# Finetune your own AM based on FastSpeech2 with multi-speakers dataset.
This example shows how to finetune your own AM based on FastSpeech2 with multi-speakers dataset. For finetuning Chinese data, we use part of csmsc's data (top 200) and Fastspeech2 pretrained model with AISHELL-3. For finetuning English data, we use part of ljspeech's data (top 200) and Fastspeech2 pretrained model with VCTK. The example is implemented according to this [discussion](https://github.com/PaddlePaddle/PaddleSpeech/discussions/1842). Thanks to the developer for the idea.
For more information on training Fastspeech2 with AISHELL-3, You can refer [examples/aishell3/tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3). For more information on training Fastspeech2 with VCTK, You can refer [examples/vctk/tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/tts3).
Assume the path to the model is `./pretrained_models`. </br>
If you want to finetune Chinese data, you need to download Fastspeech2 pretrained model with AISHELL-3: [fastspeech2_aishell3_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_ckpt_1.1.0.zip) for finetuning. Download HiFiGAN pretrained model with aishell3: [hifigan_aishell3_ckpt_0.2.0](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip) for synthesis.
If you want to finetune English data, you need to download Fastspeech2 pretrained model with VCTK: [fastspeech2_vctk_ckpt_1.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_ckpt_1.2.0.zip) for finetuning. Download HiFiGAN pretrained model with VCTK: [hifigan_vctk_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip) for synthesis.
```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).
If you want to finetune Chinese data, you need to download pretrained MFA models with aishell3: [aishell3_model.zip](https://paddlespeech.bj.bcebos.com/MFA/ernie_sat/aishell3_model.zip) and unzip it.
If you want to finetune English data, you need to download pretrained MFA models with vctk: [vctk_model.zip](https://paddlespeech.bj.bcebos.com/MFA/ernie_sat/vctk_model.zip) and unzip it.
Assume the path to the dataset is `./input` which contains a speaker folder. Speaker folder contains audio files (*.wav) and label file (labels.txt). The format of the audio file is wav. The format of the label file is: utt_id|pronunciation. </br>
If you want to finetune Chinese data, Chinese label example: 000001|ka2 er2 pu3 pei2 wai4 sun1 wan2 hua2 ti1</br>
Here is an example of the first 200 data of csmsc.
If you want to finetune English data, English label example: LJ001-0001|Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition </br>
Here is an example of the first 200 data of ljspeech.
`conf/finetune.yaml` contains some configurations for fine-tuning. You can try various options to fine better result. The value of frozen_layers can be change according `conf/fastspeech2_layers.txt` which is the model layer of fastspeech2.
-`batch_size`: finetune batch size which should be less than or equal to the number of training samples. Default: -1, means 64 which same to pretrained model
To synthesize Chinese audio, We use [HiFiGAN with aishell3](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.
To synthesize English audio, We use [HiFiGAN with vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc5) as the neural vocoder.
Assume the path to the hifigan model is `./pretrained_models`. Download the pretrained HiFiGAN model from [hifigan_vctk_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip) and unzip it.
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.
If you want to get better audio quality, you can use more audios to finetune or change configuration parameters in `conf/finetune.yaml`.</br>
More finetune results can be found on [finetune-fastspeech2-for-csmsc](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html#finetune-fastspeech2-for-csmsc).</br>
