Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+** Model Detail
+
+* Model type:
+RWKV7-0.4B-wavlmLarge-ENASR-demo is an opensource English ASR Model trained using RWKV7 architecture and WavLM-Large Encoder.
+
+* Model date: Mar,2025
+
+* Paper or resources for more information: https://github.com/JL-er/WorldRWKV
+
+* Where to send questions or comments about the model: https://github.com/JL-er/WorldRWKV/issues
+
+** Training Time
+1 hrs 4*4090
+
+
+
+
+** Inference
+
+```
+from infer.worldmodel import Worldinfer
+import librosa
+import numpy as np
+import soundfile as sf
+
+# 模型路径
+llm_path = '/home/rwkv/model/rwkv7-0.4b-wavlmlarge-enasr-demo.pth'
+encoder_path = '/home/rwkv/model/facebookhubert-large-ls960-ft'
+encoder_type = 'speech'
+
+# 初始化模型
+model = Worldinfer(model_path=llm_path, encoder_type=encoder_type, encoder_path=encoder_path)
+
+# 加载音频文件
+audio_path = './test_audio.wav'
+audio_data, sample_rate = sf.read(audio_path)
+
+# 确保音频是单声道
+if len(audio_data.shape) > 1:
+    audio_data = audio_data[:, 0]
+
+# 检查并转换音频数据为浮点数格式
+if audio_data.dtype != np.float32 and audio_data.dtype != np.float64:
+    audio_data = audio_data.astype(np.float32) / np.iinfo(audio_data.dtype).max
+
+# 重采样到 16000 Hz
+resampled_audio = librosa.resample(audio_data, orig_sr=sample_rate, target_sr=16000)
+
+# 构造提示文本
+text = '\x16Assistant:'
+
+# 生成结果
+result, _ = model.generate(text, resampled_audio)
+
+print(result)
+
+```