11 KiB
FastSpeech2 with the LJSpeech-1.1
This example contains code used to train a Fastspeech2 model with LJSpeech-1.1.
Dataset
Download and Extract
Download LJSpeech-1.1 from the official website.
Get MFA Result and Extract
We use MFA to get durations for fastspeech2. You can download from here ljspeech_alignment.tar.gz, or train your own MFA model reference to use_mfa example of our repo.
Get Started
Assume the path to the dataset is ~/datasets/LJSpeech-1.1
.
Assume the path to the MFA result of LJSpeech-1.1 is ./ljspeech_alignment
.
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, run 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
├── energy_stats.npy
├── norm
├── pitch_stats.npy
├── raw
└── speech_stats.npy
The dataset is split into 3 parts, namely train
, dev
and test
, each of which contains a norm
and raw
sub folder. The raw folder contains speech、pitch and energy features of each utterances, 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/*_stats.npy
.
Also there is a metadata.jsonl
in each subfolder. It is a table-like file which contains phones, text_lengths, speech_lengths, durations, path of speech features, path of pitch features, path of energy 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] [--verbose VERBOSE] [--phones-dict PHONES_DICT]
[--speaker-dict SPEAKER_DICT]
Train a FastSpeech2 model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG fastspeech2 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.
--verbose VERBOSE verbose.
--phones-dict PHONES_DICT
phone vocabulary file.
--speaker-dict SPEAKER_DICT
speaker id map file for multiple speaker model.
--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 save 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 parallel wavegan as the neural vocoder. Download pretrained parallel wavegan model from pwg_ljspeech_ckpt_0.5.zip and unzip it.
unzip pwg_ljspeech_ckpt_0.5.zip
Parallel WaveGAN checkpoint contains files listed below.
pwg_ljspeech_ckpt_0.5
├── pwg_default.yaml # default config used to train parallel wavegan
├── pwg_snapshot_iter_400000.pdz # generator parameters of parallel wavegan
└── pwg_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
./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] [--fastspeech2-config FASTSPEECH2_CONFIG]
[--fastspeech2-checkpoint FASTSPEECH2_CHECKPOINT]
[--fastspeech2-stat FASTSPEECH2_STAT]
[--pwg-config PWG_CONFIG]
[--pwg-checkpoint PWG_CHECKPOINT] [--pwg-stat PWG_STAT]
[--phones-dict PHONES_DICT] [--speaker-dict SPEAKER_DICT]
[--test-metadata TEST_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU] [--verbose VERBOSE]
Synthesize with fastspeech2 & parallel wavegan.
optional arguments:
-h, --help show this help message and exit
--fastspeech2-config FASTSPEECH2_CONFIG
fastspeech2 config file.
--fastspeech2-checkpoint FASTSPEECH2_CHECKPOINT
fastspeech2 checkpoint to load.
--fastspeech2-stat FASTSPEECH2_STAT
mean and standard deviation used to normalize
spectrogram when training fastspeech2.
--pwg-config PWG_CONFIG
parallel wavegan config file.
--pwg-checkpoint PWG_CHECKPOINT
parallel wavegan generator parameters to load.
--pwg-stat PWG_STAT mean and standard deviation used to normalize
spectrogram when training parallel wavegan.
--phones-dict PHONES_DICT
phone vocabulary file.
--speaker-dict SPEAKER_DICT
speaker id map file for multiple speaker model.
--test-metadata TEST_METADATA
test metadata.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
--verbose VERBOSE verbose.
./local/synthesize_e2e.sh
calls ${BIN_DIR}/synthesize_e2e_en.py
, which can synthesize waveform from text file.
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name}
usage: synthesize_e2e.py [-h] [--fastspeech2-config FASTSPEECH2_CONFIG]
[--fastspeech2-checkpoint FASTSPEECH2_CHECKPOINT]
[--fastspeech2-stat FASTSPEECH2_STAT]
[--pwg-config PWG_CONFIG]
[--pwg-checkpoint PWG_CHECKPOINT]
[--pwg-stat PWG_STAT] [--phones-dict PHONES_DICT]
[--text TEXT] [--output-dir OUTPUT_DIR]
[--inference-dir INFERENCE_DIR] [--ngpu NGPU]
[--verbose VERBOSE]
Synthesize with fastspeech2 & parallel wavegan.
optional arguments:
-h, --help show this help message and exit
--fastspeech2-config FASTSPEECH2_CONFIG
fastspeech2 config file.
--fastspeech2-checkpoint FASTSPEECH2_CHECKPOINT
fastspeech2 checkpoint to load.
--fastspeech2-stat FASTSPEECH2_STAT
mean and standard deviation used to normalize
spectrogram when training fastspeech2.
--pwg-config PWG_CONFIG
parallel wavegan config file.
--pwg-checkpoint PWG_CHECKPOINT
parallel wavegan generator parameters to load.
--pwg-stat PWG_STAT mean and standard deviation used to normalize
spectrogram when training parallel wavegan.
--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.
--inference-dir INFERENCE_DIR
dir to save inference models
--ngpu NGPU if ngpu == 0, use cpu.
--verbose VERBOSE verbose.
--fastspeech2-config
,--fastspeech2-checkpoint
,--fastspeech2-stat
and--phones-dict
are arguments for fastspeech2, which correspond to the 4 files in the fastspeech2 pretrained model.--pwg-config
,--pwg-checkpoint
,--pwg-stat
are arguments for parallel wavegan, which correspond to the 3 files in the parallel wavegan 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 FastSpeech2 model with no silence in the edge of audios. fastspeech2_nosil_ljspeech_ckpt_0.5.zip
Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/pitch_loss | eval/energy_loss |
---|---|---|---|---|---|---|
default | 2(gpu) x 100000 | 1.505682 | 0.612104 | 0.045505 | 0.62792 | 0.220147 |
FastSpeech2 checkpoint contains files listed below.
fastspeech2_nosil_ljspeech_ckpt_0.5
├── default.yaml # default config used to train fastspeech2
├── phone_id_map.txt # phone vocabulary file when training fastspeech2
├── snapshot_iter_100000.pdz # model parameters and optimizer states
└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2
You can use the following scripts to synthesize for ${BIN_DIR}/../sentences_en.txt
using pretrained fastspeech2 and parallel wavegan models.
source path.sh
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize_e2e_en.py \
--fastspeech2-config=fastspeech2_nosil_ljspeech_ckpt_0.5/default.yaml \
--fastspeech2-checkpoint=fastspeech2_nosil_ljspeech_ckpt_0.5/snapshot_iter_100000.pdz \
--fastspeech2-stat=fastspeech2_nosil_ljspeech_ckpt_0.5/speech_stats.npy \
--pwg-config=pwg_ljspeech_ckpt_0.5/pwg_default.yaml \
--pwg-checkpoint=pwg_ljspeech_ckpt_0.5/pwg_snapshot_iter_400000.pdz \
--pwg-stat=pwg_ljspeech_ckpt_0.5/pwg_stats.npy \
--text=${BIN_DIR}/../sentences_en.txt \
--output-dir=exp/default/test_e2e \
--phones-dict=fastspeech2_nosil_ljspeech_ckpt_0.5/phone_id_map.txt