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--- |
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license: mpl-2.0 |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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base_model: |
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- meta-llama/Llama-3.2-1B-Instruct |
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--- |
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# Model Card for Diva Llama 3.2 1B |
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<!-- Provide a quick summary of what the model is/does. [Optional] --> |
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This is an end-to-end Voice Assistant Model which can handle speech and text as inputs. It is trained using distillation loss. More details in the [pre-print](https://arxiv.org/abs/2410.02678) here. |
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See the model in action at [diva-audio.github.io](https://diva-audio.github.io) or look at the full training logs on [Weights&Biases](https://wandb.ai/i18nlp/levanter/runs/jnxp463y?nw=nwuserheld). |
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## Citation |
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**BibTeX:** |
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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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### Inference Example |
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```python |
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from transformers import AutoModel |
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import librosa |
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import wget |
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filename = wget.download( |
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"https://github.com/ffaisal93/SD-QA/raw/refs/heads/master/dev/eng/irl/wav_eng/-1008642825401516622.wav" |
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) |
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speech_data, _ = librosa.load(filename, sr=16_000) |
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model = AutoModel.from_pretrained("WillHeld/DiVA-llama-3.2-1b", trust_remote_code=True) |
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print(model.generate([speech_data])) |
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print(model.generate([speech_data], ["Reply Briefly Like A Pirate"])) |
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filename = wget.download( |
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"https://github.com/ffaisal93/SD-QA/raw/refs/heads/master/dev/eng/irl/wav_eng/-2426554427049983479.wav" |
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) |
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speech_data2, _ = librosa.load(filename, sr=16_000) |
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print( |
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model.generate( |
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[speech_data, speech_data2], |
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["Reply Briefly Like A Pirate", "Reply Briefly Like A New Yorker"], |
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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) |
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- [Citation](#citation) |
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- [Table of Contents](#table-of-contents) |
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- [Training Details](#training-details) |
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- [Training Data](#training-data) |
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- [Training Procedure](#training-procedure) |
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- [Environmental Impact](#environmental-impact) |
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- [Technical Specifications [optional]](#technical-specifications-optional) |
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- [Model Architecture and Objective](#model-architecture-and-objective) |
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- [Compute Infrastructure](#compute-infrastructure) |
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- [Hardware](#hardware) |
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- [Software](#software) |
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- [Model Card Contact](#model-card-contact) |
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## Training Details |
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### Training Data |
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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This model was trained on the [CommonVoice](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1) corpus. |
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### Training Procedure |
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This model was trained for 4.3k gradient steps with a batch size of 512 Recordings and a linearly decaying learning rate from 5e-4 to zero, with a linear warmup of 70 steps. |
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### Environmental Impact |
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- **Hardware Type:** V4-64 TPU |
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- **Hours used:** 3 Hours |
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- **Cloud Provider:** Google Cloud. |
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- **Compute Region:** US Central C |
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### Hardware |
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This model was trained on at V4-64 TPU on Google Cloud. |
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### Software |
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This model was trained with [Levanter](https://github.com/stanford-crfm/levanter) |
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## Model Card Authors [optional] |
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<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> |
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Will Held |
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## Model Card Contact |
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held@stanford.edu |
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