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README.md

@@ -59,19 +59,15 @@ The model should be loaded with `trust_remote_code=True`. The official inference
 
 ## Performance
 
-The following results are from the `open-audio-opd` evaluation. Lower CER/WER is better. Bold numbers mark the best result within the 0.6B group.
-
-| Model | aishell-1 (CER) | Wenet-meeting (CER) | Wenet-net (CER) | Libri-clean (WER) | Libri-other (WER) |
-| --- | ---: | ---: | ---: | ---: | ---: |
-| *0.6B models* | | | | | |
-| Ark-Base (0.6B) | 3.48% | 10.22% | 7.74% | 3.75% | 7.17% |
-| Ark-Base+OPD (0.6B) | 3.00% | 7.18% | 6.13% | 2.88% | 5.50% |
-| **Ark-Base+TD+OPD (0.6B)** | **1.95%** | 5.92% | **5.39%** | **2.45%** | **4.56%** |
-| Qwen3-ASR-0.6B | 2.07% | **5.57%** | 5.45% | 2.81% | 5.05% |
-| *Larger reference model* | | | | | |
-| Qwen3-ASR-1.7B | 1.50% | 4.69% | 4.55% | 2.20% | 4.05% |
-
-`Ark-Base` is the 0.6B supervised ASR checkpoint trained on 100k hours of ASR audio. `TD` denotes teacher-data adaptation using 2,000 hours of teacher-generated ASR data. `OPD` denotes on-policy distillation with a Qwen-ASR teacher.
+The following results are from the `open-audio-opd` evaluation. Lower CER/WER is better.
+
+| Model | AMI WER | Earnings22 WER | GigaSpeech WER | LS Clean WER | LS Other WER | SPGISpeech WER | VoxPopuli WER | English Avg WER | AISHELL-1 CER | Wenet-meeting CER | Wenet-net CER | Chinese Avg CER |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| Ark-ASR | 11.67% | 10.11% | 8.93% | 1.93% | 4.00% | 2.89% | 6.96% | 6.64% | 2.02% | 5.92% | 4.96% | 4.30% |
+| Qwen3-ASR-1.7B | 10.56% | 10.25% | 8.74% | 1.63% | 3.40% | 2.84% | 6.35% | 6.25% | 1.50% | 4.69% | 4.55% | 3.58% |
+| Qwen3-ASR-0.6B | 11.66% | 11.06% | 9.14% | 2.13% | 4.45% | 3.03% | 7.07% | 6.93% | 2.07% | 5.57% | 5.45% | 4.36% |
+
+`Ark-ASR` is the 0.6B ASR checkpoint trained with teacher-data adaptation and on-policy distillation from `open-audio-opd`.
 
 ## Inference