diff --git a/daras_ai_v2/asr.py b/daras_ai_v2/asr.py
index a8b6656da..1bf919e12 100644
--- a/daras_ai_v2/asr.py
+++ b/daras_ai_v2/asr.py
@@ -283,6 +283,7 @@ class AsrModels(Enum):
     ghana_nlp_asr_v2 = "Ghana NLP ASR v2"
     lelapa = "Vulavula (Lelapa AI)"
     whisper_sunbird_large_v3 = "Sunbird Ugandan Whisper v3 (Sunbird AI)"
+    whisper_akera_large_v3 = "Akera Whisper v3 (akera)"
     whisper_swahili_medium_v3 = "Jacaranda Health Swahili Whisper v3 (Jacaranda Health)"
     mbaza_ctc_large = "Mbaza Conformer LG (MbazaNLP)"
 
@@ -336,6 +337,7 @@ def supports_input_prompt(self) -> bool:
 
 
 asr_model_ids = {
+    AsrModels.whisper_akera_large_v3: "akera/whisper-large-v3-kik-full_v2",
     AsrModels.gpt_4_o_audio: "gpt-4o-transcribe",
     AsrModels.gpt_4_o_mini_audio: "gpt-4o-mini-transcribe",
     AsrModels.whisper_large_v3: "vaibhavs10/incredibly-fast-whisper:3ab86df6c8f54c11309d4d1f930ac292bad43ace52d10c80d87eb258b3c9f79c",
@@ -362,6 +364,7 @@ def supports_input_prompt(self) -> bool:
     AsrModels.vakyansh_bhojpuri: "bho",
     AsrModels.nemo_english: "en",
     AsrModels.nemo_hindi: "hi",
+    AsrModels.whisper_akera_large_v3: "kik",
 }
 
 asr_supported_languages = {
@@ -386,6 +389,7 @@ def supports_input_prompt(self) -> bool:
     AsrModels.lelapa: LELAPA_ASR_SUPPORTED,
     AsrModels.whisper_sunbird_large_v3: SUNBIRD_SUPPORTED_LANGUAGES,
     AsrModels.whisper_swahili_medium_v3: {"sw", "en"},
+    AsrModels.whisper_akera_large_v3: {"kik"},
     AsrModels.mbaza_ctc_large: {"sw", "rw", "lg"},
 }
 
@@ -1286,13 +1290,17 @@ def run_asr(
         )
     # call one of the self-hosted models
     else:
-        kwargs = {}
+        kwargs = {"task": "translate" if speech_translation_target else "transcribe"}
         if "vakyansh" in selected_model.name:
             # fixes https://github.com/huggingface/transformers/issues/15275#issuecomment-1624879632
             kwargs["decoder_kwargs"] = dict(skip_special_tokens=True)
             kwargs["chunk_length_s"] = 60
             kwargs["stride_length_s"] = (6, 0)
             kwargs["batch_size"] = 32
+        elif selected_model == AsrModels.whisper_akera_large_v3:
+            # don't pass language or task
+            kwargs.pop("task", None)
+            kwargs["max_length"] = 448
         elif "whisper" in selected_model.name:
             forced_lang = forced_asr_languages.get(selected_model)
             if forced_lang:
@@ -1308,7 +1316,6 @@ def run_asr(
             ),
             inputs=dict(
                 audio=audio_url,
-                task="translate" if speech_translation_target else "transcribe",
                 return_timestamps=output_format != AsrOutputFormat.text,
                 **kwargs,
             ),