You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
138 lines
7.2 KiB
138 lines
7.2 KiB
# FastSpeech2 + AISHELL-3 Voice Cloning
|
|
This example contains code used to train a [FastSpeech2](https://arxiv.org/abs/2006.04558) model with [AISHELL-3](http://www.aishelltech.com/aishell_3). The trained model can be used in Voice Cloning Task, We refer to the model structure of [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf). The general steps are as follows:
|
|
1. Speaker Encoder: We use Speaker Verification to train a speaker encoder. Datasets used in this task are different from those used in `FastSpeech2` because the transcriptions are not needed, we use more datasets, refer to [ge2e](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/ge2e).
|
|
2. Synthesizer: We use the trained speaker encoder to generate speaker embedding for each sentence in AISHELL-3. This embedding is an extra input of `FastSpeech2` which will be concated with encoder outputs.
|
|
3. Vocoder: We use [Parallel Wave GAN](http://arxiv.org/abs/1910.11480) as the neural Vocoder, refer to [voc1](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1).
|
|
|
|
## Dataset
|
|
### Download and Extract
|
|
Download AISHELL-3.
|
|
```bash
|
|
wget https://www.openslr.org/resources/93/data_aishell3.tgz
|
|
```
|
|
Extract AISHELL-3.
|
|
```bash
|
|
mkdir data_aishell3
|
|
tar zxvf data_aishell3.tgz -C data_aishell3
|
|
```
|
|
### Get MFA Result and Extract
|
|
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
|
|
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
|
|
|
|
## Pretrained GE2E Model
|
|
We use pretrained GE2E model to generate speaker embedding for each sentence.
|
|
|
|
Download pretrained GE2E model from here [ge2e_ckpt_0.3.zip](https://bj.bcebos.com/paddlespeech/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip), and `unzip` it.
|
|
|
|
## Get Started
|
|
Assume the path to the dataset is `~/datasets/data_aishell3`.
|
|
Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
|
|
Assume the path to the pretrained ge2e model is `./ge2e_ckpt_0.3`.
|
|
|
|
Run the command below to
|
|
1. **source path**.
|
|
2. preprocess the dataset.
|
|
3. train the model.
|
|
4. synthesize waveform from `metadata.jsonl`.
|
|
5. start a voice cloning inference.
|
|
```bash
|
|
./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.
|
|
```bash
|
|
./run.sh --stage 0 --stop-stage 0
|
|
```
|
|
### Data Preprocessing
|
|
```bash
|
|
CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${ge2e_ckpt_path}
|
|
```
|
|
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
|
|
```text
|
|
dump
|
|
├── dev
|
|
│ ├── norm
|
|
│ └── raw
|
|
├── embed
|
|
│ ├── SSB0005
|
|
│ ├── SSB0009
|
|
│ ├── ...
|
|
│ └── ...
|
|
├── phone_id_map.txt
|
|
├── speaker_id_map.txt
|
|
├── test
|
|
│ ├── norm
|
|
│ └── raw
|
|
└── train
|
|
├── energy_stats.npy
|
|
├── norm
|
|
├── pitch_stats.npy
|
|
├── raw
|
|
└── speech_stats.npy
|
|
```
|
|
The `embed` contains the generated speaker embedding for each sentence in AISHELL-3, which has the same file structure with wav files and the format is `.npy`.
|
|
|
|
The computing time of utterance embedding can be x hours.
|
|
|
|
The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains speech、pitch and energy features of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/*_stats.npy`.
|
|
|
|
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, text_lengths, speech_lengths, durations, the path of speech features, the path of pitch features, the path of energy features, speaker, and id of each utterance.
|
|
|
|
The preprocessing step is very similar to that one of [tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3), but there is one more `ge2e/inference` step here.
|
|
|
|
### Model Training
|
|
`./local/train.sh` calls `${BIN_DIR}/train.py`.
|
|
```bash
|
|
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
|
|
```
|
|
The training step is very similar to that one of [tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/train.py`.
|
|
|
|
### Synthesizing
|
|
We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1) as the neural vocoder.
|
|
Download pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip) and unzip it.
|
|
```bash
|
|
unzip pwg_aishell3_ckpt_0.5.zip
|
|
```
|
|
Parallel WaveGAN checkpoint contains files listed below.
|
|
```text
|
|
pwg_aishell3_ckpt_0.5
|
|
├── default.yaml # default config used to train parallel wavegan
|
|
├── feats_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
|
|
└── snapshot_iter_1000000.pdz # generator parameters of parallel wavegan
|
|
```
|
|
`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
|
|
```bash
|
|
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
|
|
```
|
|
The synthesizing step is very similar to that one of [tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/../synthesize.py`.
|
|
|
|
### Voice Cloning
|
|
Assume there are some reference audios in `./ref_audio`
|
|
```text
|
|
ref_audio
|
|
├── 001238.wav
|
|
├── LJ015-0254.wav
|
|
└── audio_self_test.mp3
|
|
```
|
|
`./local/voice_cloning.sh` calls `${BIN_DIR}/../voice_cloning.py`
|
|
|
|
```bash
|
|
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${ref_audio_dir}
|
|
```
|
|
## Pretrained Model
|
|
[fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip)
|
|
|
|
Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/pitch_loss| eval/energy_loss
|
|
:-------------:| :------------:| :-----: | :-----: | :--------: |:--------:|:---------:
|
|
default|2(gpu) x 96400|0.99699|0.62013|0.53057|0.11954| 0.20426|
|
|
|
|
FastSpeech2 checkpoint contains files listed below.
|
|
(There is no need for `speaker_id_map.txt` here )
|
|
|
|
```text
|
|
fastspeech2_nosil_aishell3_ckpt_vc1_0.5
|
|
├── default.yaml # default config used to train fastspeech2
|
|
├── phone_id_map.txt # phone vocabulary file when training fastspeech2
|
|
├── snapshot_iter_96400.pdz # model parameters and optimizer states
|
|
└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2
|
|
```
|