mirror of https://github.com/coqui-ai/TTS.git
Merge branch 'Edresson-dev' into dev
This commit is contained in:
erogol
commit
c6440c257e
|
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@ -0,0 +1,19 @@
|
|||
# Number of days of inactivity before an issue becomes stale
|
||||
daysUntilStale: 60
|
||||
# Number of days of inactivity before a stale issue is closed
|
||||
daysUntilClose: 7
|
||||
# Issues with these labels will never be considered stale
|
||||
exemptLabels:
|
||||
- pinned
|
||||
- security
|
||||
# Label to use when marking an issue as stale
|
||||
staleLabel: wontfix
|
||||
# Comment to post when marking an issue as stale. Set to `false` to disable
|
||||
markComment: >
|
||||
This issue has been automatically marked as stale because it has not had
|
||||
recent activity. It will be closed if no further activity occurs. Thank you
|
||||
for your contributions. You might also look our discourse page for further help.
|
||||
https://discourse.mozilla.org/c/tts
|
||||
# Comment to post when closing a stale issue. Set to `false` to disable
|
||||
closeComment: false
|
||||
|
||||
28
README.md
28
README.md
|
|
@ -115,10 +115,7 @@ In case of any error or intercepted execution, if there is no checkpoint yet und
|
|||
|
||||
You can also enjoy Tensorboard, if you point Tensorboard argument```--logdir``` to the experiment folder.
|
||||
|
||||
## Testing
|
||||
Best way to test your network is to use Notebooks under ```notebooks``` folder.
|
||||
|
||||
There is also a good [CoLab](https://colab.research.google.com/github/tugstugi/dl-colab-notebooks/blob/master/notebooks/Mozilla_TTS_WaveRNN.ipynb) sample using pre-trained models (by @tugstugi).
|
||||
## [Testing and Examples](https://github.com/mozilla/TTS/wiki/Examples-using-TTS)
|
||||
|
||||
## Contribution guidelines
|
||||
This repository is governed by Mozilla's code of conduct and etiquette guidelines. For more details, please read the [Mozilla Community Participation Guidelines.](https://www.mozilla.org/about/governance/policies/participation/)
|
||||
|
|
@ -139,28 +136,7 @@ If you like to use TTS to try a new idea and like to share your experiments with
|
|||
- Share your results as you proceed. (Tensorboard log files, audio results, visuals etc.)
|
||||
- Use LJSpeech dataset (for English) if you like to compare results with the released models. (It is the most open scalable dataset for quick experimentation)
|
||||
|
||||
## Contact/Getting Help
|
||||
- [Wiki](https://github.com/mozilla/TTS/wiki)
|
||||
|
||||
- [Discourse Forums](https://discourse.mozilla.org/c/tts) - If your question is not addressed in the Wiki, the Discourse Forums is the next place to look. They contain conversations on General Topics, Using TTS, and TTS Development.
|
||||
|
||||
- [Issues](https://github.com/mozilla/TTS/issues) - Finally, if all else fails, you can open an issue in our repo.
|
||||
|
||||
<!--## What is new with TTS
|
||||
If you train TTS with LJSpeech dataset, you start to hear reasonable results after 12.5K iterations with batch size 32. This is the fastest training with character-based methods up to our knowledge. Out implementation is also quite robust against long sentences.
|
||||
- Location sensitive attention ([ref](https://arxiv.org/pdf/1506.07503.pdf)). Attention is a vital part of text2speech models. Therefore, it is important to use an attention mechanism that suits the diagonal nature of the problem where the output strictly aligns with the text monotonically. Location sensitive attention performs better by looking into the previous alignment vectors and learns diagonal attention more easily. Yet, I believe there is a good space for research at this front to find a better solution.
|
||||
- Attention smoothing with sigmoid ([ref](https://arxiv.org/pdf/1506.07503.pdf)). Attention weights are computed by normalized sigmoid values instead of softmax for sharper values. That enables the model to pick multiple highly scored inputs for alignments while reducing the noise.
|
||||
- Weight decay ([ref](http://www.fast.ai/2018/07/02/adam-weight-decay/)). After a certain point of the training, you might observe the model over-fitting. That is, the model is able to pronounce words probably better but the quality of the speech quality gets lower and sometimes attention alignment gets disoriented.
|
||||
- Stop token prediction with an additional module. The original Tacotron model does not propose a stop token to stop the decoding process. Therefore, you need to use heuristic measures to stop the decoder. Here, we prefer to use additional layers at the end to decide when to stop.
|
||||
- Applying sigmoid to the model outputs. Since the output values are expected to be in the range [0, 1], we apply sigmoid to make things easier to approximate the expected output distribution.
|
||||
- Phoneme based training is enabled for easier learning and robust pronunciation. It also makes easier to adapt TTS to the most languages without worrying about language specific characters.
|
||||
- Configurable attention windowing at inference-time for robust alignment. It enforces network to only consider a certain window of encoder steps per iteration.
|
||||
- Detailed Tensorboard stats for activation, weight and gradient values per layer. It is useful to detect defects and compare networks.
|
||||
- Constant history window. Instead of using only the last frame of predictions, define a constant history queue. It enables training with gradually decreasing prediction frame (r=5 -> r=1) by only changing the last layer. For instance, you can train the model with r=5 and then fine-tune it with r=1 without any performance loss. It also solves well-known PreNet problem [#50](https://github.com/mozilla/TTS/issues/50).
|
||||
- Initialization of hidden decoder states with Embedding layers instead of zero initialization.
|
||||
One common question is to ask why we don't use Tacotron2 architecture. According to our ablation experiments, nothing, except Location Sensitive Attention, improves the performance, given the increase in the model size.
|
||||
Please feel free to offer new changes and pull things off. We are happy to discuss and make things better.
|
||||
-->
|
||||
## [Contact/Getting Help](https://github.com/mozilla/TTS/wiki/Contact-and-Getting-Help)
|
||||
|
||||
## Major TODOs
|
||||
- [x] Implement the model.
|
||||
|
|
|
|||
21
config.json
21
config.json
|
|
@ -11,6 +11,8 @@
|
|||
"sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
|
||||
"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.
|
||||
"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
|
||||
"min_level_db": -100, // normalization range
|
||||
"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
|
||||
|
|
@ -19,7 +21,7 @@
|
|||
// Normalization parameters
|
||||
"signal_norm": true, // normalize the spec values in range [0, 1]
|
||||
"symmetric_norm": true, // move normalization to range [-1, 1]
|
||||
"max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
|
||||
"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.
|
||||
"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
|
||||
"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
|
||||
|
|
@ -27,6 +29,18 @@
|
|||
"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
|
||||
},
|
||||
|
||||
// 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",
|
||||
|
|
@ -36,11 +50,12 @@
|
|||
"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'.
|
||||
"batch_size": 2, // 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.
|
||||
"loss_masking": true, // enable / disable loss masking against the sequence padding.
|
||||
"grad_accum": 2, // if N > 1, enable gradient accumulation for N iterations. It is useful for low memory GPUs.
|
||||
|
||||
// VALIDATION
|
||||
"run_eval": true,
|
||||
|
|
@ -49,7 +64,7 @@
|
|||
|
||||
// OPTIMIZER
|
||||
"noam_schedule": false, // use noam warmup and lr schedule.
|
||||
"grad_clip": 1, // upper limit for gradients for clipping.
|
||||
"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.
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ class MyDataset(Dataset):
|
|||
text_cleaner,
|
||||
ap,
|
||||
meta_data,
|
||||
tp=None,
|
||||
batch_group_size=0,
|
||||
min_seq_len=0,
|
||||
max_seq_len=float("inf"),
|
||||
|
|
@ -49,6 +50,7 @@ class MyDataset(Dataset):
|
|||
self.min_seq_len = min_seq_len
|
||||
self.max_seq_len = max_seq_len
|
||||
self.ap = ap
|
||||
self.tp = tp
|
||||
self.use_phonemes = use_phonemes
|
||||
self.phoneme_cache_path = phoneme_cache_path
|
||||
self.phoneme_language = phoneme_language
|
||||
|
|
@ -75,13 +77,13 @@ class MyDataset(Dataset):
|
|||
|
||||
def _generate_and_cache_phoneme_sequence(self, text, cache_path):
|
||||
"""generate a phoneme sequence from text.
|
||||
|
||||
since the usage is for subsequent caching, we never add bos and
|
||||
eos chars here. Instead we add those dynamically later; based on the
|
||||
config option."""
|
||||
phonemes = phoneme_to_sequence(text, [self.cleaners],
|
||||
language=self.phoneme_language,
|
||||
enable_eos_bos=False)
|
||||
enable_eos_bos=False,
|
||||
tp=self.tp)
|
||||
phonemes = np.asarray(phonemes, dtype=np.int32)
|
||||
np.save(cache_path, phonemes)
|
||||
return phonemes
|
||||
|
|
@ -101,7 +103,7 @@ class MyDataset(Dataset):
|
|||
phonemes = self._generate_and_cache_phoneme_sequence(text,
|
||||
cache_path)
|
||||
if self.enable_eos_bos:
|
||||
phonemes = pad_with_eos_bos(phonemes)
|
||||
phonemes = pad_with_eos_bos(phonemes, tp=self.tp)
|
||||
phonemes = np.asarray(phonemes, dtype=np.int32)
|
||||
return phonemes
|
||||
|
||||
|
|
@ -113,7 +115,7 @@ class MyDataset(Dataset):
|
|||
text = self._load_or_generate_phoneme_sequence(wav_file, text)
|
||||
else:
|
||||
text = np.asarray(
|
||||
text_to_sequence(text, [self.cleaners]), dtype=np.int32)
|
||||
text_to_sequence(text, [self.cleaners], tp=self.tp), dtype=np.int32)
|
||||
|
||||
assert text.size > 0, self.items[idx][1]
|
||||
assert wav.size > 0, self.items[idx][1]
|
||||
|
|
@ -193,7 +195,7 @@ class MyDataset(Dataset):
|
|||
mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
|
||||
linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
|
||||
|
||||
mel_lengths = [m.shape[1] for m in mel]
|
||||
mel_lengths = [m.shape[1] for m in mel]
|
||||
|
||||
# compute 'stop token' targets
|
||||
stop_targets = [
|
||||
|
|
|
|||
|
|
@ -60,22 +60,6 @@ def tweb(root_path, meta_file):
|
|||
# return {'text': texts, 'wavs': wavs}
|
||||
|
||||
|
||||
def mozilla_old(root_path, meta_file):
|
||||
"""Normalizes Mozilla meta data files to TTS format"""
|
||||
txt_file = os.path.join(root_path, meta_file)
|
||||
items = []
|
||||
speaker_name = "mozilla_old"
|
||||
with open(txt_file, 'r') as ttf:
|
||||
for line in ttf:
|
||||
cols = line.split('|')
|
||||
batch_no = int(cols[1].strip().split("_")[0])
|
||||
wav_folder = "batch{}".format(batch_no)
|
||||
wav_file = os.path.join(root_path, wav_folder, "wavs_no_processing", cols[1].strip())
|
||||
text = cols[0].strip()
|
||||
items.append([text, wav_file, speaker_name])
|
||||
return items
|
||||
|
||||
|
||||
def mozilla(root_path, meta_file):
|
||||
"""Normalizes Mozilla meta data files to TTS format"""
|
||||
txt_file = os.path.join(root_path, meta_file)
|
||||
|
|
@ -91,6 +75,22 @@ def mozilla(root_path, meta_file):
|
|||
return items
|
||||
|
||||
|
||||
def mozilla_de(root_path, meta_file):
|
||||
"""Normalizes Mozilla meta data files to TTS format"""
|
||||
txt_file = os.path.join(root_path, meta_file)
|
||||
items = []
|
||||
speaker_name = "mozilla"
|
||||
with open(txt_file, 'r', encoding="ISO 8859-1") as ttf:
|
||||
for line in ttf:
|
||||
cols = line.strip().split('|')
|
||||
wav_file = cols[0].strip()
|
||||
text = cols[1].strip()
|
||||
folder_name = f"BATCH_{wav_file.split('_')[0]}_FINAL"
|
||||
wav_file = os.path.join(root_path, folder_name, wav_file)
|
||||
items.append([text, wav_file, speaker_name])
|
||||
return items
|
||||
|
||||
|
||||
def mailabs(root_path, meta_files=None):
|
||||
"""Normalizes M-AI-Labs meta data files to TTS format"""
|
||||
speaker_regex = re.compile("by_book/(male|female)/(?P<speaker_name>[^/]+)/")
|
||||
|
|
|
|||
|
|
@ -64,7 +64,6 @@ class Encoder(nn.Module):
|
|||
def forward(self, x, input_lengths):
|
||||
x = self.convolutions(x)
|
||||
x = x.transpose(1, 2)
|
||||
input_lengths = input_lengths.cpu().numpy()
|
||||
x = nn.utils.rnn.pack_padded_sequence(x,
|
||||
input_lengths,
|
||||
batch_first=True)
|
||||
|
|
|
|||
|
|
@ -132,7 +132,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"# LOAD TTS MODEL\n",
|
||||
"from TTS.utils.text.symbols import symbols, phonemes\n",
|
||||
"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
|
||||
"\n",
|
||||
"# multi speaker \n",
|
||||
"if CONFIG.use_speaker_embedding:\n",
|
||||
|
|
@ -142,6 +142,10 @@
|
|||
" speakers = []\n",
|
||||
" speaker_id = None\n",
|
||||
"\n",
|
||||
"# if the vocabulary was passed, replace the default\n",
|
||||
"if 'characters' in CONFIG.keys():\n",
|
||||
" symbols, phonemes = make_symbols(**CONFIG.characters)\n",
|
||||
"\n",
|
||||
"# load the model\n",
|
||||
"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
|
||||
"model = setup_model(num_chars, len(speakers), CONFIG)\n",
|
||||
|
|
|
|||
|
|
@ -65,6 +65,7 @@
|
|||
"from TTS.utils.text import text_to_sequence\n",
|
||||
"from TTS.utils.synthesis import synthesis\n",
|
||||
"from TTS.utils.visual import visualize\n",
|
||||
"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
|
||||
"\n",
|
||||
"import IPython\n",
|
||||
"from IPython.display import Audio\n",
|
||||
|
|
@ -81,13 +82,15 @@
|
|||
"source": [
|
||||
"def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n",
|
||||
" t_1 = time.time()\n",
|
||||
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, False, CONFIG.enable_eos_bos_chars)\n",
|
||||
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None, \n",
|
||||
" truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars,\n",
|
||||
" use_griffin_lim=use_gl)\n",
|
||||
" if CONFIG.model == \"Tacotron\" and not use_gl:\n",
|
||||
" # coorect the normalization differences b/w TTS and the Vocoder.\n",
|
||||
" mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
|
||||
" mel_postnet_spec = ap._denormalize(mel_postnet_spec)\n",
|
||||
" mel_postnet_spec = ap_vocoder._normalize(mel_postnet_spec)\n",
|
||||
" if not use_gl:\n",
|
||||
" mel_postnet_spec = ap._denormalize(mel_postnet_spec)\n",
|
||||
" mel_postnet_spec = ap_vocoder._normalize(mel_postnet_spec)\n",
|
||||
" waveform = wavernn.generate(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0).cuda(), batched=batched_wavernn, target=8000, overlap=400)\n",
|
||||
"\n",
|
||||
" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
|
||||
|
|
@ -108,7 +111,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"# Set constants\n",
|
||||
"ROOT_PATH = '/media/erogol/data_ssd/Models/libri_tts/5099/'\n",
|
||||
"ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-bn-December-23-2019_08+34AM-ffea133/'\n",
|
||||
"MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n",
|
||||
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
|
||||
"OUT_FOLDER = '/home/erogol/Dropbox/AudioSamples/benchmark_samples/'\n",
|
||||
|
|
@ -116,7 +119,7 @@
|
|||
"VOCODER_MODEL_PATH = \"/media/erogol/data_ssd/Models/wavernn/ljspeech/mold_ljspeech_best_model/checkpoint_433000.pth.tar\"\n",
|
||||
"VOCODER_CONFIG_PATH = \"/media/erogol/data_ssd/Models/wavernn/ljspeech/mold_ljspeech_best_model/config.json\"\n",
|
||||
"VOCODER_CONFIG = load_config(VOCODER_CONFIG_PATH)\n",
|
||||
"use_cuda = False\n",
|
||||
"use_cuda = True\n",
|
||||
"\n",
|
||||
"# Set some config fields manually for testing\n",
|
||||
"# CONFIG.windowing = False\n",
|
||||
|
|
@ -127,7 +130,7 @@
|
|||
"# CONFIG.stopnet = True\n",
|
||||
"\n",
|
||||
"# Set the vocoder\n",
|
||||
"use_gl = False # use GL if True\n",
|
||||
"use_gl = True # use GL if True\n",
|
||||
"batched_wavernn = True # use batched wavernn inference if True"
|
||||
]
|
||||
},
|
||||
|
|
@ -138,8 +141,6 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"# LOAD TTS MODEL\n",
|
||||
"from utils.text.symbols import symbols, phonemes\n",
|
||||
"\n",
|
||||
"# multi speaker \n",
|
||||
"if CONFIG.use_speaker_embedding:\n",
|
||||
" speakers = json.load(open(f\"{ROOT_PATH}/speakers.json\", 'r'))\n",
|
||||
|
|
@ -148,6 +149,10 @@
|
|||
" speakers = []\n",
|
||||
" speaker_id = None\n",
|
||||
"\n",
|
||||
"# if the vocabulary was passed, replace the default\n",
|
||||
"if 'characters' in CONFIG.keys():\n",
|
||||
" symbols, phonemes = make_symbols(**CONFIG.characters)\n",
|
||||
"\n",
|
||||
"# load the model\n",
|
||||
"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
|
||||
"model = setup_model(num_chars, len(speakers), CONFIG)\n",
|
||||
|
|
@ -181,7 +186,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# LOAD WAVERNN\n",
|
||||
"# LOAD WAVERNN - Make sure you downloaded the model and installed the module\n",
|
||||
"if use_gl == False:\n",
|
||||
" from WaveRNN.models.wavernn import Model\n",
|
||||
" from WaveRNN.utils.audio import AudioProcessor as AudioProcessorVocoder\n",
|
||||
|
|
@ -533,7 +538,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@
|
|||
"from TTS.utils.audio import AudioProcessor\n",
|
||||
"from TTS.utils.visual import plot_spectrogram\n",
|
||||
"from TTS.utils.generic_utils import load_config, setup_model, sequence_mask\n",
|
||||
"from TTS.utils.text.symbols import symbols, phonemes\n",
|
||||
"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
|
||||
"\n",
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
|
|
@ -94,6 +94,10 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# if the vocabulary was passed, replace the default\n",
|
||||
"if 'characters' in C.keys():\n",
|
||||
" symbols, phonemes = make_symbols(**C.characters)\n",
|
||||
"\n",
|
||||
"# load the model\n",
|
||||
"num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n",
|
||||
"# TODO: multiple speaker\n",
|
||||
|
|
@ -116,7 +120,7 @@
|
|||
"preprocessor = importlib.import_module('TTS.datasets.preprocess')\n",
|
||||
"preprocessor = getattr(preprocessor, DATASET.lower())\n",
|
||||
"meta_data = preprocessor(DATA_PATH,METADATA_FILE)\n",
|
||||
"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",
|
||||
"dataset = MyDataset(checkpoint['r'], C.text_cleaner, ap, meta_data,tp=C.characters if 'characters' 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",
|
||||
"loader = DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False)"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -100,7 +100,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"# LOAD TTS MODEL\n",
|
||||
"from TTS.utils.text.symbols import symbols, phonemes\n",
|
||||
"from TTS.utils.text.symbols import make_symbols, symbols, phonemes\n",
|
||||
"\n",
|
||||
"# multi speaker \n",
|
||||
"if CONFIG.use_speaker_embedding:\n",
|
||||
|
|
@ -110,6 +110,10 @@
|
|||
" speakers = []\n",
|
||||
" speaker_id = None\n",
|
||||
"\n",
|
||||
"# if the vocabulary was passed, replace the default\n",
|
||||
"if 'characters' in CONFIG.keys():\n",
|
||||
" symbols, phonemes = make_symbols(**CONFIG.characters)\n",
|
||||
"\n",
|
||||
"# load the model\n",
|
||||
"num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
|
||||
"model = setup_model(num_chars, len(speakers), CONFIG)\n",
|
||||
|
|
|
|||
|
|
@ -6,6 +6,10 @@ Instructions below are based on a Ubuntu 18.04 machine, but it should be simple
|
|||
|
||||
#### Development server:
|
||||
|
||||
##### Using server.py
|
||||
If you have the environment set already for TTS, then you can directly call ```setup.py```.
|
||||
|
||||
##### Using .whl
|
||||
1. apt-get install -y espeak libsndfile1 python3-venv
|
||||
2. python3 -m venv /tmp/venv
|
||||
3. source /tmp/venv/bin/activate
|
||||
|
|
|
|||
|
|
@ -14,10 +14,13 @@ def create_argparser():
|
|||
parser.add_argument('--tts_checkpoint', type=str, help='path to TTS checkpoint file')
|
||||
parser.add_argument('--tts_config', type=str, help='path to TTS config.json file')
|
||||
parser.add_argument('--tts_speakers', type=str, help='path to JSON file containing speaker ids, if speaker ids are used in the model')
|
||||
parser.add_argument('--wavernn_lib_path', type=str, help='path to WaveRNN project folder to be imported. If this is not passed, model uses Griffin-Lim for synthesis.')
|
||||
parser.add_argument('--wavernn_file', type=str, help='path to WaveRNN checkpoint file.')
|
||||
parser.add_argument('--wavernn_config', type=str, help='path to WaveRNN config file.')
|
||||
parser.add_argument('--wavernn_lib_path', type=str, default=None, help='path to WaveRNN project folder to be imported. If this is not passed, model uses Griffin-Lim for synthesis.')
|
||||
parser.add_argument('--wavernn_file', type=str, default=None, help='path to WaveRNN checkpoint file.')
|
||||
parser.add_argument('--wavernn_config', type=str, default=None, help='path to WaveRNN config file.')
|
||||
parser.add_argument('--is_wavernn_batched', type=convert_boolean, default=False, help='true to use batched WaveRNN.')
|
||||
parser.add_argument('--pwgan_lib_path', type=str, default=None, help='path to ParallelWaveGAN project folder to be imported. If this is not passed, model uses Griffin-Lim for synthesis.')
|
||||
parser.add_argument('--pwgan_file', type=str, default=None, help='path to ParallelWaveGAN checkpoint file.')
|
||||
parser.add_argument('--pwgan_config', type=str, default=None, help='path to ParallelWaveGAN config file.')
|
||||
parser.add_argument('--port', type=int, default=5002, help='port to listen on.')
|
||||
parser.add_argument('--use_cuda', type=convert_boolean, default=False, help='true to use CUDA.')
|
||||
parser.add_argument('--debug', type=convert_boolean, default=False, help='true to enable Flask debug mode.')
|
||||
|
|
@ -26,28 +29,35 @@ def create_argparser():
|
|||
|
||||
synthesizer = None
|
||||
|
||||
embedded_model_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'model')
|
||||
checkpoint_file = os.path.join(embedded_model_folder, 'checkpoint.pth.tar')
|
||||
config_file = os.path.join(embedded_model_folder, 'config.json')
|
||||
embedded_models_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'model')
|
||||
|
||||
# Default options with embedded model files
|
||||
if os.path.isfile(checkpoint_file):
|
||||
default_tts_checkpoint = checkpoint_file
|
||||
else:
|
||||
default_tts_checkpoint = None
|
||||
embedded_tts_folder = os.path.join(embedded_models_folder, 'tts')
|
||||
tts_checkpoint_file = os.path.join(embedded_tts_folder, 'checkpoint.pth.tar')
|
||||
tts_config_file = os.path.join(embedded_tts_folder, 'config.json')
|
||||
|
||||
if os.path.isfile(config_file):
|
||||
default_tts_config = config_file
|
||||
else:
|
||||
default_tts_config = None
|
||||
embedded_wavernn_folder = os.path.join(embedded_models_folder, 'wavernn')
|
||||
wavernn_checkpoint_file = os.path.join(embedded_wavernn_folder, 'checkpoint.pth.tar')
|
||||
wavernn_config_file = os.path.join(embedded_wavernn_folder, 'config.json')
|
||||
|
||||
embedded_pwgan_folder = os.path.join(embedded_models_folder, 'pwgan')
|
||||
pwgan_checkpoint_file = os.path.join(embedded_pwgan_folder, 'checkpoint.pkl')
|
||||
pwgan_config_file = os.path.join(embedded_pwgan_folder, 'config.yml')
|
||||
|
||||
args = create_argparser().parse_args()
|
||||
|
||||
# If these were not specified in the CLI args, use default values
|
||||
if not args.tts_checkpoint:
|
||||
args.tts_checkpoint = default_tts_checkpoint
|
||||
if not args.tts_config:
|
||||
args.tts_config = default_tts_config
|
||||
# If these were not specified in the CLI args, use default values with embedded model files
|
||||
if not args.tts_checkpoint and os.path.isfile(tts_checkpoint_file):
|
||||
args.tts_checkpoint = tts_checkpoint_file
|
||||
if not args.tts_config and os.path.isfile(tts_config_file):
|
||||
args.tts_config = tts_config_file
|
||||
if not args.wavernn_file and os.path.isfile(wavernn_checkpoint_file):
|
||||
args.wavernn_file = wavernn_checkpoint_file
|
||||
if not args.wavernn_config and os.path.isfile(wavernn_config_file):
|
||||
args.wavernn_config = wavernn_config_file
|
||||
if not args.pwgan_file and os.path.isfile(pwgan_checkpoint_file):
|
||||
args.pwgan_file = pwgan_checkpoint_file
|
||||
if not args.pwgan_config and os.path.isfile(pwgan_config_file):
|
||||
args.pwgan_config = pwgan_config_file
|
||||
|
||||
synthesizer = Synthesizer(args)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,17 +1,20 @@
|
|||
import io
|
||||
import os
|
||||
import re
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import sys
|
||||
import yaml
|
||||
|
||||
from TTS.utils.audio import AudioProcessor
|
||||
from TTS.utils.generic_utils import load_config, setup_model
|
||||
from TTS.utils.text import phonemes, symbols
|
||||
from TTS.utils.speakers import load_speaker_mapping
|
||||
# pylint: disable=unused-wildcard-import
|
||||
# pylint: disable=wildcard-import
|
||||
from TTS.utils.synthesis import *
|
||||
|
||||
import re
|
||||
from TTS.utils.text import make_symbols, phonemes, symbols
|
||||
|
||||
alphabets = r"([A-Za-z])"
|
||||
prefixes = r"(Mr|St|Mrs|Ms|Dr)[.]"
|
||||
suffixes = r"(Inc|Ltd|Jr|Sr|Co)"
|
||||
|
|
@ -23,6 +26,7 @@ websites = r"[.](com|net|org|io|gov)"
|
|||
class Synthesizer(object):
|
||||
def __init__(self, config):
|
||||
self.wavernn = None
|
||||
self.pwgan = None
|
||||
self.config = config
|
||||
self.use_cuda = self.config.use_cuda
|
||||
if self.use_cuda:
|
||||
|
|
@ -30,28 +34,38 @@ class Synthesizer(object):
|
|||
self.load_tts(self.config.tts_checkpoint, self.config.tts_config,
|
||||
self.config.use_cuda)
|
||||
if self.config.wavernn_lib_path:
|
||||
self.load_wavernn(self.config.wavernn_lib_path, self.config.wavernn_path,
|
||||
self.config.wavernn_file, self.config.wavernn_config,
|
||||
self.config.use_cuda)
|
||||
self.load_wavernn(self.config.wavernn_lib_path, self.config.wavernn_file,
|
||||
self.config.wavernn_config, self.config.use_cuda)
|
||||
if self.config.pwgan_lib_path:
|
||||
self.load_pwgan(self.config.pwgan_lib_path, self.config.pwgan_file,
|
||||
self.config.pwgan_config, self.config.use_cuda)
|
||||
|
||||
def load_tts(self, tts_checkpoint, tts_config, use_cuda):
|
||||
# pylint: disable=global-statement
|
||||
global symbols, phonemes
|
||||
|
||||
print(" > Loading TTS model ...")
|
||||
print(" | > model config: ", tts_config)
|
||||
print(" | > checkpoint file: ", tts_checkpoint)
|
||||
|
||||
self.tts_config = load_config(tts_config)
|
||||
self.use_phonemes = self.tts_config.use_phonemes
|
||||
self.ap = AudioProcessor(**self.tts_config.audio)
|
||||
|
||||
if 'characters' in self.tts_config.keys():
|
||||
symbols, phonemes = make_symbols(**self.tts_config.characters)
|
||||
|
||||
if self.use_phonemes:
|
||||
self.input_size = len(phonemes)
|
||||
else:
|
||||
self.input_size = len(symbols)
|
||||
# load speakers
|
||||
# TODO: fix this for multi-speaker model - load speakers
|
||||
if self.config.tts_speakers is not None:
|
||||
self.tts_speakers = load_speaker_mapping(os.path.join(model_path, self.config.tts_speakers))
|
||||
self.tts_speakers = load_speaker_mapping(self.config.tts_speakers)
|
||||
num_speakers = len(self.tts_speakers)
|
||||
else:
|
||||
num_speakers = 0
|
||||
self.tts_model = setup_model(self.input_size, num_speakers=num_speakers, c=self.tts_config)
|
||||
self.tts_model = setup_model(self.input_size, num_speakers=num_speakers, c=self.tts_config)
|
||||
# load model state
|
||||
cp = torch.load(tts_checkpoint, map_location=torch.device('cpu'))
|
||||
# load the model
|
||||
|
|
@ -63,16 +77,17 @@ class Synthesizer(object):
|
|||
if 'r' in cp:
|
||||
self.tts_model.decoder.set_r(cp['r'])
|
||||
|
||||
def load_wavernn(self, lib_path, model_path, model_file, model_config, use_cuda):
|
||||
def load_wavernn(self, lib_path, model_file, model_config, use_cuda):
|
||||
# TODO: set a function in wavernn code base for model setup and call it here.
|
||||
sys.path.append(lib_path) # set this if TTS is not installed globally
|
||||
sys.path.append(lib_path) # set this if WaveRNN is not installed globally
|
||||
#pylint: disable=import-outside-toplevel
|
||||
from WaveRNN.models.wavernn import Model
|
||||
wavernn_config = os.path.join(model_path, model_config)
|
||||
model_file = os.path.join(model_path, model_file)
|
||||
print(" > Loading WaveRNN model ...")
|
||||
print(" | > model config: ", wavernn_config)
|
||||
print(" | > model config: ", model_config)
|
||||
print(" | > model file: ", model_file)
|
||||
self.wavernn_config = load_config(wavernn_config)
|
||||
self.wavernn_config = load_config(model_config)
|
||||
# This is the default architecture we use for our models.
|
||||
# You might need to update it
|
||||
self.wavernn = Model(
|
||||
rnn_dims=512,
|
||||
fc_dims=512,
|
||||
|
|
@ -80,7 +95,7 @@ class Synthesizer(object):
|
|||
mulaw=self.wavernn_config.mulaw,
|
||||
pad=self.wavernn_config.pad,
|
||||
use_aux_net=self.wavernn_config.use_aux_net,
|
||||
use_upsample_net = self.wavernn_config.use_upsample_net,
|
||||
use_upsample_net=self.wavernn_config.use_upsample_net,
|
||||
upsample_factors=self.wavernn_config.upsample_factors,
|
||||
feat_dims=80,
|
||||
compute_dims=128,
|
||||
|
|
@ -91,18 +106,35 @@ class Synthesizer(object):
|
|||
).cuda()
|
||||
|
||||
check = torch.load(model_file)
|
||||
self.wavernn.load_state_dict(check['model'])
|
||||
self.wavernn.load_state_dict(check['model'], map_location="cpu")
|
||||
if use_cuda:
|
||||
self.wavernn.cuda()
|
||||
self.wavernn.eval()
|
||||
|
||||
def load_pwgan(self, lib_path, model_file, model_config, use_cuda):
|
||||
sys.path.append(lib_path) # set this if ParallelWaveGAN is not installed globally
|
||||
#pylint: disable=import-outside-toplevel
|
||||
from parallel_wavegan.models import ParallelWaveGANGenerator
|
||||
print(" > Loading PWGAN model ...")
|
||||
print(" | > model config: ", model_config)
|
||||
print(" | > model file: ", model_file)
|
||||
with open(model_config) as f:
|
||||
self.pwgan_config = yaml.load(f, Loader=yaml.Loader)
|
||||
self.pwgan = ParallelWaveGANGenerator(**self.pwgan_config["generator_params"])
|
||||
self.pwgan.load_state_dict(torch.load(model_file, map_location="cpu")["model"]["generator"])
|
||||
self.pwgan.remove_weight_norm()
|
||||
if use_cuda:
|
||||
self.pwgan.cuda()
|
||||
self.pwgan.eval()
|
||||
|
||||
def save_wav(self, wav, path):
|
||||
# wav *= 32767 / max(1e-8, np.max(np.abs(wav)))
|
||||
wav = np.array(wav)
|
||||
self.ap.save_wav(wav, path)
|
||||
|
||||
def split_into_sentences(self, text):
|
||||
text = " " + text + " "
|
||||
@staticmethod
|
||||
def split_into_sentences(text):
|
||||
text = " " + text + " <stop>"
|
||||
text = text.replace("\n", " ")
|
||||
text = re.sub(prefixes, "\\1<prd>", text)
|
||||
text = re.sub(websites, "<prd>\\1", text)
|
||||
|
|
@ -129,15 +161,13 @@ class Synthesizer(object):
|
|||
text = text.replace("<prd>", ".")
|
||||
sentences = text.split("<stop>")
|
||||
sentences = sentences[:-1]
|
||||
sentences = [s.strip() for s in sentences]
|
||||
sentences = list(filter(None, [s.strip() for s in sentences])) # remove empty sentences
|
||||
return sentences
|
||||
|
||||
def tts(self, text):
|
||||
wavs = []
|
||||
sens = self.split_into_sentences(text)
|
||||
print(sens)
|
||||
if not sens:
|
||||
sens = [text+'.']
|
||||
for sen in sens:
|
||||
# preprocess the given text
|
||||
inputs = text_to_seqvec(sen, self.tts_config, self.use_cuda)
|
||||
|
|
@ -148,9 +178,16 @@ class Synthesizer(object):
|
|||
postnet_output, decoder_output, _ = parse_outputs(
|
||||
postnet_output, decoder_output, alignments)
|
||||
|
||||
if self.wavernn:
|
||||
postnet_output = postnet_output[0].data.cpu().numpy()
|
||||
wav = self.wavernn.generate(torch.FloatTensor(postnet_output.T).unsqueeze(0).cuda(), batched=self.config.is_wavernn_batched, target=11000, overlap=550)
|
||||
if self.pwgan:
|
||||
vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
|
||||
if self.use_cuda:
|
||||
vocoder_input.cuda()
|
||||
wav = self.pwgan.inference(vocoder_input, hop_size=self.ap.hop_length)
|
||||
elif self.wavernn:
|
||||
vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
|
||||
if self.use_cuda:
|
||||
vocoder_input.cuda()
|
||||
wav = self.wavernn.generate(vocoder_input, batched=self.config.is_wavernn_batched, target=11000, overlap=550)
|
||||
else:
|
||||
wav = inv_spectrogram(postnet_output, self.ap, self.tts_config)
|
||||
# trim silence
|
||||
|
|
|
|||
7
setup.py
7
setup.py
|
|
@ -61,10 +61,11 @@ package_data = ['server/templates/*']
|
|||
if 'bdist_wheel' in unknown_args and args.checkpoint and args.model_config:
|
||||
print('Embedding model in wheel file...')
|
||||
model_dir = os.path.join('server', 'model')
|
||||
os.makedirs(model_dir, exist_ok=True)
|
||||
embedded_checkpoint_path = os.path.join(model_dir, 'checkpoint.pth.tar')
|
||||
tts_dir = os.path.join(model_dir, 'tts')
|
||||
os.makedirs(tts_dir, exist_ok=True)
|
||||
embedded_checkpoint_path = os.path.join(tts_dir, 'checkpoint.pth.tar')
|
||||
shutil.copy(args.checkpoint, embedded_checkpoint_path)
|
||||
embedded_config_path = os.path.join(model_dir, 'config.json')
|
||||
embedded_config_path = os.path.join(tts_dir, 'config.json')
|
||||
shutil.copy(args.model_config, embedded_config_path)
|
||||
package_data.extend([embedded_checkpoint_path, embedded_config_path])
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
# pylint: disable=redefined-outer-name, unused-argument
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
|
|
@ -7,7 +8,7 @@ import string
|
|||
|
||||
from TTS.utils.synthesis import synthesis
|
||||
from TTS.utils.generic_utils import load_config, setup_model
|
||||
from TTS.utils.text.symbols import symbols, phonemes
|
||||
from TTS.utils.text.symbols import make_symbols, symbols, phonemes
|
||||
from TTS.utils.audio import AudioProcessor
|
||||
|
||||
|
||||
|
|
@ -25,14 +26,16 @@ def tts(model,
|
|||
t_1 = time.time()
|
||||
use_vocoder_model = vocoder_model is not None
|
||||
waveform, alignment, _, postnet_output, stop_tokens = synthesis(
|
||||
model, text, C, use_cuda, ap, speaker_id, False,
|
||||
C.enable_eos_bos_chars)
|
||||
model, text, C, use_cuda, ap, speaker_id, style_wav=False,
|
||||
truncated=False, enable_eos_bos_chars=C.enable_eos_bos_chars,
|
||||
use_griffin_lim=(not use_vocoder_model), do_trim_silence=True)
|
||||
|
||||
if C.model == "Tacotron" and use_vocoder_model:
|
||||
postnet_output = ap.out_linear_to_mel(postnet_output.T).T
|
||||
# correct if there is a scale difference b/w two models
|
||||
postnet_output = ap._denormalize(postnet_output)
|
||||
postnet_output = ap_vocoder._normalize(postnet_output)
|
||||
if use_vocoder_model:
|
||||
postnet_output = ap._denormalize(postnet_output)
|
||||
postnet_output = ap_vocoder._normalize(postnet_output)
|
||||
vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
|
||||
waveform = vocoder_model.generate(
|
||||
vocoder_input.cuda() if use_cuda else vocoder_input,
|
||||
|
|
@ -45,6 +48,8 @@ def tts(model,
|
|||
|
||||
if __name__ == "__main__":
|
||||
|
||||
global symbols, phonemes
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('text', type=str, help='Text to generate speech.')
|
||||
parser.add_argument('config_path',
|
||||
|
|
@ -58,7 +63,7 @@ if __name__ == "__main__":
|
|||
parser.add_argument(
|
||||
'out_path',
|
||||
type=str,
|
||||
help='Path to save final wav file.',
|
||||
help='Path to save final wav file. Wav file will be names as the text given.',
|
||||
)
|
||||
parser.add_argument('--use_cuda',
|
||||
type=bool,
|
||||
|
|
@ -102,6 +107,10 @@ if __name__ == "__main__":
|
|||
# load the audio processor
|
||||
ap = AudioProcessor(**C.audio)
|
||||
|
||||
# if the vocabulary was passed, replace the default
|
||||
if 'characters' in C.keys():
|
||||
symbols, phonemes = make_symbols(**C.characters)
|
||||
|
||||
# load speakers
|
||||
if args.speakers_json != '':
|
||||
speakers = json.load(open(args.speakers_json, 'r'))
|
||||
|
|
|
|||
|
|
@ -3,9 +3,11 @@
|
|||
"tts_config":"dummy_model_config.json", // tts config.json file
|
||||
"tts_speakers": null, // json file listing speaker ids. null if no speaker embedding.
|
||||
"wavernn_lib_path": null, // Rootpath to wavernn project folder to be imported. If this is null, model uses GL for speech synthesis.
|
||||
"wavernn_path": null, // wavernn model root path
|
||||
"wavernn_file": null, // wavernn checkpoint file name
|
||||
"wavernn_config": null, // wavernn config file
|
||||
"pwgan_lib_path": null,
|
||||
"pwgan_file": null,
|
||||
"pwgan_config": null,
|
||||
"is_wavernn_batched":true,
|
||||
"port": 5002,
|
||||
"use_cuda": false,
|
||||
|
|
|
|||
|
|
@ -19,6 +19,16 @@
|
|||
"mel_fmax": 7600, // maximum freq level for mel-spec. Tune for dataset!!
|
||||
"do_trim_silence": false
|
||||
},
|
||||
|
||||
"characters":{
|
||||
"pad": "_",
|
||||
"eos": "~",
|
||||
"bos": "^",
|
||||
"characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
|
||||
"punctuations":"!'(),-.:;? ",
|
||||
"phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
|
||||
},
|
||||
|
||||
"hidden_size": 128,
|
||||
"embedding_size": 256,
|
||||
"text_cleaner": "english_cleaners",
|
||||
|
|
|
|||
|
|
@ -5,13 +5,19 @@ import torch as T
|
|||
|
||||
from TTS.server.synthesizer import Synthesizer
|
||||
from TTS.tests import get_tests_input_path, get_tests_output_path
|
||||
from TTS.utils.text.symbols import phonemes, symbols
|
||||
from TTS.utils.text.symbols import make_symbols, phonemes, symbols
|
||||
from TTS.utils.generic_utils import load_config, save_checkpoint, setup_model
|
||||
|
||||
|
||||
class DemoServerTest(unittest.TestCase):
|
||||
# pylint: disable=R0201
|
||||
def _create_random_model(self):
|
||||
# pylint: disable=global-statement
|
||||
global symbols, phonemes
|
||||
config = load_config(os.path.join(get_tests_output_path(), 'dummy_model_config.json'))
|
||||
if 'characters' in config.keys():
|
||||
symbols, phonemes = make_symbols(**config.characters)
|
||||
|
||||
num_chars = len(phonemes) if config.use_phonemes else len(symbols)
|
||||
model = setup_model(num_chars, 0, config)
|
||||
output_path = os.path.join(get_tests_output_path())
|
||||
|
|
|
|||
|
|
@ -37,7 +37,8 @@ class TestTTSDataset(unittest.TestCase):
|
|||
r,
|
||||
c.text_cleaner,
|
||||
ap=self.ap,
|
||||
meta_data=items,
|
||||
meta_data=items,
|
||||
tp=c.characters if 'characters' in c.keys() else None,
|
||||
batch_group_size=bgs,
|
||||
min_seq_len=c.min_seq_len,
|
||||
max_seq_len=float("inf"),
|
||||
|
|
@ -137,9 +138,7 @@ class TestTTSDataset(unittest.TestCase):
|
|||
# NOTE: Below needs to check == 0 but due to an unknown reason
|
||||
# there is a slight difference between two matrices.
|
||||
# TODO: Check this assert cond more in detail.
|
||||
assert abs((abs(mel.T)
|
||||
- abs(mel_dl)
|
||||
).sum()) < 1e-5, (abs(mel.T) - abs(mel_dl)).sum()
|
||||
assert abs(mel.T - mel_dl).max() < 1e-5, abs(mel.T - mel_dl).max()
|
||||
|
||||
# check mel-spec correctness
|
||||
mel_spec = mel_input[0].cpu().numpy()
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ source /tmp/venv/bin/activate
|
|||
pip install --quiet --upgrade pip setuptools wheel
|
||||
|
||||
rm -f dist/*.whl
|
||||
python setup.py bdist_wheel --checkpoint tests/outputs/checkpoint_10.pth.tar --model_config tests/outputs/dummy_model_config.json
|
||||
python setup.py --quiet bdist_wheel --checkpoint tests/outputs/checkpoint_10.pth.tar --model_config tests/outputs/dummy_model_config.json
|
||||
pip install --quiet dist/TTS*.whl
|
||||
|
||||
python -m TTS.server.server &
|
||||
|
|
|
|||
|
|
@ -1,7 +1,14 @@
|
|||
import os
|
||||
# pylint: disable=unused-wildcard-import
|
||||
# pylint: disable=wildcard-import
|
||||
# pylint: disable=unused-import
|
||||
import unittest
|
||||
import torch as T
|
||||
|
||||
from TTS.utils.text import *
|
||||
from TTS.tests import get_tests_path
|
||||
from TTS.utils.generic_utils import load_config
|
||||
|
||||
TESTS_PATH = get_tests_path()
|
||||
conf = load_config(os.path.join(TESTS_PATH, 'test_config.json'))
|
||||
|
||||
def test_phoneme_to_sequence():
|
||||
text = "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase, the grey matter in the parts of the brain responsible for emotional regulation and learning!"
|
||||
|
|
@ -9,67 +16,80 @@ def test_phoneme_to_sequence():
|
|||
lang = "en-us"
|
||||
sequence = phoneme_to_sequence(text, text_cleaner, lang)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
|
||||
gt = "ɹiːsənt ɹɪsɜːtʃ æt hɑːɹvɚd hɐz ʃoʊn mɛdᵻteɪɾɪŋ fɔːɹ æz lɪɾəl æz eɪt wiːks kæn æktʃuːəli ɪnkɹiːs, ðə ɡɹeɪ mæɾɚɹ ɪnðə pɑːɹts ʌvðə bɹeɪn ɹɪspɑːnsəbəl fɔːɹ ɪmoʊʃənəl ɹɛɡjuːleɪʃən ænd lɜːnɪŋ!"
|
||||
assert text_hat == gt
|
||||
assert text_hat == text_hat_with_params == gt
|
||||
|
||||
# multiple punctuations
|
||||
text = "Be a voice, not an! echo?"
|
||||
sequence = phoneme_to_sequence(text, text_cleaner, lang)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ?"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
assert text_hat == gt
|
||||
assert text_hat == text_hat_with_params == gt
|
||||
|
||||
# not ending with punctuation
|
||||
text = "Be a voice, not an! echo"
|
||||
sequence = phoneme_to_sequence(text, text_cleaner, lang)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
assert text_hat == gt
|
||||
assert text_hat == text_hat_with_params == gt
|
||||
|
||||
# original
|
||||
text = "Be a voice, not an echo!"
|
||||
sequence = phoneme_to_sequence(text, text_cleaner, lang)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn ɛkoʊ!"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
assert text_hat == gt
|
||||
assert text_hat == text_hat_with_params == gt
|
||||
|
||||
# extra space after the sentence
|
||||
text = "Be a voice, not an! echo. "
|
||||
sequence = phoneme_to_sequence(text, text_cleaner, lang)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ."
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
assert text_hat == gt
|
||||
assert text_hat == text_hat_with_params == gt
|
||||
|
||||
# extra space after the sentence
|
||||
text = "Be a voice, not an! echo. "
|
||||
sequence = phoneme_to_sequence(text, text_cleaner, lang, True)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
|
||||
gt = "^biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ.~"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
assert text_hat == gt
|
||||
assert text_hat == text_hat_with_params == gt
|
||||
|
||||
# padding char
|
||||
text = "_Be a _voice, not an! echo_"
|
||||
sequence = phoneme_to_sequence(text, text_cleaner, lang)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
sequence_with_params = phoneme_to_sequence(text, text_cleaner, lang, tp=conf.characters)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence, tp=conf.characters)
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
assert text_hat == gt
|
||||
|
||||
assert text_hat == text_hat_with_params == gt
|
||||
|
||||
def test_text2phone():
|
||||
text = "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase, the grey matter in the parts of the brain responsible for emotional regulation and learning!"
|
||||
gt = "ɹ|iː|s|ə|n|t| |ɹ|ɪ|s|ɜː|tʃ| |æ|t| |h|ɑːɹ|v|ɚ|d| |h|ɐ|z| |ʃ|oʊ|n| |m|ɛ|d|ᵻ|t|eɪ|ɾ|ɪ|ŋ| |f|ɔː|ɹ| |æ|z| |l|ɪ|ɾ|əl| |æ|z| |eɪ|t| |w|iː|k|s| |k|æ|n| |æ|k|tʃ|uː|əl|i| |ɪ|n|k|ɹ|iː|s|,| |ð|ə| |ɡ|ɹ|eɪ| |m|æ|ɾ|ɚ|ɹ| |ɪ|n|ð|ə| |p|ɑːɹ|t|s| |ʌ|v|ð|ə| |b|ɹ|eɪ|n| |ɹ|ɪ|s|p|ɑː|n|s|ə|b|əl| |f|ɔː|ɹ| |ɪ|m|oʊ|ʃ|ə|n|əl| |ɹ|ɛ|ɡ|j|uː|l|eɪ|ʃ|ə|n| |æ|n|d| |l|ɜː|n|ɪ|ŋ|!"
|
||||
lang = "en-us"
|
||||
phonemes = text2phone(text, lang)
|
||||
assert gt == phonemes, f"\n{phonemes} \n vs \n{gt}"
|
||||
ph = text2phone(text, lang)
|
||||
assert gt == ph, f"\n{phonemes} \n vs \n{gt}"
|
||||
11
train.py
11
train.py
|
|
@ -20,12 +20,12 @@ from TTS.utils.generic_utils import (
|
|||
get_git_branch, load_config, remove_experiment_folder, save_best_model,
|
||||
save_checkpoint, adam_weight_decay, set_init_dict, copy_config_file,
|
||||
setup_model, gradual_training_scheduler, KeepAverage,
|
||||
set_weight_decay)
|
||||
set_weight_decay, check_config)
|
||||
from TTS.utils.logger import Logger
|
||||
from TTS.utils.speakers import load_speaker_mapping, save_speaker_mapping, \
|
||||
get_speakers
|
||||
from TTS.utils.synthesis import synthesis
|
||||
from TTS.utils.text.symbols import phonemes, symbols
|
||||
from TTS.utils.text.symbols import make_symbols, phonemes, symbols
|
||||
from TTS.utils.visual import plot_alignment, plot_spectrogram
|
||||
from TTS.datasets.preprocess import load_meta_data
|
||||
from TTS.utils.radam import RAdam
|
||||
|
|
@ -49,6 +49,7 @@ def setup_loader(ap, r, is_val=False, verbose=False):
|
|||
c.text_cleaner,
|
||||
meta_data=meta_data_eval if is_val else meta_data_train,
|
||||
ap=ap,
|
||||
tp=c.characters if 'characters' in c.keys() else None,
|
||||
batch_group_size=0 if is_val else c.batch_group_size *
|
||||
c.batch_size,
|
||||
min_seq_len=c.min_seq_len,
|
||||
|
|
@ -515,9 +516,12 @@ def evaluate(model, criterion, criterion_st, ap, global_step, epoch):
|
|||
|
||||
# FIXME: move args definition/parsing inside of main?
|
||||
def main(args): # pylint: disable=redefined-outer-name
|
||||
global meta_data_train, meta_data_eval
|
||||
# pylint: disable=global-variable-undefined
|
||||
global meta_data_train, meta_data_eval, symbols, phonemes
|
||||
# Audio processor
|
||||
ap = AudioProcessor(**c.audio)
|
||||
if 'characters' in c.keys():
|
||||
symbols, phonemes = make_symbols(**c.characters)
|
||||
|
||||
# DISTRUBUTED
|
||||
if num_gpus > 1:
|
||||
|
|
@ -687,6 +691,7 @@ if __name__ == '__main__':
|
|||
|
||||
# setup output paths and read configs
|
||||
c = load_config(args.config_path)
|
||||
check_config(c)
|
||||
_ = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
OUT_PATH = args.continue_path
|
||||
|
|
|
|||
|
|
@ -389,3 +389,131 @@ class KeepAverage():
|
|||
def update_values(self, value_dict):
|
||||
for key, value in value_dict.items():
|
||||
self.update_value(key, value)
|
||||
|
||||
|
||||
def _check_argument(name, c, enum_list=None, max_val=None, min_val=None, restricted=False, val_type=None):
|
||||
if restricted:
|
||||
assert name in c.keys(), f' [!] {name} not defined in config.json'
|
||||
if name in c.keys():
|
||||
if max_val:
|
||||
assert c[name] <= max_val, f' [!] {name} is larger than max value {max_val}'
|
||||
if min_val:
|
||||
assert c[name] >= min_val, f' [!] {name} is smaller than min value {min_val}'
|
||||
if enum_list:
|
||||
assert c[name].lower() in enum_list, f' [!] {name} is not a valid value'
|
||||
if val_type:
|
||||
assert isinstance(c[name], val_type) or c[name] is None, f' [!] {name} has wrong type - {type(c[name])} vs {val_type}'
|
||||
|
||||
|
||||
def check_config(c):
|
||||
_check_argument('model', c, enum_list=['tacotron', 'tacotron2'], restricted=True, val_type=str)
|
||||
_check_argument('run_name', c, restricted=True, val_type=str)
|
||||
_check_argument('run_description', c, val_type=str)
|
||||
|
||||
# AUDIO
|
||||
_check_argument('audio', c, restricted=True, val_type=dict)
|
||||
|
||||
# audio processing parameters
|
||||
_check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
|
||||
_check_argument('num_freq', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
|
||||
_check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
|
||||
_check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000)
|
||||
_check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000)
|
||||
_check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
|
||||
_check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
|
||||
_check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
|
||||
_check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
|
||||
_check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
|
||||
|
||||
# vocabulary parameters
|
||||
_check_argument('characters', c, restricted=False, val_type=dict)
|
||||
_check_argument('pad', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
|
||||
_check_argument('eos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
|
||||
_check_argument('bos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
|
||||
_check_argument('characters', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
|
||||
_check_argument('phonemes', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
|
||||
_check_argument('punctuations', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
|
||||
|
||||
# normalization parameters
|
||||
_check_argument('signal_norm', c['audio'], restricted=True, val_type=bool)
|
||||
_check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool)
|
||||
_check_argument('max_norm', c['audio'], restricted=True, val_type=float, min_val=0.1, max_val=1000)
|
||||
_check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
|
||||
_check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
|
||||
_check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
|
||||
_check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
|
||||
_check_argument('trim_db', c['audio'], restricted=True, val_type=int)
|
||||
|
||||
# training parameters
|
||||
_check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
|
||||
_check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
|
||||
_check_argument('r', c, restricted=True, val_type=int, min_val=1)
|
||||
_check_argument('gradual_training', c, restricted=False, val_type=list)
|
||||
_check_argument('loss_masking', c, restricted=True, val_type=bool)
|
||||
# _check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
|
||||
|
||||
# validation parameters
|
||||
_check_argument('run_eval', c, restricted=True, val_type=bool)
|
||||
_check_argument('test_delay_epochs', c, restricted=True, val_type=int, min_val=0)
|
||||
_check_argument('test_sentences_file', c, restricted=False, val_type=str)
|
||||
|
||||
# optimizer
|
||||
_check_argument('noam_schedule', c, restricted=False, val_type=bool)
|
||||
_check_argument('grad_clip', c, restricted=True, val_type=float, min_val=0.0)
|
||||
_check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
|
||||
_check_argument('lr', c, restricted=True, val_type=float, min_val=0)
|
||||
_check_argument('wd', c, restricted=True, val_type=float, min_val=0)
|
||||
_check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
|
||||
_check_argument('seq_len_norm', c, restricted=True, val_type=bool)
|
||||
|
||||
# tacotron prenet
|
||||
_check_argument('memory_size', c, restricted=True, val_type=int, min_val=-1)
|
||||
_check_argument('prenet_type', c, restricted=True, val_type=str, enum_list=['original', 'bn'])
|
||||
_check_argument('prenet_dropout', c, restricted=True, val_type=bool)
|
||||
|
||||
# attention
|
||||
_check_argument('attention_type', c, restricted=True, val_type=str, enum_list=['graves', 'original'])
|
||||
_check_argument('attention_heads', c, restricted=True, val_type=int)
|
||||
_check_argument('attention_norm', c, restricted=True, val_type=str, enum_list=['sigmoid', 'softmax'])
|
||||
_check_argument('windowing', c, restricted=True, val_type=bool)
|
||||
_check_argument('use_forward_attn', c, restricted=True, val_type=bool)
|
||||
_check_argument('forward_attn_mask', c, restricted=True, val_type=bool)
|
||||
_check_argument('transition_agent', c, restricted=True, val_type=bool)
|
||||
_check_argument('transition_agent', c, restricted=True, val_type=bool)
|
||||
_check_argument('location_attn', c, restricted=True, val_type=bool)
|
||||
_check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
|
||||
|
||||
# stopnet
|
||||
_check_argument('stopnet', c, restricted=True, val_type=bool)
|
||||
_check_argument('separate_stopnet', c, restricted=True, val_type=bool)
|
||||
|
||||
# tensorboard
|
||||
_check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
|
||||
_check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
|
||||
_check_argument('checkpoint', c, restricted=True, val_type=bool)
|
||||
_check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
|
||||
|
||||
# dataloading
|
||||
_check_argument('text_cleaner', c, restricted=True, val_type=str, enum_list=['english_cleaners', 'phoneme_cleaners', 'transliteration_cleaners', 'basic_cleaners'])
|
||||
_check_argument('enable_eos_bos_chars', c, restricted=True, val_type=bool)
|
||||
_check_argument('num_loader_workers', c, restricted=True, val_type=int, min_val=0)
|
||||
_check_argument('num_val_loader_workers', c, restricted=True, val_type=int, min_val=0)
|
||||
_check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0)
|
||||
_check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0)
|
||||
_check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10)
|
||||
|
||||
# paths
|
||||
_check_argument('output_path', c, restricted=True, val_type=str)
|
||||
|
||||
# multi-speaker gst
|
||||
_check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
|
||||
_check_argument('style_wav_for_test', c, restricted=True, val_type=str)
|
||||
_check_argument('use_gst', c, restricted=True, val_type=bool)
|
||||
|
||||
# datasets - checking only the first entry
|
||||
_check_argument('datasets', c, restricted=True, val_type=list)
|
||||
for dataset_entry in c['datasets']:
|
||||
_check_argument('name', dataset_entry, restricted=True, val_type=str)
|
||||
_check_argument('path', dataset_entry, restricted=True, val_type=str)
|
||||
_check_argument('meta_file_train', dataset_entry, restricted=True, val_type=str)
|
||||
_check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
|
||||
|
|
@ -9,10 +9,11 @@ def text_to_seqvec(text, CONFIG, use_cuda):
|
|||
if CONFIG.use_phonemes:
|
||||
seq = np.asarray(
|
||||
phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language,
|
||||
CONFIG.enable_eos_bos_chars),
|
||||
CONFIG.enable_eos_bos_chars,
|
||||
tp=CONFIG.characters if 'characters' in CONFIG.keys() else None),
|
||||
dtype=np.int32)
|
||||
else:
|
||||
seq = np.asarray(text_to_sequence(text, text_cleaner), dtype=np.int32)
|
||||
seq = np.asarray(text_to_sequence(text, text_cleaner, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None), dtype=np.int32)
|
||||
# torch tensor
|
||||
chars_var = torch.from_numpy(seq).unsqueeze(0)
|
||||
if use_cuda:
|
||||
|
|
@ -78,6 +79,7 @@ def synthesis(model,
|
|||
style_wav=None,
|
||||
truncated=False,
|
||||
enable_eos_bos_chars=False, #pylint: disable=unused-argument
|
||||
use_griffin_lim=False,
|
||||
do_trim_silence=False):
|
||||
"""Synthesize voice for the given text.
|
||||
|
||||
|
|
@ -111,8 +113,10 @@ def synthesis(model,
|
|||
postnet_output, decoder_output, alignment = parse_outputs(
|
||||
postnet_output, decoder_output, alignments)
|
||||
# plot results
|
||||
wav = inv_spectrogram(postnet_output, ap, CONFIG)
|
||||
# trim silence
|
||||
if do_trim_silence:
|
||||
wav = trim_silence(wav, ap)
|
||||
wav = None
|
||||
if use_griffin_lim:
|
||||
wav = inv_spectrogram(postnet_output, ap, CONFIG)
|
||||
# trim silence
|
||||
if do_trim_silence:
|
||||
wav = trim_silence(wav, ap)
|
||||
return wav, alignment, decoder_output, postnet_output, stop_tokens
|
||||
|
|
|
|||
|
|
@ -4,15 +4,15 @@ import re
|
|||
import phonemizer
|
||||
from phonemizer.phonemize import phonemize
|
||||
from TTS.utils.text import cleaners
|
||||
from TTS.utils.text.symbols import symbols, phonemes, _phoneme_punctuations, _bos, \
|
||||
from TTS.utils.text.symbols import make_symbols, symbols, phonemes, _phoneme_punctuations, _bos, \
|
||||
_eos
|
||||
|
||||
# Mappings from symbol to numeric ID and vice versa:
|
||||
_SYMBOL_TO_ID = {s: i for i, s in enumerate(symbols)}
|
||||
_ID_TO_SYMBOL = {i: s for i, s in enumerate(symbols)}
|
||||
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
||||
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
||||
|
||||
_PHONEMES_TO_ID = {s: i for i, s in enumerate(phonemes)}
|
||||
_ID_TO_PHONEMES = {i: s for i, s in enumerate(phonemes)}
|
||||
_phonemes_to_id = {s: i for i, s in enumerate(phonemes)}
|
||||
_id_to_phonemes = {i: s for i, s in enumerate(phonemes)}
|
||||
|
||||
# Regular expression matching text enclosed in curly braces:
|
||||
_CURLY_RE = re.compile(r'(.*?)\{(.+?)\}(.*)')
|
||||
|
|
@ -38,14 +38,11 @@ def text2phone(text, language):
|
|||
if text[-1] == punctuations[-1]:
|
||||
for punct in punctuations[:-1]:
|
||||
ph = ph.replace('| |\n', '|'+punct+'| |', 1)
|
||||
try:
|
||||
ph = ph + punctuations[-1]
|
||||
except:
|
||||
print(text)
|
||||
else:
|
||||
for punct in punctuations:
|
||||
ph = ph.replace('| |\n', '|'+punct+'| |', 1)
|
||||
elif float(phonemizer.__version__) == 2.1:
|
||||
elif float(phonemizer.__version__) > 2.1:
|
||||
ph = phonemize(text, separator=seperator, strip=False, njobs=1, backend='espeak', language=language, preserve_punctuation=True)
|
||||
# this is a simple fix for phonemizer.
|
||||
# https://github.com/bootphon/phonemizer/issues/32
|
||||
|
|
@ -59,11 +56,25 @@ def text2phone(text, language):
|
|||
return ph
|
||||
|
||||
|
||||
def pad_with_eos_bos(phoneme_sequence):
|
||||
return [_PHONEMES_TO_ID[_bos]] + list(phoneme_sequence) + [_PHONEMES_TO_ID[_eos]]
|
||||
def pad_with_eos_bos(phoneme_sequence, tp=None):
|
||||
# pylint: disable=global-statement
|
||||
global _phonemes_to_id, _bos, _eos
|
||||
if tp:
|
||||
_bos = tp['bos']
|
||||
_eos = tp['eos']
|
||||
_, _phonemes = make_symbols(**tp)
|
||||
_phonemes_to_id = {s: i for i, s in enumerate(_phonemes)}
|
||||
|
||||
return [_phonemes_to_id[_bos]] + list(phoneme_sequence) + [_phonemes_to_id[_eos]]
|
||||
|
||||
|
||||
def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False):
|
||||
def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False, tp=None):
|
||||
# pylint: disable=global-statement
|
||||
global _phonemes_to_id
|
||||
if tp:
|
||||
_, _phonemes = make_symbols(**tp)
|
||||
_phonemes_to_id = {s: i for i, s in enumerate(_phonemes)}
|
||||
|
||||
sequence = []
|
||||
text = text.replace(":", "")
|
||||
clean_text = _clean_text(text, cleaner_names)
|
||||
|
|
@ -75,21 +86,27 @@ def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False):
|
|||
sequence += _phoneme_to_sequence(phoneme)
|
||||
# Append EOS char
|
||||
if enable_eos_bos:
|
||||
sequence = pad_with_eos_bos(sequence)
|
||||
sequence = pad_with_eos_bos(sequence, tp=tp)
|
||||
return sequence
|
||||
|
||||
|
||||
def sequence_to_phoneme(sequence):
|
||||
def sequence_to_phoneme(sequence, tp=None):
|
||||
# pylint: disable=global-statement
|
||||
'''Converts a sequence of IDs back to a string'''
|
||||
global _id_to_phonemes
|
||||
result = ''
|
||||
if tp:
|
||||
_, _phonemes = make_symbols(**tp)
|
||||
_id_to_phonemes = {i: s for i, s in enumerate(_phonemes)}
|
||||
|
||||
for symbol_id in sequence:
|
||||
if symbol_id in _ID_TO_PHONEMES:
|
||||
s = _ID_TO_PHONEMES[symbol_id]
|
||||
if symbol_id in _id_to_phonemes:
|
||||
s = _id_to_phonemes[symbol_id]
|
||||
result += s
|
||||
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
|
||||
|
|
@ -102,6 +119,12 @@ def text_to_sequence(text, cleaner_names):
|
|||
Returns:
|
||||
List of integers corresponding to the symbols in the text
|
||||
'''
|
||||
# pylint: disable=global-statement
|
||||
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:
|
||||
|
|
@ -116,12 +139,18 @@ 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'''
|
||||
# pylint: disable=global-statement
|
||||
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:
|
||||
s = _ID_TO_SYMBOL[symbol_id]
|
||||
if symbol_id in _id_to_symbol:
|
||||
s = _id_to_symbol[symbol_id]
|
||||
# Enclose ARPAbet back in curly braces:
|
||||
if len(s) > 1 and s[0] == '@':
|
||||
s = '{%s}' % s[1:]
|
||||
|
|
@ -139,11 +168,11 @@ def _clean_text(text, cleaner_names):
|
|||
|
||||
|
||||
def _symbols_to_sequence(syms):
|
||||
return [_SYMBOL_TO_ID[s] for s in syms if _should_keep_symbol(s)]
|
||||
return [_symbol_to_id[s] for s in syms if _should_keep_symbol(s)]
|
||||
|
||||
|
||||
def _phoneme_to_sequence(phons):
|
||||
return [_PHONEMES_TO_ID[s] for s in list(phons) if _should_keep_phoneme(s)]
|
||||
return [_phonemes_to_id[s] for s in list(phons) if _should_keep_phoneme(s)]
|
||||
|
||||
|
||||
def _arpabet_to_sequence(text):
|
||||
|
|
@ -151,8 +180,8 @@ def _arpabet_to_sequence(text):
|
|||
|
||||
|
||||
def _should_keep_symbol(s):
|
||||
return s in _SYMBOL_TO_ID and s not in ['~', '^', '_']
|
||||
return s in _symbol_to_id and s not in ['~', '^', '_']
|
||||
|
||||
|
||||
def _should_keep_phoneme(p):
|
||||
return p in _PHONEMES_TO_ID and p not in ['~', '^', '_']
|
||||
return p in _phonemes_to_id and p not in ['~', '^', '_']
|
||||
|
|
|
|||
|
|
@ -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='^'):# pylint: disable=redefined-outer-name
|
||||
''' Function to create symbols and phonemes '''
|
||||
_phonemes_sorted = sorted(list(phonemes))
|
||||
|
||||
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
|
||||
_arpabet = ['@' + s for s in _phonemes_sorted]
|
||||
|
||||
# Export all symbols:
|
||||
_symbols = [pad, eos, bos] + list(characters) + _arpabet
|
||||
_phonemes = [pad, eos, bos] + list(_phonemes_sorted) + 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.characters if 'characters' in CONFIG.keys() else None)
|
||||
text = sequence_to_phoneme(seq, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None)
|
||||
print(text)
|
||||
|
||||
plt.yticks(range(len(text)), list(text))
|
||||
plt.colorbar()
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue