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trfms-compatible

#1
by eustlb HF Staff - opened
base: refs/heads/main
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from: refs/pr/1
Discussion Files changed
+51136
-201560
Files changed (6) hide show
  1. README.md +10 -53
  2. config.json +72 -1301
  3. model.safetensors +2 -2
  4. processor_config.json +0 -51
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +934 -8
README.md CHANGED
@@ -147,7 +147,7 @@ inputs = processor(
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  )
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149
  with torch.inference_mode(), torch.autocast(device.type, dtype=torch.bfloat16):
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- outputs = model(**inputs.to(device), return_loss=True)
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152
  audio_embeds = outputs.audio_embeds # Audio-only embeddings
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  video_embeds = outputs.video_embeds # Video-only embeddings
@@ -157,67 +157,24 @@ text_audio_embeds = outputs.text_audio_embeds # Text embeddings aligned to audi
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  text_video_embeds = outputs.text_video_embeds # Text embeddings aligned to video
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  audio_plus_text_embeds = outputs.audio_plus_text_embeds # Joint audio and text embedding
159
  video_plus_text_embeds = outputs.video_plus_text_embeds # Joint video and text embedding
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-
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- # For classification, you can use the logits_* fields of the output
162
- audio_text_preds = outputs.logits_audio_text.sigmoid()
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-
164
- # The overall loss is also available in the output (requires passing return_loss=True)
165
- loss = outputs.loss
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-
167
  ```
168
 
169
- We also provide methods for directly encoding an individual modality:
170
 
171
  ```python
172
- def get_text_audio_embeds(self, input_ids, attention_mask=None)
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-
174
- def get_text_video_embeds(self, input_ids, attention_mask=None)
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-
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- def get_text_audio_video_embeds(self, input_ids, attention_mask=None)
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-
178
- def get_audio_embeds(self, input_values, padding_mask=None)
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-
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- def get_video_embeds(self, pixel_values_videos, padding_mask_videos=None)
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-
182
- def get_audio_video_embeds(
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- self,
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- input_values: torch.Tensor,
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- pixel_values_videos: torch.Tensor,
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- padding_mask: Optional[torch.Tensor] = None,
187
- padding_mask_videos: Optional[torch.Tensor] = None,
188
- return_audio_embeds: bool = False,
189
- return_video_embeds: bool = False,
190
- )
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-
192
- def get_audio_plus_text_embeds(
193
- self,
194
- input_ids: torch.Tensor,
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- input_values: torch.Tensor,
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- attention_mask: Optional[torch.Tensor] = None,
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- padding_mask: Optional[torch.Tensor] = None,
198
- )
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-
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- def get_video_plus_text_embeds(
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- self,
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- input_ids: torch.Tensor,
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- pixel_values_videos: torch.Tensor,
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- attention_mask: Optional[torch.Tensor] = None,
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- padding_mask_videos: Optional[torch.Tensor] = None,
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- )
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-
208
  ```
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210
  ## Citation
211
 
212
  ```bibtex
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- @misc{vyas2025pushingfrontieraudiovisualperception,
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- title={Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning},
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- author={Apoorv Vyas and Heng-Jui Chang and Cheng-Fu Yang and Po-Yao Huang and Luya Gao and Julius Richter and Sanyuan Chen and Matt Le and Piotr Dollár and Christoph Feichtenhofer and Ann Lee and Wei-Ning Hsu},
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- year={2025},
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- eprint={2512.19687},
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- archivePrefix={arXiv},
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- primaryClass={cs.SD},
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- url={https://arxiv.org/abs/2512.19687},
221
  }
222
  ```
223
 
 
147
  )
148
 
149
  with torch.inference_mode(), torch.autocast(device.type, dtype=torch.bfloat16):
150
+ outputs = model(**inputs.to(device))
151
 
152
  audio_embeds = outputs.audio_embeds # Audio-only embeddings
153
  video_embeds = outputs.video_embeds # Video-only embeddings
 
157
  text_video_embeds = outputs.text_video_embeds # Text embeddings aligned to video
158
  audio_plus_text_embeds = outputs.audio_plus_text_embeds # Joint audio and text embedding
159
  video_plus_text_embeds = outputs.video_plus_text_embeds # Joint video and text embedding
 
 
 
 
 
 
 
160
  ```
161
 
162
+ Note that arguments are not optional. We provide variant's on the `forward` method for different modality combinations:
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  ```python
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+ def forward_text_audio
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+ def forward_text_video
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+ def forward_audio_video
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
168
  ```
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  ## Citation
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  ```bibtex
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+ @article{pe-av2025,
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+ title={PEAV: An Audiovisual Perception Encoder via Large-Scale Multimodal Correspondence Learning},
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+ author={Apoorv Vyas, Heng-Jui Chang, Cheng-Fu Yang, Po-Yao Huang, Luya Gao, Julius Richter, Sanyuan Chen, Matt Le, Piotr Dollár, Christoph Feichtenhofer, Ann Lee, Wei-Ning Hsu},
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+ url={arxiv link coming soon}
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+ year={2025}
 
 
 
178
  }
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  ```
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config.json CHANGED
@@ -1,1307 +1,78 @@
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  {
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- "architectures": [
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- "PeAudioVideoModel"
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- ],
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- "audio_video_config": {
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- "attention_bias": false,
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+ "normalized": true,
863
+ "rstrip": false,
864
+ "single_word": false,
865
+ "special": false
866
+ },
867
+ "50360": {
868
+ "content": "[unused75]",
869
+ "lstrip": false,
870
+ "normalized": true,
871
+ "rstrip": false,
872
+ "single_word": false,
873
+ "special": false
874
+ },
875
+ "50361": {
876
+ "content": "[unused76]",
877
+ "lstrip": false,
878
+ "normalized": true,
879
+ "rstrip": false,
880
+ "single_word": false,
881
+ "special": false
882
+ },
883
+ "50362": {
884
+ "content": "[unused77]",
885
+ "lstrip": false,
886
+ "normalized": true,
887
+ "rstrip": false,
888
+ "single_word": false,
889
+ "special": false
890
+ },
891
+ "50363": {
892
+ "content": "[unused78]",
893
+ "lstrip": false,
894
+ "normalized": true,
895
+ "rstrip": false,
896
+ "single_word": false,
897
+ "special": false
898
+ },
899
+ "50364": {
900
+ "content": "[unused79]",
901
+ "lstrip": false,
902
+ "normalized": true,
903
+ "rstrip": false,
904
+ "single_word": false,
905
+ "special": false
906
+ },
907
+ "50365": {
908
+ "content": "[unused80]",
909
+ "lstrip": false,
910
+ "normalized": true,
911
+ "rstrip": false,
912
+ "single_word": false,
913
+ "special": false
914
+ },
915
+ "50366": {
916
+ "content": "[unused81]",
917
+ "lstrip": false,
918
+ "normalized": true,
919
+ "rstrip": false,
920
+ "single_word": false,
921
+ "special": false
922
+ },
923
+ "50367": {
924
+ "content": "[unused82]",
925
+ "lstrip": false,
926
+ "normalized": true,
927
+ "rstrip": false,
928
+ "single_word": false,
929
+ "special": false
930
+ }
931
+ },
932
  "clean_up_tokenization_spaces": true,
933
  "cls_token": "[CLS]",
 
934
  "mask_token": "[MASK]",
935
+ "model_max_length": 8192,
936
+ "pad_token": "[PAD]",
937
+ "sep_token": "[SEP]",
938
+ "tokenizer_class": "PreTrainedTokenizerFast",
939
  "model_input_names": [
940
  "input_ids",
941
  "attention_mask"
942
  ],
 
 
 
 
 
943
  "unk_token": "[UNK]"
944
  }