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Fix typo (#25966)

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* Update feature_extraction_clap.py

* changed all lenght to length
This commit is contained in:
Susnato Dhar
2023-09-05 13:42:25 +05:30
committed by GitHub
parent d8e13b3e04
commit 404ff8fc17
20 changed files with 35 additions and 35 deletions
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4
tests/models/data2vec/test_modeling_data2vec_audio.py
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@@ -283,8 +283,8 @@ class Data2VecAudioModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

4
tests/models/hubert/test_modeling_hubert.py
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@@ -252,8 +252,8 @@ class HubertModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

4
tests/models/sew/test_modeling_sew.py
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@@ -222,8 +222,8 @@ class SEWModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

4
tests/models/sew_d/test_modeling_sew_d.py
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@@ -243,8 +243,8 @@ class SEWDModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

8
tests/models/speecht5/test_feature_extraction_speecht5.py
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@@ -340,7 +340,7 @@ class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
input_lenghts = [len(x) for x in speech_inputs]
input_lengths = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]
processed = BatchFeature({input_name: speech_inputs})
@@ -350,18 +350,18 @@ class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest
processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
self.assertIn("attention_mask", processed)
self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lengths)
def test_attention_mask_with_truncation_target(self):
feat_dict = self.feat_extract_dict
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
input_lenghts = [len(x) for x in speech_inputs]
input_lengths = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]
processed = BatchFeature({input_name: speech_inputs})
max_length = min(input_lenghts)
max_length = min(input_lengths)
feat_extract.feature_size = feat_extract.num_mel_bins # hack!

4
tests/models/unispeech/test_modeling_unispeech.py
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@@ -245,8 +245,8 @@ class UniSpeechModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

4
tests/models/unispeech_sat/test_modeling_unispeech_sat.py
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@@ -265,8 +265,8 @@ class UniSpeechSatModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

4
tests/models/wav2vec2/test_modeling_wav2vec2.py
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@@ -404,8 +404,8 @@ class Wav2Vec2ModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

4
tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py
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@@ -314,8 +314,8 @@ class Wav2Vec2ConformerModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

4
tests/models/wavlm/test_modeling_wavlm.py
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@@ -256,8 +256,8 @@ class WavLMModelTester:
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss

8
tests/test_sequence_feature_extraction_common.py
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@@ -391,7 +391,7 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
input_lenghts = [len(x) for x in speech_inputs]
input_lengths = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]
processed = BatchFeature({input_name: speech_inputs})
@@ -399,18 +399,18 @@ class SequenceFeatureExtractionTestMixin(FeatureExtractionSavingTestMixin):
processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
self.assertIn("attention_mask", processed)
self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lengths)
def test_attention_mask_with_truncation(self):
feat_dict = self.feat_extract_dict
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_common()
input_lenghts = [len(x) for x in speech_inputs]
input_lengths = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]
processed = BatchFeature({input_name: speech_inputs})
max_length = min(input_lenghts)
max_length = min(input_lengths)
processed_pad = feat_extract.pad(
processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"