text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=in... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
assert attn_mask.size()[0] == (len(self.layers)), (
... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hid... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPa... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextModel(Speech2TextPreTrainedModel):
def __init__(self, config: Speech2TextConfig):
super().__init__(config)
self.encoder = Speech2TextEncoder(config)
self.decoder = Speech2TextDecoder(config)
# Initialize weights and apply final processing
self.post_init()
... | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
@add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_features: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decod... | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
Returns: | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
Example:
```python
>>> import torch
>>> from transformers import Speech2TextModel, AutoFeatureExtractor
>>> from datasets import load_dataset
>>> model = Speech2TextModel.from_pretrained("facebook/s2t-small-librispeech-asr")
>>> feature_extractor = AutoFeatureExtr... | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
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
)
use_cache = use_cache if use_cache is not None else self... | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
retu... | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# downsample encoder attention mask
if attention_mask is not None:
encoder_attention_mask = self._get_feature_vector_attention_mask(
encoder_outputs[0].shape[1], attention_mask
)
else:
encoder_attention_mask = None
# decoder outputs consists o... | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_... | 3,286 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: Speech2TextConfig):
super().__init__(config)
self.model = Speech2TextModel(config)
self.lm_head = nn... | 3,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
@add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_features: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decod... | 3,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
... | 3,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
Returns:
Example:
```python
>>> import torch
>>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
>>> from datasets import load_dataset
>>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr... | 3,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
'mister quilter is the apostle of the middle classes and we are glad to welcome his gospel'
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dic... | 3,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
outputs = self.model(
input_features,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_hea... | 3,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return Seq2SeqLMOutput(
loss=loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.d... | 3,287 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class TFConv1dSubsampler(keras.layers.Layer):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config: Speech2TextConfig, **kwargs):
super()._... | 3,288 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def call(self, input_features: tf.Tensor) -> tf.Tensor:
# TF Conv1D assumes Batch x Time x Channels, same as the input
hidden_states = tf.cast(input_features, tf.float32)
for i, conv in enumerate(self.conv_layers):
# equivalent to `padding=k // 2` on PT's `nn.Conv1d`
pad_... | 3,288 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv_layers", None) is not None:
for i, layer in enumerate(self.conv_layers):
with tf.name_scope(layer.name):
layer.build([None, None, self.in_... | 3,288 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextSinusoidalPositionalEmbedding(keras.layers.Layer):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None, **kwargs):
super().__init__(**kwargs)
self.offset = 2
... | 3,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@staticmethod
def _get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None) -> tf.Tensor:
"""
Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the
description in Section 3.5 of "Attention Is All You Need... | 3,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
return emb | 3,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def call(self, input_ids: tf.Tensor, past_key_values_length: int = 0) -> tf.Tensor:
bsz, seq_len = shape_list(input_ids)
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_k... | 3,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@staticmethod
def create_position_ids_from_input_ids(
input_ids: tf.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0
) -> tf.Tensor:
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
symbols are ignore... | 3,289 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_pro... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states) | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=(
f"Attention weights should be of size {(b... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weigh... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=(
f"`attn_output` should be of siz... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "q_proj", None) is not N... | 3,290 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextEncoderLayer(keras.layers.Layer):
def __init__(self, config: Speech2TextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFSpeech2TextAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attenti... | 3,291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def call(
self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: bool = False
):
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of ... | 3,291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states ... | 3,291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not No... | 3,291 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextDecoderLayer(keras.layers.Layer):
def __init__(self, config: Speech2TextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFSpeech2TextAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attent... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFSpeech2TextAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def call(
self,
hidden_states,
attention_mask: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cross_attn_layer_head_mask: tf.Tensor | None = None,
... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
`(decoder_attention_heads,)`
cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-a... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hi... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not No... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build([None, None, self.config.decoder_ffn_dim])
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer... | 3,292 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextPreTrainedModel(TFPreTrainedModel):
config_class = Speech2TextConfig
base_model_prefix = "model"
main_input_name = "input_features"
_keys_to_ignore_on_load_unexpected = [r"encoder.embed_positions.weights"]
def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
... | 3,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@property
def input_signature(self):
return {
"input_features": tf.TensorSpec(
(None, None, self.config.input_feat_per_channel * self.config.input_channels),
tf.float32,
name="input_features",
),
"attention_mask": tf.TensorS... | 3,293 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextEncoder(keras.layers.Layer):
config_class = Speech2TextConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFSpeech2TextEncoderLayer`].
Args:
config: Speech2TextConfig
"""
def __init__(self, config: Speech... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
self.embed_positions = TFSpeech2TextSinusoidalPositionalEmbedding(
num_positions=config.max_source_positions,
embedding_dim=embed_dim,
padding_idx=self.padding_idx,
name="embed_positions",
)
self.layers = [TFSpeech2TextEncoderLayer(config, name=f"layers.{i... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def _get_feature_vector_attention_mask(self, feature_vector_length, attention_mask):
# generate creates 3D attention mask, because of the shape of input_features
# convert it to 2D if thats the case
if len(attention_mask.shape) > 2:
attention_mask = attention_mask[:, :, -1]
... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@unpack_inputs
def call(
self,
input_features=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
"""
Args:
input_features (`tf.Tensor` of sh... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
Mask to nullify selected heads of the ... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of a... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# subsample attention mask if necessary
if attention_mask is not None:
attention_mask = self._get_feature_vector_attention_mask(tf.shape(inputs_embeds)[1], attention_mask)
padding_mask = tf.cast(tf.math.not_equal(attention_mask, 1), tf.int64)
else:
padding_mask = tf.z... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
tf.debugging.assert_equal(
shape_list(head_mask)[0],
len(self.layers),
message=(
f"The head_mask should be specified for {len(self.la... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
hidden_states, attn = encoder_layer(
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
training=training,
)
if output_attentions:
all_attentions += (attn,)
hidden_states = se... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv", None) is not None:
with tf.name_scope(self.conv.name):
self.conv.build(None)
if getattr(self, "embed_positions", None) is not None:
with... | 3,294 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextDecoder(keras.layers.Layer):
config_class = Speech2TextConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFSpeech2TextDecoderLayer`]
Args:
config: Speech2TextConfig
"""
def __init__(self, config: Speech2TextConfig, **kwarg... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
self.layers = [TFSpeech2TextDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
self.dropout = keras.layers.Dropout(config.dropout)
def get_embed_tokens(self):
return self.embed_t... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@unpack_inputs
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
ou... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
input... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
""" | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
else:
combined_attention_mask = _expand_mask(
tf.ones((input_shape[0], input_shap... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
hidden_states = inputs_embeds + positions
hidden_states = self.dropout(hidden_states, training=training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if (output_attentions and en... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
if attn_mask is not None:
tf.debugging.assert_equal(
sha... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None
cross_attn_layer_head_mask = cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
hidden_states,... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attns
... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_tokens", None) is not None:
with tf.name_scope(self.embed_tokens.name):
self.embed_tokens.build(None)
if getattr(self, "embed_positions", None) is no... | 3,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextMainLayer(keras.layers.Layer):
config_class = Speech2TextConfig
def __init__(self, config: Speech2TextConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.encoder = TFSpeech2TextEncoder(config, name="encoder")
self.decoder = TFSpeech2TextDe... | 3,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@unpack_inputs
def call(
self,
input_features=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
past_key_values=None,... | 3,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_features=input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 3,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
elif not return_dict and not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple() | 3,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# downsample encoder attention mask
if attention_mask is not None:
encoder_attention_mask = self.encoder._get_feature_vector_attention_mask(
tf.shape(encoder_outputs[0])[1], attention_mask
)
else:
encoder_attention_mask = None | 3,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=encoder_attentio... | 3,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outp... | 3,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextModel(TFSpeech2TextPreTrainedModel):
def __init__(self, config: Speech2TextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFSpeech2TextMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_... | 3,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_features: TFModelInputType |... | 3,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
**kwargs,
) -> Union[Tuple, TFSeq2SeqModelOutput]:
outputs = self.model(
input_features=input_features,
a... | 3,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
return outputs
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions)... | 3,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
... | 3,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
class TFSpeech2TextForConditionalGeneration(TFSpeech2TextPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config: Speech2TextConfig):
super().__init__(config)
self.model = TFSpeech2TextMainLayer(config, name="model")
self.lm_head = keras.layers.Dense(self.config.vocab_size,... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_features: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs,
) -> Union[Tuple, TFSeq2SeqLMOutput]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing ... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
Returns:
Example:
```python
>>> import tensorflow as tf
>>> from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> model = TFSpeech2TextForConditionalGeneration.f... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
>>> input_features = processor(
... ds["speech"][0], sampling_rate=16000, return_tensors="tf"
... ).input_features # Batch size 1
>>> generated_ids = model.generate(input_features)
>>> transcription = processor.batch_decode(generated_ids)
```"""
return_dict = return... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
outputs = self.model(
input_features=input_features,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_m... | 3,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py |
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