Feature Extraction
Transformers
Safetensors
diva
custom_code
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Commit
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Files changed (2) hide show
  1. README.md +17 -17
  2. modeling_diva.py +2 -1
README.md CHANGED
@@ -12,6 +12,22 @@ This is an end-to-end Voice Assistant Model which can handle speech and text as
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  See the model in action at [diva-audio.github.io](https://diva-audio.github.io).
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  ### Inference Example
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  ```python
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  from transformers import AutoModel
@@ -44,22 +60,6 @@ print(
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  )
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  ```
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- ## Citation
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- **BibTeX:**
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-
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- ```
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- @misc{DiVA,
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- title={{D}istilling an {E}nd-to-{E}nd {V}oice {A}ssistant {W}ithout {I}nstruction {T}raining {D}ata},
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- author={William Held and Ella Li and Michael Ryan and Weiyan Shi and Yanzhe Zhang and Diyi Yang},
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- year={2024},
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- eprint={2410.02678},
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- archivePrefix={arXiv},
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- primaryClass={cs.CL},
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- url={https://arxiv.org/abs/2410.02678},
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- }
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-
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- ```
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-
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  ## Table of Contents
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  - [Model Card for DiVA Llama 3](#model-card-for-DiVA-Llama-3)
@@ -114,4 +114,4 @@ Will Held
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  ## Model Card Contact
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- held@stanford.edu
 
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  See the model in action at [diva-audio.github.io](https://diva-audio.github.io).
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+ ## Citation
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+ **BibTeX:**
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+
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+ ```
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+ @misc{DiVA,
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+ title={{D}istilling an {E}nd-to-{E}nd {V}oice {A}ssistant {W}ithout {I}nstruction {T}raining {D}ata},
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+ author={William Held and Ella Li and Michael Ryan and Weiyan Shi and Yanzhe Zhang and Diyi Yang},
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+ year={2024},
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+ eprint={2410.02678},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2410.02678},
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+ }
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+
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+ ```
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+
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  ### Inference Example
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  ```python
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  from transformers import AutoModel
 
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  )
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  ```
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  ## Table of Contents
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  - [Model Card for DiVA Llama 3](#model-card-for-DiVA-Llama-3)
 
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  ## Model Card Contact
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+ held@stanford.edu
modeling_diva.py CHANGED
@@ -263,7 +263,8 @@ class DiVAModel(PreTrainedModel):
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  else:
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  greedy = next_token_logits.argmax(dim=-1)
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  for token_index, out in enumerate(greedy.flatten().tolist()):
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- outs[token_index].append(out)
 
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  if out == 128009:
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  complete[token_index] = True
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  else:
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  greedy = next_token_logits.argmax(dim=-1)
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  for token_index, out in enumerate(greedy.flatten().tolist()):
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+ if not complete[token_index]:
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+ outs[token_index].append(out)
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  if out == 128009:
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  complete[token_index] = True
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