add doc

README.md

@@ -178,6 +178,7 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision
   - 🧩  *Cascaded models application*: as an extension of the typical traditional audio tasks, we combine the workflows of the aforementioned tasks with other fields like Natural language processing (NLP) and Computer Vision (CV).
 
 ### Recent Update
+- 🤗 2025.08.11: Add [code-switch online model and server demo](./examples/tal_cs/asr1/).
 - 👑 2023.05.31: Add [WavLM ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr5), WavLM fine-tuning for ASR on LibriSpeech.
 - 🎉 2023.05.18: Add [Squeezeformer](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell/asr1), Squeezeformer training for ASR on Aishell.
 - 👑 2023.05.04: Add [HuBERT ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr4), HuBERT fine-tuning for ASR on LibriSpeech.

README_cn.md

@@ -183,6 +183,7 @@
   - 🧩 级联模型应用: 作为传统语音任务的扩展，我们结合了自然语言处理、计算机视觉等任务，实现更接近实际需求的产业级应用。
 
 ### 近期更新
+- 🤗 2025.08.11: 新增 [流式中英混合 tal_cs 识别模型](./examples/tal_cs/asr1/).
 - 👑 2023.05.31: 新增 [WavLM ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr5), 基于WavLM的英语识别微调，使用LibriSpeech数据集
 - 🎉 2023.05.18: 新增 [Squeezeformer](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell/asr1), 使用Squeezeformer进行训练，使用Aishell数据集
 - 👑 2023.05.04: 新增 [HuBERT ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr4), 基于HuBERT的英语识别微调，使用LibriSpeech数据集

examples/tal_cs/asr1/RESULTS.md

@@ -1,5 +1,6 @@
 # TALCS
-2023.1.6, commit id: fa724285f3b799b97b4348ad3b1084afc0764f9b
+2023.1.6, commit id: fa724285f3b799b97b4348ad3b1084afc0764f9b (conformer)
+2025.8.11, commit id: 4f62ff05b7c9974d5642b26306ff3c7140c84312 (chunk_conformer)
 
 ## Conformer
 train: Epoch 100, 3 V100-32G, best avg: 10
@@ -9,4 +10,8 @@ train: Epoch 100, 3 V100-32G, best avg: 10
 | conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | attention | 9.85091028213501 | 0.102786 |  
 | conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | ctc_greedy_search | 9.85091028213501 | 0.103538 |  
 | conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | ctc_prefix_beam_search | 9.85091028213501 | 0.103317 |  
-| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | attention_rescoring | 9.85091028213501 | 0.084374 |  
+| conformer | 47.63 M | conf/conformer.yaml | spec_aug | test-set | attention_rescoring | 9.85091028213501 | 0.084374 | 
+| chunk_conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug | test-set | attention | 9.897139549255371 | 0.080488 |
+| chunk_conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug | test-set | ctc_greedy_search | 9.897139549255371 | 0.093244 |
+| chunk_conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug | test-set | ctc_prefix_beam_search | 9.897139549255371 | 0.093251 |
+| chunk_conformer | 47.63 M | conf/chunk_conformer.yaml | spec_aug | test-set | attention_rescoring | 9.897139549255371 | 0.079193 |