Fix (#3980)

.github/ISSUE_TEMPLATE/bug-report-s2t.md

@@ -33,7 +33,7 @@ If applicable, add screenshots to help explain your problem.
  - Python Version [e.g. 3.7]
  - PaddlePaddle Version [e.g. 2.0.0]
  - Model Version [e.g. 2.0.0]
- - GPU/DRIVER Informationo [e.g. Tesla V100-SXM2-32GB/440.64.00]
+ - GPU/DRIVER Information [e.g. Tesla V100-SXM2-32GB/440.64.00]
  - CUDA/CUDNN Version [e.g. cuda-10.2]
  - MKL Version
 - TensorRT Version

.github/ISSUE_TEMPLATE/bug-report-tts.md

@@ -32,7 +32,7 @@ If applicable, add screenshots to help explain your problem.
  - Python Version [e.g. 3.7]
  - PaddlePaddle Version [e.g. 2.0.0]
  - Model Version [e.g. 2.0.0]
- - GPU/DRIVER Informationo [e.g. Tesla V100-SXM2-32GB/440.64.00]
+ - GPU/DRIVER Information [e.g. Tesla V100-SXM2-32GB/440.64.00]
  - CUDA/CUDNN Version [e.g. cuda-10.2]
  - MKL Version
 - TensorRT Version

audio/paddleaudio/backends/soundfile_backend.py

@@ -61,7 +61,7 @@ def resample(y: np.ndarray,
     if mode == 'kaiser_best':
         warnings.warn(
             f'Using resampy in kaiser_best to {src_sr}=>{target_sr}. This function is pretty slow, \
-        we recommend the mode kaiser_fast in large scale audio trainning')
+        we recommend the mode kaiser_fast in large scale audio training')
 
     if not isinstance(y, np.ndarray):
         raise ParameterError(

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 singal frame when it
+        snip_edges (bool, optional): Drop samples in the end of waveform that cann'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 singal frame when it
+        snip_edges (bool, optional): Drop samples in the end of waveform that cann'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 singal frame when it
+        snip_edges (bool, optional): Drop samples in the end of waveform that cann'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/datasets/dataset.py

@@ -47,7 +47,7 @@ class AudioClassificationDataset(paddle.io.Dataset):
             files (:obj:`List[str]`): A list of absolute path of audio files.
             labels (:obj:`List[int]`): Labels of audio files.
             feat_type (:obj:`str`, `optional`, defaults to `raw`):
-                It identifies the feature type that user wants to extrace of an audio file.
+                It identifies the feature type that user wants to extract of an audio file.
         """
         super(AudioClassificationDataset, self).__init__()
 

audio/paddleaudio/datasets/esc50.py

@@ -117,7 +117,7 @@ class ESC50(AudioClassificationDataset):
             split (:obj:`int`, `optional`, defaults to 1):
                 It specify the fold of dev dataset.
             feat_type (:obj:`str`, `optional`, defaults to `raw`):
-                It identifies the feature type that user wants to extrace of an audio file.
+                It identifies the feature type that user wants to extract of an audio file.
         """
         files, labels = self._get_data(mode, split)
         super(ESC50, self).__init__(

audio/paddleaudio/datasets/gtzan.py

@@ -67,7 +67,7 @@ class GTZAN(AudioClassificationDataset):
             split (:obj:`int`, `optional`, defaults to 1):
                 It specify the fold of dev dataset.
             feat_type (:obj:`str`, `optional`, defaults to `raw`):
-                It identifies the feature type that user wants to extrace of an audio file.
+                It identifies the feature type that user wants to extract of an audio file.
         """
         assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
         files, labels = self._get_data(mode, seed, n_folds, split)

audio/paddleaudio/datasets/tess.py

@@ -76,7 +76,7 @@ class TESS(AudioClassificationDataset):
             split (:obj:`int`, `optional`, defaults to 1):
                 It specify the fold of dev dataset.
             feat_type (:obj:`str`, `optional`, defaults to `raw`):
-                It identifies the feature type that user wants to extrace of an audio file.
+                It identifies the feature type that user wants to extract of an audio file.
         """
         assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
         files, labels = self._get_data(mode, seed, n_folds, split)

audio/paddleaudio/datasets/urban_sound.py

@@ -68,7 +68,7 @@ class UrbanSound8K(AudioClassificationDataset):
             split (:obj:`int`, `optional`, defaults to 1):
                 It specify the fold of dev dataset.
             feat_type (:obj:`str`, `optional`, defaults to `raw`):
-                It identifies the feature type that user wants to extrace of an audio file.
+                It identifies the feature type that user wants to extract of an audio file.
         """
 
     def _get_meta_info(self):

audio/paddleaudio/datasets/voxceleb.py

@@ -262,8 +262,8 @@ class VoxCeleb(Dataset):
                      split_chunks: bool=True):
         print(f'Generating csv: {output_file}')
         header = ["id", "duration", "wav", "start", "stop", "spk_id"]
-        # Note: this may occurs c++ execption, but the program will execute fine
-        # so we can ignore the execption 
+        # Note: this may occurs c++ exception, but the program will execute fine
+        # so we can ignore the exception 
         with Pool(cpu_count()) as p:
             infos = list(
                 tqdm(

audio/paddleaudio/features/layers.py

@@ -34,7 +34,7 @@ __all__ = [
 
 class Spectrogram(nn.Layer):
     """Compute spectrogram of given signals, typically audio waveforms.
-    The spectorgram is defined as the complex norm of the short-time Fourier transformation.
+    The spectrogram is defined as the complex norm of the short-time Fourier transformation.
 
     Args:
         n_fft (int, optional): The number of frequency components of the discrete Fourier transform. Defaults to 512.

audio/paddleaudio/functional/functional.py

@@ -247,7 +247,7 @@ def create_dct(n_mfcc: int,
     Args:
         n_mfcc (int): Number of mel frequency cepstral coefficients. 
         n_mels (int): Number of mel filterbanks.
-        norm (Optional[str], optional): Normalizaiton type. Defaults to 'ortho'.
+        norm (Optional[str], optional): Normalization type. Defaults to 'ortho'.
         dtype (str, optional): The data type of the return matrix. Defaults to 'float32'.
 
     Returns:

audio/paddleaudio/metric/eer.py

@@ -22,8 +22,8 @@ def compute_eer(labels: np.ndarray, scores: np.ndarray) -> List[float]:
     """Compute EER and return score threshold.
 
     Args:
-        labels (np.ndarray): the trial label, shape: [N], one-dimention, N refer to the samples num
-        scores (np.ndarray): the trial scores, shape: [N], one-dimention, N refer to the samples num
+        labels (np.ndarray): the trial label, shape: [N], one-dimension, N refer to the samples num
+        scores (np.ndarray): the trial scores, shape: [N], one-dimension, N refer to the samples num
 
     Returns:
         List[float]: eer and the specific threshold

audio/paddleaudio/sox_effects/sox_effects.py

@@ -121,8 +121,8 @@ def apply_effects_tensor(
 
     """
     tensor_np = tensor.numpy()
-    ret = paddleaudio._paddleaudio.sox_effects_apply_effects_tensor(tensor_np, sample_rate,
-                                                       effects, channels_first)
+    ret = paddleaudio._paddleaudio.sox_effects_apply_effects_tensor(
+        tensor_np, sample_rate, effects, channels_first)
     if ret is not None:
         return (paddle.to_tensor(ret[0]), ret[1])
     raise RuntimeError("Failed to apply sox effect")
@@ -139,7 +139,7 @@ def apply_effects_file(
 
     Note:
         This function works in the way very similar to ``sox`` command, however there are slight
-        differences. For example, ``sox`` commnad adds certain effects automatically (such as
+        differences. For example, ``sox`` command adds certain effects automatically (such as
         ``rate`` effect after ``speed``, ``pitch`` etc), but this function only applies the given
         effects. Therefore, to actually apply ``speed`` effect, you also need to give ``rate``
         effect with desired sampling rate, because internally, ``speed`` effects only alter sampling
@@ -228,14 +228,14 @@ def apply_effects_file(
         >>>     pass
     """
     if hasattr(path, "read"):
-        ret = paddleaudio._paddleaudio.apply_effects_fileobj(path, effects, normalize,
-                                                channels_first, format)
+        ret = paddleaudio._paddleaudio.apply_effects_fileobj(
+            path, effects, normalize, channels_first, format)
         if ret is None:
             raise RuntimeError("Failed to load audio from {}".format(path))
         return (paddle.to_tensor(ret[0]), ret[1])
     path = os.fspath(path)
-    ret = paddleaudio._paddleaudio.sox_effects_apply_effects_file(path, effects, normalize,
-                                                     channels_first, format)
+    ret = paddleaudio._paddleaudio.sox_effects_apply_effects_file(
+        path, effects, normalize, channels_first, format)
     if ret is not None:
         return (paddle.to_tensor(ret[0]), ret[1])
     raise RuntimeError("Failed to load audio from {}".format(path))

audio/paddleaudio/src/pybind/kaldi/feature_common_inl.h

@@ -26,7 +26,7 @@ template <class F>
 bool StreamingFeatureTpl<F>::ComputeFeature(
     const std::vector<float>& wav,
     std::vector<float>* feats) {
-    // append remaned waves
+    // append remained waves
     int wav_len = wav.size();
     if (wav_len == 0) return false;
     int left_len = remained_wav_.size();
@@ -38,7 +38,7 @@ bool StreamingFeatureTpl<F>::ComputeFeature(
                 wav.data(),
                 wav_len * sizeof(float));
 
-    // cache remaned waves
+    // cache remained waves
     knf::FrameExtractionOptions frame_opts = computer_.GetFrameOptions();
     int num_frames = knf::NumFrames(waves.size(), frame_opts);
     int frame_shift = frame_opts.WindowShift();

audio/paddleaudio/src/pybind/kaldi/kaldi_feature_wrapper.cc

@@ -44,5 +44,5 @@ py::array_t<float> KaldiFeatureWrapper::ComputeFbank(
     return result.reshape(shape);
 }
 
-}  // namesapce kaldi
+}  // namespace kaldi
 }  // namespace paddleaudio

audio/paddleaudio/src/pybind/sox/effects.cpp

@@ -12,9 +12,9 @@ using namespace paddleaudio::sox_utils;
 namespace paddleaudio::sox_effects {
 
 // Streaming decoding over file-like object is tricky because libsox operates on
-// FILE pointer. The folloing is what `sox` and `play` commands do
+// FILE pointer. The following is what `sox` and `play` commands do
 //  - file input -> FILE pointer
-//  - URL input -> call wget in suprocess and pipe the data -> FILE pointer
+//  - URL input -> call wget in subprocess and pipe the data -> FILE pointer
 //  - stdin -> FILE pointer
 //
 // We want to, instead, fetch byte strings chunk by chunk, consume them, and
@@ -127,12 +127,12 @@ namespace {
 
 enum SoxEffectsResourceState { NotInitialized, Initialized, ShutDown };
 SoxEffectsResourceState SOX_RESOURCE_STATE = NotInitialized;
-std::mutex SOX_RESOUCE_STATE_MUTEX;
+std::mutex SOX_RESOURCE_STATE_MUTEX;
 
 } // namespace
 
 void initialize_sox_effects() {
-  const std::lock_guard<std::mutex> lock(SOX_RESOUCE_STATE_MUTEX);
+  const std::lock_guard<std::mutex> lock(SOX_RESOURCE_STATE_MUTEX);
 
   switch (SOX_RESOURCE_STATE) {
     case NotInitialized:
@@ -150,7 +150,7 @@ void initialize_sox_effects() {
 };
 
 void shutdown_sox_effects() {
-  const std::lock_guard<std::mutex> lock(SOX_RESOUCE_STATE_MUTEX);
+  const std::lock_guard<std::mutex> lock(SOX_RESOURCE_STATE_MUTEX);
 
   switch (SOX_RESOURCE_STATE) {
     case NotInitialized:

audio/paddleaudio/src/pybind/sox/effects_chain.cpp

@@ -14,7 +14,7 @@ namespace {
 
 /// helper classes for passing the location of input tensor and output buffer
 ///
-/// drain/flow callback functions require plaing C style function signature and
+/// drain/flow callback functions require plain C style function signature and
 /// the way to pass extra data is to attach data to sox_effect_t::priv pointer.
 /// The following structs will be assigned to sox_effect_t::priv pointer which
 /// gives sox_effect_t an access to input Tensor and output buffer object.
@@ -50,7 +50,7 @@ int tensor_input_drain(sox_effect_t* effp, sox_sample_t* obuf, size_t* osamp) {
   *osamp -= *osamp % num_channels;
 
   // Slice the input Tensor
-  // refacor this module, chunk
+  // refactor this module, chunk
   auto i_frame = index / num_channels;
   auto num_frames = *osamp / num_channels;
 

audio/paddleaudio/src/pybind/sox/utils.cpp

@@ -162,7 +162,7 @@ py::dtype get_dtype(
         }
       default:
         // default to float32 for the other formats, including
-        // 32-bit flaoting-point WAV,
+        // 32-bit floating-point WAV,
         // MP3,
         // FLAC,
         // VORBIS etc...
@@ -177,7 +177,7 @@ py::array convert_to_tensor(
     const py::dtype dtype,
     const bool normalize,
     const bool channels_first) {
-  // todo refector later(SGoat)
+  // todo refactor later(SGoat)
   py::array t;
   uint64_t dummy = 0;
   SOX_SAMPLE_LOCALS;

audio/paddleaudio/src/pybind/sox/utils.h

@@ -76,7 +76,7 @@ py::dtype get_dtype(
 /// Tensor.
 /// @param dtype Target dtype. Determines the output dtype and value range in
 /// conjunction with normalization.
-/// @param noramlize Perform normalization. Only effective when dtype is not
+/// @param normalize Perform normalization. Only effective when dtype is not
 /// kFloat32. When effective, the output tensor is kFloat32 type and value range
 /// is [-1.0, 1.0]
 /// @param channels_first When True, output Tensor has shape of [num_channels,

audio/paddleaudio/third_party/sox/CMakeLists.txt

@@ -8,9 +8,9 @@ set(patch_dir ${CMAKE_CURRENT_SOURCE_DIR}/../patches)
 set(COMMON_ARGS --quiet --disable-shared --enable-static --prefix=${INSTALL_DIR} --with-pic --disable-dependency-tracking --disable-debug --disable-examples --disable-doc)
 
 # To pass custom environment variables to ExternalProject_Add command,
-# we need to do `${CMAKE_COMMAND} -E env ${envs} <COMMANAD>`.
+# we need to do `${CMAKE_COMMAND} -E env ${envs} <COMMAND>`.
 # https://stackoverflow.com/a/62437353
-# We constrcut the custom environment variables here
+# We construct the custom environment variables here
 set(envs
   "PKG_CONFIG_PATH=${INSTALL_DIR}/lib/pkgconfig"
   "LDFLAGS=-L${INSTALL_DIR}/lib $ENV{LDFLAGS}"

audio/paddleaudio/utils/download.py

@@ -41,14 +41,14 @@ def download_and_decompress(archives: List[Dict[str, str]],
                             path: str,
                             decompress: bool=True):
     """
-    Download archieves and decompress to specific path.
+    Download archives and decompress to specific path.
     """
     if not os.path.isdir(path):
         os.makedirs(path)
 
     for archive in archives:
         assert 'url' in archive and 'md5' in archive, \
-            'Dictionary keys of "url" and "md5" are required in the archive, but got: {list(archieve.keys())}'
+            'Dictionary keys of "url" and "md5" are required in the archive, but got: {list(archive.keys())}'
         download.get_path_from_url(
             archive['url'], path, archive['md5'], decompress=decompress)
 

audio/paddleaudio/utils/log.py

@@ -58,7 +58,7 @@ log_config = {
 
 class Logger(object):
     '''
-    Deafult logger in PaddleAudio
+    Default logger in PaddleAudio
     Args:
         name(str) : Logger name, default is 'PaddleAudio'
     '''

audio/paddleaudio/utils/sox_utils.py

@@ -55,7 +55,7 @@ def set_use_threads(use_threads: bool):
 
     Args:
         use_threads (bool): When ``True``, enables ``libsox``'s parallel effects channels processing.
-            To use mutlithread, the underlying ``libsox`` has to be compiled with OpenMP support.
+            To use multithread, the underlying ``libsox`` has to be compiled with OpenMP support.
 
     See Also:
         http://sox.sourceforge.net/sox.html

audio/paddleaudio/utils/tensor_utils.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Unility functions for Transformer."""
+"""Utility functions for Transformer."""
 from typing import List
 from typing import Tuple
 
@@ -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 ()
@@ -94,7 +94,7 @@ 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
@@ -103,7 +103,7 @@ 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