PaddleSpeech/examples/ljspeech/tts1

TransformerTTS with LJSpeech

Dataset

Download and Extract

Download LJSpeech-1.1 from it's Official Website and extract it to ~/datasets. Then the dataset is in the directory ~/datasets/LJSpeech-1.1.

Get Started

Assume the path to the dataset is ~/datasets/LJSpeech-1.1 and extract it to ~/datasets. Then the dataset is in the directory ~/datasets/LJSpeech-1.1.

Run the command below to

1. source path.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
   • synthesize waveform from metadata.jsonl.
   • synthesize waveform from text file.

./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       TransformerTTS config file.
  --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.

1. --config is a config file in yaml format to overwrite the default config, which can be found at conf/default.yaml.
2. --train-metadata and --dev-metadata should be the metadata file in the normalized subfolder of train and dev in the dump folder.
3. --output-dir is the directory to save the results of the experiment. Checkpoints are saved in checkpoints/ inside this directory.
4. --ngpu is the number of gpus to use, if ngpu == 0, use cpu.
5. --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.

1. --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.
2. --waveflow-config, --waveflow-checkpoint are arguments for waveflow, which correspond to the 2 files in the waveflow pretrained model.
3. --test-metadata should be the metadata file in the normalized subfolder of test in the dump folder.
4. --text is the text file, which contains sentences to synthesize.
5. --output-dir is the directory to save synthesized audio files.
6. --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