Add MedASR

model.py

@@ -182,6 +182,10 @@ def get_pretrained_model(
         return funsar_nano_31_languages_models[repo_id](
             repo_id, decoding_method=decoding_method, num_active_paths=num_active_paths
         )
+    elif repo_id in medical_english_models:
+        return medical_english_models[repo_id](
+            repo_id, decoding_method=decoding_method, num_active_paths=num_active_paths
+        )
     elif repo_id in twenty_five_languages_models:
         return twenty_five_languages_models[repo_id](
             repo_id, decoding_method=decoding_method, num_active_paths=num_active_paths
@@ -1425,6 +1429,36 @@ def _get_sherpa_onnx_omnilingual_asr_models(
     return recognizer
 
 
+@lru_cache(maxsize=10)
+def _get_medasr_model(
+    repo_id: str,
+    decoding_method: str,
+    num_active_paths: int,
+) -> sherpa_onnx.OfflineRecognizer:
+    assert repo_id in [
+        "csukuangfj/sherpa-onnx-medasr-ctc-en-int8-2025-12-25",
+        "csukuangfj/sherpa-onnx-medasr-ctc-en-2025-12-25",
+    ], repo_id
+
+    is_int8 = "int8" in repo_id
+
+    model = _get_nn_model_filename(
+        repo_id=repo_id,
+        filename="model.int8.onnx" if is_int8 else "model.onnx",
+        subfolder=".",
+    )
+
+    tokens = _get_token_filename(repo_id=repo_id, subfolder=".")
+
+    recognizer = sherpa_onnx.OfflineRecognizer.from_medasr_ctc(
+        tokens=tokens,
+        model=model,
+        num_threads=2,
+    )
+
+    return recognizer
+
+
 @lru_cache(maxsize=10)
 def _get_sherpa_onnx_nemo_transducer_models_int8(
     repo_id: str,
@@ -2293,6 +2327,11 @@ funsar_nano_31_languages_models = {
     "csukuangfj/sherpa-onnx-sense-voice-funasr-nano-2025-12-17": _get_sense_voice_pre_trained_model,
 }
 
+medical_english_models = {
+    "csukuangfj/sherpa-onnx-medasr-ctc-en-int8-2025-12-25": _get_medasr_model,
+    "csukuangfj/sherpa-onnx-medasr-ctc-en-2025-12-25": _get_medasr_model,
+}
+
 more_than_1600_languages_models = {
     "csukuangfj/sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12": _get_sherpa_onnx_omnilingual_asr_models,
     "csukuangfj/sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-2025-11-12": _get_sherpa_onnx_omnilingual_asr_models,
@@ -2414,6 +2453,7 @@ portuguese_brazlian_models = {
 all_models = {
     **funsar_nano_31_languages_models,
     **more_than_1600_languages_models,
+    **medical_english_models,
     **twenty_five_languages_models,
     **multi_lingual_models,
     **sichuan_models,
@@ -2437,6 +2477,7 @@ all_models = {
 }
 
 language_to_models = {
+    "Medical dictation English": list(medical_english_models.keys()),
     "31 languages (FunASR Nano)": list(funsar_nano_31_languages_models.keys()),
     "1600+ languages": list(more_than_1600_languages_models.keys()),
     "25 European languages": list(twenty_five_languages_models.keys()),

requirements.txt

@@ -10,7 +10,7 @@ numpy<2
 
 huggingface_hub
 
-https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/cpu/1.12.19/sherpa_onnx_core-1.12.19-py3-none-manylinux2014_x86_64.whl
-https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/cpu/1.12.19/sherpa_onnx-1.12.19-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
+https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/cpu/1.12.20/sherpa_onnx_core-1.12.20-py3-none-manylinux2014_x86_64.whl
+https://huggingface.co/csukuangfj/sherpa-onnx-wheels/resolve/main/cpu/1.12.20/sherpa_onnx-1.12.20-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
 
 #sherpa-onnx>=1.12.6