TianYuan
2 years ago
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# FastSpeech2 + AISHELL-3 Voice Cloning (ECAPA-TDNN)
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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:
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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 [ECAPA-TDNN](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0).
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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.
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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).
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## Dataset
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### Download and Extract
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Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
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### Get MFA Result and Extract
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We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
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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.
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## Get Started
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Assume the path to the dataset is `~/datasets/data_aishell3`.
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Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
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Run the command below to
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1. **source path**.
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2. preprocess the dataset.
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3. train the model.
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4. synthesize waveform from `metadata.jsonl`.
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5. start a voice cloning inference.
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```bash
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./run.sh
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```
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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.
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```bash
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./run.sh --stage 0 --stop-stage 0
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```
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### Data Preprocessing
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── embed
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│ ├── SSB0005
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│ ├── SSB0009
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│ ├── ...
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│ └── ...
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├── phone_id_map.txt
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├── speaker_id_map.txt
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── energy_stats.npy
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├── norm
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├── pitch_stats.npy
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├── raw
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└── speech_stats.npy
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```
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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`.
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The computing time of utterance embedding can be x hours.
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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`.
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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.
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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 `ECAPA-TDNN/inference` step here.
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### Model Training
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`./local/train.sh` calls `${BIN_DIR}/train.py`.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
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```
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The training step is very similar to that one of [tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/train.py`.
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### Synthesizing
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We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1) as the neural vocoder.
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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.
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```bash
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unzip pwg_aishell3_ckpt_0.5.zip
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```
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Parallel WaveGAN checkpoint contains files listed below.
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```text
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pwg_aishell3_ckpt_0.5
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├── default.yaml # default config used to train parallel wavegan
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├── feats_stats.npy # statistics used to normalize spectrogram when training parallel wavegan
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└── snapshot_iter_1000000.pdz # generator parameters of parallel wavegan
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```
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`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
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```
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The synthesizing step is very similar to that one of [tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/../synthesize.py`.
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### Voice Cloning
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Assume there are some reference audios in `./ref_audio` (the format must be wav here)
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```text
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ref_audio
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├── 001238.wav
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├── LJ015-0254.wav
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└── audio_self_test.wav
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```
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`./local/voice_cloning.sh` calls `${BIN_DIR}/../voice_cloning.py`
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ref_audio_dir}
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```
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## Pretrained Model
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- [fastspeech2_aishell3_ckpt_vc2_1.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_aishell3_ckpt_vc2_1.2.0.zip)
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Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/pitch_loss| eval/energy_loss
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:-------------:| :------------:| :-----: | :-----: | :--------: |:--------:|:---------:
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default|2(gpu) x 96400|0.991855|0.599517|0.052142|0.094877| 0.245318|
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FastSpeech2 checkpoint contains files listed below.
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(There is no need for `speaker_id_map.txt` here )
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```text
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fastspeech2_aishell3_ckpt_vc2_1.2.0
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├── default.yaml # default config used to train fastspeech2
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├── energy_stats.npy # statistics used to normalize energy when training fastspeech2
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├── phone_id_map.txt # phone vocabulary file when training fastspeech2
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├── pitch_stats.npy # statistics used to normalize pitch when training fastspeech2
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├── snapshot_iter_96400.pdz # model parameters and optimizer states
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└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2
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```
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # sr
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n_fft: 2048 # FFT size (samples).
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n_shift: 300 # Hop size (samples). 12.5ms
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win_length: 1200 # Window length (samples). 50ms
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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# Only used for feats_type != raw
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fmin: 80 # Minimum frequency of Mel basis.
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fmax: 7600 # Maximum frequency of Mel basis.
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n_mels: 80 # The number of mel basis.
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# Only used for the model using pitch features (e.g. FastSpeech2)
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f0min: 80 # Minimum f0 for pitch extraction.
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f0max: 400 # Maximum f0 for pitch extraction.
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###########################################################
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# DATA SETTING #
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###########################################################
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batch_size: 64
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num_workers: 2
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###########################################################
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# MODEL SETTING #
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###########################################################
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model:
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adim: 384 # attention dimension
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aheads: 2 # number of attention heads
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elayers: 4 # number of encoder layers
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eunits: 1536 # number of encoder ff units
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dlayers: 4 # number of decoder layers
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dunits: 1536 # number of decoder ff units
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positionwise_layer_type: conv1d # type of position-wise layer
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positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer
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duration_predictor_layers: 2 # number of layers of duration predictor
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duration_predictor_chans: 256 # number of channels of duration predictor
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duration_predictor_kernel_size: 3 # filter size of duration predictor
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postnet_layers: 5 # number of layers of postnset
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postnet_filts: 5 # filter size of conv layers in postnet
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postnet_chans: 256 # number of channels of conv layers in postnet
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use_scaled_pos_enc: True # whether to use scaled positional encoding
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encoder_normalize_before: True # whether to perform layer normalization before the input
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decoder_normalize_before: True # whether to perform layer normalization before the input
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reduction_factor: 1 # reduction factor
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init_type: xavier_uniform # initialization type
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init_enc_alpha: 1.0 # initial value of alpha of encoder scaled position encoding
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init_dec_alpha: 1.0 # initial value of alpha of decoder scaled position encoding
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transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer
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transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding
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transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer
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transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer
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transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding
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transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
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pitch_predictor_layers: 5 # number of conv layers in pitch predictor
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pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
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pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
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pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
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pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
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pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
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stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
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energy_predictor_layers: 2 # number of conv layers in energy predictor
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energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
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energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
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energy_predictor_dropout: 0.5 # dropout rate in energy predictor
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energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
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energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy
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stop_gradient_from_energy_predictor: False # whether to stop the gradient from energy predictor to encoder
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spk_embed_dim: 192 # speaker embedding dimension
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spk_embed_integration_type: concat # speaker embedding integration type
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###########################################################
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# UPDATER SETTING #
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###########################################################
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updater:
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use_masking: True # whether to apply masking for padded part in loss calculation
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###########################################################
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# OPTIMIZER SETTING #
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###########################################################
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optimizer:
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optim: adam # optimizer type
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learning_rate: 0.001 # learning rate
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###########################################################
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# TRAINING SETTING #
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###########################################################
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max_epoch: 200
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num_snapshots: 5
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###########################################################
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# OTHER SETTING #
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###########################################################
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seed: 10086
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#!/bin/bash
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stage=0
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stop_stage=100
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config_path=$1
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# gen speaker embedding
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/vc2_infer.py \
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--input=~/datasets/data_aishell3/train/wav/ \
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--output=dump/embed \
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--num-cpu=20
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fi
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# copy from tts3/preprocess
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# get durations from MFA's result
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echo "Generate durations.txt from MFA results ..."
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python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
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--inputdir=./aishell3_alignment_tone \
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--output durations.txt \
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--config=${config_path}
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# extract features
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echo "Extract features ..."
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python3 ${BIN_DIR}/preprocess.py \
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--dataset=aishell3 \
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--rootdir=~/datasets/data_aishell3/ \
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--dumpdir=dump \
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--dur-file=durations.txt \
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--config=${config_path} \
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--num-cpu=20 \
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--cut-sil=True \
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--spk_emb_dir=dump/embed
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# get features' stats(mean and std)
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echo "Get features' stats ..."
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--field-name="speech"
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--field-name="pitch"
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--field-name="energy"
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# normalize and covert phone/speaker to id, dev and test should use train's stats
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echo "Normalize ..."
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python3 ${BIN_DIR}/normalize.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--dumpdir=dump/train/norm \
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--speech-stats=dump/train/speech_stats.npy \
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--pitch-stats=dump/train/pitch_stats.npy \
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--energy-stats=dump/train/energy_stats.npy \
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--phones-dict=dump/phone_id_map.txt \
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--speaker-dict=dump/speaker_id_map.txt
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python3 ${BIN_DIR}/normalize.py \
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--metadata=dump/dev/raw/metadata.jsonl \
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--dumpdir=dump/dev/norm \
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--speech-stats=dump/train/speech_stats.npy \
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--pitch-stats=dump/train/pitch_stats.npy \
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--energy-stats=dump/train/energy_stats.npy \
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--phones-dict=dump/phone_id_map.txt \
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--speaker-dict=dump/speaker_id_map.txt
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python3 ${BIN_DIR}/normalize.py \
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--metadata=dump/test/raw/metadata.jsonl \
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--dumpdir=dump/test/norm \
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--speech-stats=dump/train/speech_stats.npy \
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--pitch-stats=dump/train/pitch_stats.npy \
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--energy-stats=dump/train/energy_stats.npy \
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--phones-dict=dump/phone_id_map.txt \
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--speaker-dict=dump/speaker_id_map.txt
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fi
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize.py \
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--am=fastspeech2_aishell3 \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_aishell3 \
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--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
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--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
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--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
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--test_metadata=dump/test/norm/metadata.jsonl \
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--output_dir=${train_output_path}/test \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--voice-cloning=True
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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python3 ${BIN_DIR}/train.py \
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--train-metadata=dump/train/norm/metadata.jsonl \
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--dev-metadata=dump/dev/norm/metadata.jsonl \
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--config=${config_path} \
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--output-dir=${train_output_path} \
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--ngpu=2 \
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--phones-dict=dump/phone_id_map.txt \
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--voice-cloning=True
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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ref_audio_dir=$4
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../voice_cloning.py \
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--am=fastspeech2_aishell3 \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_aishell3 \
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--voc_config=pwg_aishell3_ckpt_0.5/default.yaml \
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--voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
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--voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \
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--text="凯莫瑞安联合体的经济崩溃迫在眉睫。" \
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--input-dir=${ref_audio_dir} \
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--output-dir=${train_output_path}/vc_syn \
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--phones-dict=dump/phone_id_map.txt \
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--use_ecapa=True
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=fastspeech2
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
|
||||
@ -0,0 +1,39 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0,1
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_96400.pdz
|
||||
ref_audio_dir=ref_audio
|
||||
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize, vocoder is pwgan
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# synthesize, vocoder is pwgan
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ref_audio_dir} || exit -1
|
||||
fi
|
||||
@ -0,0 +1,70 @@
|
||||
import argparse
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import tqdm
|
||||
|
||||
from paddlespeech.cli.vector import VectorExecutor
|
||||
|
||||
|
||||
def _process_utterance(ifpath: Path,
|
||||
input_dir: Path,
|
||||
output_dir: Path,
|
||||
vec_executor):
|
||||
rel_path = ifpath.relative_to(input_dir)
|
||||
ofpath = (output_dir / rel_path).with_suffix(".npy")
|
||||
ofpath.parent.mkdir(parents=True, exist_ok=True)
|
||||
embed = vec_executor(audio_file=ifpath, force_yes=True)
|
||||
np.save(ofpath, embed)
|
||||
return ofpath
|
||||
|
||||
|
||||
def main(args):
|
||||
# input output preparation
|
||||
input_dir = Path(args.input).expanduser()
|
||||
ifpaths = list(input_dir.rglob(args.pattern))
|
||||
print(f"{len(ifpaths)} utterances in total")
|
||||
output_dir = Path(args.output).expanduser()
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
vec_executor = VectorExecutor()
|
||||
nprocs = args.num_cpu
|
||||
|
||||
# warm up
|
||||
vec_executor(audio_file=ifpaths[0], force_yes=True)
|
||||
|
||||
if nprocs == 1:
|
||||
results = []
|
||||
for ifpath in tqdm.tqdm(ifpaths, total=len(ifpaths)):
|
||||
_process_utterance(
|
||||
ifpath=ifpath,
|
||||
input_dir=input_dir,
|
||||
output_dir=output_dir,
|
||||
vec_executor=vec_executor)
|
||||
else:
|
||||
with ThreadPoolExecutor(nprocs) as pool:
|
||||
with tqdm.tqdm(total=len(ifpaths)) as progress:
|
||||
for ifpath in ifpaths:
|
||||
future = pool.submit(_process_utterance, ifpath, input_dir,
|
||||
output_dir, vec_executor)
|
||||
future.add_done_callback(lambda p: progress.update())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="compute utterance embed.")
|
||||
parser.add_argument(
|
||||
"--input", type=str, help="path of the audio_file folder.")
|
||||
parser.add_argument(
|
||||
"--pattern",
|
||||
type=str,
|
||||
default="*.wav",
|
||||
help="pattern to filter audio files.")
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
metavar="OUTPUT_DIR",
|
||||
help="path to save spk embedding results.")
|
||||
parser.add_argument(
|
||||
"--num-cpu", type=int, default=1, help="number of process.")
|
||||
args = parser.parse_args()
|
||||
|
||||
main(args)
|
||||
Loading…
Reference in new issue