docs: use canonical FunASR ecosystem links

Point Fun-ASR and SenseVoice links at their canonical QwenAudio repositories. Keep deployment links current; the Transformers card also pins the verified PR head.

README.md

@@ -22,7 +22,7 @@ tags:
 
 This model is part of the **FunASR** ecosystem — one industrial-grade open-source toolkit for **ASR · VAD · punctuation · speaker diarization · emotion / event · LLM-ASR**. A Star really helps the project (and keeps you updated):
 
-[**🌟 FunASR**](https://github.com/modelscope/FunASR)  ·  [**🌟 SenseVoice**](https://github.com/FunAudioLLM/SenseVoice)  ·  [**🌟 Fun-ASR**](https://github.com/FunAudioLLM/Fun-ASR)  ·  [**🌟 FunClip**](https://github.com/modelscope/FunClip)
+[**🌟 FunASR**](https://github.com/modelscope/FunASR)  ·  [**🌟 SenseVoice**](https://github.com/QwenAudio/SenseVoice)  ·  [**🌟 Fun-ASR**](https://github.com/QwenAudio/Fun-ASR)  ·  [**🌟 FunClip**](https://github.com/modelscope/FunClip)
 
 </div>
 
@@ -40,7 +40,7 @@ With [PyTorch installed for your platform](https://pytorch.org/get-started/local
 
 ```bash
 python -m pip install accelerate librosa \
-  "transformers @ https://github.com/huggingface/transformers/archive/4e199f7990ab38838210fc02726f34f6f25d0097.zip"
+  "transformers @ https://github.com/huggingface/transformers/archive/aa0a25a4c55a70cff996267bcd7e96317fe4031c.zip"
 ```
 
 ```python
@@ -85,4 +85,4 @@ inputs = processor.apply_transcription_request(
 ).to(model.device)
 ```
 
-`language` accepts Chinese, English, and Japanese as ISO codes, full English names, or native names. For batch inference, training, `torch.compile`, benchmarks, and the full model description, see the [Transformers documentation](https://github.com/huggingface/transformers/blob/4e199f7990ab38838210fc02726f34f6f25d0097/docs/source/en/model_doc/fun_asr_nano.md) and the [original model card](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512).
+`language` accepts Chinese, English, and Japanese as ISO codes, full English names, or native names. For batch inference, training, `torch.compile`, benchmarks, and the full model description, see the [Transformers documentation](https://github.com/huggingface/transformers/blob/aa0a25a4c55a70cff996267bcd7e96317fe4031c/docs/source/en/model_doc/fun_asr_nano.md) and the [original model card](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512).