Merge branch 'develop' into docker

audio/paddleaudio/compliance/kaldi.py

@@ -233,7 +233,7 @@ def spectrogram(waveform: Tensor,
         round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
             to FFT. Defaults to True.
         sr (int, optional): Sample rate of input waveform. Defaults to 16000.
-        snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a signal frame when it
+        snip_edges (bool, optional): Drop samples in the end of waveform that can't fit a signal frame when it
             is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
         subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
         window_type (str, optional): Choose type of window for FFT computation. Defaults to "povey".
@@ -443,7 +443,7 @@ def fbank(waveform: Tensor,
         round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
             to FFT. Defaults to True.
         sr (int, optional): Sample rate of input waveform. Defaults to 16000.
-        snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a signal frame when it
+        snip_edges (bool, optional): Drop samples in the end of waveform that can't fit a signal frame when it
             is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
         subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
         use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.
@@ -566,7 +566,7 @@ def mfcc(waveform: Tensor,
         round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
             to FFT. Defaults to True.
         sr (int, optional): Sample rate of input waveform. Defaults to 16000.
-        snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a signal frame when it
+        snip_edges (bool, optional): Drop samples in the end of waveform that can't fit a signal frame when it
             is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
         subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
         use_energy (bool, optional): Add an dimension with energy of spectrogram to the output. Defaults to False.

audio/paddleaudio/compliance/librosa.py

@@ -527,7 +527,7 @@ def melspectrogram(x: np.ndarray,
     if fmax is None:
         fmax = sr // 2
     if fmin < 0 or fmin >= fmax:
-        raise ParameterError('fmin and fmax must statisfy 0<fmin<fmax')
+        raise ParameterError('fmin and fmax must satisfy 0<fmin<fmax')
 
     s = stft(
         x,

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;
             }
         }
@@ -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 &&
+            (word_num >= 2 && find(must_neural_tone_words.begin(),
-             find(must_neural_tone_words.begin(),
                                    must_neural_tone_words.end(),
-                  ppspeech::wstring2utf8string(word_wstr.substr(
+                                   ppspeech::wstring2utf8string(
-                      word_num - 2))) != must_neural_tone_words.end())) {
+                                       word_wstr.substr(word_num - 2))) !=
+                                  must_neural_tone_words.end())) {
             (*finals).back() =
                 (*finals).back().replace((*finals).back().length() - 1, 1, "5");
         }

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/aishell/asr3/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

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/librispeech/asr1/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

examples/librispeech/asr2/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

examples/librispeech/asr3/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

examples/librispeech/asr4/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

examples/librispeech/asr5/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

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/mustc/st1/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

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/other/ngram_lm/s0/local/kenlm_score_test.py

@@ -97,7 +97,7 @@ def test_full_scores_words():
         if w not in model:
             print('"{0}" is an OOV'.format(w))
             oov.append(w)
-    # zh_giga.no_cna_cmn.prune01244.klm is chinese charactor LM 
+    # zh_giga.no_cna_cmn.prune01244.klm is chinese character LM 
     assert oov == ["盘点", "不怕", "网站", "❗", "️", "海淘", "向来", "便宜", "保真",
                    "！"], 'error oov'
 

examples/ted_en_zh/st1/cmd.sh

@@ -54,8 +54,8 @@ elif [ "${cmd_backend}" = sge ]; then
 # "sbatch" (Slurm)
 elif [ "${cmd_backend}" = slurm ]; then
     # The default setting is written in conf/slurm.conf.
-    # You must change "-p cpu" and "-p gpu" for the "partion" for your environment.
+    # You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
-    # To know the "partion" names, type "sinfo".
+    # To know the "partition" names, type "sinfo".
     # You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
     # The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".
 

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
+    # Note: this may occurs c++ exception, but the program will execute fine
-    # so we ignore the execption 
+    # 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/audio/utils/tensor_utils.py

@@ -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

paddlespeech/dataset/aidatatang_200zh/aidatatang_200zh.py

@@ -62,7 +62,7 @@ def create_manifest(data_dir, manifest_path_prefix):
         if line == '':
             continue
         audio_id, text = line.split(' ', 1)
-        # remove withespace, charactor text
+        # remove withespace, character text
         text = ''.join(text.split())
         transcript_dict[audio_id] = text
 

paddlespeech/dataset/aishell/aishell.py

@@ -65,7 +65,7 @@ def create_manifest(data_dir, manifest_path_prefix):
         if line == '':
             continue
         audio_id, text = line.split(' ', 1)
-        # remove withespace, charactor text
+        # remove withespace, character text
         text = ''.join(text.split())
         transcript_dict[audio_id] = text
 
@@ -159,7 +159,7 @@ def check_dataset(data_dir):
         if line == '':
             continue
         audio_id, text = line.split(' ', 1)
-        # remove withespace, charactor text
+        # remove withespace, character text
         text = ''.join(text.split())
         transcript_dict[audio_id] = text
 

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/s2t/utils/error_rate.py

@@ -171,7 +171,7 @@ def wer(reference, hypothesis, ignore_case=False, delimiter=' '):
 
 
 def cer(reference, hypothesis, ignore_case=False, remove_space=False):
-    """Calculate charactor error rate (CER). CER compares reference text and
+    """Calculate character error rate (CER). CER compares reference text and
     hypothesis text in char-level. CER is defined as:
 
     .. math::

paddlespeech/s2t/utils/tensor_utils.py

@@ -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

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/frontend/zh_frontend.py

@@ -523,7 +523,7 @@ class Frontend():
         initials = []
         finals = []
 
-        # to charactor list
+        # to character list
         words = self._split_word_to_char(words[0])
 
         for pinyin, char in zip(pinyin_spec, words):

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.
 

paddlespeech/t2s/utils/error_rate.py

@@ -159,7 +159,7 @@ def wer(reference, hypothesis, ignore_case=False, delimiter=' '):
 
 
 def cer(reference, hypothesis, ignore_case=False, remove_space=False):
-    """Calculate charactor error rate (CER). CER compares reference text and
+    """Calculate character error rate (CER). CER compares reference text and
     hypothesis text in char-level. CER is defined as:
     .. math::
         CER = (Sc + Dc + Ic) / Nc

runtime/engine/asr/decoder/ctc_prefix_beam_search_decoder.cc

@@ -157,7 +157,7 @@ void CTCPrefixBeamSearch::AdvanceDecoding(
                     next_score.v_b = prefix_score.ViterbiScore() + prob;
                     next_score.times_b = prefix_score.Times();
 
-                    // Prefix not changed, copy the context from pefix
+                    // Prefix not changed, copy the context from prefix
                     if (context_graph_ && !next_score.has_context) {
                         next_score.CopyContext(prefix_score);
                         next_score.has_context = true;
@@ -183,7 +183,7 @@ void CTCPrefixBeamSearch::AdvanceDecoding(
                         }
                     }
 
-                    // Prefix not changed, copy the context from pefix
+                    // Prefix not changed, copy the context from prefix
                     if (context_graph_ && !next_score1.has_context) {
                         next_score1.CopyContext(prefix_score);
                         next_score1.has_context = true;

runtime/engine/common/frontend/cmvn.cc

@@ -72,7 +72,7 @@ bool CMVN::Read(std::vector<BaseFloat>* feats) {
         return false;
     }
 
-    // appply cmvn
+    // apply cmvn
     kaldi::Timer timer;
     Compute(feats);
     VLOG(1) << "CMVN::Read cost: " << timer.Elapsed() << " sec.";

runtime/engine/common/frontend/cmvn.h

@@ -29,7 +29,7 @@ class CMVN : public FrontendInterface {
     // the length of feats = feature_row * feature_dim,
     // the Matrix is squashed into Vector
     virtual bool Read(std::vector<kaldi::BaseFloat>* feats);
-    // the dim_ is the feautre dim.
+    // the dim_ is the feature dim.
     virtual size_t Dim() const { return dim_; }
     virtual void SetFinished() { base_extractor_->SetFinished(); }
     virtual bool IsFinished() const { return base_extractor_->IsFinished(); }

runtime/engine/common/frontend/db_norm.h

@@ -47,7 +47,7 @@ class DecibelNormalizer : public FrontendInterface {
         std::unique_ptr<FrontendInterface> base_extractor);
     virtual void Accept(const kaldi::VectorBase<kaldi::BaseFloat>& waves);
     virtual bool Read(kaldi::Vector<kaldi::BaseFloat>* waves);
-    // noramlize audio, the dim is 1.
+    // normalize audio, the dim is 1.
     virtual size_t Dim() const { return dim_; }
     virtual void SetFinished() { base_extractor_->SetFinished(); }
     virtual bool IsFinished() const { return base_extractor_->IsFinished(); }

runtime/engine/common/matrix/kaldi-matrix.cc

@@ -244,8 +244,8 @@ void MatrixBase<Real>::SymAddMat2(const Real alpha,
   /// function will produce NaN in the output. This is a bug in the
   /// ATLAS library. To overcome this, the AddMatMat function, which calls
   /// cblas_Xgemm(...) rather than cblas_Xsyrk(...), is used in this special
-  /// sitation.
+  /// situation.
-  /// Wei Shi: Note this bug is observerd for single precision matrix
+  /// Wei Shi: Note this bug is observed for single precision matrix
   /// on a 64-bit machine
 #ifdef HAVE_ATLAS
   if (transA == kTrans && num_rows_ >= 56) {
@@ -683,7 +683,7 @@ empty.
   if (V_in == NULL) tmpV.Resize(1, this->num_cols_);  // work-space if V_in
 empty.
 
-  /// Impementation notes:
+  /// Implementation notes:
   /// Lapack works in column-order, therefore the dimensions of *this are
   /// swapped as well as the U and V matrices.
 
@@ -2378,7 +2378,7 @@ bool ReadHtk(std::istream &is, Matrix<Real> *M_ptr, HtkHeader *header_ptr)
   Matrix<Real> &M = *M_ptr;
   HtkHeader htk_hdr;
 
-  // TODO(arnab): this fails if the HTK file has CRC cheksum or is compressed.
+  // TODO(arnab): this fails if the HTK file has CRC checksum or is compressed.
   is.read((char*)&htk_hdr, sizeof(htk_hdr));  // we're being really POSIX here!
   if (is.fail()) {
     KALDI_WARN << "Could not read header from HTK feature file ";

runtime/engine/common/matrix/kaldi-vector.cc

@@ -235,7 +235,7 @@ void VectorBase<Real>::CopyRowsFromMat(const MatrixBase<Real> &mat) {
         memcpy(inc_data, mat.Data(), cols * rows * sizeof(Real));
     } else {
         for (MatrixIndexT i = 0; i < rows; i++) {
-            // copy the data to the propper position
+            // copy the data to the proper position
             memcpy(inc_data, mat.RowData(i), cols * sizeof(Real));
             // set new copy position
             inc_data += cols;

runtime/engine/common/utils/file_utils.cc

@@ -44,7 +44,7 @@ std::string ReadFile2String(const std::string& path) {
 }
 
 bool FileExists(const std::string& strFilename) { 
-    // this funciton if from:
+    // this function if from:
     // https://github.com/kaldi-asr/kaldi/blob/master/src/fstext/deterministic-fst-test.cc
     struct stat stFileInfo; 
     bool blnReturn; 

runtime/engine/kaldi/lat/kaldi-lattice.cc

@@ -407,7 +407,7 @@ bool WriteLattice(std::ostream &os, bool binary, const Lattice &t) {
     if (os.fail())
       KALDI_WARN << "Stream failure detected.";
     // Write another newline as a terminating character.  The read routine will
-    // detect this [this is a Kaldi mechanism, not somethig in the original
+    // detect this [this is a Kaldi mechanism, not something in the original
     // OpenFst code].
     os << '\n';
     return os.good();

runtime/examples/README.md

@@ -34,7 +34,7 @@ bash run.sh --stop_stage 4
 
 ## Display Model with [Netron](https://github.com/lutzroeder/netron)  
 
-If you have a model, we can using this commnd to show model graph.
+If you have a model, we can using this commend to show model graph.
 
 For example:
 ```

runtime/examples/audio_classification/README.md

@@ -74,7 +74,7 @@ includes/
 #### set path
 push resource into android phone
 
-1. change resource path in conf to gloabal path, such as:
+1. change resource path in conf to global path, such as:
 
     [CONF]
     wav_normal=true
@@ -92,9 +92,9 @@ push resource into android phone
     high_freq=14000
     dither=0.0
 2. adb push conf label_list scp test.wav /data/local/tmp/
-3. set reource path in android demo(android_demo/app/src/main/cpp/native-lib.cpp) to actual path, such as:
+3. set resource path in android demo(android_demo/app/src/main/cpp/native-lib.cpp) to actual path, such as:
 
 std::string conf_path = "/data/local/tmp/conf";
 std::string wav_path = "/data/local/tmp/test.wav";
 
-4. excecute android_demo in android studio
+4. execute android_demo in android studio

runtime/examples/text_lm/local/mmseg.py

@@ -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):
+    def isChineseChar(self, character):
-        return 0x4e00 <= ord(charater) < 0x9fa6
+        return 0x4e00 <= ord(character) < 0x9fa6
 
     #判断是否是ASCII码  
     def isASCIIChar(self, ch):
@@ -253,7 +253,6 @@ class Analysis:
                             # print(word3.length, word3.text)
                             if word3.length == -1:
                                 chunk = Chunk(word1, word2)
-                                # print("Ture")
                             else:
                                 chunk = Chunk(word1, word2, word3)
                             chunks.append(chunk)

runtime/patch/openfst/src/include/fst/flags.h

@@ -181,8 +181,8 @@ template <typename T>
 class FlagRegisterer {
  public:
   FlagRegisterer(const string &name, const FlagDescription<T> &desc) {
-    auto registr = FlagRegister<T>::GetRegister();
+    auto r = FlagRegister<T>::GetRegister();
-    registr->SetDescription(name, desc);
+    r->SetDescription(name, desc);
   }
 
  private:

tests/test_tipc/conformer/scripts/aishell_tiny.py

@@ -62,7 +62,7 @@ def create_manifest(data_dir, manifest_path_prefix):
         if line == '':
             continue
         audio_id, text = line.split(' ', 1)
-        # remove withespace, charactor text
+        # remove withespace, character text
         text = ''.join(text.split())
         transcript_dict[audio_id] = text
 

tests/unit/cli/aishell_test_prepare.py

@@ -63,7 +63,7 @@ def create_manifest(data_dir, manifest_path_prefix):
         if line == '':
             continue
         audio_id, text = line.split(' ', 1)
-        # remove withespace, charactor text
+        # remove withespace, character text
         text = ''.join(text.split())
         transcript_dict[audio_id] = text
 

tests/unit/server/offline/test_server_client.sh

@@ -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 "Service ip: $server_ip" | tee ./log/test_result.log
-echo "Sevice port: $port" | tee -a ./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
 

tests/unit/server/online/tts/check_server/test.sh

@@ -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 "Service ip: $server_ip" | tee $log/test_result.log
-echo "Sevice port: $port" | tee -a $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
 

tests/unit/tts/test_snapshot.py

@@ -30,7 +30,7 @@ def _test_snapshot():
     # use a simplest iterable object as dataloader
     dataloader = count()
 
-    # hack the training proecss: training does nothing except increse iteration
+    # hack the training proecss: training does nothing except increase iteration
     updater = StandardUpdater(model, optimizer, dataloader=dataloader)
     updater.update_core = lambda x: None
 

tools/extras/install_liblbfgs.sh

@@ -17,13 +17,13 @@ cd liblbfgs-$VER
 ./configure --prefix=`pwd`
 make
 # due to the liblbfgs project directory structure, we have to use -i
-# but the erros are completely harmless
+# but the errors are completely harmless
 make -i install
 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

tools/extras/install_srilm.sh

@@ -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

utils/format_triplet_data.py

@@ -44,7 +44,7 @@ add_arg('manifest_paths',   str,
 # bpe
 add_arg('spm_model_prefix', str, None,
      "spm model prefix, spm_model_%(bpe_mode)_%(count_threshold), only need when `unit_type` is spm")
-add_arg('output_path',  str, None, "filepath of formated manifest.", required=True)
+add_arg('output_path',  str, None, "filepath of formatted manifest.", required=True)
 # yapf: disable
 args = parser.parse_args()
 

utils/fst/ctc_token_fst.py

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

utils/fst/make_tlg.sh

@@ -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>' | \

utils/generate_infer_yaml.py

@@ -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:

utils/tokenizer.perl

@@ -79,7 +79,7 @@ if ($HELP)
         print "  -b     ... disable Perl buffering.\n";
         print "  -time  ... enable processing time calculation.\n";
         print "  -penn  ... use Penn treebank-like tokenization.\n";
-        print "  -protected FILE  ... specify file with patters to be protected in tokenisation.\n";
+        print "  -protected FILE  ... specify file with patterns to be protected in tokenisation.\n";
 	print "  -no-escape ... don't perform HTML escaping on apostrophy, quotes, etc.\n";
 	exit;
 }

utils/train_arpa_with_kenlm.sh

@@ -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, 

utils/zh_tn.py

@@ -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“