Fix

audio/paddleaudio/datasets/esc50.py

@@ -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()
 

audio/paddleaudio/datasets/gtzan.py

@@ -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

audio/paddleaudio/datasets/rirs_noises.py

@@ -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"

audio/paddleaudio/datasets/tess.py

@@ -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)):

audio/paddleaudio/datasets/urban_sound.py

@@ -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()
 

audio/paddleaudio/datasets/voxceleb.py

@@ -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):

dataset/chime3_background/chime3_background.py

@@ -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,

dataset/timit/timit.py

@@ -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):

demos/TTSCppFrontend/src/front/front_interface.cpp

@@ -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;
             }
         }

demos/audio_searching/src/milvus_helpers.py

@@ -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(

demos/speech_web/speech_server/main.py

@@ -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

demos/streaming_tts_serving_fastdeploy/streaming_tts_serving/1/model.py

@@ -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

examples/aishell3/tts3/conf/conformer.yaml

@@ -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

examples/aishell3/tts3/conf/default.yaml

@@ -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

examples/aishell3/vc1/conf/default.yaml

@@ -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

examples/aishell3/vc2/conf/default.yaml

@@ -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

examples/aishell3/voc5/conf/default.yaml

@@ -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.

examples/canton/tts3/conf/default.yaml

@@ -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

examples/csmsc/jets/conf/default.yaml

@@ -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

examples/csmsc/tts3/conf/cnndecoder.yaml

@@ -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

examples/csmsc/tts3/conf/conformer.yaml

@@ -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

examples/csmsc/tts3/conf/default.yaml

@@ -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

examples/csmsc/voc5/conf/default.yaml

@@ -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.

examples/csmsc/voc5/conf/finetune.yaml

@@ -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.

examples/csmsc/voc5/conf/iSTFT.yaml

@@ -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.

examples/ljspeech/tts1/conf/default.yaml

@@ -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

examples/ljspeech/tts3/conf/default.yaml

@@ -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

examples/ljspeech/voc5/conf/default.yaml

@@ -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.

examples/opencpop/svs1/conf/default.yaml

@@ -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

examples/opencpop/voc5/conf/default.yaml

@@ -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.

examples/opencpop/voc5/conf/finetune.yaml

@@ -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.

examples/vctk/tts3/conf/default.yaml

@@ -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

examples/vctk/voc5/conf/default.yaml

@@ -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.

examples/voxceleb/sv0/local/data_prepare.py

@@ -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(

examples/zh_en_tts/tts3/conf/default.yaml

@@ -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

paddlespeech/s2t/exps/hubert/model.py

@@ -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)):

paddlespeech/s2t/exps/wav2vec2/model.py

@@ -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)):

paddlespeech/s2t/exps/wavlm/model.py

@@ -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)):

paddlespeech/s2t/training/trainer.py

@@ -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"]
 

paddlespeech/server/utils/audio_handler.py

@@ -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.

paddlespeech/server/ws/asr_api.py

@@ -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

paddlespeech/server/ws/tts_api.py

@@ -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()
 

paddlespeech/t2s/models/vits/residual_coupling.py

@@ -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.