From 816e7ee69803285566eeb0d25f534633326b4e15 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Wed, 5 May 2021 15:23:11 +0200 Subject: [PATCH] remove default configs.json as replacing with Coqpit configs --- .../ljspeech_tacotron2_dynamic_conv_attn.json | 173 ------------------ TTS/tts/configs/speedy_speech_ljspeech.json | 153 ---------------- 2 files changed, 326 deletions(-) delete mode 100644 TTS/tts/configs/ljspeech_tacotron2_dynamic_conv_attn.json delete mode 100644 TTS/tts/configs/speedy_speech_ljspeech.json diff --git a/TTS/tts/configs/ljspeech_tacotron2_dynamic_conv_attn.json b/TTS/tts/configs/ljspeech_tacotron2_dynamic_conv_attn.json deleted file mode 100644 index 947462aa..00000000 --- a/TTS/tts/configs/ljspeech_tacotron2_dynamic_conv_attn.json +++ /dev/null @@ -1,173 +0,0 @@ -{ - "model": "Tacotron2", - "run_name": "ljspeech-dcattn", - "run_description": "tacotron2 with dynamic convolution attention.", - - // AUDIO PARAMETERS - "audio":{ - // stft parameters - "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. - "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 (true), 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": 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, - - // 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": "/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 - }, - - // 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. - "mixed_precision": true, // 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. - - // LOSS SETTINGS - "loss_masking": true, // enable / disable loss masking against the sequence padding. - "decoder_loss_alpha": 0.5, // original decoder loss weight. If > 0, it is enabled - "postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled - "postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled - "decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled - "decoder_ssim_alpha": 0.5, // decoder ssim loss weight. If > 0, it is enabled - "postnet_ssim_alpha": 0.25, // postnet ssim loss weight. If > 0, it is enabled - "ga_alpha": 0.0, // weight for guided attention loss. If > 0, guided attention is enabled. - "stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples. - - - // 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": false, // enable/disable dropout at prenet. - - // TACOTRON ATTENTION - "attention_type": "dynamic_convolution", // 'original' , 'graves', 'dynamic_convolution' - "attention_heads": 4, // number of attention heads (only for 'graves') - "attention_norm": "softmax", // softmax or sigmoid. - "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": false, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/ - "ddc_r": 7, // reduction rate for coarse decoder. - - // 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 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": 10000, // 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": 4, //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 - "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. - - // PATHS - "output_path": "/home/erogol/Models/LJSpeech/", - - // PHONEMES - "phoneme_cache_path": "/home/erogol/Models/phoneme_cache/", // 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_gst": false, // use global style tokens - "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 - "external_speaker_embedding_file": "../../speakers-vctk-en.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 - "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_num_style_tokens). - "gst_embedding_dim": 512, - "gst_num_heads": 4, - "gst_num_style_tokens": 10, - "gst_use_speaker_embedding": false - }, - - // DATASETS - "datasets": // List of datasets. They all merged and they get different speaker_ids. - [ - { - "name": "ljspeech", - "path": "/home/erogol/Data/LJSpeech-1.1/", - "meta_file_train": "metadata.csv", // for vtck if list, ignore speakers id in list for train, its useful for test cloning with new speakers - "meta_file_val": null - } - ] -} - diff --git a/TTS/tts/configs/speedy_speech_ljspeech.json b/TTS/tts/configs/speedy_speech_ljspeech.json deleted file mode 100644 index f61f35cd..00000000 --- a/TTS/tts/configs/speedy_speech_ljspeech.json +++ /dev/null @@ -1,153 +0,0 @@ -{ - "model": "speedy_speech", - "run_name": "speedy-speech-ljspeech", - "run_description": "speedy-speech model for LJSpeech dataset.", - - // AUDIO PARAMETERS - "audio":{ - // stft parameters - "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. - "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 (true), 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": 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, - - // 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": "/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 - }, - - // VOCABULARY PARAMETERS - // if custom character set is not defined, - // default set in symbols.py is used - // "characters":{ - // "pad": "_", - // "eos": "&", - // "bos": "*", - // "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ", - // "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 - "positional_encoding": true, - "hidden_channels": 128, // defined globally all the hidden channels of the model - 128 default - "encoder_type": "residual_conv_bn", - "encoder_params":{ - "kernel_size": 4, - "dilations": [1, 2, 4, 1, 2, 4, 1, 2, 4, 1, 2, 4, 1], - "num_conv_blocks": 2, - "num_res_blocks": 13 - }, - "decoder_type": "residual_conv_bn", - "decoder_params":{ - "kernel_size": 4, - "dilations": [1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1], - "num_conv_blocks": 2, - "num_res_blocks": 17 - }, - - // TRAINING - "batch_size":64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'. - "eval_batch_size":32, - "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. - - // LOSS PARAMETERS - "ssim_alpha": 1, - "l1_alpha": 1, - "huber_alpha": 1, - - // VALIDATION - "run_eval": true, - "test_delay_epochs": -1, //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": 1.0, // upper limit for gradients for clipping. - "epochs": 10000, // total number of epochs to train. - "lr": 0.002, // Initial learning rate. If Noam decay is active, maximum learning rate. - "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr" - - // 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.:set n - "mixed_precision": false, - - // 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": 4, //Number of batches to shuffle after bucketing. - "min_seq_len": 2, // DATASET-RELATED: minimum text length to use in training - "max_seq_len": 300, // DATASET-RELATED: maximum text length - "compute_f0": false, // compute f0 values in data-loader - "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. - - // PATHS - "output_path": "/home/erogol/Models/ljspeech/", - - // PHONEMES - "phoneme_cache_path": "/home/erogol/Models/ljspeech_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 pronoun[ciation. - "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, // 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": "/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 - - - // DATASETS - "datasets": // List of datasets. They all merged and they get different s$ - [ - { - "name": "ljspeech", - "path": "/home/erogol/Data/LJSpeech-1.1/", - "meta_file_train": "metadata.csv", - "meta_file_val": null, - "meta_file_attn_mask": "/home/erogol/Data/LJSpeech-1.1/metadata_attn_mask.txt" // created by bin/compute_attention_masks.py - } - ] -} \ No newline at end of file