up (#14008)

docs/source/model_doc/speech_to_text.rst

@@ -66,7 +66,7 @@ be installed as follows: ``apt install libsndfile1-dev``
         ...     batch["speech"] = speech
         ...     return batch
 
-        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.map(map_to_array)
 
         >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
@@ -98,7 +98,7 @@ be installed as follows: ``apt install libsndfile1-dev``
         ...     batch["speech"] = speech
         ...     return batch
 
-        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.map(map_to_array)
 
         >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")

docs/source/model_doc/speech_to_text_2.rst

@@ -68,7 +68,7 @@ predicted token ids.
         ...     batch["speech"] = speech
         ...     return batch
 
-        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.map(map_to_array)
 
         >>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
@@ -86,7 +86,7 @@ predicted token ids.
         >>> from datasets import load_dataset
         >>> from transformers import pipeline
 
-        >>> librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-de", feature_extractor="facebook/s2t-wav2vec2-large-en-de")
 
         >>> translation_de = asr(librispeech_en[0]["file"])

examples/pytorch/test_examples.py

@@ -391,7 +391,7 @@ class ExamplesTests(TestCasePlus):
             run_speech_recognition_ctc.py
             --output_dir {tmp_dir}
             --model_name_or_path hf-internal-testing/tiny-random-wav2vec2
-            --dataset_name patrickvonplaten/librispeech_asr_dummy
+            --dataset_name hf-internal-testing/librispeech_asr_dummy
             --dataset_config_name clean
             --train_split_name validation
             --eval_split_name validation
@@ -460,7 +460,7 @@ class ExamplesTests(TestCasePlus):
             run_wav2vec2_pretraining_no_trainer.py
             --output_dir {tmp_dir}
             --model_name_or_path hf-internal-testing/tiny-random-wav2vec2
-            --dataset_name patrickvonplaten/librispeech_asr_dummy
+            --dataset_name hf-internal-testing/librispeech_asr_dummy
             --dataset_config_names clean
             --dataset_split_names validation
             --learning_rate 1e-4

examples/research_projects/wav2vec2/README.md

@@ -155,7 +155,7 @@ run_asr.py \
 --per_device_eval_batch_size=2 --evaluation_strategy=steps --save_steps=500 --eval_steps=100 \
 --logging_steps=5 --learning_rate=5e-4 --warmup_steps=3000 \
 --model_name_or_path=patrickvonplaten/wav2vec2_tiny_random_robust \
---dataset_name=patrickvonplaten/librispeech_asr_dummy --dataset_config_name=clean \
+--dataset_name=hf-internal-testing/librispeech_asr_dummy --dataset_config_name=clean \
 --train_split_name=validation --validation_split_name=validation --orthography=timit \
 --preprocessing_num_workers=1 --group_by_length --freeze_feature_extractor --verbose_logging \
 --deepspeed ds_config_wav2vec2_zero2.json
@@ -179,7 +179,7 @@ run_asr.py \
 --per_device_eval_batch_size=2 --evaluation_strategy=steps --save_steps=500 --eval_steps=100 \
 --logging_steps=5 --learning_rate=5e-4 --warmup_steps=3000 \
 --model_name_or_path=patrickvonplaten/wav2vec2_tiny_random_robust \
---dataset_name=patrickvonplaten/librispeech_asr_dummy --dataset_config_name=clean \
+--dataset_name=hf-internal-testing/librispeech_asr_dummy --dataset_config_name=clean \
 --train_split_name=validation --validation_split_name=validation --orthography=timit \
 --preprocessing_num_workers=1 --group_by_length --freeze_feature_extractor --verbose_logging \
 --deepspeed ds_config_wav2vec2_zero3.json

examples/research_projects/wav2vec2/test_wav2vec2_deepspeed.py

@@ -155,7 +155,7 @@ class TestDeepSpeedWav2Vec2(TestCasePlus):
         output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
         args = f"""
             --model_name_or_path {model_name}
-            --dataset_name patrickvonplaten/librispeech_asr_dummy
+            --dataset_name hf-internal-testing/librispeech_asr_dummy
             --dataset_config_name clean
             --train_split_name validation
             --validation_split_name validation

src/transformers/models/hubert/modeling_hubert.py

@@ -953,7 +953,7 @@ class HubertModel(HubertPreTrainedModel):
             ...     batch["speech"] = speech
             ...     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
@@ -1059,7 +1059,7 @@ class HubertForCTC(HubertPreTrainedModel):
             ...     batch["speech"] = speech
             ...     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1

src/transformers/models/hubert/modeling_tf_hubert.py

@@ -1412,7 +1412,7 @@ class TFHubertModel(TFHubertPreTrainedModel):
             ...     batch["speech"] = speech
             ...     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
@@ -1522,7 +1522,7 @@ class TFHubertForCTC(TFHubertPreTrainedModel):
             ...     batch["speech"] = speech
             ...     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values # Batch size 1

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -414,7 +414,7 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
             >>>     batch["speech"] = speech
             >>>     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1

src/transformers/models/speech_to_text/modeling_speech_to_text.py

@@ -1306,7 +1306,7 @@ class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel):
             >>>     batch["speech"] = speech
             >>>     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_features = processor(ds["speech"][0], sampling_rate=16000, return_tensors="pt").input_features  # Batch size 1

src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py

@@ -944,7 +944,7 @@ FLAX_WAV2VEC2_MODEL_DOCSTRING = """
         >>>     batch["speech"] = speech
         >>>     return batch
 
-        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="np").input_values  # Batch size 1
@@ -1045,7 +1045,7 @@ FLAX_WAV2VEC2_FOR_CTC_DOCSTRING = """
         >>>     batch["speech"] = speech
         >>>     return batch
 
-        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="np").input_values  # Batch size 1
@@ -1233,7 +1233,7 @@ FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING = """
         ...     return batch
 
 
-        >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values  # Batch size 1

src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py

@@ -1406,7 +1406,7 @@ class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
             >>>     batch["speech"] = speech
             >>>     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
@@ -1516,7 +1516,7 @@ class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
             >>>     batch["speech"] = speech
             >>>     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values # Batch size 1

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1146,7 +1146,7 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
             >>>     batch["speech"] = speech
             >>>     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
@@ -1280,7 +1280,7 @@ class Wav2Vec2ForPreTraining(Wav2Vec2PreTrainedModel):
             ...     return batch
 
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = feature_extractor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
@@ -1442,7 +1442,7 @@ class Wav2Vec2ForMaskedLM(Wav2Vec2PreTrainedModel):
             >>>     batch["speech"] = speech
             >>>     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
@@ -1536,7 +1536,7 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
             >>>     batch["speech"] = speech
             >>>     return batch
 
-            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             >>> ds = ds.map(map_to_array)
 
             >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1

tests/test_modeling_flax_wav2vec2.py

@@ -366,7 +366,7 @@ class FlaxWav2Vec2ModelIntegrationTest(unittest.TestCase):
             batch["speech"] = speech
             return batch
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 
         ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
 

tests/test_modeling_hubert.py

@@ -623,7 +623,7 @@ class HubertModelIntegrationTest(unittest.TestCase):
             batch["speech"] = speech
             return batch
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 
         ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
 

tests/test_modeling_speech_to_text.py

@@ -723,7 +723,7 @@ class Speech2TextModelIntegrationTests(unittest.TestCase):
             batch["speech"] = speech
             return batch
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         ds = ds.sort("id").select(range(num_samples)).map(map_to_array)
 
         return ds["speech"][:num_samples]

tests/test_modeling_tf_hubert.py

@@ -489,7 +489,7 @@ class TFHubertModelIntegrationTest(unittest.TestCase):
             batch["speech"] = speech
             return batch
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 
         ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
 

tests/test_modeling_tf_wav2vec2.py

@@ -489,7 +489,7 @@ class TFWav2Vec2ModelIntegrationTest(unittest.TestCase):
             batch["speech"] = speech
             return batch
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 
         ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
 

tests/test_modeling_wav2vec2.py

@@ -910,7 +910,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
             batch["speech"] = speech
             return batch
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 
         ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
 

tests/test_pipelines_audio_classification.py

@@ -62,7 +62,7 @@ class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         )
 
         # test with a local file
-        dataset = datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+        dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         filename = dataset[0]["file"]
         output = audio_classifier(filename)
         self.assertEqual(

tests/test_pipelines_automatic_speech_recognition.py

@@ -74,7 +74,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
 
         from datasets import load_dataset
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
@@ -92,7 +92,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
 
         from datasets import load_dataset
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": 'Ein Mann sagte zum Universum : " Sir, ich existiert! "'})
@@ -114,7 +114,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
         output = asr(waveform)
         self.assertEqual(output, {"text": ""})
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = asr(filename)
         self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
@@ -144,7 +144,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
         output = asr(waveform)
         self.assertEqual(output, {"text": "(Applausi)"})
 
-        ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = asr(filename)
         self.assertEqual(output, {"text": "Un uomo disse all'universo: \"Signore, io esisto."})