update readme

README.md

@@ -50,4 +50,76 @@ Notes:
 
 `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).
+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)
+```