Begin to support CTC models (#119)

Please see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/index.html for a list of pre-trained CTC models from NeMo.

sherpa-onnx/python/sherpa_onnx/offline_recognizer.py

@@ -4,12 +4,15 @@ from typing import List
 
 from _sherpa_onnx import (
     OfflineFeatureExtractorConfig,
-    OfflineRecognizer as _Recognizer,
+    OfflineModelConfig,
+    OfflineNemoEncDecCtcModelConfig,
+    OfflineParaformerModelConfig,
+)
+from _sherpa_onnx import OfflineRecognizer as _Recognizer
+from _sherpa_onnx import (
     OfflineRecognizerConfig,
     OfflineStream,
-    OfflineModelConfig,
     OfflineTransducerModelConfig,
-    OfflineParaformerModelConfig,
 )
 
 
@@ -75,7 +78,6 @@ class OfflineRecognizer(object):
                 decoder_filename=decoder,
                 joiner_filename=joiner,
             ),
-            paraformer=OfflineParaformerModelConfig(model=""),
             tokens=tokens,
             num_threads=num_threads,
             debug=debug,
@@ -119,7 +121,7 @@ class OfflineRecognizer(object):
                 symbol integer_id
 
           paraformer:
-            Path to ``paraformer.onnx``.
+            Path to ``model.onnx``.
           num_threads:
             Number of threads for neural network computation.
           sample_rate:
@@ -133,9 +135,6 @@ class OfflineRecognizer(object):
         """
         self = cls.__new__(cls)
         model_config = OfflineModelConfig(
-            transducer=OfflineTransducerModelConfig(
-                encoder_filename="", decoder_filename="", joiner_filename=""
-            ),
             paraformer=OfflineParaformerModelConfig(model=paraformer),
             tokens=tokens,
             num_threads=num_threads,
@@ -155,6 +154,64 @@ class OfflineRecognizer(object):
         self.recognizer = _Recognizer(recognizer_config)
         return self
 
+    @classmethod
+    def from_nemo_ctc(
+        cls,
+        model: str,
+        tokens: str,
+        num_threads: int,
+        sample_rate: int = 16000,
+        feature_dim: int = 80,
+        decoding_method: str = "greedy_search",
+        debug: bool = False,
+    ):
+        """
+        Please refer to
+        `<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
+        to download pre-trained models for different languages, e.g., Chinese,
+        English, etc.
+
+        Args:
+          tokens:
+            Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
+            columns::
+
+                symbol integer_id
+
+          model:
+            Path to ``model.onnx``.
+          num_threads:
+            Number of threads for neural network computation.
+          sample_rate:
+            Sample rate of the training data used to train the model.
+          feature_dim:
+            Dimension of the feature used to train the model.
+          decoding_method:
+            Valid values are greedy_search, modified_beam_search.
+          debug:
+            True to show debug messages.
+        """
+        self = cls.__new__(cls)
+        model_config = OfflineModelConfig(
+            nemo_ctc=OfflineNemoEncDecCtcModelConfig(model=model),
+            tokens=tokens,
+            num_threads=num_threads,
+            debug=debug,
+        )
+
+        feat_config = OfflineFeatureExtractorConfig(
+            sampling_rate=sample_rate,
+            feature_dim=feature_dim,
+        )
+
+        recognizer_config = OfflineRecognizerConfig(
+            feat_config=feat_config,
+            model_config=model_config,
+            decoding_method=decoding_method,
+        )
+        self.recognizer = _Recognizer(recognizer_config)
+        return self
+
     def create_stream(self):
         return self.recognizer.create_stream()