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PaddleSpeech
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Fix typos (#4021)

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* Fix

* Fix

* Fix
pull/4024/head
co63oc 6 months ago committed by GitHub
parent de3851336f
commit c2dc4dae2d
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GPG Key ID: B5690EEEBB952194
55 changed files with 136 additions and 136 deletions
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4
audio/paddleaudio/datasets/esc50.py
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@ -35,7 +35,7 @@ class ESC50(AudioClassificationDataset):
http://dx.doi.org/10.1145/2733373.2806390
"""
archieves = [
archives = [
{
'url':
'https://paddleaudio.bj.bcebos.com/datasets/ESC-50-master.zip',
@ -133,7 +133,7 @@ class ESC50(AudioClassificationDataset):
def _get_data(self, mode: str, split: int) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves, DATA_HOME)
download_and_decompress(self.archives, DATA_HOME)
meta_info = self._get_meta_info()

4
audio/paddleaudio/datasets/gtzan.py
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@ -35,7 +35,7 @@ class GTZAN(AudioClassificationDataset):
https://ieeexplore.ieee.org/document/1021072/
"""
archieves = [
archives = [
{
'url': 'http://opihi.cs.uvic.ca/sound/genres.tar.gz',
'md5': '5b3d6dddb579ab49814ab86dba69e7c7',
@ -85,7 +85,7 @@ class GTZAN(AudioClassificationDataset):
split) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves, DATA_HOME)
download_and_decompress(self.archives, DATA_HOME)
meta_info = self._get_meta_info()
random.seed(seed) # shuffle samples to split data

4
audio/paddleaudio/datasets/rirs_noises.py
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@ -30,7 +30,7 @@ __all__ = ['OpenRIRNoise']
class OpenRIRNoise(Dataset):
archieves = [
archives = [
{
'url': 'http://www.openslr.org/resources/28/rirs_noises.zip',
'md5': 'e6f48e257286e05de56413b4779d8ffb',
@ -76,7 +76,7 @@ class OpenRIRNoise(Dataset):
print(f"rirs noises base path: {self.base_path}")
if not os.path.isdir(self.base_path):
download_and_decompress(
self.archieves, self.base_path, decompress=True)
self.archives, self.base_path, decompress=True)
else:
print(
f"{self.base_path} already exists, we will not download and decompress again"

4
audio/paddleaudio/datasets/tess.py
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@ -37,7 +37,7 @@ class TESS(AudioClassificationDataset):
https://doi.org/10.5683/SP2/E8H2MF
"""
archieves = [
archives = [
{
'url':
'https://bj.bcebos.com/paddleaudio/datasets/TESS_Toronto_emotional_speech_set.zip',
@ -93,7 +93,7 @@ class TESS(AudioClassificationDataset):
def _get_data(self, mode, seed, n_folds,
split) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)):
download_and_decompress(self.archieves, DATA_HOME)
download_and_decompress(self.archives, DATA_HOME)
wav_files = []
for root, _, files in os.walk(os.path.join(DATA_HOME, self.audio_path)):

4
audio/paddleaudio/datasets/urban_sound.py
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@ -35,7 +35,7 @@ class UrbanSound8K(AudioClassificationDataset):
https://dl.acm.org/doi/10.1145/2647868.2655045
"""
archieves = [
archives = [
{
'url':
'https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz',
@ -81,7 +81,7 @@ class UrbanSound8K(AudioClassificationDataset):
def _get_data(self, mode: str, split: int) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves, DATA_HOME)
download_and_decompress(self.archives, DATA_HOME)
meta_info = self._get_meta_info()

12
audio/paddleaudio/datasets/voxceleb.py
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@ -34,7 +34,7 @@ __all__ = ['VoxCeleb']
class VoxCeleb(Dataset):
source_url = 'https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/'
archieves_audio_dev = [
archives_audio_dev = [
{
'url': source_url + 'vox1_dev_wav_partaa',
'md5': 'e395d020928bc15670b570a21695ed96',
@ -52,13 +52,13 @@ class VoxCeleb(Dataset):
'md5': '7bb1e9f70fddc7a678fa998ea8b3ba19',
},
]
archieves_audio_test = [
archives_audio_test = [
{
'url': source_url + 'vox1_test_wav.zip',
'md5': '185fdc63c3c739954633d50379a3d102',
},
]
archieves_meta = [
archives_meta = [
{
'url':
'https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt',
@ -135,11 +135,11 @@ class VoxCeleb(Dataset):
if not os.path.isdir(self.wav_path):
print("start to download the voxceleb1 dataset")
download_and_decompress( # multi-zip parts concatenate to vox1_dev_wav.zip
self.archieves_audio_dev,
self.archives_audio_dev,
self.base_path,
decompress=False)
download_and_decompress( # download the vox1_test_wav.zip and unzip
self.archieves_audio_test,
self.archives_audio_test,
self.base_path,
decompress=True)
@ -157,7 +157,7 @@ class VoxCeleb(Dataset):
if not os.path.isdir(self.meta_path):
print("prepare the meta data")
download_and_decompress(
self.archieves_meta, self.meta_path, decompress=False)
self.archives_meta, self.meta_path, decompress=False)
# Data preparation.
if not os.path.isdir(self.csv_path):

2
dataset/chime3_background/chime3_background.py
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@ -109,7 +109,7 @@ def create_manifest(data_dir, manifest_path):
def prepare_chime3(url, md5sum, target_dir, manifest_path):
"""Download, unpack and create summmary manifest file."""
"""Download, unpack and create summary manifest file."""
if not os.path.exists(os.path.join(target_dir, "CHiME3")):
# download
filepath = download(url, md5sum, target_dir,

2
dataset/timit/timit.py
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@ -210,7 +210,7 @@ def create_manifest(data_dir, manifest_path_prefix):
def prepare_dataset(url, md5sum, target_dir, manifest_path):
"""Download, unpack and create summmary manifest file.
"""Download, unpack and create summary manifest file.
"""
filepath = os.path.join(target_dir, "TIMIT.zip")
if not os.path.exists(filepath):

22
demos/TTSCppFrontend/src/front/front_interface.cpp
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@ -115,27 +115,27 @@ int FrontEngineInterface::init() {
// 生成词典（词到音素的映射）
if (0 != GenDict(_word2phone_path, &word_phone_map)) {
LOG(ERROR) << "Genarate word2phone dict failed";
LOG(ERROR) << "Generate word2phone dict failed";
return -1;
}
// 生成音素字典（音素到音素id的映射）
if (0 != GenDict(_phone2id_path, &phone_id_map)) {
LOG(ERROR) << "Genarate phone2id dict failed";
LOG(ERROR) << "Generate phone2id dict failed";
return -1;
}
// 生成音调字典（音调到音调id的映射）
if (_separate_tone == "true") {
if (0 != GenDict(_tone2id_path, &tone_id_map)) {
LOG(ERROR) << "Genarate tone2id dict failed";
LOG(ERROR) << "Generate tone2id dict failed";
return -1;
}
}
// 生成繁简字典（繁体到简体id的映射）
if (0 != GenDict(_trand2simp_path, &trand_simp_map)) {
LOG(ERROR) << "Genarate trand2simp dict failed";
LOG(ERROR) << "Generate trand2simp dict failed";
return -1;
}
@ -263,7 +263,7 @@ int FrontEngineInterface::GetWordsIds(
if (0 !=
GetInitialsFinals(word, &word_initials, &word_finals)) {
LOG(ERROR)
<< "Genarate the word_initials and word_finals of "
<< "Generate the word_initials and word_finals of "
<< word << " failed";
return -1;
}
@ -304,7 +304,7 @@ int FrontEngineInterface::GetWordsIds(
// 音素到音素id
if (0 != Phone2Phoneid(phone, phoneids, toneids)) {
LOG(ERROR) << "Genarate the phone id of " << word << " failed";
LOG(ERROR) << "Generate the phone id of " << word << " failed";
return -1;
}
}
@ -916,11 +916,11 @@ int FrontEngineInterface::NeuralSandhi(const std::string &word,
if (find(must_neural_tone_words.begin(),
must_neural_tone_words.end(),
word) != must_neural_tone_words.end() ||
(word_num >= 2 &&
find(must_neural_tone_words.begin(),
must_neural_tone_words.end(),
ppspeech::wstring2utf8string(word_wstr.substr(
word_num - 2))) != must_neural_tone_words.end())) {
(word_num >= 2 && find(must_neural_tone_words.begin(),
must_neural_tone_words.end(),
ppspeech::wstring2utf8string(
word_wstr.substr(word_num - 2))) !=
must_neural_tone_words.end())) {
(*finals).back() =
(*finals).back().replace((*finals).back().length() - 1, 1, "5");
}

4
demos/audio_searching/src/milvus_helpers.py
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@ -77,13 +77,13 @@ class MilvusHelper:
field1 = FieldSchema(
name="id",
dtype=DataType.INT64,
descrition="int64",
description="int64",
is_primary=True,
auto_id=True)
field2 = FieldSchema(
name="embedding",
dtype=DataType.FLOAT_VECTOR,
descrition="speaker embeddings",
description="speaker embeddings",
dim=VECTOR_DIMENSION,
is_primary=False)
schema = CollectionSchema(

4
demos/speech_web/speech_server/main.py
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@ -225,7 +225,7 @@ async def websocket_endpoint_online(websocket: WebSocket):
websocket (WebSocket): the websocket instance
"""
#1. the interface wait to accept the websocket protocal header
#1. the interface wait to accept the websocket protocol header
# and only we receive the header, it establish the connection with specific thread
await websocket.accept()
@ -238,7 +238,7 @@ async def websocket_endpoint_online(websocket: WebSocket):
connection_handler = None
try:
#4. we do a loop to process the audio package by package according the protocal
#4. we do a loop to process the audio package by package according the protocol
# and only if the client send finished signal, we will break the loop
while True:
# careful here, changed the source code from starlette.websockets

2
demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/1/model.py
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@ -75,7 +75,7 @@ class TritonPythonModel:
def initialize(self, args):
"""`initialize` is called only once when the model is being loaded.
Implementing `initialize` function is optional. This function allows
the model to intialize any state associated with this model.
the model to initialize any state associated with this model.
Parameters
----------
args : dict

6
examples/aishell3/tts3/conf/conformer.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
encoder_normalize_before: True # whether to perform layer normalization before the input
@ -66,14 +66,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

6
examples/aishell3/tts3/conf/default.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

6
examples/aishell3/vc1/conf/default.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

6
examples/aishell3/vc2/conf/default.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

4
examples/aishell3/voc5/conf/default.yaml
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@ -39,7 +39,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -77,7 +77,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

6
examples/canton/tts3/conf/default.yaml
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@ -45,7 +45,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -63,14 +63,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

4
examples/csmsc/jets/conf/default.yaml
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@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

6
examples/csmsc/tts3/conf/cnndecoder.yaml
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@ -43,7 +43,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -65,14 +65,14 @@ model:
cnn_decoder_embedding_dim: 256
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

6
examples/csmsc/tts3/conf/conformer.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
encoder_normalize_before: True # whether to perform layer normalization before the input
@ -66,14 +66,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

6
examples/csmsc/tts3/conf/default.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

4
examples/csmsc/voc5/conf/default.yaml
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@ -38,7 +38,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -76,7 +76,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

4
examples/csmsc/voc5/conf/finetune.yaml
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@ -38,7 +38,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -76,7 +76,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

4
examples/csmsc/voc5/conf/iSTFT.yaml
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@ -42,7 +42,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -83,7 +83,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

2
examples/ljspeech/tts1/conf/default.yaml
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@ -34,7 +34,7 @@ model: # keyword arguments for the selected model
dunits: 1024 # number of decoder ff units
positionwise_layer_type: conv1d # type of position-wise layer
positionwise_conv_kernel_size: 1 # kernel size of position wise conv layer
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding

6
examples/ljspeech/tts3/conf/default.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

4
examples/ljspeech/voc5/conf/default.yaml
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@ -38,7 +38,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -76,7 +76,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

4
examples/opencpop/svs1/conf/default.yaml
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@ -68,14 +68,14 @@ model:
duration_predictor_dropout_rate: 0.5 # dropout rate in energy predictor
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

4
examples/opencpop/voc5/conf/default.yaml
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@ -38,7 +38,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -76,7 +76,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

4
examples/opencpop/voc5/conf/finetune.yaml
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@ -38,7 +38,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -76,7 +76,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

6
examples/vctk/tts3/conf/default.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

4
examples/vctk/voc5/conf/default.yaml
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@ -39,7 +39,7 @@ generator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
@ -77,7 +77,7 @@ discriminator_params:
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.
nonlinear_activation_params: # Nonlinear activation parameters.
negative_slope: 0.1
use_weight_norm: True # Whether to apply weight normalization.
use_spectral_norm: False # Whether to apply spectral normalization.

4
examples/voxceleb/sv0/local/data_prepare.py
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@ -32,8 +32,8 @@ def main(args, config):
seed_everything(config.seed)
# stage 1: generate the voxceleb csv file
# Note: this may occurs c++ execption, but the program will execute fine
# so we ignore the execption
# Note: this may occurs c++ exception, but the program will execute fine
# so we ignore the exception
# we explicitly pass the vox2 base path to data prepare and generate the audio info
logger.info("start to generate the voxceleb dataset info")
train_dataset = VoxCeleb(

6
examples/zh_en_tts/tts3/conf/default.yaml
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@ -42,7 +42,7 @@ model:
duration_predictor_layers: 2 # number of layers of duration predictor
duration_predictor_chans: 256 # number of channels of duration predictor
duration_predictor_kernel_size: 3 # filter size of duration predictor
postnet_layers: 5 # number of layers of postnset
postnet_layers: 5 # number of layers of postnet
postnet_filts: 5 # filter size of conv layers in postnet
postnet_chans: 256 # number of channels of conv layers in postnet
use_scaled_pos_enc: True # whether to use scaled positional encoding
@ -60,14 +60,14 @@ model:
transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer
pitch_predictor_layers: 5 # number of conv layers in pitch predictor
pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor
pitch_predictor_kernel_size: 5 # kernel size of conv layers in pitch predictor
pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor
pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch
pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch
stop_gradient_from_pitch_predictor: True # whether to stop the gradient from pitch predictor to encoder
energy_predictor_layers: 2 # number of conv layers in energy predictor
energy_predictor_chans: 256 # number of channels of conv layers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor
energy_predictor_kernel_size: 3 # kernel size of conv layers in energy predictor
energy_predictor_dropout: 0.5 # dropout rate in energy predictor
energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy
energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy

6
paddlespeech/audio/utils/tensor_utils.py
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@ -79,7 +79,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
# assuming trailing dimensions and type of all the Tensors
# in sequences are same and fetching those from sequences[0]
max_size = paddle.shape(sequences[0])
# (TODO Hui Zhang): slice not supprot `end==start`
# (TODO Hui Zhang): slice not support `end==start`
# trailing_dims = max_size[1:]
trailing_dims = tuple(
max_size[1:].numpy().tolist()) if sequences[0].ndim >= 2 else ()
@ -93,7 +93,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
length = tensor.shape[0]
# use index notation to prevent duplicate references to the tensor
if batch_first:
# TODO (Hui Zhang): set_value op not supprot `end==start`
# TODO (Hui Zhang): set_value op not support `end==start`
# TODO (Hui Zhang): set_value op not support int16
# TODO (Hui Zhang): set_varbase 2 rank not support [0,0,...]
# out_tensor[i, :length, ...] = tensor
@ -102,7 +102,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
else:
out_tensor[i, length] = tensor
else:
# TODO (Hui Zhang): set_value op not supprot `end==start`
# TODO (Hui Zhang): set_value op not support `end==start`
# out_tensor[:length, i, ...] = tensor
if length != 0:
out_tensor[:length, i] = tensor

8
paddlespeech/s2t/exps/hubert/model.py
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@ -362,7 +362,7 @@ class HubertASRTrainer(Trainer):
scratch = None
if self.args.resume:
# just restore ckpt
# lr will resotre from optimizer ckpt
# lr will restore from optimizer ckpt
resume_json_path = os.path.join(self.checkpoint_dir,
self.args.resume + '.json')
with open(resume_json_path, 'r', encoding='utf8') as f:
@ -370,20 +370,20 @@ class HubertASRTrainer(Trainer):
self.iteration = 0
self.epoch = resume_json["epoch"]
# resotre model from *.pdparams
# restore model from *.pdparams
params_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdparams'
model_dict = paddle.load(params_path)
self.model.set_state_dict(model_dict)
# resotre optimizer from *.pdopt
# restore optimizer from *.pdopt
optimizer_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdopt'
optimizer_dict = paddle.load(optimizer_path)
self.model_optimizer.set_state_dict(optimizer_dict['model'])
self.hubert_optimizer.set_state_dict(optimizer_dict['hubert'])
# resotre lr_scheduler from *.pdlrs
# restore lr_scheduler from *.pdlrs
scheduler_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdlrs'
if os.path.isfile(os.path.join(scheduler_path)):

8
paddlespeech/s2t/exps/wav2vec2/model.py
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@ -361,7 +361,7 @@ class Wav2Vec2ASRTrainer(Trainer):
scratch = None
if self.args.resume:
# just restore ckpt
# lr will resotre from optimizer ckpt
# lr will restore from optimizer ckpt
resume_json_path = os.path.join(self.checkpoint_dir,
self.args.resume + '.json')
with open(resume_json_path, 'r', encoding='utf8') as f:
@ -369,20 +369,20 @@ class Wav2Vec2ASRTrainer(Trainer):
self.iteration = 0
self.epoch = resume_json["epoch"]
# resotre model from *.pdparams
# restore model from *.pdparams
params_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdparams'
model_dict = paddle.load(params_path)
self.model.set_state_dict(model_dict)
# resotre optimizer from *.pdopt
# restore optimizer from *.pdopt
optimizer_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdopt'
optimizer_dict = paddle.load(optimizer_path)
self.model_optimizer.set_state_dict(optimizer_dict['model'])
self.wav2vec2_optimizer.set_state_dict(optimizer_dict['wav2vec2'])
# resotre lr_scheduler from *.pdlrs
# restore lr_scheduler from *.pdlrs
scheduler_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdlrs'
if os.path.isfile(os.path.join(scheduler_path)):

8
paddlespeech/s2t/exps/wavlm/model.py
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@ -361,7 +361,7 @@ class WavLMASRTrainer(Trainer):
scratch = None
if self.args.resume:
# just restore ckpt
# lr will resotre from optimizer ckpt
# lr will restore from optimizer ckpt
resume_json_path = os.path.join(self.checkpoint_dir,
self.args.resume + '.json')
with open(resume_json_path, 'r', encoding='utf8') as f:
@ -369,20 +369,20 @@ class WavLMASRTrainer(Trainer):
self.iteration = 0
self.epoch = resume_json["epoch"]
# resotre model from *.pdparams
# restore model from *.pdparams
params_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdparams'
model_dict = paddle.load(params_path)
self.model.set_state_dict(model_dict)
# resotre optimizer from *.pdopt
# restore optimizer from *.pdopt
optimizer_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdopt'
optimizer_dict = paddle.load(optimizer_path)
self.model_optimizer.set_state_dict(optimizer_dict['model'])
self.wavlm_optimizer.set_state_dict(optimizer_dict['wavlm'])
# resotre lr_scheduler from *.pdlrs
# restore lr_scheduler from *.pdlrs
scheduler_path = os.path.join(self.checkpoint_dir,
"{}".format(self.epoch)) + '.pdlrs'
if os.path.isfile(os.path.join(scheduler_path)):

2
paddlespeech/s2t/training/trainer.py
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@ -215,7 +215,7 @@ class Trainer():
checkpoint_path=self.args.checkpoint_path)
if infos:
# just restore ckpt
# lr will resotre from optimizer ckpt
# lr will restore from optimizer ckpt
self.iteration = infos["step"]
self.epoch = infos["epoch"]

6
paddlespeech/s2t/utils/tensor_utils.py
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@ -80,7 +80,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
# assuming trailing dimensions and type of all the Tensors
# in sequences are same and fetching those from sequences[0]
max_size = paddle.shape(sequences[0])
# (TODO Hui Zhang): slice not supprot `end==start`
# (TODO Hui Zhang): slice not support `end==start`
# trailing_dims = max_size[1:]
trailing_dims = tuple(
max_size[1:].numpy().tolist()) if sequences[0].ndim >= 2 else ()
@ -98,7 +98,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
f"length {length}, out_tensor {out_tensor.shape}, tensor {tensor.shape}"
)
if batch_first:
# TODO (Hui Zhang): set_value op not supprot `end==start`
# TODO (Hui Zhang): set_value op not support `end==start`
# TODO (Hui Zhang): set_value op not support int16
# TODO (Hui Zhang): set_varbase 2 rank not support [0,0,...]
# out_tensor[i, :length, ...] = tensor
@ -107,7 +107,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
else:
out_tensor[i, length] = tensor
else:
# TODO (Hui Zhang): set_value op not supprot `end==start`
# TODO (Hui Zhang): set_value op not support `end==start`
# out_tensor[:length, i, ...] = tensor
if length != 0:
out_tensor[:length, i] = tensor

10
paddlespeech/server/utils/audio_handler.py
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@ -79,7 +79,7 @@ class ASRWsAudioHandler:
punc_server_ip=None,
punc_server_port=None):
"""PaddleSpeech Online ASR Server Client audio handler
Online asr server use the websocket protocal
Online asr server use the websocket protocol
Args:
url (str, optional): the server ip. Defaults to None.
port (int, optional): the server port. Defaults to None.
@ -144,10 +144,10 @@ class ASRWsAudioHandler:
logger.error("No asr server, please input valid ip and port")
return ""
# 1. send websocket handshake protocal
# 1. send websocket handshake protocol
start_time = time.time()
async with websockets.connect(self.url) as ws:
# 2. server has already received handshake protocal
# 2. server has already received handshake protocol
# client start to send the command
audio_info = json.dumps(
{
@ -255,7 +255,7 @@ class ASRHttpHandler:
class TTSWsHandler:
def __init__(self, server="127.0.0.1", port=8092, play: bool=False):
"""PaddleSpeech Online TTS Server Client audio handler
Online tts server use the websocket protocal
Online tts server use the websocket protocol
Args:
server (str, optional): the server ip. Defaults to "127.0.0.1".
port (int, optional): the server port. Defaults to 8092.
@ -405,7 +405,7 @@ class TTSWsHandler:
class TTSHttpHandler:
def __init__(self, server="127.0.0.1", port=8092, play: bool=False):
"""PaddleSpeech Online TTS Server Client audio handler
Online tts server use the websocket protocal
Online tts server use the websocket protocol
Args:
server (str, optional): the server ip. Defaults to "127.0.0.1".
port (int, optional): the server port. Defaults to 8092.

4
paddlespeech/server/ws/asr_api.py
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@ -31,7 +31,7 @@ async def websocket_endpoint(websocket: WebSocket):
websocket (WebSocket): the websocket instance
"""
#1. the interface wait to accept the websocket protocal header
#1. the interface wait to accept the websocket protocol header
# and only we receive the header, it establish the connection with specific thread
await websocket.accept()
@ -45,7 +45,7 @@ async def websocket_endpoint(websocket: WebSocket):
connection_handler = None
try:
#4. we do a loop to process the audio package by package according the protocal
#4. we do a loop to process the audio package by package according the protocol
# and only if the client send finished signal, we will break the loop
while True:
# careful here, changed the source code from starlette.websockets

2
paddlespeech/server/ws/tts_api.py
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@ -32,7 +32,7 @@ async def websocket_endpoint(websocket: WebSocket):
websocket (WebSocket): the websocket instance
"""
#1. the interface wait to accept the websocket protocal header
#1. the interface wait to accept the websocket protocol header
# and only we receive the header, it establish the connection with specific thread
await websocket.accept()

4
paddlespeech/t2s/models/vits/residual_coupling.py
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@ -76,7 +76,7 @@ class ResidualAffineCouplingBlock(nn.Layer):
use_weight_norm (bool):
Whether to use weight normalization in WaveNet.
bias (bool):
Whether to use bias paramters in WaveNet.
Whether to use bias parameters in WaveNet.
use_only_mean (bool):
Whether to estimate only mean.
@ -169,7 +169,7 @@ class ResidualAffineCouplingLayer(nn.Layer):
use_weight_norm (bool):
Whether to use weight normalization in WaveNet.
bias (bool):
Whether to use bias paramters in WaveNet.
Whether to use bias parameters in WaveNet.
use_only_mean (bool):
Whether to estimate only mean.

4
runtime/examples/text_lm/local/mmseg.py
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@ -156,8 +156,8 @@ class Analysis:
return self.text[self.pos]
#判断该字符是否是中文字符（不包括中文标点）
def isChineseChar(self, charater):
return 0x4e00 <= ord(charater) < 0x9fa6
def isChineseChar(self, character):
return 0x4e00 <= ord(character) < 0x9fa6
#判断是否是ASCII码
def isASCIIChar(self, ch):

6
tests/unit/server/offline/test_server_client.sh
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@ -66,8 +66,8 @@ config_file=./conf/application.yaml
server_ip=$(cat $config_file | grep "host" | awk -F " " '{print $2}')
port=$(cat $config_file | grep "port" | awk '/port:/ {print $2}')
echo "Sevice ip: $server_ip" | tee ./log/test_result.log
echo "Sevice port: $port" | tee -a ./log/test_result.log
echo "Service ip: $server_ip" | tee ./log/test_result.log
echo "Service port: $port" | tee -a ./log/test_result.log
# whether a process is listening on $port
pid=`lsof -i :"$port"|grep -v "PID" | awk '{print $2}'`
@ -190,7 +190,7 @@ echo "**************************************************************************
echo "All tests completed." | tee -a ./log/test_result.log
# sohw all the test results
# show all the test results
echo "***************** Here are all the test results ********************"
cat ./log/test_result.log

6
tests/unit/server/online/tts/check_server/test.sh
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@ -76,8 +76,8 @@ config_file=./conf/application.yaml
server_ip=$(cat $config_file | grep "host" | awk -F " " '{print $2}')
port=$(cat $config_file | grep "port" | awk '/port:/ {print $2}')
echo "Sevice ip: $server_ip" | tee $log/test_result.log
echo "Sevice port: $port" | tee -a $log/test_result.log
echo "Service ip: $server_ip" | tee $log/test_result.log
echo "Service port: $port" | tee -a $log/test_result.log
# whether a process is listening on $port
pid=`lsof -i :"$port"|grep -v "PID" | awk '{print $2}'`
@ -307,7 +307,7 @@ echo "**************************************************************************
echo "All tests completed." | tee -a $log/test_result.log
# sohw all the test results
# show all the test results
echo "***************** Here are all the test results ********************"
cat $log/test_result.log

2
tools/extras/install_liblbfgs.sh
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@ -23,7 +23,7 @@ cd ..
(
[ ! -z "${LIBLBFGS}" ] && \
echo >&2 "LIBLBFGS variable is aleady defined. Undefining..." && \
echo >&2 "LIBLBFGS variable is already defined. Undefining..." && \
unset LIBLBFGS
[ -f ./env.sh ] && . ./env.sh

2
tools/extras/install_srilm.sh
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@ -68,7 +68,7 @@ make || exit
cd ..
(
[ ! -z "${SRILM}" ] && \
echo >&2 "SRILM variable is aleady defined. Undefining..." && \
echo >&2 "SRILM variable is already defined. Undefining..." && \
unset SRILM
[ -f ./env.sh ] && . ./env.sh

2
utils/fst/ctc_token_fst.py
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@ -32,7 +32,7 @@ def main(args):
# leaving `token`
print('{} {} {} {}'.format(node, 2, '<eps>', '<eps>'))
node += 1
# Fianl node
# Final node
print('0')

2
utils/fst/make_tlg.sh
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@ -21,7 +21,7 @@ cp -r $src_lang $tgt_lang
# eps2disambig.pl: replace epsilons on the input side with the special disambiguation symbol #0.
# s2eps.pl: replaces <s> and </s> with <eps> (on both input and output sides), for the G.fst acceptor.
# G.fst, the disambiguation symbol #0 only appears on the input side
# do eps2disambig.pl and s2eps.pl maybe just for fallowing `fstrmepsilon`.
# do eps2disambig.pl and s2eps.pl maybe just for following `fstrmepsilon`.
cat $arpa_lm | \
grep -v '<s> <s>' | \
grep -v '</s> <s>' | \

4
utils/generate_infer_yaml.py
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@ -3,7 +3,7 @@
'''
Merge training configs into a single inference config.
The single inference config is for CLI, which only takes a single config to do inferencing.
The trainig configs includes: model config, preprocess config, decode config, vocab file and cmvn file.
The training configs includes: model config, preprocess config, decode config, vocab file and cmvn file.
Process:
# step 1: prepare dir
@ -11,7 +11,7 @@
cp -r exp conf data release_dir
cd release_dir
# step 2: get "model.yaml" which conatains all configuration info.
# step 2: get "model.yaml" which contains all configuration info.
# if does not contain preprocess.yaml file. e.g ds2:
python generate_infer_yaml.py --cfg_pth conf/deepspeech2_online.yaml --dcd_pth conf/tuning/chunk_decode.yaml --vb_pth data/lang_char/vocab.txt --cmvn_pth data/mean_std.json --save_pth model.yaml --pre_pth null
# if contains preprocess.yaml file. e.g u2:

4
utils/train_arpa_with_kenlm.sh
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@ -37,7 +37,7 @@ fi
# the text should be properly pre-processed, e.g:
# cleand, normalized and possibly word-segmented
# get rid off irrelavent symbols
# get rid off irrelevant symbols
grep -v '<eps>' $symbol_table \
| grep -v '#0' \
| grep -v '<unk>' | grep -v '<UNK>' \
@ -51,7 +51,7 @@ grep -v '<eps>' $symbol_table \
#
# TL;DR reason:
# Unlike SRILM's -limit-vocab, kenlm's --limit_vocab_file option
# spcifies a *valid* set of vocabulary, whereas *valid but unseen*
# specifies a *valid* set of vocabulary, whereas *valid but unseen*
# words are discarded in final arpa.
# So the trick is,
# we explicitly add kaldi's vocab(one word per line) to training text,

2
utils/zh_tn.py
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@ -1288,7 +1288,7 @@ def normalize_corpus(corpus,
def char_token(s: Text) -> List[Text]:
"""chinese charactor
"""chinese character
Args:
s (Text): "我爱中国“

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