зеркало из https://github.com/mozilla/TTS.git
add text parameters in config.json
This commit is contained in:
Edresson Casanova
Родитель
e7ef4e9050
Коммит
8feb326a60
10
config.json
10
config.json
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@ -27,6 +27,16 @@
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"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
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},
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// VOCABULARY PARAMETERS
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"text":{
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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":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
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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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@ -15,6 +15,7 @@ class MyDataset(Dataset):
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text_cleaner,
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ap,
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meta_data,
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tp=None,
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batch_group_size=0,
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min_seq_len=0,
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max_seq_len=float("inf"),
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@ -49,6 +50,7 @@ class MyDataset(Dataset):
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self.min_seq_len = min_seq_len
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self.max_seq_len = max_seq_len
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self.ap = ap
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self.tp = tp
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self.use_phonemes = use_phonemes
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self.phoneme_cache_path = phoneme_cache_path
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self.phoneme_language = phoneme_language
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@ -81,7 +83,8 @@ class MyDataset(Dataset):
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config option."""
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phonemes = phoneme_to_sequence(text, [self.cleaners],
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language=self.phoneme_language,
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enable_eos_bos=False)
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enable_eos_bos=False,
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tp=self.tp)
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phonemes = np.asarray(phonemes, dtype=np.int32)
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np.save(cache_path, phonemes)
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return phonemes
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@ -101,7 +104,7 @@ class MyDataset(Dataset):
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phonemes = self._generate_and_cache_phoneme_sequence(text,
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cache_path)
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if self.enable_eos_bos:
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phonemes = pad_with_eos_bos(phonemes)
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phonemes = pad_with_eos_bos(phonemes, tp=self.tp)
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phonemes = np.asarray(phonemes, dtype=np.int32)
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return phonemes
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@ -113,7 +116,7 @@ class MyDataset(Dataset):
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text = self._load_or_generate_phoneme_sequence(wav_file, text)
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else:
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text = np.asarray(
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text_to_sequence(text, [self.cleaners]), dtype=np.int32)
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text_to_sequence(text, [self.cleaners], tp=self.tp), dtype=np.int32)
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assert text.size > 0, self.items[idx][1]
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assert wav.size > 0, self.items[idx][1]
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@ -132,7 +132,7 @@
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"outputs": [],
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"source": [
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"# LOAD TTS MODEL\n",
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"from TTS.utils.text.symbols import symbols, phonemes\n",
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"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
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"\n",
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"# multi speaker \n",
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"if CONFIG.use_speaker_embedding:\n",
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@ -142,6 +142,10 @@
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" speakers = []\n",
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" speaker_id = None\n",
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"\n",
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"# if the vocabulary was passed, replace the default\n",
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"if 'text' in CONFIG.keys():\n",
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" symbols, phonemes = make_symbols(**CONFIG.text)\n",
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"\n",
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"# load the model\n",
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"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
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"model = setup_model(num_chars, len(speakers), CONFIG)\n",
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@ -65,7 +65,7 @@
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"from TTS.utils.text import text_to_sequence\n",
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"from TTS.utils.synthesis import synthesis\n",
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"from TTS.utils.visual import visualize\n",
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"from TTS.utils.text.symbols import symbols, phonemes\n",
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"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
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"\n",
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"import IPython\n",
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"from IPython.display import Audio\n",
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@ -149,6 +149,10 @@
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" speakers = []\n",
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" speaker_id = None\n",
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"\n",
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"# if the vocabulary was passed, replace the default\n",
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"if 'text' in CONFIG.keys():\n",
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" symbols, phonemes = make_symbols(**CONFIG.text)\n",
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"\n",
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"# load the model\n",
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"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
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"model = setup_model(num_chars, len(speakers), CONFIG)\n",
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@ -37,7 +37,7 @@
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.visual import plot_spectrogram\n",
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"from TTS.utils.generic_utils import load_config, setup_model, sequence_mask\n",
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"from TTS.utils.text.symbols import symbols, phonemes\n",
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"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
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"\n",
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"%matplotlib inline\n",
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"\n",
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@ -94,6 +94,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# if the vocabulary was passed, replace the default\n",
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"if 'text' in C.keys():\n",
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" symbols, phonemes = make_symbols(**C.text)\n",
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"\n",
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"# load the model\n",
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"num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
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"# TODO: multiple speaker\n",
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@ -116,7 +120,7 @@
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"preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
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"preprocessor = getattr(preprocessor, DATASET.lower())\n",
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"meta_data = preprocessor(DATA_PATH,METADATA_FILE)\n",
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"dataset = MyDataset(checkpoint['r'], C.text_cleaner, ap, meta_data, use_phonemes=C.use_phonemes, phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n",
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"dataset = MyDataset(checkpoint['r'], C.text_cleaner, ap, meta_data,tp=C.text if 'text' in C.keys() else None, use_phonemes=C.use_phonemes, phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n",
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"loader = DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False)"
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]
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},
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@ -100,7 +100,7 @@
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"outputs": [],
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"source": [
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"# LOAD TTS MODEL\n",
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"from TTS.utils.text.symbols import symbols, phonemes\n",
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"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
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"\n",
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"# multi speaker \n",
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"if CONFIG.use_speaker_embedding:\n",
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@ -110,6 +110,10 @@
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" speakers = []\n",
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" speaker_id = None\n",
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"\n",
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"# if the vocabulary was passed, replace the default\n",
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"if 'text' in CONFIG.keys():\n",
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" symbols, phonemes = make_symbols(**CONFIG.text)\n",
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"\n",
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"# load the model\n",
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"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
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"model = setup_model(num_chars, len(speakers), CONFIG)\n",
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@ -10,7 +10,7 @@ from TTS.utils.audio import AudioProcessor
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from TTS.utils.generic_utils import load_config, setup_model
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from TTS.utils.speakers import load_speaker_mapping
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from TTS.utils.synthesis import *
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from TTS.utils.text import phonemes, symbols
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from TTS.utils.text import make_symbols, phonemes, symbols
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alphabets = r"([A-Za-z])"
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prefixes = r"(Mr|St|Mrs|Ms|Dr)[.]"
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@ -38,12 +38,19 @@ class Synthesizer(object):
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self.config.pwgan_config, self.config.use_cuda)
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def load_tts(self, tts_checkpoint, tts_config, use_cuda):
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global symbols, phonemes
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print(" > Loading TTS model ...")
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print(" | > model config: ", tts_config)
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print(" | > checkpoint file: ", tts_checkpoint)
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self.tts_config = load_config(tts_config)
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self.use_phonemes = self.tts_config.use_phonemes
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self.ap = AudioProcessor(**self.tts_config.audio)
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if 'text' in self.tts_config.keys():
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symbols, phonemes = make_symbols(**self.tts_config.text)
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if self.use_phonemes:
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self.input_size = len(phonemes)
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else:
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@ -8,7 +8,7 @@ import string
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from TTS.utils.synthesis import synthesis
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from TTS.utils.generic_utils import load_config, setup_model
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from TTS.utils.text.symbols import symbols, phonemes
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from TTS.utils.text.symbols import make_symbols, symbols, phonemes
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from TTS.utils.audio import AudioProcessor
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@ -48,6 +48,8 @@ def tts(model,
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if __name__ == "__main__":
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global symbols, phonemes
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parser = argparse.ArgumentParser()
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parser.add_argument('text', type=str, help='Text to generate speech.')
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parser.add_argument('config_path',
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@ -105,6 +107,10 @@ if __name__ == "__main__":
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# load the audio processor
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ap = AudioProcessor(**C.audio)
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# if the vocabulary was passed, replace the default
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if 'text' in C.keys():
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symbols, phonemes = make_symbols(**C.text)
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# load speakers
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if args.speakers_json != '':
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speakers = json.load(open(args.speakers_json, 'r'))
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@ -5,13 +5,16 @@ import torch as T
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from TTS.server.synthesizer import Synthesizer
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from TTS.tests import get_tests_input_path, get_tests_output_path
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from TTS.utils.text.symbols import phonemes, symbols
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from TTS.utils.text.symbols import make_symbols, phonemes, symbols
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from TTS.utils.generic_utils import load_config, save_checkpoint, setup_model
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class DemoServerTest(unittest.TestCase):
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def _create_random_model(self):
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config = load_config(os.path.join(get_tests_output_path(), 'dummy_model_config.json'))
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if 'text' in config.keys():
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symbols, phonemes = make_symbols(**config.text)
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num_chars = len(phonemes) if config.use_phonemes else len(symbols)
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model = setup_model(num_chars, 0, config)
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output_path = os.path.join(get_tests_output_path())
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@ -38,6 +38,7 @@ class TestTTSDataset(unittest.TestCase):
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c.text_cleaner,
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ap=self.ap,
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meta_data=items,
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tp=c.text if 'text' in c.keys() else None,
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batch_group_size=bgs,
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min_seq_len=c.min_seq_len,
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max_seq_len=float("inf"),
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8
train.py
8
train.py
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@ -25,7 +25,7 @@ from TTS.utils.logger import Logger
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from TTS.utils.speakers import load_speaker_mapping, save_speaker_mapping, \
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get_speakers
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from TTS.utils.synthesis import synthesis
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from TTS.utils.text.symbols import phonemes, symbols
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from TTS.utils.text.symbols import make_symbols, phonemes, symbols
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from TTS.utils.visual import plot_alignment, plot_spectrogram
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from TTS.datasets.preprocess import load_meta_data
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from TTS.utils.radam import RAdam
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@ -49,6 +49,7 @@ def setup_loader(ap, r, is_val=False, verbose=False):
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c.text_cleaner,
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meta_data=meta_data_eval if is_val else meta_data_train,
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ap=ap,
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tp=c.text if 'text' in c.keys() else None,
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batch_group_size=0 if is_val else c.batch_group_size *
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c.batch_size,
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min_seq_len=c.min_seq_len,
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@ -515,9 +516,12 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
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# FIXME: move args definition/parsing inside of main?
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def main(args): # pylint: disable=redefined-outer-name
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global meta_data_train, meta_data_eval
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global meta_data_train, meta_data_eval, symbols, phonemes
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# Audio processor
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ap = AudioProcessor(**c.audio)
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if 'text' in c.keys():
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symbols, phonemes = make_symbols(**c.text)
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# DISTRUBUTED
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if num_gpus > 1:
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@ -425,6 +425,15 @@ def check_config(c):
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_check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
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_check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
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# vocabulary parameters
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_check_argument('text', c, restricted=False, val_type=dict) # parameter not mandatory
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_check_argument('pad', c['text'] if 'text' in c.keys() else {}, restricted=True if 'text' in c.keys() else False, val_type=str) # mandatory if "text parameters" else no mandatory
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_check_argument('eos', c['text'] if 'text' in c.keys() else {}, restricted=True if 'text' in c.keys() else False, val_type=str)
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_check_argument('bos', c['text'] if 'text' in c.keys() else {}, restricted=True if 'text' in c.keys() else False, val_type=str)
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_check_argument('characters', c['text'] if 'text' in c.keys() else {}, restricted=True if 'text' in c.keys() else False, val_type=str)
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_check_argument('phonemes', c['text'] if 'text' in c.keys() else {}, restricted=True if 'text' in c.keys() else False, val_type=str)
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_check_argument('punctuations', c['text'] if 'text' in c.keys() else {}, restricted=True if 'text' in c.keys() else False, val_type=str)
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# normalization parameters
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_check_argument('signal_norm', c['audio'], restricted=True, val_type=bool)
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_check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool)
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@ -9,10 +9,11 @@ def text_to_seqvec(text, CONFIG, use_cuda):
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if CONFIG.use_phonemes:
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seq = np.asarray(
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phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language,
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CONFIG.enable_eos_bos_chars),
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CONFIG.enable_eos_bos_chars,
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tp=CONFIG.text if 'text' in CONFIG.keys() else None),
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dtype=np.int32)
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else:
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seq = np.asarray(text_to_sequence(text, text_cleaner), dtype=np.int32)
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seq = np.asarray(text_to_sequence(text, text_cleaner, tp=CONFIG.text if 'text' in CONFIG.keys() else None), dtype=np.int32)
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# torch tensor
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chars_var = torch.from_numpy(seq).unsqueeze(0)
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if use_cuda:
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@ -4,7 +4,7 @@ import re
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import phonemizer
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from phonemizer.phonemize import phonemize
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from TTS.utils.text import cleaners
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from TTS.utils.text.symbols import symbols, phonemes, _phoneme_punctuations, _bos, \
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from TTS.utils.text.symbols import make_symbols, symbols, phonemes, _phoneme_punctuations, _bos, \
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_eos
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# Mappings from symbol to numeric ID and vice versa:
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@ -56,11 +56,23 @@ def text2phone(text, language):
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return ph
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def pad_with_eos_bos(phoneme_sequence):
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def pad_with_eos_bos(phoneme_sequence, tp=None):
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global _PHONEMES_TO_ID, _bos, _eos
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if tp:
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_bos = tp['bos']
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_eos = tp['eos']
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_, phonemes = make_symbols(**tp)
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_PHONEMES_TO_ID = {s: i for i, s in enumerate(phonemes)}
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return [_PHONEMES_TO_ID[_bos]] + list(phoneme_sequence) + [_PHONEMES_TO_ID[_eos]]
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def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False):
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def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False, tp=None):
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global _PHONEMES_TO_ID
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if tp:
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_, phonemes = make_symbols(**tp)
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_PHONEMES_TO_ID = {s: i for i, s in enumerate(phonemes)}
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sequence = []
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text = text.replace(":", "")
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clean_text = _clean_text(text, cleaner_names)
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@ -72,13 +84,18 @@ def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False):
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sequence += _phoneme_to_sequence(phoneme)
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# Append EOS char
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if enable_eos_bos:
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sequence = pad_with_eos_bos(sequence)
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sequence = pad_with_eos_bos(sequence, tp=tp)
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return sequence
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def sequence_to_phoneme(sequence):
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def sequence_to_phoneme(sequence, tp=None):
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'''Converts a sequence of IDs back to a string'''
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global _ID_TO_PHONEMES
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result = ''
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if tp:
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_, phonemes = make_symbols(**tp)
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_ID_TO_PHONEMES = {i: s for i, s in enumerate(phonemes)}
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for symbol_id in sequence:
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if symbol_id in _ID_TO_PHONEMES:
|
||||
s = _ID_TO_PHONEMES[symbol_id]
|
||||
|
|
@ -86,7 +103,7 @@ def sequence_to_phoneme(sequence):
|
|||
return result.replace('}{', ' ')
|
||||
|
||||
|
||||
def text_to_sequence(text, cleaner_names):
|
||||
def text_to_sequence(text, cleaner_names, tp=None):
|
||||
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
||||
|
||||
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
|
||||
|
|
@ -99,6 +116,11 @@ def text_to_sequence(text, cleaner_names):
|
|||
Returns:
|
||||
List of integers corresponding to the symbols in the text
|
||||
'''
|
||||
global _SYMBOL_TO_ID
|
||||
if tp:
|
||||
symbols, _ = make_symbols(**tp)
|
||||
_SYMBOL_TO_ID = {s: i for i, s in enumerate(symbols)}
|
||||
|
||||
sequence = []
|
||||
# Check for curly braces and treat their contents as ARPAbet:
|
||||
while text:
|
||||
|
|
@ -113,8 +135,13 @@ def text_to_sequence(text, cleaner_names):
|
|||
return sequence
|
||||
|
||||
|
||||
def sequence_to_text(sequence):
|
||||
def sequence_to_text(sequence, tp=None):
|
||||
'''Converts a sequence of IDs back to a string'''
|
||||
global _ID_TO_SYMBOL
|
||||
if tp:
|
||||
symbols, _ = make_symbols(**tp)
|
||||
_ID_TO_SYMBOL = {i: s for i, s in enumerate(symbols)}
|
||||
|
||||
result = ''
|
||||
for symbol_id in sequence:
|
||||
if symbol_id in _ID_TO_SYMBOL:
|
||||
|
|
|
|||
|
|
@ -5,6 +5,18 @@ Defines the set of symbols used in text input to the model.
|
|||
The default is a set of ASCII characters that works well for English or text that has been run
|
||||
through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details.
|
||||
'''
|
||||
def make_symbols(characters, phonemes, punctuations='!\'(),-.:;? ', pad='_', eos='~', bos='^'):
|
||||
''' Function to create symbols and phonemes '''
|
||||
_phonemes = sorted(list(phonemes))
|
||||
|
||||
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
|
||||
_arpabet = ['@' + s for s in _phonemes]
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [pad, eos, bos] + list(characters) + _arpabet
|
||||
phonemes = [pad, eos, bos] + list(_phonemes) + list(punctuations)
|
||||
|
||||
return symbols, phonemes
|
||||
|
||||
_pad = '_'
|
||||
_eos = '~'
|
||||
|
|
@ -20,14 +32,9 @@ _pulmonic_consonants = 'pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðsz
|
|||
_suprasegmentals = 'ˈˌːˑ'
|
||||
_other_symbols = 'ʍwɥʜʢʡɕʑɺɧ'
|
||||
_diacrilics = 'ɚ˞ɫ'
|
||||
_phonemes = sorted(list(_vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics))
|
||||
_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics
|
||||
|
||||
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
|
||||
_arpabet = ['@' + s for s in _phonemes]
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [_pad, _eos, _bos] + list(_characters) + _arpabet
|
||||
phonemes = [_pad, _eos, _bos] + list(_phonemes) + list(_punctuations)
|
||||
symbols, phonemes = make_symbols( _characters, _phonemes,_punctuations, _pad, _eos, _bos)
|
||||
|
||||
# Generate ALIEN language
|
||||
# from random import shuffle
|
||||
|
|
|
|||
|
|
@ -54,9 +54,10 @@ def visualize(alignment, spectrogram_postnet, stop_tokens, text, hop_length, CON
|
|||
plt.xlabel("Decoder timestamp", fontsize=label_fontsize)
|
||||
plt.ylabel("Encoder timestamp", fontsize=label_fontsize)
|
||||
if CONFIG.use_phonemes:
|
||||
seq = phoneme_to_sequence(text, [CONFIG.text_cleaner], CONFIG.phoneme_language, CONFIG.enable_eos_bos_chars)
|
||||
text = sequence_to_phoneme(seq)
|
||||
seq = phoneme_to_sequence(text, [CONFIG.text_cleaner], CONFIG.phoneme_language, CONFIG.enable_eos_bos_chars, tp=CONFIG.text if 'text' in CONFIG.keys() else None)
|
||||
text = sequence_to_phoneme(seq, tp=CONFIG.text if 'text' in CONFIG.keys() else None)
|
||||
print(text)
|
||||
|
||||
plt.yticks(range(len(text)), list(text))
|
||||
plt.colorbar()
|
||||
|
||||
|
|
|
|||
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