# 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). ## Prepare ### Download Pretrained model Assume the path to the model is `./pretrained_models`.
If you want to finetune Chinese pretrained model, 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.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip) for synthesis. ```bash mkdir -p pretrained_models && cd pretrained_models # pretrained fastspeech2 model 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 # pretrained hifigan model 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 ../ ``` If you want to finetune English pretrained model, 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 # pretrained fastspeech2 model wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_vctk_ckpt_1.2.0.zip unzip fastspeech2_vctk_ckpt_1.2.0.zip # pretrained hifigan model wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip unzip hifigan_vctk_ckpt_0.2.0.zip cd ../ ``` If you want to finetune Chinese-English Mixed pretrained model, you need to download Fastspeech2 pretrained model with mix datasets: [fastspeech2_mix_ckpt_1.2.0.zip](https://paddlespeech.bj.bcebos.com/t2s/chinse_english_mixed/models/fastspeech2_mix_ckpt_1.2.0.zip) for finetuning. Download HiFiGAN pretrained model with aishell3: [hifigan_aishell3_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip) for synthesis. ```bash mkdir -p pretrained_models && cd pretrained_models # pretrained fastspeech2 model wget https://paddlespeech.bj.bcebos.com/t2s/chinse_english_mixed/models/fastspeech2_mix_ckpt_1.2.0.zip unzip fastspeech2_mix_ckpt_1.2.0.zip # pretrained hifigan model 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 ../ ``` ### Prepare your data 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.
If you want to finetune Chinese pretrained model, you need to prepare Chinese data. Chinese label example: ``` 000001|ka2 er2 pu3 pei2 wai4 sun1 wan2 hua2 ti1 ``` 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 ../ ``` If you want to finetune English pretrained model, you need to prepare 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 ``` Here is an example of the first 200 data of ljspeech. ```bash mkdir -p input && cd input wget https://paddlespeech.bj.bcebos.com/datasets/ljspeech_mini.zip unzip ljspeech_mini.zip cd ../ ``` If you want to finetune Chinese-English Mixed pretrained model, you need to prepare Chinese data or English data. Here is an example of the first 12 data of SSB0005 (the speaker of aishell3). ```bash mkdir -p input && cd input wget https://paddlespeech.bj.bcebos.com/datasets/SSB0005_mini.zip unzip SSB0005_mini.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). ```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 mkdir -p aligner && cd aligner ``` If you want to get mfa result of 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. ```bash # pretrained mfa model for Chinese data 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 ../../ ``` If you want to get mfa result of 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. ```bash # pretrained mfa model for English data wget https://paddlespeech.bj.bcebos.com/MFA/ernie_sat/vctk_model.zip unzip vctk_model.zip wget https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/cmudict-0.7b cd ../../ ``` When "Prepare" done. The structure of the current directory is similar to the following. ```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 │ ├── hifigan_aishell3_ckpt_0.2.0 │ │ ├── default.yaml │ │ ├── feats_stats.npy │ │ └── snapshot_iter_2500000.pdz │ └── hifigan_aishell3_ckpt_0.2.0.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 `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. Arguments: - `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 - `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 For finetuning Chinese pretrained model, execute `./run.sh`. For finetuning English pretrained model, execute `./run_en.sh`. For finetuning Chinese-English Mixed pretrained model, execute `./run_mix.sh`.
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 5 ``` `stage 5` of `run.sh` calls `local/finetune.py`, here's the complete help message. ```text usage: finetune.py [-h] [--pretrained_model_dir PRETRAINED_MODEL_DIR] [--dump_dir DUMP_DIR] [--output_dir OUTPUT_DIR] [--ngpu NGPU] [--epoch EPOCH] [--finetune_config FINETUNE_CONFIG] optional arguments: -h, --help Show this help message and exit --pretrained_model_dir PRETRAINED_MODEL_DIR Path to pretrained model --dump_dir DUMP_DIR directory to save feature files and metadata --output_dir OUTPUT_DIR Directory to save finetune model --ngpu NGPU The number of gpu, if ngpu=0, use cpu --epoch EPOCH The epoch of finetune --finetune_config FINETUNE_CONFIG Path to finetune config file ``` 1. `--pretrained_model_dir` is the directory incluing pretrained fastspeech2_aishell3 model. 2. `--dump_dir` is the directory including audio feature and metadata. 3. `--output_dir` is the directory to save finetune model. 4. `--ngpu` is the number of gpu, if ngpu=0, use cpu 5. `--epoch` is the epoch of finetune. 6. `--finetune_config` is the path to finetune config file ### Synthesizing 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. Modify `ckpt` in `run.sh` to the final model in `exp/default/checkpoints`. ```bash ./run.sh --stage 6 --stop-stage 6 ``` `stage 6` of `run.sh` calls `${BIN_DIR}/../synthesize_e2e.py`, which can synthesize waveform from text file. ```text usage: synthesize_e2e.py [-h] [--am {fastspeech2_aishell3,fastspeech2_vctk}] [--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_aishell3, pwgan_vctk, hifigan_aishell3, hifigan_vctk}] [--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 {fastspeech2_aishell3, fastspeech2_vctk} 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_aishell3, pwgan_vctk, hifigan_aishell3, hifigan_vctk} 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 or change configuration parameters in `conf/finetune.yaml`.
More finetune results can be found on [finetune-fastspeech2-for-csmsc](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html#finetune-fastspeech2-for-csmsc).
The results show the effect on csmsc_mini: Freeze encoder > Non Frozen > Freeze encoder && duration_predictor.