Add SAC model 16k 37.5Hz, README, config

README.md

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 license: mit
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+
+<div align="center">
+  <h1>
+  SAC: Neural Speech Codec with Semantic-Acoustic Dual-Stream Quantization
+  </h1>
+
+<p>
+    <a href="https://github.com/Soul-AILab/SAC">
+        <img src="https://img.shields.io/badge/SAC-GitHub-black?logo=github&logoColor=white" alt="GitHub Repo">
+    </a>
+    <a href="https://sac-codec.github.io/">
+        <img src="https://img.shields.io/badge/🌐%20Demo-Page-brightgreen" alt="Demo Page">
+    </a>
+    <a href="https://arxiv.org/abs/2510.00000">
+        <img src="https://img.shields.io/badge/arXiv-2510.00000-blueviolet?logo=arxiv&logoColor=white" alt="arXiv">
+    </a>
+    <a href="https://huggingface.co/Soul/SAC">
+        <img src="https://img.shields.io/badge/🤗%20SAC-Models-yellow" alt="Hugging Face">
+    </a>
+</p>
+
+  <p align="center">
+    <i>A semantic–acoustic dual-stream speech codec achieving state-of-the-art performance in speech reconstruction and semantic representation across bitrates.</i>
+  </p>
+</div>
+
+
+## 🛠️ Environment Setup
+```bash
+conda create -n sac python=3.10
+conda activate sac
+pip install -r requirements.txt  # pip version == 24.0
+```
+
+
+## 🧩 Model Checkpoints
+
+To use SAC, you need to prepare the pretrained dependencies, including the [GLM-4-Voice-Tokenizer](https://huggingface.co/zai-org/glm-4-voice-tokenizer) for semantic tokenization and the [ERes2Net](https://modelscope.cn/models/iic/speech_eres2net_sv_en_voxceleb_16k) speaker encoder for speaker feature extraction (during codec training). Make sure the corresponding model paths are correctly set in your configuration file (e.g., `configs/xxx.yaml`).
+
+The following table lists the available SAC checkpoints:
+
+| Model Name | Hugging Face | Sample Rate | Token Rate | BPS |
+|:-----------:|:------------:|:------------:|:-----------:|:---:|
+| SAC | [🤗 Soul/SAC-16k-37.5hz](https://huggingface.co/Soul/SAC-16k-37.5hz) | 16 kHz | 37.5 Hz | 525 |
+| SAC | [🤗 Soul/SAC-16k-62.5hz](https://huggingface.co/Soul/SAC-16k-62.5hz) | 16 kHz | 62.5 Hz | 875 |
+
+
+## 🎧 Inference
+
+To perform audio reconstruction, you can use the following command:
+
+```bash
+python -m bins.infer
+```
+
+We also provide batch scripts for [audio reconstruction](./scripts/batch/reconstruct.sh), [encoding](./scripts/batch/encode.sh), [decoding](./scripts/batch/decode.sh), and [embedding extraction](./scripts/batch/extract_embeddings.sh) in the `scripts/batch` directory as references (you can refer to the [batch scripts guide](./docs/batch_scripts_guide.md) for details).
+
+
+## 🧪 Evaluation
+
+You can run the following command to perform evaluation:
+
+```bash
+bash scripts/eval.sh
+```
+
+For details on dataset preparation and evaluation setup, please first refer to the [evaluation guide](./docs/evaluation_guide.md).
+
+
+## 🚀 Training
+### Step 1: Prepare training data
+Before training, organize your dataset in **JSONL** format. You can refer to `example/training_data.jsonl`. Each entry should include:
+- **utt** — unique utterance ID (customizable)
+- **wav_path** — path to raw audio
+- **ssl_path** — path to offline-extracted Whisper features (for semantic supervision)
+- **semantic_token_path** — path to offline-extracted semantic tokens
+
+To accelerate training, you need to **extract semantic tokens and Whisper features offline** first before starting.  Refer to the [feature extraction guide](./docs/feature_extraction_guide.md) for detailed instructions.
+
+### Step 2: Modify configuration files
+You can adjust training and DeepSpeed configurations by editing:
+- [`configs/xxx.yaml`](./configs) — main training configuration  
+- [`configs/ds_stage2.json`](./configs/ds_stage2.json) — DeepSpeed configuration
+
+### Step 3: Start training
+Run the following script to start SAC training:
+
+```bash
+bash scripts/train.sh
+```
+
+
+## 🙏 Acknowledgement
+Our codebase builds upon the awesome [SparkVox](https://github.com/SparkAudio/SparkVox) and [DAC](https://github.com/descriptinc/descript-audio-codec). We thank the authors for their excellent work.
+
+## 🔖 Citation
+If you find this work useful in your research, please consider citing:
+```bibtex
+```
+
+## 📜 License
+This project is licensed under the Apache 2.0 License.

SAC-16k-37_5Hz.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:16ff29d557d7cd6be36358d2694bbcd83a3d79c3766bff4d9a9e99ee5523fae2
+size 2553952728

config.json

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+{
+  "_name_or_path": "Soul-AILab/SAC-16k-37_5Hz",
+  "architectures": [
+    "CustomModel"
+  ],
+  "model_type": "custom",
+  "description": "SAC model at 37.5 Hz",
+  "sample_rate": 16000
+}