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README.md
FastSpeech2 with Cantonese language
Dataset
Download and Extract
If you don't have the Cantonese datasets mentioned above, please download and unzip Guangzhou_Cantonese_Scripted_Speech_Corpus_Daily_Use_Sentence and Guangzhou_Cantonese_Scripted_Speech_Corpus_in_Vehicle under ~/datasets/
.
To obtain better performance, please combine these two datasets together as follows:
mkdir -p ~/datasets/canton_all/WAV
cp -r ~/datasets/Guangzhou_Cantonese_Scripted_Speech_Corpus_Daily_Use_Sentence/WAV/* ~/datasets/canton_all/WAV
cp -r ~/datasets/Guangzhou_Cantonese_Scripted_Speech_Corpus_in_Vehicle/WAV/* ~/datasets/canton_all/WAV
After that, it should be look like:
~/datasets/canton_all
│ └── WAV
│ └──G0001
│ └──G0002
│ ...
│ └──G0071
│ └──G0072
Get MFA Result and Extract
We use MFA1.x to get durations for canton_fastspeech2. You can train your MFA model reference to canton_mfa example (use MFA1.x now) of our repo. We here provide the MFA results of these two datasets. canton_alignment.zip
Get Started
Assume the path to the Cantonese MFA result of the two datsets mentioned above is ./canton_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, 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
├── 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
subfolder. The raw folder contains speech、pitch and energy features 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/*_stats.npy
.
Also, there is a metadata.jsonl
in each subfolder. It is a table-like file that contains phones, text_lengths, speech_lengths, durations, the path of speech features, the path of pitch features, a path of energy features, speaker, and id of each utterance.
Training details can refer to the script of examples/aishell3/tts3.
Pretrained Model
Pretrained FastSpeech2 model with no silence in the edge of audios:
The static model can be downloaded here:
The ONNX model can be downloaded here:
FastSpeech2 checkpoint contains files listed below.
fastspeech2_canton_ckpt_1.4.0
├── default.yaml # default config used to train fastspeech2
├── energy_stats.npy # statistics used to normalize energy when training fastspeech2
├── phone_id_map.txt # phone vocabulary file when training fastspeech2
├── pitch_stats.npy # statistics used to normalize pitch when training fastspeech2
├── snapshot_iter_140000.pdz # model parameters and optimizer states
├── speaker_id_map.txt # speaker id map file when training a multi-speaker fastspeech2
└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2
We use parallel wavegan as the neural vocoder. Download the pretrained parallel wavegan model from pwg_aishell3_ckpt_0.5.zip and unzip it.
unzip pwg_aishell3_ckpt_0.5.zip
You can use the following scripts to synthesize for ${BIN_DIR}/../../assets/sentences_canton.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 ${BIN_DIR}/../synthesize_e2e.py \
--am=fastspeech2_aishell3 \
--am_config=fastspeech2_canton_ckpt_1.4.0/default.yaml \
--am_ckpt=fastspeech2_canton_ckpt_1.4.0/snapshot_iter_140000.pdz \
--am_stat=fastspeech2_canton_ckpt_1.4.0/speech_stats.npy \
--voc=pwgan_aishell3 \
--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
--lang=canton \
--text=${BIN_DIR}/../../assets/sentences_canton.txt \
--output_dir=exp/default/test_e2e \
--phones_dict=fastspeech2_canton_ckpt_1.4.0/phone_id_map.txt \
--speaker_dict=fastspeech2_canton_ckpt_1.4.0/speaker_id_map.txt \
--spk_id=10 \
--inference_dir=exp/default/inference