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Unverified Commit 7fc1cb15 authored by Sylvain Gugger's avatar Sylvain Gugger Committed by GitHub
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Remove all hf-internal-testing checkpoints that can be removed (#21199) 

* Remove all hf-internal-testing checkpoints that can be removed

* Fix copies

* Put back processor_class in TF example

* Address review comment
parent 142ad1a1
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Showing with 43 additions and 212 deletions
src/transformers/models/hubert/modeling_hubert.py
View file @ 7fc1cb15
......@@ -41,14 +41,10 @@ from .configuration_hubert import HubertConfig
logger = logging.get_logger(__name__)
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_HIDDEN_STATES_START_POSITION = 1
# General docstring
_CONFIG_FOR_DOC = "HubertConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/hubert-large-ls960-ft"
......@@ -59,7 +55,6 @@ _CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND
_CTC_EXPECTED_LOSS = 22.68
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "superb/hubert-base-superb-ks"
_SEQ_CLASS_EXPECTED_OUTPUT = "'_unknown_'"
_SEQ_CLASS_EXPECTED_LOSS = 8.53
......@@ -1145,7 +1140,6 @@ class HubertForCTC(HubertPreTrainedModel):
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1280,7 +1274,6 @@ class HubertForSequenceClassification(HubertPreTrainedModel):
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/longformer/modeling_longformer.py
View file @ 7fc1cb15
......@@ -41,7 +41,6 @@ logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "allenai/longformer-base-4096"
_CONFIG_FOR_DOC = "LongformerConfig"
_TOKENIZER_FOR_DOC = "LongformerTokenizer"
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
"allenai/longformer-base-4096",
......@@ -1903,7 +1902,6 @@ class LongformerForSequenceClassification(LongformerPreTrainedModel):
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="jpwahle/longformer-base-plagiarism-detection",
output_type=LongformerSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2172,7 +2170,6 @@ class LongformerForTokenClassification(LongformerPreTrainedModel):
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="brad1141/Longformer-finetuned-norm",
output_type=LongformerTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2260,7 +2257,6 @@ class LongformerForMultipleChoice(LongformerPreTrainedModel):
LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=LongformerMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/longformer/modeling_tf_longformer.py
View file @ 7fc1cb15
......@@ -2381,11 +2381,9 @@ class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSeque
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="hf-internal-testing/tiny-random-longformer",
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFLongformerSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'LABEL_1'",
expected_loss=0.69,
)
def call(
self,
......@@ -2636,15 +2634,9 @@ class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenCla
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="hf-internal-testing/tiny-random-longformer",
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFLongformerTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=(
"['LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1',"
" 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1',"
" 'LABEL_1', 'LABEL_1']"
),
expected_loss=0.59,
)
def call(
self,
......
src/transformers/models/mbart/modeling_mbart.py
View file @ 7fc1cb15
......@@ -49,22 +49,10 @@ logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/mbart-large-cc25"
_CONFIG_FOR_DOC = "MBartConfig"
_TOKENIZER_FOR_DOC = "MBartTokenizer"
# Base model docstring
_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
# SequenceClassification docstring
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "hf-internal-testing/tiny-random-mbart"
_SEQ_CLASS_EXPECTED_LOSS = 0.69
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
# QuestionAsnwering docstring
_CHECKPOINT_FOR_QA = "hf-internal-testing/tiny-random-mbart"
_QA_EXPECTED_LOSS = 3.55
_QA_EXPECTED_OUTPUT = "'? Jim Henson was a'"
MBART_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/mbart-large-cc25",
# See all MBART models at https://huggingface.co/models?filter=mbart
......@@ -1187,7 +1175,6 @@ class MBartModel(MBartPreTrainedModel):
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1467,12 +1454,9 @@ class MBartForSequenceClassification(MBartPreTrainedModel):
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
# Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification.forward
def forward(
......@@ -1596,12 +1580,9 @@ class MBartForQuestionAnswering(MBartPreTrainedModel):
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_QA,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_loss=_QA_EXPECTED_LOSS,
expected_output=_QA_EXPECTED_OUTPUT,
)
# Copied from transformers.models.bart.modeling_bart.BartForQuestionAnswering.forward
def forward(
......
src/transformers/models/plbart/modeling_plbart.py
View file @ 7fc1cb15
......@@ -48,16 +48,6 @@ logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "uclanlp/plbart-base"
_CONFIG_FOR_DOC = "PLBartConfig"
_TOKENIZER_FOR_DOC = "PLBartTokenizer"
# Base model docstring
_EXPECTED_OUTPUT_SHAPE = [1, 8, 768]
# SequenceClassification docstring
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "hf-internal-testing/tiny-plbart"
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
_SEQ_CLASS_EXPECTED_LOSS = 0.69
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST = [
"uclanlp/plbart-base",
......@@ -1161,7 +1151,6 @@ class PLBartModel(PLBartPreTrainedModel):
@add_start_docstrings_to_model_forward(PLBART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1438,12 +1427,9 @@ class PLBartForSequenceClassification(PLBartPreTrainedModel):
@add_start_docstrings_to_model_forward(PLBART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
# Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification.forward
def forward(
......
src/transformers/models/reformer/modeling_reformer.py
View file @ 7fc1cb15
......@@ -49,7 +49,6 @@ logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/reformer-crime-and-punishment"
_CONFIG_FOR_DOC = "ReformerConfig"
_TOKENIZER_FOR_DOC = "ReformerTokenizer"
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/reformer-crime-and-punishment",
......@@ -2009,7 +2008,6 @@ class ReformerModel(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=ReformerModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2220,7 +2218,6 @@ class ReformerModelWithLMHead(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2360,13 +2357,20 @@ class ReformerForMaskedLM(ReformerPreTrainedModel):
Returns:
<Tip warning={true}>
This example uses a false checkpoint since we don't have any available pretrained model for the masked language
modeling task with the Reformer architecture.
</Tip>
Example:
```python
>>> import torch
>>> from transformers import ReformerTokenizer, ReformerForMaskedLM
>>> from transformers import AutoTokenizer, ReformerForMaskedLM
>>> tokenizer = ReformerTokenizer.from_pretrained("hf-internal-testing/tiny-random-reformer")
>>> tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-reformer")
>>> model = ReformerForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-reformer")
>>> # add mask_token
......@@ -2479,10 +2483,10 @@ class ReformerForSequenceClassification(ReformerPreTrainedModel):
```python
>>> import torch
>>> from transformers import ReformerTokenizer, ReformerForSequenceClassification
>>> from transformers import AutoTokenizer, ReformerForSequenceClassification
>>> tokenizer = ReformerTokenizer.from_pretrained("hf-internal-testing/tiny-random-reformer")
>>> model = ReformerForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-reformer")
>>> tokenizer = AutoTokenizer.from_pretrained("google/reformer-crime-and-punishment")
>>> model = ReformerForSequenceClassification.from_pretrained("google/reformer-crime-and-punishment")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
......@@ -2491,59 +2495,20 @@ class ReformerForSequenceClassification(ReformerPreTrainedModel):
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'LABEL_1'
'LABEL_0'
```
```python
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = ReformerForSequenceClassification.from_pretrained(
... "hf-internal-testing/tiny-random-reformer", num_labels=num_labels
... "google/reformer-crime-and-punishment", num_labels=num_labels
... )
>>> labels = torch.tensor(1)
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.69
```
Example of multi-label classification:
```python
>>> import torch
>>> from transformers import ReformerTokenizer, ReformerForSequenceClassification
>>> tokenizer = ReformerTokenizer.from_pretrained("hf-internal-testing/tiny-random-reformer")
>>> model = ReformerForSequenceClassification.from_pretrained(
... "hf-internal-testing/tiny-random-reformer", problem_type="multi_label_classification"
... )
>>> # add pad_token
>>> tokenizer.add_special_tokens({"pad_token": "[PAD]"}) # doctest: +IGNORE_RESULT
>>> inputs = tokenizer("Hello, my dog is cute", max_length=100, padding="max_length", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'LABEL_1'
```
```python
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = ReformerForSequenceClassification.from_pretrained(
... "hf-internal-testing/tiny-random-reformer", num_labels=num_labels
... )
>>> model.train() # doctest: +IGNORE_RESULT
>>> num_labels = len(model.config.id2label)
>>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
... torch.float
... )
>>> loss = model(**inputs, labels=labels).loss
>>> loss.backward() # doctest: +IGNORE_RESULT
0.68
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
......@@ -2641,12 +2606,9 @@ class ReformerForQuestionAnswering(ReformerPreTrainedModel):
@add_start_docstrings_to_model_forward(REFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint="hf-internal-testing/tiny-random-reformer",
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="''",
expected_loss=3.28,
)
def forward(
self,
......
src/transformers/models/sew/modeling_sew.py
View file @ 7fc1cb15
......@@ -36,16 +36,11 @@ from .configuration_sew import SEWConfig
logger = logging.get_logger(__name__)
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_HIDDEN_STATES_START_POSITION = 1
# General docstring
_CONFIG_FOR_DOC = "SEWConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "asapp/sew-tiny-100k-ft-ls100h"
......@@ -58,7 +53,6 @@ _CTC_EXPECTED_OUTPUT = (
_CTC_EXPECTED_LOSS = 0.42
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "anton-l/sew-mid-100k-ft-keyword-spotting"
_SEQ_CLASS_EXPECTED_OUTPUT = "'_unknown_'"
_SEQ_CLASS_EXPECTED_LOSS = 9.52
......@@ -916,7 +910,6 @@ class SEWModel(SEWPreTrainedModel):
@add_start_docstrings_to_model_forward(SEW_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1020,7 +1013,6 @@ class SEWForCTC(SEWPreTrainedModel):
@add_start_docstrings_to_model_forward(SEW_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1155,7 +1147,6 @@ class SEWForSequenceClassification(SEWPreTrainedModel):
@add_start_docstrings_to_model_forward(SEW_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/sew_d/modeling_sew_d.py
View file @ 7fc1cb15
......@@ -42,7 +42,6 @@ _HIDDEN_STATES_START_POSITION = 1
# General docstring
_CONFIG_FOR_DOC = "SEWDConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "asapp/sew-d-tiny-100k-ft-ls100h"
......@@ -53,7 +52,6 @@ _CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTIL OF THE MIDDLE CLASSES AND
_CTC_EXPECTED_LOSS = 0.21
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "anton-l/sew-d-mid-400k-ft-keyword-spotting"
_SEQ_CLASS_EXPECTED_OUTPUT = "'_unknown_'"
_SEQ_CLASS_EXPECTED_LOSS = 3.16
......@@ -1453,7 +1451,6 @@ class SEWDModel(SEWDPreTrainedModel):
@add_start_docstrings_to_model_forward(SEWD_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1557,7 +1554,6 @@ class SEWDForCTC(SEWDPreTrainedModel):
@add_start_docstrings_to_model_forward(SEWD_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1692,7 +1688,6 @@ class SEWDForSequenceClassification(SEWDPreTrainedModel):
@add_start_docstrings_to_model_forward(SEWD_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/unispeech/modeling_unispeech.py
View file @ 7fc1cb15
......@@ -48,7 +48,6 @@ _HIDDEN_STATES_START_POSITION = 2
# General docstring
_CONFIG_FOR_DOC = "UniSpeechConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "patrickvonplaten/unispeech-large-1500h-cv-timit"
......@@ -58,12 +57,6 @@ _EXPECTED_OUTPUT_SHAPE = [1, 292, 1024]
_CTC_EXPECTED_OUTPUT = "'mister quilter is the apposl of the midle classes and weare glad to welcom his gosepl'"
_CTC_EXPECTED_LOSS = 17.17
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "hf-internal-testing/tiny-random-unispeech"
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'" # TODO(anton) - could you quickly fine-tune a KS WavLM Model
_SEQ_CLASS_EXPECTED_LOSS = 0.66 # TODO(anton) - could you quickly fine-tune a KS WavLM Model
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST = [
"microsoft/unispeech-large-1500h-cv",
"microsoft/unispeech-large-multi-lingual-1500h-cv",
......@@ -1143,7 +1136,6 @@ class UniSpeechModel(UniSpeechPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1286,12 +1278,9 @@ class UniSpeechForPreTraining(UniSpeechPreTrainedModel):
```python
>>> import torch
>>> from transformers import Wav2Vec2FeatureExtractor, UniSpeechForPreTraining
>>> from transformers.models.unispeech.modeling_unispeech import _compute_mask_indices
>>> from transformers import AutoFeatureExtractor, UniSpeechForPreTraining
>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
... "hf-internal-testing/tiny-random-unispeech-sat"
... )
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/unispeech-large-1500h-cv")
>>> model = UniSpeechForPreTraining.from_pretrained("microsoft/unispeech-large-1500h-cv")
>>> # TODO: Add full pretraining example
```"""
......@@ -1395,7 +1384,6 @@ class UniSpeechForCTC(UniSpeechPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1482,7 +1470,6 @@ class UniSpeechForCTC(UniSpeechPreTrainedModel):
""",
UNISPEECH_START_DOCSTRING,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification with Wav2Vec2->UniSpeech, wav2vec2->unispeech, WAV_2_VEC_2->UNISPEECH
class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
def __init__(self, config):
super().__init__(config)
......@@ -1501,6 +1488,7 @@ class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_extractor
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
......@@ -1513,6 +1501,7 @@ class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
)
self.freeze_feature_encoder()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->unispeech
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
......@@ -1520,6 +1509,7 @@ class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
"""
self.unispeech.feature_extractor._freeze_parameters()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_base_model with wav2vec2->unispeech
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
......@@ -1530,14 +1520,12 @@ class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->UniSpeech, wav2vec2->unispeech
def forward(
self,
input_values: Optional[torch.Tensor],
......
src/transformers/models/unispeech_sat/modeling_unispeech_sat.py
View file @ 7fc1cb15
......@@ -55,7 +55,6 @@ _HIDDEN_STATES_START_POSITION = 2
# General docstring
_CONFIG_FOR_DOC = "UniSpeechSatConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "microsoft/unispeech-sat-base-100h-libri-ft"
......@@ -65,12 +64,6 @@ _EXPECTED_OUTPUT_SHAPE = [1, 292, 768]
_CTC_EXPECTED_OUTPUT = "'MISTER QUILDER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
_CTC_EXPECTED_LOSS = 39.88
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "hf-internal-testing/tiny-random-unispeech-sat"
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'" # TODO(anton) - could you quickly fine-tune a KS WavLM Model
_SEQ_CLASS_EXPECTED_LOSS = 0.71 # TODO(anton) - could you quickly fine-tune a KS WavLM Model
# Frame class docstring
_FRAME_CLASS_CHECKPOINT = "microsoft/unispeech-sat-base-plus-sd"
_FRAME_EXPECTED_OUTPUT = [0, 0]
......@@ -1158,7 +1151,6 @@ class UniSpeechSatModel(UniSpeechSatPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1299,10 +1291,10 @@ class UniSpeechSatForPreTraining(UniSpeechSatPreTrainedModel):
```python
>>> import torch
>>> from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForPreTraining
>>> from transformers import AutoFeatureExtractor, UniSpeechSatForPreTraining
>>> from transformers.models.unispeech_sat.modeling_unispeech_sat import _compute_mask_indices
>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/unispeech-sat-base")
>>> model = UniSpeechSatForPreTraining.from_pretrained("microsoft/unispeech-sat-base")
>>> # TODO: Add full pretraining example
```"""
......@@ -1399,7 +1391,6 @@ class UniSpeechSatForCTC(UniSpeechSatPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1486,7 +1477,6 @@ class UniSpeechSatForCTC(UniSpeechSatPreTrainedModel):
""",
UNISPEECH_SAT_START_DOCSTRING,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat, WAV_2_VEC_2->UNISPEECH_SAT
class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
......@@ -1505,6 +1495,7 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_extractor
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
......@@ -1517,6 +1508,7 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
)
self.freeze_feature_encoder()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->unispeech_sat
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
......@@ -1524,6 +1516,7 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
"""
self.unispeech_sat.feature_extractor._freeze_parameters()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_base_model with wav2vec2->unispeech_sat
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
......@@ -1534,14 +1527,12 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat
def forward(
self,
input_values: Optional[torch.Tensor],
......@@ -1658,7 +1649,6 @@ class UniSpeechSatForAudioFrameClassification(UniSpeechSatPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_FRAME_CLASS_CHECKPOINT,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1841,7 +1831,6 @@ class UniSpeechSatForXVector(UniSpeechSatPreTrainedModel):
@add_start_docstrings_to_model_forward(UNISPEECH_SAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_XVECTOR_CHECKPOINT,
output_type=XVectorOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/wav2vec2/modeling_wav2vec2.py
View file @ 7fc1cb15
......@@ -56,7 +56,6 @@ _HIDDEN_STATES_START_POSITION = 2
# General docstring
_CONFIG_FOR_DOC = "Wav2Vec2Config"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h"
......@@ -67,7 +66,6 @@ _CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND
_CTC_EXPECTED_LOSS = 53.48
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "superb/wav2vec2-base-superb-ks"
_SEQ_CLASS_EXPECTED_OUTPUT = "'_unknown_'"
_SEQ_CLASS_EXPECTED_LOSS = 6.54
......@@ -1279,7 +1277,6 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1655,7 +1652,6 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1789,7 +1785,6 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1911,7 +1906,6 @@ class Wav2Vec2ForAudioFrameClassification(Wav2Vec2PreTrainedModel):
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_FRAME_CLASS_CHECKPOINT,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -2091,7 +2085,6 @@ class Wav2Vec2ForXVector(Wav2Vec2PreTrainedModel):
@add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_XVECTOR_CHECKPOINT,
output_type=XVectorOutput,
config_class=_CONFIG_FOR_DOC,
......
src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
View file @ 7fc1cb15
......@@ -54,7 +54,6 @@ _HIDDEN_STATES_START_POSITION = 2
# General docstring
_CONFIG_FOR_DOC = "Wav2Vec2ConformerConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/wav2vec2-conformer-rope-large-960h-ft"
......@@ -64,20 +63,6 @@ _EXPECTED_OUTPUT_SHAPE = [1, 292, 1024]
_CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
_CTC_EXPECTED_LOSS = 64.21
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "hf-internal-testing/wav2vec2-conformer-seq-class"
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
_SEQ_CLASS_EXPECTED_LOSS = 0.68
# Frame class docstring
_FRAME_CLASS_CHECKPOINT = "hf-internal-testing/wav2vec2-conformer-frame-class"
_FRAME_EXPECTED_OUTPUT = [1, 0]
# Speaker Verification docstring
_XVECTOR_CHECKPOINT = "hf-internal-testing/wav2vec2-conformer-xvector"
_XVECTOR_EXPECTED_OUTPUT = 1.0
WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
"facebook/wav2vec2-conformer-rel-pos-large",
......@@ -1324,7 +1309,6 @@ class Wav2Vec2ConformerModel(Wav2Vec2ConformerPreTrainedModel):
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1643,7 +1627,6 @@ class Wav2Vec2ConformerForCTC(Wav2Vec2ConformerPreTrainedModel):
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1769,13 +1752,10 @@ class Wav2Vec2ConformerForSequenceClassification(Wav2Vec2ConformerPreTrainedMode
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
def forward(
......@@ -1884,12 +1864,10 @@ class Wav2Vec2ConformerForAudioFrameClassification(Wav2Vec2ConformerPreTrainedMo
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_FRAME_CLASS_CHECKPOINT,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_FRAME_EXPECTED_OUTPUT,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.forward with wav2vec2->wav2vec2_conformer
def forward(
......@@ -2058,12 +2036,10 @@ class Wav2Vec2ConformerForXVector(Wav2Vec2ConformerPreTrainedModel):
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_XVECTOR_CHECKPOINT,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=XVectorOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_XVECTOR_EXPECTED_OUTPUT,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
def forward(
......
src/transformers/models/wavlm/modeling_wavlm.py
View file @ 7fc1cb15
......@@ -48,7 +48,6 @@ _HIDDEN_STATES_START_POSITION = 2
# General docstring
_CONFIG_FOR_DOC = "WavLMConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"
# Base docstring
_CHECKPOINT_FOR_DOC = "patrickvonplaten/wavlm-libri-clean-100h-base-plus"
......@@ -58,12 +57,6 @@ _EXPECTED_OUTPUT_SHAPE = [1, 292, 768]
_CTC_EXPECTED_OUTPUT = "'mister quilter is the aposle of the middle classes and we are glad to welcome his gospel'"
_CTC_EXPECTED_LOSS = 12.51
# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "hf-internal-testing/tiny-random-wavlm"
_SEQ_CLASS_EXPECTED_OUTPUT = "'no'" # TODO(anton) - could you quickly fine-tune a KS WavLM Model
_SEQ_CLASS_EXPECTED_LOSS = 0.7 # TODO(anton) - could you quickly fine-tune a KS WavLM Model
# Frame class docstring
_FRAME_CLASS_CHECKPOINT = "microsoft/wavlm-base-plus-sd"
_FRAME_EXPECTED_OUTPUT = [0, 0]
......@@ -1212,7 +1205,6 @@ class WavLMModel(WavLMPreTrainedModel):
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1320,7 +1312,6 @@ class WavLMForCTC(WavLMPreTrainedModel):
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1407,7 +1398,6 @@ class WavLMForCTC(WavLMPreTrainedModel):
""",
WAVLM_START_DOCSTRING,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification with Wav2Vec2->WavLM, wav2vec2->wavlm, WAV_2_VEC_2->WAVLM
class WavLMForSequenceClassification(WavLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
......@@ -1426,6 +1416,7 @@ class WavLMForSequenceClassification(WavLMPreTrainedModel):
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_extractor
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
......@@ -1438,6 +1429,7 @@ class WavLMForSequenceClassification(WavLMPreTrainedModel):
)
self.freeze_feature_encoder()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->wavlm
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
......@@ -1445,6 +1437,7 @@ class WavLMForSequenceClassification(WavLMPreTrainedModel):
"""
self.wavlm.feature_extractor._freeze_parameters()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_base_model with wav2vec2->wavlm
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
......@@ -1455,14 +1448,12 @@ class WavLMForSequenceClassification(WavLMPreTrainedModel):
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_SEQ_CLASS_CHECKPOINT,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->WavLM, wav2vec2->wavlm
def forward(
self,
input_values: Optional[torch.Tensor],
......@@ -1578,7 +1569,6 @@ class WavLMForAudioFrameClassification(WavLMPreTrainedModel):
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_FRAME_CLASS_CHECKPOINT,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
......@@ -1761,7 +1751,6 @@ class WavLMForXVector(WavLMPreTrainedModel):
@add_start_docstrings_to_model_forward(WAVLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
checkpoint=_XVECTOR_CHECKPOINT,
output_type=XVectorOutput,
config_class=_CONFIG_FOR_DOC,
......
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