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Whisper encoder + No 30 second padding (#5)
* enable whisper model (no need for max_padding) * bugfix: boolq_in didn't have audio_transcript -> use _get_transcribe_sample * bugfix: move model to device before merging lora weights * rename modified_whisper -> whisper_model_modified
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
| # 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, | ||
| ) |