they are all comptible.

david-eom commented 3 years ago

Hi Eren, thank you for your reply! I switched over to the latest version earlier on (v0.1.2), and it seems like the issue persists.

Tensorboard output: avg_align_error stays constant at 0.09933.
Training script (identical to recipes/ljspeech/glow_tts/train_glowtts.py):
```
import os
```

from TTS.tts.configs import GlowTTSConfig from TTS.tts.configs import BaseDatasetConfig from TTS.trainer import init_training, Trainer, TrainingArgs

output_path = "/home/david_yan/coqui_ai_tts/glow_tts_models" dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv", path="/home/david_yan/mozilla_tts/TTS/tts/datasets/LJSpeech-1.1/") config = GlowTTSConfig( batch_size=32, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, test_delay_epochs=-1, epochs=1000, text_cleaner="english_cleaners", use_phonemes=False, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), print_step=25, print_eval=True, mixed_precision=False, output_path=output_path, datasets=[dataset_config] ) args, config, outputpath, , c_logger, tb_logger = init_training(TrainingArgs(), config) trainer = Trainer(args, config, output_path, c_logger, tb_logger) trainer.fit()

erogol commented 3 years ago

BTW I just realized that the align_error does not really make sense with models using duration predictors since it actually measures how diagonal and decisive the alignment is but since the alignment is 0-1 valued for such models, it computes the same error for every step.

So the solution to this issue is to removing align_error from these models. But for now, you can just ignore it.

BoneGoat commented 3 years ago

"I just realized that the align_error does not really make sense with models using duration predictors..." - Is this also true for Taco2 DCA? Because I'm experiencing the same issue where avg_align_error isn't changing at all efter each epoch.

This is a new dataset I'm using and I had some success with it using Nvidia Nemo but so far Coqui GlowTTS and Taco2 is just producing noise.

erogol commented 3 years ago

It'd just produce noise until the attention aligns.

coqui-ai / TTS