text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if decoder_position_ids is not None:
decoder_position_ids = decoder_position_ids[:, remove_prefix_length:]
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride durin... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
# recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
decoder_input_ids = decoder_input_ids.contiguous()
if (
isins... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
decoder_attention_mask = self.get_decoder()._prepare_4d_causal_attention_mask_with_cache_position(
decoder_attention_mask,
sequence_length=sequence_length,
target_length=past_key_values.self_attention_cache.get_max_cache_shape(),
dtype=self.proj_out.weight... | 9,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperDecoderWrapper(WhisperPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
... | 9,904 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperForCausalLM(WhisperPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["proj_out.weight"]
main_input_name = "input_ids"
def __init__(self, config):
super().__init__(config)
config.is_encoder_decoder = False
self.model = WhisperDecoderWrapper(config)
self.p... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
head_mask:... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids)... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence mod... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
labels (`torch.Lon... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
- 0 for tokens that are **masked**.
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*):
... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
Returns:
Example:
```python
>>> from transformers import WhisperForCausalLM, WhisperForConditionalGeneration, WhisperProcessor
>>> import torch
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
>>>... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'
```"""
output_attentions = output_attentions if outpu... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_outputs,
head_mask=head_mask,
cross_attn_head_mask=cross_att... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs... | 9,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class WhisperForAudioClassification(WhisperPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.encoder = WhisperEncoder(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
se... | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
def get_input_embeddings(self) -> nn.Module:
return self.encoder.get_input_embeddings()
def set_input_embeddings(self, value: nn.Module):
self.encoder.set_input_embeddings(value) | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_features: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
... | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoFeatureExtractor, WhisperForAudioClassification
>>> from datasets import load_dataset
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-i... | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
>>> predicted_class_ids = torch.argmax(logits).item()
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> predicted_label
'Afrikaans'
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_... | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_features,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if ... | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
if labels is not None:
loss_fct = CrossEntropyLoss()
# move labels to correct device to enable PP
labels = labels.to(logits.device)
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + enco... | 9,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py |
class TFWhisperPositionalEmbedding(keras.layers.Layer):
def __init__(
self,
num_positions: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
embedding_initializer=None,
**kwargs,
):
super().__init__(**kwargs)
self.num_positions = num_posi... | 9,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
def call(self, input_ids, past_key_values_length=0):
past_key_values_length = tf.cast(past_key_values_length, tf.int32)
gather_indices = tf.range(tf.shape(input_ids)[1], delta=1) + past_key_values_length
return tf.gather(self.weight, gather_indices) | 9,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperAttention(keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
self.k_proj = keras.layers.Dense(embed_dim, use_bias=False, name="k_proj")
self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_pr... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention.call with BART->whisper
def call(
self,
hidden_states: tf.Tensor,
key_value_states: tf.Tensor | None = None,
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None,
attention_mask: tf.Tensor | None = None,... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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_... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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, "v_proj", None) is not N... | 9,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperEncoderLayer(keras.layers.Layer):
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFWhisperAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, ... | 9,909 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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 ... | 9,909 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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 ... | 9,909 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,909 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperDecoderLayer(keras.layers.Layer):
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFWhisperAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFWhisperAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_dec... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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,
... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperPreTrainedModel(TFPreTrainedModel):
config_class = WhisperConfig
base_model_prefix = "model"
main_input_name = "input_features"
def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor) -> int:
"""
Computes the output length of the convolutional layers
... | 9,911 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
@property
def input_signature(self):
return {
"input_features": tf.TensorSpec((None, self.config.num_mel_bins, None), tf.float32, name="input_features"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.Ten... | 9,911 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperEncoder(keras.layers.Layer):
config_class = WhisperConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFWhisperEncoderLayer`].
Args:
config: WhisperConfig
embed_tokens (TFWhisperEmbedding): output embedding
... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
# Padding is added in call() to match the PyTorch implementation
self.conv1 = keras.layers.Conv1D(self.embed_dim, kernel_size=3, strides=1, padding="valid", name="conv1")
self.conv2 = keras.layers.Conv1D(self.embed_dim, kernel_size=3, strides=2, padding="valid", name="conv2")
self.embed_positio... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
@unpack_inputs
def call(
self,
input_features=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
r"""
Args:
input_features (`tf.Tensor` of shape `(batch_size, feature_si... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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
)
return_dict = return_dict if return_dict is not None els... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
hidden_states = inputs_embeds + embed_pos
hidden_states = self.dropout(hidden_states, training=training)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
for idx, encoder_layer in enumerate(self.encoder_layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if train... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
) | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv1", None) is not None:
with tf.name_scope(self.conv1.name):
self.conv1.build([None, None, self.num_mel_bins])
if getattr(self, "conv2", None) is not No... | 9,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperDecoder(keras.layers.Layer):
config_class = WhisperConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFWhisperDecoderLayer`]
Args:
config: WhisperConfig
"""
def __init__(self, config: WhisperConfig, **kwargs):
super().... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
self.embed_tokens = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model,
embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std),
name="embed_tokens",
)
self.embed_positions = TFWhisperPositionalEmb... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
batch_size, seq_len = input_shape[0], input_shape[1]
combined_attention_mask = tf.cond(
tf.math.greater... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
encoder_hidden_states=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_atten... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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**,
... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
[What are attention masks?](../glossary#attention-mask)
position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embed... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates th... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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.
"""
output_attentions = output_a... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
# 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 = tf.shape(input_ids)
input_... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
# embed positions
filled_past_positions = past_key_values_length if position_ids is None else position_ids[0, -1]
positions = self.embed_positions(input_ids, past_key_values_length=filled_past_positions)
hidden_states = inputs_embeds + positions
hidden_states = self.dropout(hidden_state... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
# check if head_mask/cross_attn_head_mask has 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(
shape_li... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
for idx, decoder_layer in enumerate(self.decoder_layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if training and (drop... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if use_cache:
next_decoder_cache += (layer_outputs[3],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layer_norm... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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 TFBaseModelOutputWith... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperMainLayer(keras.layers.Layer):
config_class = WhisperConfig
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.encoder = TFWhisperEncoder(config, name="encoder")
self.decoder = TFWhisperDecoder(config, name="d... | 9,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@unpack_inputs
def call(
self,
input_features=None,
decoder_input_ids=None,
decoder_attention_mask=None,
decoder_po... | 9,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
>>> model = TFWhisperModel.from_pretrained("openai/whisper-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(ds[0]["audio"]["... | 9,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_features,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=t... | 9,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
... | 9,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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... | 9,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperModel(TFWhisperPreTrainedModel):
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = TFWhisperMainLayer(config, name="model")
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embedd... | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
@unpack_inputs
def call(
self,
input_features: TFModelInputType | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None ... | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
r"""
Returns: | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
Example:
```python
>>> import tensorflow as tf
>>> from transformers import TFWhisperModel, AutoFeatureExtractor
>>> from datasets import load_dataset | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
>>> model = TFWhisperModel.from_pretrained("openai/whisper-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(ds[0]["audio"]["... | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_s... | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
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) if self.config.outp... | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.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,
... | 9,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class TFWhisperForConditionalGeneration(TFWhisperPreTrainedModel, TFCausalLanguageModelingLoss):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
r"encoder.version",
r"decoder.version",
r"proj_out.weight",
]
_keys_to_ignore_on_save = [
r"proj_out.weight",
... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@unpack_inputs
def call(
self,
input_features: TFModelInputType | None = None,
decoder_input_ids: np.ndarray | tf.Tensor | None = N... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
Returns:
Example:
```python
>>> import tensorflow as tf
>>> from transformers import AutoProcessor, TFWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = TFWhi... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. 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... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
outputs = self.model(
input_features,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=head_mask,
decoder_head_mask... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSeq2SeqLMOutput(
loss=loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
def generate(
self,
inputs: Optional[tf.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[TFLogitsProcessorList] = None,
seed: Optional[List[int]] = None,
return_timestamps: Optional[bool] = None,
task: Optional[str] ... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
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