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[TTS]【Hackathon + No.190】 + 模型复现：iSTFTNet (#3006)

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* iSTFTNet implementation based on hifigan, not affect the function and execution of HIFIGAN

* modify the comment in iSTFT.yaml

* add the comments in hifigan

* iSTFTNet implementation based on hifigan, not affect the function and execution of HIFIGAN

* modify the comment in iSTFT.yaml

* add the comments in hifigan

* add iSTFTNet.md

* modify the format of iSTFTNet.md

* modify iSTFT.yaml and hifigan.py

* Format code using pre-commit

* modify hifigan.py,delete the unused self.istft_layer_id , move the self.output_conv behind else, change conv_post to output_conv

* update iSTFTNet_csmsc_ckpt.zip download link

* modify iSTFTNet.md

* modify hifigan.py and iSTFT.yaml

* modify iSTFTNet.md
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174
examples/csmsc/voc5/conf/iSTFT.yaml
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# This is the configuration file for CSMSC dataset.
# This configuration is based on HiFiGAN V1, which is an official configuration.
# But I found that the optimizer setting does not work well with my implementation.
# So I changed optimizer settings as follows:
# - AdamW -> Adam
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
# - Scheduler: ExponentialLR -> MultiStepLR
# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 24000 # Sampling rate.
n_fft: 2048 # FFT size (samples).
n_shift: 300 # Hop size (samples). 12.5ms
win_length: 1200 # Window length (samples). 50ms
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
n_mels: 80 # Number of mel basis.
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
###########################################################
# GENERATOR NETWORK ARCHITECTURE SETTING #
###########################################################
generator_params:
use_istft: True # Use iSTFTNet.
istft_layer_id: 2 # Use istft after istft_layer_id layers of upsample layer if use_istft=True.
n_fft: 2048 # FFT size (samples) in feature extraction.
win_length: 1200 # Window length (samples) in feature extraction.
in_channels: 80 # Number of input channels.
out_channels: 1 # Number of output channels.
channels: 512 # Number of initial channels.
kernel_size: 7 # Kernel size of initial and final conv layers.
upsample_scales: [5, 5, 4, 3] # Upsampling scales.
upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers.
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
resblock_dilations: # Dilations for residual blocks.
- [1, 3, 5]
- [1, 3, 5]
- [1, 3, 5]
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
bias: True # Whether to use bias parameter in conv.
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
nonlinear_activation_params: # Nonlinear activation paramters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
###########################################################
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
###########################################################
discriminator_params:
scales: 3 # Number of multi-scale discriminator.
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
scale_downsample_pooling_params:
kernel_size: 4 # Pooling kernel size.
stride: 2 # Pooling stride.
padding: 2 # Padding size.
scale_discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
channels: 128 # Initial number of channels.
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
max_groups: 16 # Maximum number of groups in downsampling conv layers.
bias: True
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
nonlinear_activation: "leakyrelu" # Nonlinear activation.
nonlinear_activation_params:
negative_slope: 0.1
follow_official_norm: True # Whether to follow the official norm setting.
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
period_discriminator_params:
in_channels: 1 # Number of input channels.
out_channels: 1 # Number of output channels.
kernel_sizes: [5, 3] # List of kernel sizes.
channels: 32 # Initial number of channels.
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
bias: True # Whether to use bias parameter in conv layer."
nonlinear_activation: "leakyrelu" # Nonlinear activation.
nonlinear_activation_params: # Nonlinear activation paramters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.
###########################################################
# STFT LOSS SETTING #
###########################################################
use_stft_loss: False # Whether to use multi-resolution STFT loss.
use_mel_loss: True # Whether to use Mel-spectrogram loss.
mel_loss_params:
fs: 24000
fft_size: 2048
hop_size: 300
win_length: 1200
window: "hann"
num_mels: 80
fmin: 0
fmax: 12000
log_base: null
generator_adv_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
discriminator_adv_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
use_feat_match_loss: True
feat_match_loss_params:
average_by_discriminators: False # Whether to average loss by #discriminators.
average_by_layers: False # Whether to average loss by #layers in each discriminator.
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 16 # Batch size.
batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
num_workers: 2 # Number of workers in DataLoader.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
generator_optimizer_params:
beta1: 0.5
beta2: 0.9
weight_decay: 0.0 # Generator's weight decay coefficient.
generator_scheduler_params:
learning_rate: 2.0e-4 # Generator's learning rate.
gamma: 0.5 # Generator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 200000
- 400000
- 600000
- 800000
generator_grad_norm: -1 # Generator's gradient norm.
discriminator_optimizer_params:
beta1: 0.5
beta2: 0.9
weight_decay: 0.0 # Discriminator's weight decay coefficient.
discriminator_scheduler_params:
learning_rate: 2.0e-4 # Discriminator's learning rate.
gamma: 0.5 # Discriminator's scheduler gamma.
milestones: # At each milestone, lr will be multiplied by gamma.
- 200000
- 400000
- 600000
- 800000
discriminator_grad_norm: -1 # Discriminator's gradient norm.
###########################################################
# INTERVAL SETTING #
###########################################################
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
train_max_steps: 2500000 # Number of training steps.
save_interval_steps: 5000 # Interval steps to save checkpoint.
eval_interval_steps: 1000 # Interval steps to evaluate the network.
###########################################################
# OTHER SETTING #
###########################################################
num_snapshots: 10 # max number of snapshots to keep while training
seed: 42 # random seed for paddle, random, and np.random

145
examples/csmsc/voc5/iSTFTNet.md
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# iSTFTNet with CSMSC
This example contains code used to train a [iSTFTNet](https://arxiv.org/abs/2203.02395) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
## Dataset
### Download and Extract
Download CSMSC from it's [official website](https://test.data-baker.com/data/index/TNtts/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/BZNSYP`.
### Get MFA Result and Extract
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence at the edge of audio.
You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo.
## Get Started
Assume the path to the dataset is `~/datasets/BZNSYP`.
Assume the path to the MFA result of CSMSC is `./baker_alignment_tone`.
Run the command below to
1. **source path**.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
- synthesize waveform from `metadata.jsonl`.
```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
./local/preprocess.sh ${conf_path}
```
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
```text
dump
├── dev
│ ├── norm
│ └── raw
├── test
│ ├── norm
│ └── raw
└── train
├── norm
├── raw
└── feats_stats.npy
```
The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
### Model Training
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
```
`./local/train.sh` calls `${BIN_DIR}/train.py`.
Here's the complete help message.
```text
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU]
Train a HiFiGAN model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG HiFiGAN config file.
--train-metadata TRAIN_METADATA
training data.
--dev-metadata DEV_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
```
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/iSTFT.yaml`.
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
### Synthesizing
`./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}
```
```text
usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
Synthesize with GANVocoder.
optional arguments:
-h, --help show this help message and exit
--generator-type GENERATOR_TYPE
type of GANVocoder, should in {pwgan, mb_melgan,
style_melgan, } now
--config CONFIG GANVocoder config file.
--checkpoint CHECKPOINT
snapshot to load.
--test-metadata TEST_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
```
1. `--config` config file. You should use the same config with which the model is trained.
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
4. `--output-dir` is the directory to save the synthesized audio files.
5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
## Pretrained Models
The pretrained model can be downloaded here:
- [iSTFTNet_csmsc_ckpt.zip](https://pan.baidu.com/s/1SNDlRWOGOcbbrKf5w-TJaA?pwd=r1e5)
iSTFTNet checkpoint contains files listed below.
```text
iSTFTNet_csmsc_ckpt
├── iSTFT.yaml                  # config used to train iSTFTNet
├── feats_stats.npy               # statistics used to normalize spectrogram when training hifigan
└── snapshot_iter_50000.pdz     # generator parameters of hifigan
```
A Comparison between iSTFTNet and Hifigan
| Model | Step | eval/generator_loss | eval/mel_loss | eval/feature_matching_loss | rtf |
|:--------:|:--------------:|:-------------------:|:-------------:|:--------------------------:| :---: |
| hifigan | 1(gpu) x 50000 | 13.989 | 0.14683 | 1.3484 | 0.01767 |
| istftNet | 1(gpu) x 50000 | 13.319 | 0.14818 | 1.1069 | 0.01069 |
> Rtf is tested on the CSMSC test dataset, and the test environment is aistudio v100 16G 1GPU, the test command is `./run.sh --stage 2 --stop-stage 2`
The pretained hifigan model int the comparison can be downloaded here:
- [hifigan_csmsc_ckpt.zip](https://pan.baidu.com/s/1pGY6RYV7yEB_5hRI_JoWig?pwd=tcaj)
## Acknowledgement
We adapted some code from https://github.com/rishikksh20/iSTFTNet-pytorch.git.

82
paddlespeech/t2s/models/hifigan/hifigan.py
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@ -37,8 +37,8 @@ class HiFiGANGenerator(nn.Layer):
channels: int=512,
global_channels: int=-1,
kernel_size: int=7,
upsample_scales: List[int]=(8, 8, 2, 2),
upsample_kernel_sizes: List[int]=(16, 16, 4, 4),
upsample_scales: List[int]=(5, 5, 4, 3),
upsample_kernel_sizes: List[int]=(10, 10, 8, 6),
resblock_kernel_sizes: List[int]=(3, 7, 11),
resblock_dilations: List[List[int]]=[(1, 3, 5), (1, 3, 5),
(1, 3, 5)],
@ -47,8 +47,13 @@ class HiFiGANGenerator(nn.Layer):
nonlinear_activation: str="leakyrelu",
nonlinear_activation_params: Dict[str, Any]={"negative_slope": 0.1},
use_weight_norm: bool=True,
init_type: str="xavier_uniform", ):
init_type: str="xavier_uniform",
use_istft: bool=False,
istft_layer_id: int=2,
n_fft: int=2048,
win_length: int=1200, ):
"""Initialize HiFiGANGenerator module.
Args:
in_channels (int):
Number of input channels.
@ -79,6 +84,14 @@ class HiFiGANGenerator(nn.Layer):
use_weight_norm (bool):
Whether to use weight norm.
If set to true, it will be applied to all of the conv layers.
use_istft (bool):
If set to true, it will be a iSTFTNet based on hifigan.
istft_layer_id (int):
Use istft after istft_layer_id layers of upsample layer if use_istft=True
n_fft (int):
Number of fft points in feature extraction
win_length (int):
Window length in feature extraction
"""
super().__init__()
@ -89,9 +102,11 @@ class HiFiGANGenerator(nn.Layer):
assert kernel_size % 2 == 1, "Kernel size must be odd number."
assert len(upsample_scales) == len(upsample_kernel_sizes)
assert len(resblock_dilations) == len(resblock_kernel_sizes)
assert len(upsample_scales) >= istft_layer_id if use_istft else True
# define modules
self.num_upsamples = len(upsample_kernel_sizes)
self.num_upsamples = len(
upsample_kernel_sizes) if not use_istft else istft_layer_id
self.num_blocks = len(resblock_kernel_sizes)
self.input_conv = nn.Conv1D(
in_channels,
@ -101,7 +116,7 @@ class HiFiGANGenerator(nn.Layer):
padding=(kernel_size - 1) // 2, )
self.upsamples = nn.LayerList()
self.blocks = nn.LayerList()
for i in range(len(upsample_kernel_sizes)):
for i in range(self.num_upsamples):
assert upsample_kernel_sizes[i] == 2 * upsample_scales[i]
self.upsamples.append(
nn.Sequential(
@ -126,15 +141,36 @@ class HiFiGANGenerator(nn.Layer):
nonlinear_activation=nonlinear_activation,
nonlinear_activation_params=nonlinear_activation_params,
))
self.output_conv = nn.Sequential(
nn.LeakyReLU(),
nn.Conv1D(
self.use_istft = use_istft
if self.use_istft:
self.istft_hop_size = 1
for j in range(istft_layer_id, len(upsample_scales)):
self.istft_hop_size *= upsample_scales[j]
s = 1
for j in range(istft_layer_id):
s *= upsample_scales[j]
self.istft_n_fft = int(n_fft / s) if (
n_fft / s) % 2 == 0 else int((n_fft / s + 2) - n_fft / s % 2)
self.istft_win_length = int(win_length / s) if (
win_length /
s) % 2 == 0 else int((win_length / s + 2) - win_length / s % 2)
self.reflection_pad = nn.Pad1D(padding=[1, 0], mode='reflect')
self.output_conv = nn.Conv1D(
channels // (2**(i + 1)),
out_channels,
(self.istft_n_fft // 2 + 1) * 2,
kernel_size,
1,
padding=(kernel_size - 1) // 2, ),
nn.Tanh(), )
padding=(kernel_size - 1) // 2, )
else:
self.output_conv = nn.Sequential(
nn.LeakyReLU(),
nn.Conv1D(
channels // (2**(i + 1)),
out_channels,
kernel_size,
1,
padding=(kernel_size - 1) // 2, ),
nn.Tanh(), )
if global_channels > 0:
self.global_conv = nn.Conv1D(global_channels, channels, 1)
@ -167,7 +203,29 @@ class HiFiGANGenerator(nn.Layer):
for j in range(self.num_blocks):
cs += self.blocks[i * self.num_blocks + j](c)
c = cs / self.num_blocks
c = self.output_conv(c)
if self.use_istft:
c = F.leaky_relu(c)
c = self.reflection_pad(c)
c = self.output_conv(c)
"""
Input of Exp operator, an N-D Tensor, with data type float32, float64 or float16.
https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/exp_en.html
Use Euler's formula to implement spec*paddle.exp(1j*phase)
"""
spec = paddle.exp(c[:, :self.istft_n_fft // 2 + 1, :])
phase = paddle.sin(c[:, self.istft_n_fft // 2 + 1:, :])
c = paddle.complex(spec * (paddle.cos(phase)),
spec * (paddle.sin(phase)))
c = paddle.signal.istft(
c,
n_fft=self.istft_n_fft,
hop_length=self.istft_hop_size,
win_length=self.istft_win_length)
c = c.unsqueeze(1)
else:
c = self.output_conv(c)
return c

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