# Finetune your own AM based on FastSpeech2 with AISHELL-3.
This example shows how to finetune your own AM based on FastSpeech2 with AISHELL-3. We use part of csmsc's data (top 200) as finetune data in this example. The example is implemented according to this [discussion](https://github.com/PaddlePaddle/PaddleSpeech/discussions/1842). Thanks to the developer for the idea.
We use AISHELL-3 to train a multi-speaker fastspeech2 model. You can refer [examples/aishell3/tts3](https://github.com/lym0302/PaddleSpeech/tree/develop/examples/aishell3/tts3) to train multi-speaker fastspeech2 from scratch.
## Prepare
### Download Pretrained Fastspeech2 model
Assume the path to the model is `./pretrained_models`. Download pretrained fastspeech2 model with aishell3: [fastspeech2_aishell3_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_ckpt_1.1.0.zip).
```bash
mkdir -p pretrained_models && cd pretrained_models
wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_ckpt_1.1.0.zip
unzip fastspeech2_aishell3_ckpt_1.1.0.zip
cd ../
```
### Download MFA tools and pretrained model
Assume the path to the MFA tool is `./tools`. Download [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/download/v1.0.1/montreal-forced-aligner_linux.tar.gz) and pretrained MFA models with aishell3: [aishell3_model.zip](https://paddlespeech.bj.bcebos.com/MFA/ernie_sat/aishell3_model.zip).
```bash
mkdir -p tools && cd tools
# mfa tool
wget https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/download/v1.0.1/montreal-forced-aligner_linux.tar.gz
tar xvf montreal-forced-aligner_linux.tar.gz
cp montreal-forced-aligner/lib/libpython3.6m.so.1.0 montreal-forced-aligner/lib/libpython3.6m.so
# pretrained mfa model
mkdir -p aligner && cd aligner
wget https://paddlespeech.bj.bcebos.com/MFA/ernie_sat/aishell3_model.zip
unzip aishell3_model.zip
wget https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/simple.lexicon
cd ../../
```
### Prepare your data
Assume the path to the dataset is `./input`. This directory contains audio files (*.wav) and label file (labels.txt). The audio file is in wav format. The format of the label file is: utt_id|pinyin. Here is an example of the first 200 data of csmsc.
```bash
mkdir -p input && cd input
wget https://paddlespeech.bj.bcebos.com/datasets/csmsc_mini.zip
unzip csmsc_mini.zip
cd ../
```
When "Prepare" done. The structure of the current directory is listed below.
```text
├── input
│ ├── csmsc_mini
│ │ ├── 000001.wav
│ │ ├── 000002.wav
│ │ ├── 000003.wav
│ │ ├── ...
│ │ ├── 000200.wav
│ │ ├── labels.txt
│ └── csmsc_mini.zip
├── pretrained_models
│ ├── fastspeech2_aishell3_ckpt_1.1.0
│ │ ├── default.yaml
│ │ ├── energy_stats.npy
│ │ ├── phone_id_map.txt
│ │ ├── pitch_stats.npy
│ │ ├── snapshot_iter_96400.pdz
│ │ ├── speaker_id_map.txt
│ │ └── speech_stats.npy
│ └── fastspeech2_aishell3_ckpt_1.1.0.zip
└── tools
├── aligner
│ ├── aishell3_model
│ ├── aishell3_model.zip
│ └── simple.lexicon
├── montreal-forced-aligner
│ ├── bin
│ ├── lib
│ └── pretrained_models
└── montreal-forced-aligner_linux.tar.gz
...
```
### Set finetune.yaml
`finetune.yaml` contains some configurations for fine-tuning. You can try various options to fine better result.
Arguments:
- `batch_size`: finetune batch size. Default: -1, means 64 which same to pretrained model
- `learning_rate`: learning rate. Default: 0.0001
- `num_snapshots`: number of save models. Default: -1, means 5 which same to pretrained model
- `frozen_layers`: frozen layers. must be a list. If you don't want to frozen any layer, set [].
## Get Started
Run the command below to
1. **source path**.
2. finetune the model.
3. synthesize wavs.
- synthesize waveform from text file.
```bash
./run.sh
```
You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to run only one stage.
### Model Finetune
Finetune a FastSpeech2 model.
```bash
./run.sh --stage 0 --stop-stage 0
```
`stage 0` of `run.sh` calls `finetune.py`, here's the complete help message.
```text
usage: finetune.py [-h] [--input_dir INPUT_DIR] [--pretrained_model_dir PRETRAINED_MODEL_DIR]
[--mfa_dir MFA_DIR] [--dump_dir DUMP_DIR]
[--output_dir OUTPUT_DIR] [--lang LANG]
[--ngpu NGPU]
optional arguments:
-h, --help show this help message and exit
--input_dir INPUT_DIR
directory containing audio and label file
--pretrained_model_dir PRETRAINED_MODEL_DIR
Path to pretrained model
--mfa_dir MFA_DIR directory to save aligned files
--dump_dir DUMP_DIR
directory to save feature files and metadata
--output_dir OUTPUT_DIR
directory to save finetune model
--lang LANG Choose input audio language, zh or en
--ngpu NGPU if ngpu=0, use cpu
--epoch EPOCH the epoch of finetune
--batch_size BATCH_SIZE
the batch size of finetune, default -1 means same as pretrained model
```
1. `--input_dir` is the directory containing audio and label file.
2. `--pretrained_model_dir` is the directory incluing pretrained fastspeech2_aishell3 model.
3. `--mfa_dir` is the directory to save the results of aligning from pretrained MFA_aishell3 model.
4. `--dump_dir` is the directory including audio feature and metadata.
5. `--output_dir` is the directory to save finetune model.
6. `--lang` is the language of input audio, zh or en.
7. `--ngpu` is the number of gpu.
8. `--epoch` is the epoch of finetune.
9. `--batch_size` is the batch size of finetune.
### Synthesizing
We use [HiFiGAN](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc5) as the neural vocoder.
Assume the path to the hifigan model is `./pretrained_models`. Download the pretrained HiFiGAN model from [hifigan_aishell3_ckpt_0.2.0](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip) and unzip it.
```bash
cd pretrained_models
wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip
unzip hifigan_aishell3_ckpt_0.2.0.zip
cd ../
```
HiFiGAN checkpoint contains files listed below.
```text
hifigan_aishell3_ckpt_0.2.0
├── default.yaml # default config used to train HiFiGAN
├── feats_stats.npy # statistics used to normalize spectrogram when training HiFiGAN
└── snapshot_iter_2500000.pdz # generator parameters of HiFiGAN
```
Modify `ckpt` in `run.sh` to the final model in `exp/default/checkpoints`.
```bash
./run.sh --stage 1 --stop-stage 1
```
`stage 1` of `run.sh` calls `${BIN_DIR}/../synthesize_e2e.py`, which can synthesize waveform from text file.
```text
usage: synthesize_e2e.py [-h]
[--am {speedyspeech_csmsc,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}]
[--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
[--am_stat AM_STAT] [--phones_dict PHONES_DICT]
[--tones_dict TONES_DICT]
[--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID]
[--voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc,style_melgan_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,wavernn_csmsc}]
[--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
[--voc_stat VOC_STAT] [--lang LANG]
[--inference_dir INFERENCE_DIR] [--ngpu NGPU]
[--text TEXT] [--output_dir OUTPUT_DIR]
Synthesize with acoustic model & vocoder
optional arguments:
-h, --help show this help message and exit
--am {speedyspeech_csmsc,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}
Choose acoustic model type of tts task.
--am_config AM_CONFIG
Config of acoustic model.
--am_ckpt AM_CKPT Checkpoint file of acoustic model.
--am_stat AM_STAT mean and standard deviation used to normalize
spectrogram when training acoustic model.
--phones_dict PHONES_DICT
phone vocabulary file.
--tones_dict TONES_DICT
tone vocabulary file.
--speaker_dict SPEAKER_DICT
speaker id map file.
--spk_id SPK_ID spk id for multi speaker acoustic model
--voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc,style_melgan_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,wavernn_csmsc}
Choose vocoder type of tts task.
--voc_config VOC_CONFIG
Config of voc.
--voc_ckpt VOC_CKPT Checkpoint file of voc.
--voc_stat VOC_STAT mean and standard deviation used to normalize
spectrogram when training voc.
--lang LANG Choose model language. zh or en
--inference_dir INFERENCE_DIR
dir to save inference models
--ngpu NGPU if ngpu == 0, use cpu.
--text TEXT text to synthesize, a 'utt_id sentence' pair per line.
--output_dir OUTPUT_DIR
output dir.
```
1. `--am` is acoustic model type with the format {model_name}_{dataset}
2. `--am_config`, `--am_ckpt`, `--am_stat`, `--phones_dict` `--speaker_dict` are arguments for acoustic model, which correspond to the 5 files in the fastspeech2 pretrained model.
3. `--voc` is vocoder type with the format {model_name}_{dataset}
4. `--voc_config`, `--voc_ckpt`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
5. `--lang` is the model language, which can be `zh` or `en`.
6. `--text` is the text file, which contains sentences to synthesize.
7. `--output_dir` is the directory to save synthesized audio files.
8. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
### Tips
If you want to get better audio quality, you can use more audios to finetune.