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Add type hints for UniSpeech (#16399)

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* Add type hints for UniSpeech

* Added type hints for UniSpeechSat

* Added type hints for Wave2Vec2 (PT)

* Added type hints for models dependent of wave2vec
This commit is contained in:
Dan Tegzes 2022-03-29 19:02:46 +02:00 committed by GitHub
parent 875e07a9e3
commit 0540d1b6c0
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 240 additions and 240 deletions
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68
src/transformers/models/data2vec/modeling_data2vec_audio.py
View File

@ -977,13 +977,13 @@ class Data2VecAudioModel(Data2VecAudioPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Data2VecAudioBaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1085,13 +1085,13 @@ class Data2VecAudioForCTC(Data2VecAudioPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1221,13 +1221,13 @@ class Data2VecAudioForSequenceClassification(Data2VecAudioPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -1342,12 +1342,12 @@ class Data2VecAudioForAudioFrameClassification(Data2VecAudioPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -1519,13 +1519,13 @@ class Data2VecAudioForXVector(Data2VecAudioPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, XVectorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

42
src/transformers/models/hubert/modeling_hubert.py
View File

@ -1008,13 +1008,13 @@ class HubertModel(HubertPreTrainedModel):
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
"""
Returns:
@ -1135,13 +1135,13 @@ class HubertForCTC(HubertPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1271,13 +1271,13 @@ class HubertForSequenceClassification(HubertPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

42
src/transformers/models/sew/modeling_sew.py
View File

@ -914,13 +914,13 @@ class SEWModel(SEWPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1018,13 +1018,13 @@ class SEWForCTC(SEWPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1154,13 +1154,13 @@ class SEWForSequenceClassification(SEWPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

42
src/transformers/models/sew_d/modeling_sew_d.py
View File

@ -1427,13 +1427,13 @@ class SEWDModel(SEWDPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1531,13 +1531,13 @@ class SEWDForCTC(SEWDPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1667,13 +1667,13 @@ class SEWDForSequenceClassification(SEWDPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

54
src/transformers/models/unispeech/modeling_unispeech.py
View File

@ -1160,13 +1160,13 @@ class UniSpeechModel(UniSpeechPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, UniSpeechBaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1274,12 +1274,12 @@ class UniSpeechForPreTraining(UniSpeechPreTrainedModel):
@replace_return_docstrings(output_type=UniSpeechForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, UniSpeechForPreTrainingOutput]:
r"""
mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
@ -1412,13 +1412,13 @@ class UniSpeechForCTC(UniSpeechPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1548,13 +1548,13 @@ class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

80
src/transformers/models/unispeech_sat/modeling_unispeech_sat.py
View File

@ -1199,13 +1199,13 @@ class UniSpeechSatModel(UniSpeechSatPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, UniSpeechSatBaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1318,12 +1318,12 @@ class UniSpeechSatForPreTraining(UniSpeechSatPreTrainedModel):
@replace_return_docstrings(output_type=UniSpeechSatForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, UniSpeechSatForPreTrainingOutput]:
r"""
Returns:
@ -1440,13 +1440,13 @@ class UniSpeechSatForCTC(UniSpeechSatPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1576,13 +1576,13 @@ class UniSpeechSatForSequenceClassification(UniSpeechSatPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -1697,12 +1697,12 @@ class UniSpeechSatForAudioFrameClassification(UniSpeechSatPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -1874,13 +1874,13 @@ class UniSpeechSatForXVector(UniSpeechSatPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, XVectorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

84
src/transformers/models/wav2vec2/modeling_wav2vec2.py
View File

@ -1331,13 +1331,13 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Wav2Vec2BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1448,14 +1448,14 @@ class Wav2Vec2ForPreTraining(Wav2Vec2PreTrainedModel):
@replace_return_docstrings(output_type=Wav2Vec2ForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
sampled_negative_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.BoolTensor] = None,
sampled_negative_indices: Optional[torch.BoolTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Wav2Vec2ForPreTrainingOutput]:
r"""
mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
@ -1696,13 +1696,13 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1831,13 +1831,13 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -1951,12 +1951,12 @@ class Wav2Vec2ForAudioFrameClassification(Wav2Vec2PreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -2125,13 +2125,13 @@ class Wav2Vec2ForXVector(Wav2Vec2PreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, XVectorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,

68
src/transformers/models/wavlm/modeling_wavlm.py
View File

@ -1282,13 +1282,13 @@ class WavLMModel(WavLMPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
mask_time_indices=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, WavLMBaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1390,13 +1390,13 @@ class WavLMForCTC(WavLMPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@ -1526,13 +1526,13 @@ class WavLMForSequenceClassification(WavLMPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -1647,12 +1647,12 @@ class WavLMForAudioFrameClassification(WavLMPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@ -1824,13 +1824,13 @@ class WavLMForXVector(WavLMPreTrainedModel):
)
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, XVectorOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,