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README.md
TransformerTTS with LJSpeech
Dataset
We experiment with the LJSpeech dataset. Download and unzip LJSpeech.
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
tar xjvf LJSpeech-1.1.tar.bz2
Get Started
Assume the path to the dataset is ~/datasets/LJSpeech-1.1
.
Run the command below to
- source path.
- preprocess the dataset.
- train the model.
- synthesize wavs.
- synthesize waveform from
metadata.jsonl
. - synthesize waveform from text file.
- synthesize waveform from
./run.sh
You can choose a range of stages you want to run, or set stage
equal to stop-stage
to use only one stage, for example, running the following command will only preprocess the dataset.
./run.sh --stage 0 --stop-stage 0
Data Preprocessing
./local/preprocess.sh ${conf_path}
When it is done. A dump
folder is created in the current directory. The structure of the dump folder is listed below.
dump
├── dev
│ ├── norm
│ └── raw
├── phone_id_map.txt
├── speaker_id_map.txt
├── test
│ ├── norm
│ └── raw
└── train
├── norm
├── raw
└── speech_stats.npy
The dataset is split into 3 parts, namely train
, dev
, and test
, each of which contains a norm
and raw
subfolder. The raw folder contains the speech feature of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in dump/train/speech_stats.npy
.
Also, there is a metadata.jsonl
in each subfolder. It is a table-like file that contains phones, text_lengths, speech_lengths, the path of speech features, speaker, and id of each utterance.
Model Training
./local/train.sh
calls ${BIN_DIR}/train.py
.
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
Here's the complete help message.
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU] [--phones-dict PHONES_DICT]
Train a TransformerTTS model with LJSpeech TTS dataset.
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file to overwrite default config.
--train-metadata TRAIN_METADATA
training data.
--dev-metadata DEV_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
--phones-dict PHONES_DICT
phone vocabulary file.
--config
is a config file in yaml format to overwrite the default config, which can be found atconf/default.yaml
.--train-metadata
and--dev-metadata
should be the metadata file in the normalized subfolder oftrain
anddev
in thedump
folder.--output-dir
is the directory to save the results of the experiment. Checkpoints are saved incheckpoints/
inside this directory.--ngpu
is the number of gpus to use, if ngpu == 0, use cpu.--phones-dict
is the path of the phone vocabulary file.
Synthesizing
We use waveflow as the neural vocoder. Download Pretrained WaveFlow Model with residual channel equals 128 from waveflow_ljspeech_ckpt_0.3.zip and unzip it.
unzip waveflow_ljspeech_ckpt_0.3.zip
WaveFlow checkpoint contains files listed below.
waveflow_ljspeech_ckpt_0.3
├── config.yaml # default config used to train waveflow
└── step-2000000.pdparams # model parameters of waveflow
./local/synthesize.sh
calls ${BIN_DIR}/synthesize.py
, which can synthesize waveform from metadata.jsonl
.
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
usage: synthesize.py [-h] [--transformer-tts-config TRANSFORMER_TTS_CONFIG]
[--transformer-tts-checkpoint TRANSFORMER_TTS_CHECKPOINT]
[--transformer-tts-stat TRANSFORMER_TTS_STAT]
[--waveflow-config WAVEFLOW_CONFIG]
[--waveflow-checkpoint WAVEFLOW_CHECKPOINT]
[--phones-dict PHONES_DICT]
[--test-metadata TEST_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU]
Synthesize with transformer tts & waveflow.
optional arguments:
-h, --help show this help message and exit
--transformer-tts-config TRANSFORMER_TTS_CONFIG
transformer tts config file.
--transformer-tts-checkpoint TRANSFORMER_TTS_CHECKPOINT
transformer tts checkpoint to load.
--transformer-tts-stat TRANSFORMER_TTS_STAT
mean and standard deviation used to normalize
spectrogram when training transformer tts.
--waveflow-config WAVEFLOW_CONFIG
waveflow config file.
--waveflow-checkpoint WAVEFLOW_CHECKPOINT
waveflow checkpoint to load.
--phones-dict PHONES_DICT
phone vocabulary file.
--test-metadata TEST_METADATA
test metadata.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
./local/synthesize_e2e.sh
calls ${BIN_DIR}/synthesize_e2e.py
, which can synthesize waveform from text file.
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name}
usage: synthesize_e2e.py [-h]
[--transformer-tts-config TRANSFORMER_TTS_CONFIG]
[--transformer-tts-checkpoint TRANSFORMER_TTS_CHECKPOINT]
[--transformer-tts-stat TRANSFORMER_TTS_STAT]
[--waveflow-config WAVEFLOW_CONFIG]
[--waveflow-checkpoint WAVEFLOW_CHECKPOINT]
[--phones-dict PHONES_DICT] [--text TEXT]
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
Synthesize with transformer tts & waveflow.
optional arguments:
-h, --help show this help message and exit
--transformer-tts-config TRANSFORMER_TTS_CONFIG
transformer tts config file.
--transformer-tts-checkpoint TRANSFORMER_TTS_CHECKPOINT
transformer tts checkpoint to load.
--transformer-tts-stat TRANSFORMER_TTS_STAT
mean and standard deviation used to normalize
spectrogram when training transformer tts.
--waveflow-config WAVEFLOW_CONFIG
waveflow config file.
--waveflow-checkpoint WAVEFLOW_CHECKPOINT
waveflow checkpoint to load.
--phones-dict PHONES_DICT
phone vocabulary file.
--text TEXT text to synthesize, a 'utt_id sentence' pair per line.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
--transformer-tts-config
,--transformer-tts-checkpoint
,--transformer-tts-stat
and--phones-dict
are arguments for transformer_tts, which correspond to the 4 files in the transformer_tts pretrained model.--waveflow-config
,--waveflow-checkpoint
are arguments for waveflow, which correspond to the 2 files in the waveflow pretrained model.--test-metadata
should be the metadata file in the normalized subfolder oftest
in thedump
folder.--text
is the text file, which contains sentences to synthesize.--output-dir
is the directory to save synthesized audio files.--ngpu
is the number of gpus to use, if ngpu == 0, use cpu.
Pretrained Model
Pretrained Model can be downloaded here. transformer_tts_ljspeech_ckpt_0.4.zip
TransformerTTS checkpoint contains files listed below.
transformer_tts_ljspeech_ckpt_0.4
├── default.yaml # default config used to train transformer_tts
├── phone_id_map.txt # phone vocabulary file when training transformer_tts
├── snapshot_iter_201500.pdz # model parameters and optimizer states
└── speech_stats.npy # statistics used to normalize spectrogram when training transformer_tts
You can use the following scripts to synthesize for ${BIN_DIR}/../sentences_en.txt
using pretrained transformer_tts and waveflow models.
source path.sh
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize_e2e.py \
--transformer-tts-config=transformer_tts_ljspeech_ckpt_0.4/default.yaml \
--transformer-tts-checkpoint=transformer_tts_ljspeech_ckpt_0.4/snapshot_iter_201500.pdz \
--transformer-tts-stat=transformer_tts_ljspeech_ckpt_0.4/speech_stats.npy \
--waveflow-config=waveflow_ljspeech_ckpt_0.3/config.yaml \
--waveflow-checkpoint=waveflow_ljspeech_ckpt_0.3/step-2000000.pdparams \
--text=${BIN_DIR}/../sentences_en.txt \
--output-dir=exp/default/test_e2e \
--phones-dict=transformer_tts_ljspeech_ckpt_0.4/phone_id_map.txt