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# modified from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
# see this issue for the commentary: https://github.com/huggingface/transformers/issues/25744
#
# Copyright 2022 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
import transformers
import transformers.modeling_outputs
from transformers.models.whisper import modeling_whisper as whisper
class WhisperEncoder(whisper.WhisperEncoder):
"""
Encoder portion of OpenAI's Whisper model.
This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes:
1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder
2. allow less than 30 second of audio padding to be passed in:
- relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal
- embed_pos is now sliced to match the length of `inputs_embeds`
Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
"""
base_model_prefix = "model.encoder"
def forward(
self,
input_features,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
expected_seq_length = (
self.config.max_source_positions
* self.conv1.stride[0]
* self.conv2.stride[0]
)
if input_features.shape[-1] > expected_seq_length:
raise ValueError(
f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
)
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 else self.config.use_return_dict
)
inputs_embeds = nn.functional.gelu(self.conv1(input_features))
inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
inputs_embeds = inputs_embeds.permute(0, 2, 1)
embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)]
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(
hidden_states, p=self.dropout, training=self.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
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
None,
(head_mask[idx] if head_mask is not None else None),
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
None,
layer_head_mask=(
head_mask[idx] if head_mask is not None else None
),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, encoder_states, all_attentions]
if v is not None
)
return transformers.modeling_outputs.BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_states,
attentions=all_attentions,
)
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