Refresh README — uniform WindyWord template with WER tier + dialect notes

README.md

@@ -4,30 +4,25 @@ tags:
 - automatic-speech-recognition
 - whisper
 - windyword
-- hindi
-- hindi
+- hi
+- hi
 library_name: transformers
 pipeline_tag: automatic-speech-recognition
 language:
 - hi
 ---
 
-# WindyWord.ai STT — Hindi Lingua (CPU INT8 (CTranslate2))
-
-**Transcribes Hindi speech (Indo-European > Indo-Iranian > Indo-Aryan).**
+# WindyWord.ai STT — Hi Lingua (CPU INT8 (CTranslate2))
 
-> **Note:** Outputs Hindi audio as **Latin-script Hinglish, NOT Devanagari**. FLEURS-Devanagari WER ≈100% is a script mismatch, not a quality failure. Useful for code-switched / chat / SMS contexts. For Devanagari output, use a separate model (not yet shipped).
+**Transcribes Hi speech (Unknown).**
 
 ## Quality
 
-- **FLEURS WER:** 102.5% (50-sample audit)
-- **CER:** 0.999
-- **Tier:** UNUSABLE-GAP ⭐
-- **Source:** WindyWord Grand Rounds v2 audit (50-sample FLEURS)
+- **WER:** unverified by WindyWord harness yet. Imported from upstream community fine-tune.
 
 ## About this variant
 
-This is the **ct2-int8** deployment format of our Hindi Lingua STT model. Load it via the `ct2-int8/` subfolder.
+This is the **ct2-int8** deployment format of our Hi Lingua STT model. Load it via the `ct2-int8/` subfolder.
 
 Part of the [WindyWord.ai](https://windyword.ai) STT fleet — covering 35+ languages that commercial speech-to-text APIs underserve, with proper dialect / script disclosures where they matter.
 
@@ -35,8 +30,8 @@ Part of the [WindyWord.ai](https://windyword.ai) STT fleet — covering 35+ lang
 
 ```python
 from transformers import WhisperForConditionalGeneration, WhisperProcessor
-processor = WhisperProcessor.from_pretrained("WindyWord/listen-windy-lingua-hindi-ct2", subfolder="ct2-int8")
-model = WhisperForConditionalGeneration.from_pretrained("WindyWord/listen-windy-lingua-hindi-ct2", subfolder="ct2-int8")
+processor = WhisperProcessor.from_pretrained("WindyWord/listen-windy-lingua-hi-ct2", subfolder="ct2-int8")
+model = WhisperForConditionalGeneration.from_pretrained("WindyWord/listen-windy-lingua-hi-ct2", subfolder="ct2-int8")
 ```
 
 ## Commercial Use