mirror of https://github.com/coqui-ai/TTS.git
Japanese Tacotron 2 model
This commit is contained in:
Katsuya Iida
parent
5482a0f62d
commit
0536aa6d0f
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{
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"model": "Tacotron2",
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"run_name": "kokoro-ddc",
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"run_description": "tacotron2 with DDC and differential spectral loss.",
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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": "./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": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
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"punctuations": "!'(),-.:;? ",
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"phonemes": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
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},
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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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// TRAINING
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"batch_size": 32, // 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":16,
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"r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
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"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.
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"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.
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// LOSS SETTINGS
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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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"decoder_loss_alpha": 0.5, // original decoder loss weight. If > 0, it is enabled
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"postnet_loss_alpha": 0.25, // original postnet loss weight. If > 0, it is enabled
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"postnet_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
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"decoder_diff_spec_alpha": 0.25, // differential spectral loss weight. If > 0, it is enabled
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"decoder_ssim_alpha": 0.5, // decoder ssim loss weight. If > 0, it is enabled
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"postnet_ssim_alpha": 0.25, // postnet ssim loss weight. If > 0, it is enabled
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"ga_alpha": 5.0, // weight for guided attention loss. If > 0, guided attention is enabled.
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"stopnet_pos_weight": 15.0, // pos class weight for stopnet loss since there are way more negative samples than positive samples.
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": 10, //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": false, // 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": 1000, // total number of epochs to train.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"wd": 0.000001, // Weight decay weight.
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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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// TACOTRON PRENET
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"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.
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"prenet_type": "original", // "original" or "bn".
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"prenet_dropout": true, // enable/disable dropout at prenet.
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// TACOTRON ATTENTION
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"attention_type": "original", // 'original' , 'graves', 'dynamic_convolution'
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"attention_heads": 4, // number of attention heads (only for 'graves')
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"attention_norm": "sigmoid", // softmax or sigmoid.
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"windowing": false, // Enables attention windowing. Used only in eval mode.
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"use_forward_attn": false, // if it uses forward attention. In general, it aligns faster.
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"forward_attn_mask": false, // Additional masking forcing monotonicity only in eval mode.
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"transition_agent": false, // enable/disable transition agent of forward attention.
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"location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
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"bidirectional_decoder": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
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"double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
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"ddc_r": 7, // reduction rate for coarse decoder.
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// STOPNET
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"stopnet": true, // Train stopnet predicting the end of synthesis.
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"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.
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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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"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": 10000, // 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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"keep_all_best": false, // If true, keeps all best_models after keep_after steps
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"keep_after": 10000, // Global step after which to keep best models if keep_all_best is true
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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.
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// DATA LOADING
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"text_cleaner": "basic_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": 4, // 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": 4, // number of evaluation data loader processes.
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"batch_group_size": 4, //Number of batches to shuffle after bucketing.
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"min_seq_len": 6, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 153, // DATASET-RELATED: maximum text length
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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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"use_noise_augment": true,
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// PATHS
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"output_path": "./Models/Kokoro/",
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// PHONEMES
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"phoneme_cache_path": "./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 pronounciation.
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"phoneme_language": "ja-jp", // 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_gst": false, // use global style tokens
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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": "../../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
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"gst": { // gst parameter if gst is enabled
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"gst_style_input": null, // Condition the style input either on a
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// -> wave file [path to wave] or
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// -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
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// with the dictionary being len(dict) <= len(gst_style_tokens).
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"gst_embedding_dim": 512,
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"gst_num_heads": 4,
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"gst_style_tokens": 10,
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"gst_use_speaker_embedding": false
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},
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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": "kokoro",
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"path": "./kokoro-speech-v1_1-small/",
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"meta_file_train": "metadata.csv", // for vtck if list, ignore speakers id in list for train, its useful for test cloning with new speakers
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"meta_file_val": null
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}
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]
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}
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@ -424,3 +424,17 @@ def baker(root_path: str, meta_file: str) -> List[List[str]]:
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wav_path = os.path.join(root_path, "clips_22", wav_name)
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items.append([text, wav_path, speaker_name])
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return items
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def kokoro(root_path, meta_file):
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"""Japanese single-speaker dataset from https://github.com/kaiidams/Kokoro-Speech-Dataset"""
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txt_file = os.path.join(root_path, meta_file)
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items = []
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speaker_name = "kokoro"
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with open(txt_file, "r") as ttf:
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for line in ttf:
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cols = line.split("|")
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wav_file = os.path.join(root_path, "wavs", cols[0] + '.wav')
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text = cols[2].replace(" ", "")
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items.append([text, wav_file, speaker_name])
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return items
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from .text import japanese_text2phone
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# Convert Japanese text to phonemes which is
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# compatible with Julius https://github.com/julius-speech/segmentation-kit
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import re
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import MeCab
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from typing import List, Tuple
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_CONVRULES = [
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# Conversion of 2 letters
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'アァ/ a a',
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'イィ/ i i',
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'イェ/ i e',
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'イャ/ y a',
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'ウゥ/ u:',
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'エェ/ e e',
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'オォ/ o:',
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'カァ/ k a:',
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'キィ/ k i:',
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'クゥ/ k u:',
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'クャ/ ky a',
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'クュ/ ky u',
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'クョ/ ky o',
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'ケェ/ k e:',
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'コォ/ k o:',
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'ガァ/ g a:',
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'ギィ/ g i:',
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'グゥ/ g u:',
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'グャ/ gy a',
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'グュ/ gy u',
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'グョ/ gy o',
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'ゲェ/ g e:',
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'ゴォ/ g o:',
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'サァ/ s a:',
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'シィ/ sh i:',
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'スゥ/ s u:',
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'スャ/ sh a',
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'スュ/ sh u',
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'スョ/ sh o',
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'セェ/ s e:',
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'ソォ/ s o:',
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'ザァ/ z a:',
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'ジィ/ j i:',
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'ズゥ/ z u:',
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'ズャ/ zy a',
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'ズュ/ zy u',
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'ズョ/ zy o',
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'ゼェ/ z e:',
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'ゾォ/ z o:',
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'タァ/ t a:',
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'チィ/ ch i:',
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'ツァ/ ts a',
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'ツィ/ ts i',
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'ツゥ/ ts u:',
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'ツャ/ ch a',
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'ツュ/ ch u',
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'ツョ/ ch o',
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'ツェ/ ts e',
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'ツォ/ ts o',
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'テェ/ t e:',
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'トォ/ t o:',
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'ダァ/ d a:',
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'ヂィ/ j i:',
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'ヅゥ/ d u:',
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'ヅャ/ zy a',
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'ヅュ/ zy u',
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'ヅョ/ zy o',
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'デェ/ d e:',
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'ドォ/ d o:',
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'ナァ/ n a:',
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'ニィ/ n i:',
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'ヌゥ/ n u:',
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'ヌャ/ ny a',
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'ヌュ/ ny u',
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'ヌョ/ ny o',
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'ネェ/ n e:',
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'ノォ/ n o:',
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'ハァ/ h a:',
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'ヒィ/ h i:',
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'フゥ/ f u:',
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'フャ/ hy a',
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'フュ/ hy u',
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'フョ/ hy o',
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'ヘェ/ h e:',
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'ホォ/ h o:',
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'バァ/ b a:',
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'ビィ/ b i:',
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'ブゥ/ b u:',
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'フャ/ hy a',
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'ブュ/ by u',
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'フョ/ hy o',
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'ベェ/ b e:',
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'ボォ/ b o:',
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'パァ/ p a:',
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'ピィ/ p i:',
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'プゥ/ p u:',
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'プャ/ py a',
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'プュ/ py u',
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'プョ/ py o',
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'ペェ/ p e:',
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'ポォ/ p o:',
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'マァ/ m a:',
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'ミィ/ m i:',
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'ムゥ/ m u:',
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'ムャ/ my a',
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'ムュ/ my u',
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'ムョ/ my o',
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'メェ/ m e:',
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'モォ/ m o:',
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'ヤァ/ y a:',
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'ユゥ/ y u:',
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'ユャ/ y a:',
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'ユュ/ y u:',
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'ユョ/ y o:',
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'ヨォ/ y o:',
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'ラァ/ r a:',
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'リィ/ r i:',
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'ルゥ/ r u:',
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'ルャ/ ry a',
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'ルュ/ ry u',
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'ルョ/ ry o',
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'レェ/ r e:',
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'ロォ/ r o:',
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'ワァ/ w a:',
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'ヲォ/ o:',
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'ディ/ d i',
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'デェ/ d e:',
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'デャ/ dy a',
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'デュ/ dy u',
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'デョ/ dy o',
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'ティ/ t i',
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'テェ/ t e:',
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'テャ/ ty a',
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'テュ/ ty u',
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'テョ/ ty o',
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'スィ/ s i',
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'ズァ/ z u a',
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'ズィ/ z i',
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'ズゥ/ z u',
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'ズャ/ zy a',
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'ズュ/ zy u',
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'ズョ/ zy o',
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'ズェ/ z e',
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'ズォ/ z o',
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'キャ/ ky a',
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'キュ/ ky u',
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'キョ/ ky o',
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'シャ/ sh a',
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'シュ/ sh u',
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'シェ/ sh e',
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'ショ/ sh o',
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'チャ/ ch a',
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'チュ/ ch u',
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'チェ/ ch e',
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'チョ/ ch o',
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'トゥ/ t u',
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'トャ/ ty a',
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'トュ/ ty u',
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'トョ/ ty o',
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'ドァ/ d o a',
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'ドゥ/ d u',
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'ドャ/ dy a',
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'ドュ/ dy u',
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'ドョ/ dy o',
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'ドォ/ d o:',
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'ニャ/ ny a',
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'ニュ/ ny u',
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'ニョ/ ny o',
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'ヒャ/ hy a',
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'ヒュ/ hy u',
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'ヒョ/ hy o',
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'ミャ/ my a',
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'ミュ/ my u',
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'ミョ/ my o',
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'リャ/ ry a',
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'リュ/ ry u',
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'リョ/ ry o',
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'ギャ/ gy a',
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'ギュ/ gy u',
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'ギョ/ gy o',
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'ヂェ/ j e',
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'ヂャ/ j a',
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'ヂュ/ j u',
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'ヂョ/ j o',
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'ジェ/ j e',
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'ジャ/ j a',
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'ジュ/ j u',
|
||||
'ジョ/ j o',
|
||||
'ビャ/ by a',
|
||||
'ビュ/ by u',
|
||||
'ビョ/ by o',
|
||||
'ピャ/ py a',
|
||||
'ピュ/ py u',
|
||||
'ピョ/ py o',
|
||||
'ウァ/ u a',
|
||||
'ウィ/ w i',
|
||||
'ウェ/ w e',
|
||||
'ウォ/ w o',
|
||||
'ファ/ f a',
|
||||
'フィ/ f i',
|
||||
'フゥ/ f u',
|
||||
'フャ/ hy a',
|
||||
'フュ/ hy u',
|
||||
'フョ/ hy o',
|
||||
'フェ/ f e',
|
||||
'フォ/ f o',
|
||||
'ヴァ/ b a',
|
||||
'ヴィ/ b i',
|
||||
'ヴェ/ b e',
|
||||
'ヴォ/ b o',
|
||||
'ヴュ/ by u',
|
||||
|
||||
# Conversion of 1 letter
|
||||
'ア/ a',
|
||||
'イ/ i',
|
||||
'ウ/ u',
|
||||
'エ/ e',
|
||||
'オ/ o',
|
||||
'カ/ k a',
|
||||
'キ/ k i',
|
||||
'ク/ k u',
|
||||
'ケ/ k e',
|
||||
'コ/ k o',
|
||||
'サ/ s a',
|
||||
'シ/ sh i',
|
||||
'ス/ s u',
|
||||
'セ/ s e',
|
||||
'ソ/ s o',
|
||||
'タ/ t a',
|
||||
'チ/ ch i',
|
||||
'ツ/ ts u',
|
||||
'テ/ t e',
|
||||
'ト/ t o',
|
||||
'ナ/ n a',
|
||||
'ニ/ n i',
|
||||
'ヌ/ n u',
|
||||
'ネ/ n e',
|
||||
'ノ/ n o',
|
||||
'ハ/ h a',
|
||||
'ヒ/ h i',
|
||||
'フ/ f u',
|
||||
'ヘ/ h e',
|
||||
'ホ/ h o',
|
||||
'マ/ m a',
|
||||
'ミ/ m i',
|
||||
'ム/ m u',
|
||||
'メ/ m e',
|
||||
'モ/ m o',
|
||||
'ラ/ r a',
|
||||
'リ/ r i',
|
||||
'ル/ r u',
|
||||
'レ/ r e',
|
||||
'ロ/ r o',
|
||||
'ガ/ g a',
|
||||
'ギ/ g i',
|
||||
'グ/ g u',
|
||||
'ゲ/ g e',
|
||||
'ゴ/ g o',
|
||||
'ザ/ z a',
|
||||
'ジ/ j i',
|
||||
'ズ/ z u',
|
||||
'ゼ/ z e',
|
||||
'ゾ/ z o',
|
||||
'ダ/ d a',
|
||||
'ヂ/ j i',
|
||||
'ヅ/ z u',
|
||||
'デ/ d e',
|
||||
'ド/ d o',
|
||||
'バ/ b a',
|
||||
'ビ/ b i',
|
||||
'ブ/ b u',
|
||||
'ベ/ b e',
|
||||
'ボ/ b o',
|
||||
'パ/ p a',
|
||||
'ピ/ p i',
|
||||
'プ/ p u',
|
||||
'ペ/ p e',
|
||||
'ポ/ p o',
|
||||
'ヤ/ y a',
|
||||
'ユ/ y u',
|
||||
'ヨ/ y o',
|
||||
'ワ/ w a',
|
||||
'ヰ/ i',
|
||||
'ヱ/ e',
|
||||
'ヲ/ o',
|
||||
'ン/ N',
|
||||
'ッ/ q',
|
||||
'ヴ/ b u',
|
||||
'ー/:',
|
||||
|
||||
# Try converting broken text
|
||||
'ァ/ a',
|
||||
'ィ/ i',
|
||||
'ゥ/ u',
|
||||
'ェ/ e',
|
||||
'ォ/ o',
|
||||
'ヮ/ w a',
|
||||
'ォ/ o',
|
||||
|
||||
# Symbols
|
||||
'、/ ,',
|
||||
'。/ .',
|
||||
'！/ !',
|
||||
'？/ ?',
|
||||
'・/ ,'
|
||||
]
|
||||
|
||||
_COLON_RX = re.compile(':+')
|
||||
_REJECT_RX = re.compile('[^ a-zA-Z:,.?]')
|
||||
|
||||
def _makerulemap():
|
||||
l = [tuple(x.split('/')) for x in _CONVRULES]
|
||||
return tuple(
|
||||
{k: v for k, v in l if len(k) == i}
|
||||
for i in (1, 2)
|
||||
)
|
||||
|
||||
_RULEMAP1, _RULEMAP2 = _makerulemap()
|
||||
|
||||
def kata2phoneme(text: str) -> str:
|
||||
"""Convert katakana text to phonemes.
|
||||
"""
|
||||
text = text.strip()
|
||||
res = ''
|
||||
while text:
|
||||
if len(text) >= 2:
|
||||
x = _RULEMAP2.get(text[:2])
|
||||
if x is not None:
|
||||
text = text[2:]
|
||||
res += x
|
||||
continue
|
||||
x = _RULEMAP1.get(text[0])
|
||||
if x is not None:
|
||||
text = text[1:]
|
||||
res += x
|
||||
continue
|
||||
res += ' ' + text[0]
|
||||
text = text[1:]
|
||||
res = _COLON_RX.sub(':', res)
|
||||
return res[1:]
|
||||
|
||||
_KATAKANA = ''.join(chr(ch) for ch in range(ord('ァ'), ord('ン') + 1))
|
||||
_HIRAGANA = ''.join(chr(ch) for ch in range(ord('ぁ'), ord('ん') + 1))
|
||||
_HIRA2KATATRANS = str.maketrans(_HIRAGANA, _KATAKANA)
|
||||
|
||||
def hira2kata(text: str) -> str:
|
||||
text = text.translate(_HIRA2KATATRANS)
|
||||
return text.replace('う゛', 'ヴ')
|
||||
|
||||
_SYMBOL_TOKENS = set(list('・、。？！'))
|
||||
_NO_YOMI_TOKENS = set(list('「」『』―（）［］[] …'))
|
||||
_TAGGER = MeCab.Tagger()
|
||||
|
||||
def text2kata(text: str) -> str:
|
||||
parsed = _TAGGER.parse(text)
|
||||
res = []
|
||||
for line in parsed.split('\n'):
|
||||
if line == 'EOS':
|
||||
break
|
||||
parts = line.split('\t')
|
||||
|
||||
word, yomi = parts[0], parts[1]
|
||||
if yomi:
|
||||
res.append(yomi)
|
||||
else:
|
||||
if word in _SYMBOL_TOKENS:
|
||||
res.append(word)
|
||||
elif word == 'っ' or word == 'ッ':
|
||||
res.append('ッ')
|
||||
elif word in _NO_YOMI_TOKENS:
|
||||
pass
|
||||
else:
|
||||
res.append(word)
|
||||
return hira2kata(''.join(res))
|
||||
|
||||
def japanese_text2phone(text: str) -> str:
|
||||
"""Convert Japanese text to phonemes.
|
||||
"""
|
||||
res = text2kata(text)
|
||||
res = kata2phoneme(res)
|
||||
return res.replace(' ', '')
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
import unittest
|
||||
from . import japanese_text2phone
|
||||
|
||||
_TEST_CASES = '''
|
||||
どちらに行きますか？/dochiraniikimasuka?
|
||||
今日は温泉に、行きます。/kyo:waoNseNni,ikimasu.
|
||||
「A」から「Z」までです。/AkaraZmadedesu.
|
||||
そうですね！/so:desune!
|
||||
クジラは哺乳類です。/kujirawahonyu:ruidesu.
|
||||
ヴィディオを見ます。/bidioomimasu.
|
||||
ky o: w a o N s e N n i , i k i m a s u ./kyo:waoNseNni,ikimasu.
|
||||
'''
|
||||
|
||||
class TestText(unittest.TestCase):
|
||||
|
||||
def test_text2phone(self):
|
||||
for line in _TEST_CASES.strip().split('\n'):
|
||||
text, phone = line.split('/')
|
||||
self.assertEqual(japanese_text2phone(text), phone)
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
|
@ -39,6 +39,11 @@ def text2phone(text, language):
|
|||
if language == "zh-CN":
|
||||
ph = chinese_text_to_phonemes(text)
|
||||
return ph
|
||||
elif language == "ja-jp":
|
||||
from TTS.tts.utils.japanese import japanese_text2phone
|
||||
ph = japanese_text2phone(text)
|
||||
return ph
|
||||
|
||||
raise ValueError(f" [!] Language {language} is not supported for phonemization.")
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -19,3 +19,5 @@ numba==0.52
|
|||
umap-learn==0.4.6
|
||||
unidecode==0.4.20
|
||||
coqpit
|
||||
mecab-python3
|
||||
unidic-lite
|
||||
|
|
|
|||
Loading…
Reference in New Issue