Update repo references after rename to include nict-tib1

README.md

@@ -17,7 +17,7 @@ metrics:
 - cer
 - wer
 model-index:
-- name: tibetan-asr-whisper-tiny-lora-8bit
+- name: tibetan-asr-nict-tib1-whisper-tiny-lora-8bit
   results:
   - task:
       type: automatic-speech-recognition
@@ -41,7 +41,7 @@ Fine-tuned **`openai/whisper-tiny`** for automatic speech recognition (ASR) on *
 
 > J. Moore, S. Li and P. Lauren, "Evaluating Tibetan ASR With Segmented Word Error Rate: Beyond Character-Level Metrics," in *IEEE Access*, vol. 14, pp. 101790-101805, 2026, doi: 10.1109/ACCESS.2026.3709206.
 
-This repo is one of 14 model checkpoints released with the paper, benchmarking 5 ASR architectures (Whisper Tiny/Base/Small, Wav2Vec 2.0 Base, HuBERT Base) under full fine-tuning, LoRA, and QLoRA (8-bit/4-bit) adaptation. See [billingsmoore/tibetan-asr-*](https://huggingface.co/billingsmoore) for the full set.
+This repo is one of 14 model checkpoints released with the paper, benchmarking 5 ASR architectures (Whisper Tiny/Base/Small, Wav2Vec 2.0 Base, HuBERT Base) under full fine-tuning, LoRA, and QLoRA (8-bit/4-bit) adaptation. See [billingsmoore/tibetan-asr-nict-tib1-*](https://huggingface.co/billingsmoore) for the full set.
 
 ## Model description
 
@@ -82,15 +82,15 @@ Evaluated on the 1,547-utterance NICT-Tib1 test set under five metrics: Characte
 
 | Model | Repo | CER | SER | BoTok-SWER | BERT-SWER | Gem-SWER |
 |---|---|---|---|---|---|---|
-| HuBERT Base | [tibetan-asr-hubert-base](https://huggingface.co/billingsmoore/tibetan-asr-hubert-base) | 0.1352 | 0.3690 | 0.4477 | 0.161 | 0.9653 |
-| Wav2Vec 2.0 Base | [tibetan-asr-wav2vec2-base](https://huggingface.co/billingsmoore/tibetan-asr-wav2vec2-base) | 0.0745 | 0.2152 | 0.2747 | 0.097 | 0.7447 |
-| Whisper Tiny | [tibetan-asr-whisper-tiny](https://huggingface.co/billingsmoore/tibetan-asr-whisper-tiny) | 0.1560 | 0.2351 | 0.2975 | 0.118 | 0.6759 |
-| Whisper Base | [tibetan-asr-whisper-base](https://huggingface.co/billingsmoore/tibetan-asr-whisper-base) | 0.1417 | 0.2083 | 0.2600 | 0.105 | 0.6314 |
-| **Whisper Small** | [tibetan-asr-whisper-small](https://huggingface.co/billingsmoore/tibetan-asr-whisper-small) | **0.1185** | **0.1692** | **0.2042** | **0.086** | **0.5337** |
+| HuBERT Base | [tibetan-asr-nict-tib1-hubert-base](https://huggingface.co/billingsmoore/tibetan-asr-nict-tib1-hubert-base) | 0.1352 | 0.3690 | 0.4477 | 0.161 | 0.9653 |
+| Wav2Vec 2.0 Base | [tibetan-asr-nict-tib1-wav2vec2-base](https://huggingface.co/billingsmoore/tibetan-asr-nict-tib1-wav2vec2-base) | 0.0745 | 0.2152 | 0.2747 | 0.097 | 0.7447 |
+| Whisper Tiny | [tibetan-asr-nict-tib1-whisper-tiny](https://huggingface.co/billingsmoore/tibetan-asr-nict-tib1-whisper-tiny) | 0.1560 | 0.2351 | 0.2975 | 0.118 | 0.6759 |
+| Whisper Base | [tibetan-asr-nict-tib1-whisper-base](https://huggingface.co/billingsmoore/tibetan-asr-nict-tib1-whisper-base) | 0.1417 | 0.2083 | 0.2600 | 0.105 | 0.6314 |
+| **Whisper Small** | [tibetan-asr-nict-tib1-whisper-small](https://huggingface.co/billingsmoore/tibetan-asr-nict-tib1-whisper-small) | **0.1185** | **0.1692** | **0.2042** | **0.086** | **0.5337** |
 
 
 > [!WARNING]
-> **This is a LoRA/QLoRA fine-tuned checkpoint. In the paper's benchmark, all LoRA and QLoRA configurations showed catastrophic word-level degradation** relative to full fine-tuning of the same base model, despite retaining partial character-level accuracy. This repo is published for reproducibility of that (negative) result, not as a recommended deployment artifact. If you need a usable Tibetan ASR model, use the [standard fine-tuned checkpoint](https://huggingface.co/billingsmoore/tibetan-asr-whisper-tiny) instead.
+> **This is a LoRA/QLoRA fine-tuned checkpoint. In the paper's benchmark, all LoRA and QLoRA configurations showed catastrophic word-level degradation** relative to full fine-tuning of the same base model, despite retaining partial character-level accuracy. This repo is published for reproducibility of that (negative) result, not as a recommended deployment artifact. If you need a usable Tibetan ASR model, use the [standard fine-tuned checkpoint](https://huggingface.co/billingsmoore/tibetan-asr-nict-tib1-whisper-tiny) instead.
 
 ## How to use
 
@@ -103,7 +103,7 @@ bnb_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=None)
 base_model = WhisperForConditionalGeneration.from_pretrained(
     "openai/whisper-tiny", quantization_config=bnb_config, device_map="auto"
 )
-model = PeftModel.from_pretrained(base_model, "billingsmoore/tibetan-asr-whisper-tiny-lora-8bit")
+model = PeftModel.from_pretrained(base_model, "billingsmoore/tibetan-asr-nict-tib1-whisper-tiny-lora-8bit")
 processor = WhisperProcessor.from_pretrained("openai/whisper-tiny", language="bo", task="transcribe")
 
 # generate as usual, e.g.: