Add DuHow Whisper turbo cat evaluations and runner support

asr/evals/duhow_whisper_large_v3_turbo_cat.json

@@ -0,0 +1,19 @@
+{
+  "model": "duhow/whisper-large-v3-turbo-cat",
+  "params_b": null,
+  "memory_gb": null,
+  "hardware": "AMD RYZEN AI MAX+ PRO 395 w/ Radeon 8060S",
+  "evaluated_at": "2026-10-29T22:14:07+00:00",
+  "benchmarks": {
+    "fleurs_ca": {
+      "wer": 0.0983,
+      "cer": 0.0715,
+      "n": 400
+    },
+    "openslr69_ca": {
+      "wer": 0.048,
+      "cer": 0.017,
+      "n": 400
+    }
+  }
+}

asr/hf-eval.py

@@ -80,6 +80,7 @@ class EvalResult:
     "whisper-large-v3",
     "whisper-large-v3-turbo",
     "projecte-aina/whisper-large-v3-ca-3catparla",
+    "duhow/whisper-large-v3-turbo-cat",
 ]
 
 GEMMA_MODELS = [
@@ -113,7 +114,7 @@ class WhisperWrapper:
     def __init__(self, model_name: str, device: str):
         from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
-        if "aina" not in model_name:
+        if "/" not in model_name:
             model_id = f"openai/{model_name}"
         else:
             model_id = model_name

asr/run_evals.py

@@ -56,6 +56,11 @@
         "args": ["projecte-aina/whisper-large-v3-ca-3catparla"],
         "output": "evals/whisper_large_v3_ca.json",
     },
+    {
+        "label": "duhow/whisper-large-v3-turbo-cat",
+        "args": ["duhow/whisper-large-v3-turbo-cat"],
+        "output": "evals/duhow_whisper_large_v3_turbo_cat.json",
+    },
     {
         "label": "omniASR_CTC_300M",
         "args": ["omniASR_CTC_300M"],

docs/asr.md

@@ -36,6 +36,7 @@ uv run python hf-eval.py --list-models
 ```bash
 uv run python hf-eval.py whisper-large-v3 --device cuda --output evals/whisper_large_v3.json
 uv run python hf-eval.py whisper-small --output evals/whisper_small.json
+uv run python hf-eval.py duhow/whisper-large-v3-turbo-cat --device cuda --output evals/duhow_whisper_large_v3_turbo_cat.json
 ```
 
 **Avaluar un model d'API:**