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  If not already stored in your system, the SeamlessM4T model will be downloaded automatically when
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  running the script. The output will be saved in `${OUT_DIR}`.
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- We suggest to run the inference using a GPU to speed up the process but the system can be run on
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  any device (e.g., CPU) supported by SimulEval and HuggingFace.
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  ## 💬 Inference using docker
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- To run SimulSeamless using docker, as required by the IWSLT 2024 Simultaneous track, follow the
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- steps below:
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  1. Download the docker file by cloning this repository
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  2. Load the docker image:
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  ```bash
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  ```
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  To set, `${TGT_LANG}`, `${FRAME}`, `${LAYER}`, `${BLEU_TOKENIZER}`, `${LATENCY_UNIT}`,
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  `${LIST_OF_AUDIO}`, `${TGT_FILE}`, `${SEG_SIZE}`, and `${OUT_DIR}` refer to
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- [🤖 Inference using your environment](#-inference-using-your-environment).
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  ## 📍Citation
 
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  If not already stored in your system, the SeamlessM4T model will be downloaded automatically when
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  running the script. The output will be saved in `${OUT_DIR}`.
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+ We suggest running the inference using a GPU to speed up the process but the system can be run on
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  any device (e.g., CPU) supported by SimulEval and HuggingFace.
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  ## 💬 Inference using docker
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+ To run SimulSeamless using docker, follow the steps below:
 
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  1. Download the docker file by cloning this repository
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  2. Load the docker image:
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  ```bash
 
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  ```
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  To set, `${TGT_LANG}`, `${FRAME}`, `${LAYER}`, `${BLEU_TOKENIZER}`, `${LATENCY_UNIT}`,
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  `${LIST_OF_AUDIO}`, `${TGT_FILE}`, `${SEG_SIZE}`, and `${OUT_DIR}` refer to
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+ [🤖 Inference using your environment](#🤖-inference-using-your-environment).
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  ## 📍Citation