# 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.
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
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).
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.
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.
--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.
