---
title: GLM-ASR-Nano-2512
canonical_url: "https://www.modelscope.cn/models/ZhipuAI/GLM-ASR-Nano-2512"
md_url: "https://www.modelscope.cn/models/ZhipuAI/GLM-ASR-Nano-2512.md"
repository: ZhipuAI/GLM-ASR-Nano-2512
chinese_name: GLM-ASR-Nano-2512
last_updated: 2026-04-08
license: mit
model_type:
  - glmasr
architectures:
  - GlmAsrForConditionalGeneration
parameters: 2.3B
tensor_type:
  - BF16
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - Pytorch
language:
  - en
  - zh
downloads: 60110
stars: 77
---

# GLM-ASR-Nano-2512

> GLM-ASR-Nano-2512 - ZhipuAI 在 ModelScope 开源的模型。👋 Join our WeChat community

ZhipuAI/GLM-ASR-Nano-2512 是 ModelScope 魔搭社区上的 2.3B 参数机器学习模型，采用 mit 许可。

- **Repository**: ZhipuAI/GLM-ASR-Nano-2512
- **License**: mit
- **Parameters**: 2.3B
- **Downloads**: 60110
- **Stars**: 77
- **Last updated**: 2026-04-08

Source: https://www.modelscope.cn/models/ZhipuAI/GLM-ASR-Nano-2512

---

# GLM-ASR-Nano-2512

<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-ASR/refs/heads/main/resources/logo.svg width="20%"/>
</div>
<p align="center">
    👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-ASR/refs/heads/main/resources/wechat.png" target="_blank">WeChat</a> community
</p>

## Model Introduction

**GLM-ASR-Nano-2512** is a robust, open-source speech recognition model with **1.5B parameters**. Designed for
real-world complexity, it outperforms OpenAI Whisper V3 on multiple benchmarks while maintaining a compact size.

Key capabilities include:

* **Exceptional Dialect Support:**
  Beyond standard Mandarin and English, the model is highly optimized for **Cantonese (粤语)** and other dialects,
  effectively bridging the gap in dialectal speech recognition.

* **Low-Volume Speech Robustness:**
  Specifically trained for **"Whisper/Quiet Speech"** scenarios. It captures and accurately transcribes extremely
  low-volume audio that traditional models often miss.

* **SOTA Performance:**
  Achieves the **lowest average error rate (4.10)** among comparable open-source models, showing significant advantages
  in Chinese benchmarks (Wenet Meeting, Aishell-1, etc..).

## Benchmark

We evaluated GLM-ASR-Nano against leading open-source and closed-source models. The results demonstrate that *
*GLM-ASR-Nano (1.5B)** achieves superior performance, particularly in challenging acoustic environments.

![Benchmark results](https://raw.githubusercontent.com/zai-org/GLM-ASR/refs/heads/main/resources/bench.png)

Notes:

- Wenet Meeting reflects real-world meeting scenarios with noise and overlapping speech.
- Aishell-1 is a standard Mandarin benchmark.

## Inference

`GLM-ASR-Nano-2512` can be easily integrated using the `transformers` library.  
We will support `transformers 5.x` as well as inference frameworks such as `vLLM` and `SGLang`.
you can check more code in [Github](https://github.com/zai-org/GLM-ASR).

### Transformers 🤗

Install `transformers` from source:
```bash
pip install git+https://github.com/huggingface/transformers
```

#### Basic Usage

```python
from transformers import AutoModelForSeq2SeqLM, AutoProcessor

processor = AutoProcessor.from_pretrained("zai-org/GLM-ASR-Nano-2512")
model = AutoModelForSeq2SeqLM.from_pretrained("zai-org/GLM-ASR-Nano-2512", dtype="auto", device_map="auto")

inputs = processor.apply_transcription_request("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")

inputs = inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
print(decoded_outputs)
```

#### Using Audio Arrays Directly

You can also use audio arrays directly:

```python
from transformers import GlmAsrForConditionalGeneration, AutoProcessor
from datasets import load_dataset
from datasets import Audio

processor = AutoProcessor.from_pretrained("zai-org/GLM-ASR-Nano-2512")
model = GlmAsrForConditionalGeneration.from_pretrained("zai-org/GLM-ASR-Nano-2512", dtype="auto", device_map="auto")

# loading audio directly from dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
audio_array = ds[0]["audio"]["array"]

inputs = processor.apply_transcription_request(audio_array)

inputs = inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
print(decoded_outputs)
```

#### Batched Inference

You can process multiple audio files at once:

```python
from transformers import GlmAsrForConditionalGeneration, AutoProcessor

processor = AutoProcessor.from_pretrained("zai-org/GLM-ASR-Nano-2512")
model = GlmAsrForConditionalGeneration.from_pretrained("zai-org/GLM-ASR-Nano-2512", dtype="auto", device_map="auto")

inputs = processor.apply_transcription_request([
    "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
    "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
])

inputs = inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
print(decoded_outputs)
```