Fixes the typos in tasks internal files.

PiperOrigin-RevId: 526063515

mediapipe/tasks/python/audio/core/audio_record.py

@@ -47,11 +47,11 @@ class AudioRecord(object):
       raise sd_error
 
     if channels <= 0:
-      raise ValueError('channels must be postive.')
+      raise ValueError('channels must be positive.')
     if sampling_rate <= 0:
-      raise ValueError('sampling_rate must be postive.')
+      raise ValueError('sampling_rate must be positive.')
     if buffer_size <= 0:
-      raise ValueError('buffer_size must be postive.')
+      raise ValueError('buffer_size must be positive.')
 
     self._audio_buffer = []
     self._buffer_size = buffer_size

mediapipe/tasks/python/test/audio/audio_classifier_test.py

@@ -47,7 +47,7 @@ _TEST_DATA_DIR = 'mediapipe/tasks/testdata/audio'
 _TWO_HEADS_WAV_16K_MONO = 'two_heads_16000_hz_mono.wav'
 _TWO_HEADS_WAV_44K_MONO = 'two_heads_44100_hz_mono.wav'
 _YAMNET_NUM_OF_SAMPLES = 15600
-_MILLSECONDS_PER_SECOND = 1000
+_MILLISECONDS_PER_SECOND = 1000
 
 
 class AudioClassifierTest(parameterized.TestCase):
@@ -75,7 +75,7 @@ class AudioClassifierTest(parameterized.TestCase):
       end = min(start + (int)(step_size), len(buffer))
       audio_data_list.append((_AudioData.create_from_array(
           buffer[start:end].astype(float) / np.iinfo(np.int16).max,
-          sample_rate), (int)(start / sample_rate * _MILLSECONDS_PER_SECOND)))
+          sample_rate), (int)(start / sample_rate * _MILLISECONDS_PER_SECOND)))
       start = end
     return audio_data_list
 

mediapipe/tasks/python/test/audio/audio_embedder_test.py

@@ -45,7 +45,7 @@ _SPEECH_WAV_48K_MONO = 'speech_48000_hz_mono.wav'
 _TWO_HEADS_WAV_16K_MONO = 'two_heads_16000_hz_mono.wav'
 _TEST_DATA_DIR = 'mediapipe/tasks/testdata/audio'
 _YAMNET_NUM_OF_SAMPLES = 15600
-_MILLSECONDS_PER_SECOND = 1000
+_MILLISECONDS_PER_SECOND = 1000
 # Tolerance for embedding vector coordinate values.
 _EPSILON = 3e-6
 
@@ -78,7 +78,7 @@ class AudioEmbedderTest(parameterized.TestCase):
       end = min(start + (int)(step_size), len(buffer))
       audio_data_list.append((_AudioData.create_from_array(
           buffer[start:end].astype(float) / np.iinfo(np.int16).max,
-          sample_rate), (int)(start / sample_rate * _MILLSECONDS_PER_SECOND)))
+          sample_rate), (int)(start / sample_rate * _MILLISECONDS_PER_SECOND)))
       start = end
     return audio_data_list
 

mediapipe/tasks/python/vision/core/base_vision_task_api.py

@@ -188,7 +188,7 @@ class BaseVisionTaskApi(object):
     # For 90° and 270° rotations, we need to swap width and height.
     # This is due to the internal behavior of ImageToTensorCalculator, which:
     # - first denormalizes the provided rect by multiplying the rect width or
-    #   height by the image width or height, repectively.
+    #   height by the image width or height, respectively.
     # - then rotates this by denormalized rect by the provided rotation, and
     #   uses this for cropping,
     # - then finally rotates this back.

mediapipe/tasks/web/vision/core/vision_task_runner.ts

@@ -186,7 +186,7 @@ export abstract class VisionTaskRunner extends TaskRunner {
       // For 90° and 270° rotations, we need to swap width and height.
       // This is due to the internal behavior of ImageToTensorCalculator, which:
       // - first denormalizes the provided rect by multiplying the rect width or
-      //   height by the image width or height, repectively.
+      //   height by the image width or height, respectively.
       // - then rotates this by denormalized rect by the provided rotation, and
       //   uses this for cropping,
       // - then finally rotates this back.

mediapipe/tasks/web/vision/image_classifier/image_classifier_test.ts

@@ -117,12 +117,12 @@ describe('ImageClassifier', () => {
     classifcations.setHeadIndex(1);
     classifcations.setHeadName('headName');
     const classificationList = new ClassificationList();
-    const clasification = new Classification();
+    const classification = new Classification();
-    clasification.setIndex(1);
+    classification.setIndex(1);
-    clasification.setScore(0.2);
+    classification.setScore(0.2);
-    clasification.setDisplayName('displayName');
+    classification.setDisplayName('displayName');
-    clasification.setLabel('categoryName');
+    classification.setLabel('categoryName');
-    classificationList.addClassification(clasification);
+    classificationList.addClassification(classification);
     classifcations.setClassificationList(classificationList);
     classificationResult.addClassifications(classifcations);