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config update, audio.py update and modularize synthesize.py

pull/10/head
Eren Golge 2019-06-14 16:18:49 +02:00
parent e061ed091a
commit 037ec13453
5 changed files with 184 additions and 143 deletions
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6
.compute
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@ -10,7 +10,7 @@ wget https://www.dropbox.com/s/wqn5v3wkktw9lmo/install.sh?dl=0 -O install.sh
sudo sh install.sh
python3 setup.py develop
# cp -R ${USER_DIR}/GermanData ../tmp/
# python3 distribute.py --config_path config_tacotron_de.json --data_path ../tmp/GermanData/karlsson/
cp -R ${USER_DIR}/Mozilla_22050 ../tmp/
python3 distribute.py --config_path config_tacotron_gst.json --data_path ../tmp/Mozilla_22050/
python3 distribute.py --config_path config_tacotron_de.json --data_path ${USER_DIR}/GermanData/karlsson/
# cp -R ${USER_DIR}/Mozilla_22050 ../tmp/
# python3 distribute.py --config_path config_tacotron_gst.json --data_path ../tmp/Mozilla_22050/
while true; do sleep 1000000; done

198
config_tacotron_de.json
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@ -1,85 +1,117 @@
{
"run_name": "german-tacotron-tagent-bn",
"run_description": "train german",
"audio":{
// Audio processing parameters
"num_mels": 80, // size of the mel spec frame.
"num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"frame_length_ms": 50, // stft window length in ms.
"frame_shift_ms": 12.5, // stft window hop-lengh in ms.
"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.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
// Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": false, // move normalization to range [-1, 1]
"max_norm": 1, // 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!!
"do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
},
"distributed":{
"backend": "nccl",
"url": "tcp:\/\/localhost:54321"
},
"reinit_layers": [],
"model": "Tacotron", // one of the model in models/
"grad_clip": 1, // 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.
"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"windowing": false, // Enables attention windowing. Used only in eval mode.
"memory_size": 5, // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
"attention_norm": "sigmoid", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
"prenet_type": "original", // ONLY TACOTRON2 - "original" or "bn".
"prenet_dropout": true, // ONLY TACOTRON2 - enable/disable dropout at prenet.
"use_forward_attn": true, // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
"transition_agent": false, // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
"location_attn": false, // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default.
"loss_masking": true, // enable / disable loss masking against the sequence padding.
"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
"stopnet": true, // Train stopnet predicting the end of synthesis.
"separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"github_branch":"tacotron-gst-softmax",
"run_name": "german-tacotron-gst-softmax",
"run_description": "train german with all of the german dataset",
"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
"eval_batch_size":16,
"r": 5, // Number of frames to predict for step.
"wd": 0.000001, // Weight decay weight.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
"print_step": 10, // Number of steps to log traning on console.
"batch_group_size": 0, //Number of batches to shuffle after bucketing.
"run_eval": false,
"test_sentences_file": "de_sentences.txt", // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
"test_delay_epochs": 5, //Until attention is aligned, testing only wastes computation time.
"data_path": "/media/erogol/data_ssd/Data/Mozilla/", // DATASET-RELATED: can overwritten from command argument
"meta_file_train": [
"grune_haus/metadata.csv",
"kleine_lord/metadata.csv",
"toten_seelen/metadata.csv",
"werde_die_du_bist/metadata.csv"
], // DATASET-RELATED: metafile for training dataloader.
"meta_file_val": "metadata_val.txt", // DATASET-RELATED: metafile for evaluation dataloader.
"dataset": "mailabs", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
"min_seq_len": 0, // DATASET-RELATED: minimum text length to use in training
"max_seq_len": 200, // DATASET-RELATED: maximum text length
"output_path": "/media/erogol/data_ssd/Data/models/german/", // DATASET-RELATED: output path for all training outputs.
"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"num_val_loader_workers": 4, // number of evaluation data loader processes.
"phoneme_cache_path": "phoneme_cache", // phoneme computation is slow, therefore, it caches results in the given folder.
"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
"phoneme_language": "de", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
"text_cleaner": "phoneme_cleaners"
}
"audio":{
// Audio processing parameters
"num_mels": 80, // size of the mel spec frame.
"num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"frame_length_ms": 50, // stft window length in ms.
"frame_shift_ms": 12.5, // stft window hop-lengh in ms.
"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.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
// Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": false, // move normalization to range [-1, 1]
"max_norm": 1, // 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!!
"do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
},
"distributed":{
"backend": "nccl",
"url": "tcp:\/\/localhost:54321"
},
"reinit_layers": [],
"model": "Tacotron", // one of the model in models/
"grad_clip": 1, // upper limit for gradients for clipping.
"epochs": 10000, // total number of epochs to train.
"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"windowing": false, // Enables attention windowing. Used only in eval mode.
"memory_size": 5, // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
"attention_norm": "softmax", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
"prenet_type": "original", // ONLY TACOTRON2 - "original" or "bn".
"prenet_dropout": true, // ONLY TACOTRON2 - enable/disable dropout at prenet.
"use_forward_attn": true, // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
"transition_agent": false, // ONLY TACOTRON2 - enable/disable transition agent of forward attention.
"forward_attn_mask": true,
"location_attn": false, // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default.
"loss_masking": true, // enable / disable loss masking against the sequence padding.
"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
"stopnet": true, // Train stopnet predicting the end of synthesis.
"separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
"eval_batch_size":32,
"r": 5, // Number of frames to predict for step.
"wd": 0.000001, // Weight decay weight.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
"print_step": 10, // Number of steps to log traning on console.
"batch_group_size": 0, //Number of batches to shuffle after bucketing.
"run_eval": false,
"test_sentences_file": "de_sentences.txt", // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
"test_delay_epochs": 5, //Until attention is aligned, testing only wastes computation time.
"data_path": "/home/erogol/Data/m-ai-labs/de_DE/by_book/" , // DATASET-RELATED: can overwritten from command argument
"meta_file_train": [
"/home/erogol/Data/m-ai-labs/de_DE/by_book/mix/erzaehlungen_poe/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/mix/auf_zwei_planeten/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/kleinzaches/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/spiegel_kaetzchen/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/herrnarnesschatz/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/maedchen_von_moorhof/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/koenigsgaukler/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/altehous/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/odysseus/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/undine/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/reise_tilsit/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/schmied_seines_glueckes/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/kammmacher/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/unterm_birnbaum/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/liebesbriefe/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/sandmann/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/kleine_lord/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/toten_seelen/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/werde_die_du_bist/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/grune_haus/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/rebecca_braunert_plunkett/das_letzte_marchen/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/rebecca_braunert_plunkett/ferien_vom_ich/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/rebecca_braunert_plunkett/maerchen/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/rebecca_braunert_plunkett/mein_weg_als_deutscher_und_jude/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/caspar/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/sterben/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/weihnachtsabend/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/frankenstein/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/tschun/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/menschenhasser/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/grune_gesicht/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/tom_sawyer/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/ramona_deininger/alter_afrikaner/metadata.csv",
"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/angela_merkel/merkel_alone/metadata.csv"
], // DATASET-RELATED: metafile for training dataloader.
"meta_file_val": null, // DATASET-RELATED: metafile for evaluation dataloader.
"dataset": "mailabs", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
"min_seq_len": 15, // DATASET-RELATED: minimum text length to use in training
"max_seq_len": 200, // DATASET-RELATED: maximum text length
"output_path": "/media/erogol/data_ssd/Data/models/mozilla_models/", // DATASET-RELATED: output path for all training outputs.
"num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"num_val_loader_workers": 4, // number of evaluation data loader processes.
"phoneme_cache_path": "phoneme_cache", // phoneme computation is slow, therefore, it caches results in the given folder.
"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
"phoneme_language": "de", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
"text_cleaner": "phoneme_cleaners"
}

11
datasets/preprocess.py
View File

@ -63,7 +63,9 @@ def mailabs(root_path, meta_files):
"""Normalizes M-AI-Labs meta data files to TTS format"""
if meta_files is None:
meta_files = glob(root_path+"/**/metadata.csv", recursive=True)
folders = [os.path.dirname(f.strip()) for f in meta_files]
folders = [f.strip().split("/")[-2] for f in meta_files]
else:
folders = [f.strip().split("by_book")[1][1:] for f in meta_files]
# meta_files = [f.strip() for f in meta_files.split(",")]
items = []
for idx, meta_file in enumerate(meta_files):
@ -73,13 +75,12 @@ def mailabs(root_path, meta_files):
with open(txt_file, 'r') as ttf:
for line in ttf:
cols = line.split('|')
wav_file = os.path.join(root_path, folder, 'wavs',
cols[0] + '.wav')
wav_file = os.path.join(root_path, folder.replace("metadata.csv", ""), 'wavs', cols[0] + '.wav')
if os.path.isfile(wav_file):
text = cols[1]
text = cols[1].strip()
items.append([text, wav_file])
else:
continue
raise RuntimeError("> File %s is not exist!"%(wav_file))
return items

17
utils/audio.py
View File

@ -216,32 +216,23 @@ class AudioProcessor(object):
return librosa.effects.trim(
wav, top_db=40, frame_length=1024, hop_length=256)[0]
def mulaw_encode(self, wav, qc):
@staticmethod
def mulaw_encode(wav, qc):
mu = 2 ** qc - 1
# wav_abs = np.minimum(np.abs(wav), 1.0)
signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1. + mu)
# Quantize signal to the specified number of levels.
signal = (signal + 1) / 2 * mu + 0.5
return np.floor(signal,)
return np.floor(signal)
@staticmethod
def mulaw_decode(wav, qc):
"""Recovers waveform from quantized values."""
# from IPython.core.debugger import set_trace
# set_trace()
mu = 2 ** qc - 1
x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
return x
# mu = 2 ** qc - 1.
# # Map values back to [-1, 1].
# # casted = wav.astype(np.float32)
# # signal = 2 * casted / mu - 1
# # Perform inverse of mu-law transformation.
# magnitude = (1 / mu) * ((1 + mu) ** abs(wav) - 1)
# return np.sign(wav) * magnitude
def load_wav(self, filename, encode=False):
x, sr = sf.read(filename)
# x, sr = librosa.load(filename, sr=self.sample_rate)
if self.do_trim_silence:
x = self.trim_silence(x)
# sr, x = io.wavfile.read(filename)

95
utils/synthesis.py
View File

@ -10,36 +10,73 @@ from matplotlib import pylab as plt
def text_to_seqvec(text, CONFIG, use_cuda):
text_cleaner = [CONFIG.text_cleaner]
# text ot phonemes to sequence vector
if CONFIG.use_phonemes:
seq = np.asarray(
phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language, enable_eos_bos_chars),
phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language,
CONFIG.enable_eos_bos_chars),
dtype=np.int32)
else:
seq = np.asarray(text_to_sequence(text, text_cleaner), dtype=np.int32)
# torch tensor
chars_var = torch.from_numpy(seq).unsqueeze(0)
if use_cuda:
chars_var = chars_var.cuda()
return chars_var.long()
def compute_style_mel(style_wav, ap):
style_mel = torch.FloatTensor(ap.melspectrogram(ap.load_wav(style_wav))).unsqueeze(0)
return style_mel
def compute_style_mel(style_wav, ap, use_cuda):
print(style_wav)
style_mel = torch.FloatTensor(ap.melspectrogram(
ap.load_wav(style_wav))).unsqueeze(0)
if use_cuda:
return style_mel.cuda()
else:
return style_mel
def run_model():
pass
def run_model(model, inputs, CONFIG, truncated, style_mel=None):
if CONFIG.model == "TacotronGST" and style_mel is not None:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
inputs, style_mel)
else:
if truncated:
decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
inputs)
else:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
inputs)
return decoder_output, postnet_output, alignments, stop_tokens
def parse_outputs():
pass
def parse_outputs(postnet_output, decoder_output, alignments):
postnet_output = postnet_output[0].data.cpu().numpy()
decoder_output = decoder_output[0].data.cpu().numpy()
alignment = alignments[0].cpu().data.numpy()
return postnet_output, decoder_output, alignment
def trim_silence():
pass
def trim_silence(wav):
return wav[:ap.find_endpoint(wav)]
def synthesis(model, text, CONFIG, use_cuda, ap, style_wav=None, truncated=False, enable_eos_bos_chars=False, trim_silence=False):
def inv_spectrogram(postnet_output, ap, CONFIG):
if CONFIG.model in ["Tacotron", "TacotronGST"]:
wav = ap.inv_spectrogram(postnet_output.T)
else:
wav = ap.inv_mel_spectrogram(postnet_output.T)
return wav
def synthesis(model,
text,
CONFIG,
use_cuda,
ap,
style_wav=None,
truncated=False,
enable_eos_bos_chars=False,
trim_silence=False):
"""Synthesize voice for the given text.
Args:
@ -57,38 +94,18 @@ def synthesis(model, text, CONFIG, use_cuda, ap, style_wav=None, truncated=False
"""
# GST processing
if CONFIG.model == "TacotronGST" and style_wav is not None:
style_mel = compute_style_mel(style_wav, ap)
style_mel = compute_style_mel(style_wav, ap, use_cuda)
# preprocess the given text
text_cleaner = [CONFIG.text_cleaner]
if CONFIG.use_phonemes:
seq = np.asarray(
phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language, enable_eos_bos_chars),
dtype=np.int32)
else:
seq = np.asarray(text_to_sequence(text, text_cleaner), dtype=np.int32)
chars_var = torch.from_numpy(seq).unsqueeze(0)
inputs = text_to_seqvec(text, CONFIG, use_cuda)
# synthesize voice
if CONFIG.model == "TacotronGST" and style_wav is not None:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
chars_var.long(), style_mel)
else:
if truncated:
decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
chars_var.long())
else:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
chars_var.long())
decoder_output, postnet_output, alignments, stop_tokens = run_model(
model, inputs, CONFIG, truncated, style_mel)
# convert outputs to numpy
postnet_output = postnet_output[0].data.cpu().numpy()
decoder_output = decoder_output[0].data.cpu().numpy()
alignment = alignments[0].cpu().data.numpy()
postnet_output, decoder_output, alignment = parse_outputs(
postnet_output, decoder_output, alignments)
# plot results
if CONFIG.model in ["Tacotron", "TacotronGST"]:
wav = ap.inv_spectrogram(postnet_output.T)
else:
wav = ap.inv_mel_spectrogram(postnet_output.T)
wav = inv_spectrogram(postnet_output, ap, CONFIG)
# trim silence
if trim_silence:
wav = wav[:ap.find_endpoint(wav)]
wav = trim_silence(wav)
return wav, alignment, decoder_output, postnet_output, stop_tokens