Add lm rescore to online-modified-beam-search (#133)

android/SherpaOnnx/app/src/main/java/com/k2fsa/sherpa/onnx/MainActivity.kt

@@ -182,9 +182,10 @@ class MainActivity : AppCompatActivity() {
         val config = OnlineRecognizerConfig(
             featConfig = getFeatureConfig(sampleRate = sampleRateInHz, featureDim = 80),
             modelConfig = getModelConfig(type = type)!!,
+            lmConfig = getOnlineLMConfig(type = type),
             endpointConfig = getEndpointConfig(),
             enableEndpoint = true,
-            decodingMethod = "greedy_search",
+            decodingMethod = "modified_beam_search",
             maxActivePaths = 4,
         )
 

android/SherpaOnnx/app/src/main/java/com/k2fsa/sherpa/onnx/SherpaOnnx.kt

@@ -23,6 +23,11 @@ data class OnlineTransducerModelConfig(
     var debug: Boolean = false,
 )
 
+data class OnlineLMConfig(
+    var model: String = "",
+    var scale: Float = 0.5f,
+)
+
 data class FeatureConfig(
     var sampleRate: Int = 16000,
     var featureDim: Int = 80,
@@ -31,6 +36,7 @@ data class FeatureConfig(
 data class OnlineRecognizerConfig(
     var featConfig: FeatureConfig = FeatureConfig(),
     var modelConfig: OnlineTransducerModelConfig,
+    var lmConfig : OnlineLMConfig,
     var endpointConfig: EndpointConfig = EndpointConfig(),
     var enableEndpoint: Boolean = true,
     var decodingMethod: String = "greedy_search",
@@ -151,6 +157,32 @@ fun getModelConfig(type: Int): OnlineTransducerModelConfig? {
     return null;
 }
 
+/*
+Please see
+https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
+for a list of pre-trained models.
+
+We only add a few here. Please change the following code
+to add your own LM model. (It should be straightforward to train a new NN LM model
+by following the code, https://github.com/k2-fsa/icefall/blob/master/icefall/rnn_lm/train.py)
+
+@param type
+0 - sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20 (Bilingual, Chinese + English)
+    https://k2-fsa.github.io/sherpa/onnx/pretrained_models/zipformer-transducer-models.html#sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20-bilingual-chinese-english
+ */
+fun getOnlineLMConfig(type : Int): OnlineLMConfig {
+    when (type) {
+        0 -> {
+            val modelDir = "sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20"
+            return OnlineLMConfig(
+                model = "$modelDir/with-state-epoch-99-avg-1.int8.onnx",
+                scale = 0.5f,
+            )
+        }
+    }
+    return OnlineLMConfig();
+}
+
 fun getEndpointConfig(): EndpointConfig {
     return EndpointConfig(
         rule1 = EndpointRule(false, 2.4f, 0.0f),