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https://github.com/wassname/TTS.git
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pylint and test fixes
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erogol
@@ -86,7 +86,7 @@
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"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.
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"encoder_type": "gatedconv",
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"encoder_type": "transformer",
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// TENSORBOARD and LOGGING
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"print_step": 25, // Number of steps to log training on console.
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@@ -0,0 +1,149 @@
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{
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"model": "speedy_speech",
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"run_name": "test_sample_dataset_run",
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"run_description": "sample dataset test run",
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// AUDIO PARAMETERS
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"audio":{
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// stft parameters
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"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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// Audio processing parameters
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"sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
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"preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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// Silence trimming
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"do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
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"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
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// Griffin-Lim
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// MelSpectrogram parameters
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"num_mels": 80, // size of the mel spec frame.
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"mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
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"spec_gain": 1,
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// Normalization parameters
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"signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
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"min_level_db": -100, // lower bound for normalization
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"stats_path": "/home/erogol/Data/LJSpeech-1.1/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
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},
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// VOCABULARY PARAMETERS
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// if custom character set is not defined,
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// default set in symbols.py is used
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// "characters":{
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// "pad": "_",
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// "eos": "&",
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// "bos": "*",
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// "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ",
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// "punctuations":"!'(),-.:;? ",
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// "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ'̃' "
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// },
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"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.
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// DISTRIBUTED TRAINING
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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54321"
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},
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"reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
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// MODEL PARAMETERS
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"positional_encoding": true,
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"encoder_type": "residual_conv_bn",
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"encoder_params":{
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"kernel_size": 4,
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"dilations": [1, 2, 4, 1, 2, 4, 1, 2, 4, 1, 2, 4, 1],
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"num_conv_blocks": 2,
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"num_res_blocks": 13
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},
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"decoder_residual_conv_bn_params":{
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"kernel_size": 4,
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"dilations": [1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1],
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"num_conv_blocks": 2,
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"num_res_blocks": 17
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},
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// TRAINING
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"batch_size":64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"eval_batch_size":32,
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"r": 1, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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// LOSS PARAMETERS
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"ssim_alpha": 1,
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"l1_alpha": 1,
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"huber_alpha": 1,
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": -1, //Until attention is aligned, testing only wastes computation time.
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"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.
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// OPTIMIZER
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"noam_schedule": true, // use noam warmup and lr schedule.
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"grad_clip": 1.0, // upper limit for gradients for clipping.
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"epochs": 1, // total number of epochs to train.
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"lr": 0.002, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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// TENSORBOARD and LOGGING
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"print_step": 1, // Number of steps to log training on console.
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"tb_plot_step": 100, // Number of steps to plot TB training figures.
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"print_eval": false, // If True, it prints intermediate loss values in evalulation.
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"save_step": 5000, // Number of training steps expected to save traninpg stats and checkpoints.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.:set n
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"mixed_precision": false,
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// DATA LOADING
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"text_cleaner": "english_cleaners",
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"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
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"num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 0, // number of evaluation data loader processes.
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"batch_group_size": 0, //Number of batches to shuffle after bucketing.
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"min_seq_len": 2, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 300, // DATASET-RELATED: maximum text length
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"compute_f0": false, // compute f0 values in data-loader
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"compute_input_seq_cache": false, // if true, text sequences are computed before starting training. If phonemes are enabled, they are also computed at this stage.
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// PATHS
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"output_path": "tests/train_outputs/",
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// PHONEMES
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"phoneme_cache_path": "tests/train_outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
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"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronoun[ciation.
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"phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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// MULTI-SPEAKER and GST
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"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
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"use_external_speaker_embedding_file": false, // 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
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"external_speaker_embedding_file": "/home/erogol/Data/libritts/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
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// DATASETS
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"datasets": // List of datasets. They all merged and they get different speaker_ids.
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[
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{
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"name": "ljspeech",
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"path": "tests/data/ljspeech/",
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"meta_file_train": "metadata.csv",
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"meta_file_val": "metadata.csv",
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"meta_file_attn_mask": "tests/data/ljspeech/metadata_attn_mask.txt"
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}
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]
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}
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@@ -44,10 +44,9 @@ class GlowTTSTrainTest(unittest.TestCase):
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# model to train
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model = GlowTts(num_chars=32,
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hidden_channels=128,
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filter_channels=32,
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filter_channels_dp=32,
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hidden_channels_ffn=32,
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hidden_channels_dp=32,
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out_channels=80,
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kernel_size=3,
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num_heads=2,
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num_layers_enc=6,
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dropout_p=0.1,
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@@ -72,10 +71,9 @@ class GlowTTSTrainTest(unittest.TestCase):
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# reference model to compare model weights
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model_ref = GlowTts(num_chars=32,
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hidden_channels=128,
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filter_channels=32,
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filter_channels_dp=32,
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hidden_channels_ffn=32,
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hidden_channels_dp=32,
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out_channels=80,
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kernel_size=3,
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num_heads=2,
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num_layers_enc=6,
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dropout_p=0.1,
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@@ -0,0 +1,98 @@
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import torch
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from TTS.tts.layers.speedy_speech.encoder import Encoder
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from TTS.tts.layers.speedy_speech.decoder import Decoder
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from TTS.tts.layers.speedy_speech.duration_predictor import DurationPredictor
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from TTS.tts.utils.generic_utils import sequence_mask
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from TTS.tts.models.speedy_speech import SpeedySpeech
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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def test_encoder():
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input_dummy = torch.rand(8, 14, 37).to(device)
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input_lengths = torch.randint(31, 37, (8, )).long().to(device)
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input_lengths[-1] = 37
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input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)),
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1).to(device)
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# residual bn conv encoder
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layer = Encoder(out_channels=11,
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hidden_channels=14,
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encoder_type='residual_conv_bn').to(device)
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output = layer(input_dummy, input_mask)
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assert list(output.shape)==[8, 11, 37]
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# transformer encoder
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layer = Encoder(out_channels=11,
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hidden_channels=14,
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encoder_type='transformer',
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encoder_params={
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'hidden_channels_ffn': 768,
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'num_heads': 2,
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"kernel_size": 3,
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"dropout_p": 0.1,
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"num_layers": 6,
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"rel_attn_window_size": 4,
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"input_length": None
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}).to(device)
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output = layer(input_dummy, input_mask)
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assert list(output.shape)==[8, 11, 37]
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def test_decoder():
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input_dummy = torch.rand(8, 128, 37).to(device)
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input_lengths = torch.randint(31, 37, (8, )).long().to(device)
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input_lengths[-1] = 37
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input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
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layer = Decoder(out_channels=11, hidden_channels=128).to(device)
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output = layer(input_dummy, input_mask)
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assert list(output.shape) == [8, 11, 37]
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def test_duration_predictor():
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input_dummy = torch.rand(8, 128, 27).to(device)
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input_lengths = torch.randint(20, 27, (8, )).long().to(device)
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input_lengths[-1] = 27
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x_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
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layer = DurationPredictor(hidden_channels=128).to(device)
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output = layer(input_dummy, x_mask)
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assert list(output.shape)==[8, 1, 27]
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def test_speedy_speech():
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num_chars = 7
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B = 8
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T_en = 37
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T_de = 74
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x_dummy = torch.randint(0, 7, (B, T_en)).long().to(device)
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x_lengths = torch.randint(31, T_en, (B, )).long().to(device)
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x_lengths[-1] = T_en
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# set durations. max total duration should be equal to T_de
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durations = torch.randint(1, 4, (B, T_en))
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durations = durations * (T_de / durations.sum(1)).unsqueeze(1)
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durations = durations.to(torch.long).to(device)
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max_dur = durations.sum(1).max()
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durations[:, 0] += T_de - max_dur if T_de > max_dur else 0
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y_lengths = durations.sum(1)
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model = SpeedySpeech(num_chars, out_channels=80, hidden_channels=128)
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if use_cuda:
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model.cuda()
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# forward pass
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o_de, o_dr, attn = model(x_dummy, x_lengths, y_lengths, durations)
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assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
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assert list(attn.shape) == [B, T_de, T_en]
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assert list(o_dr.shape) == [B, T_en]
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