Instructions to use yxdu/ESRT-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yxdu/ESRT-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="yxdu/ESRT-4B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yxdu/ESRT-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -7,25 +7,41 @@ ESRT supports many-to-many speech-to-text translation across **45 languages** (4
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[](https://arxiv.org/abs/2605.28642)
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[](https://huggingface.co/yxdu/ESRT-4B)
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## Setup
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```bash
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uv venv --python 3.10
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source .venv/bin/activate
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uv pip install -r requirements.txt
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```
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## Test Data
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```bash
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```
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## Inference
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Two-stage inference: edge side and cloud side.
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```bash
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#Offline for Quick Testing
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#Online deployment guide coming soon.
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```
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[](https://arxiv.org/abs/2605.28642)
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[](https://huggingface.co/yxdu/ESRT-4B)
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## Timeline
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- **2026-05-29** — macOS CPU support added
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- **2026-05-28** — ESRT-4B has been released on [Hugging Face](https://huggingface.co/yxdu/ESRT-4B) with GPU support.
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## Setup
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```bash
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git clone https://github.com/yxduir/ESRT
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cd ESRT
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uv venv --python 3.10
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source .venv/bin/activate
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uv pip install -r requirements.txt
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# uv pip install -r requirements_mac.txt
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```
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> **Note**: The GPU setup includes `vllm`. macOS uses a CPU backend with `transformers`.
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## Test Data
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```bash
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hf download --repo-type dataset yxdu/fleurs_eng_test --local-dir ./fleurs_eng_test
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```
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## Inference
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Two-stage inference: edge side and cloud side.
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```bash
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#Offline for Quick Testing
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bash run_test.sh
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#bash run_test_mac.sh
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#Online deployment guide coming soon.
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```
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