Java api update for adding modelType in config class (#228)

zhaomingwork · GitHub
Commit 5a549615df273adfcef8a8aa83c15fddd5df19ba 5a549615 1 parent daffdab5
java-api-examples/modelconfig.cfg
java-api-examples/src/DecodeFile.java
sherpa-onnx/java-api/src/com/k2fsa/sherpa/onnx/OnlineRecognizer.java
sherpa-onnx/java-api/src/com/k2fsa/sherpa/onnx/OnlineTransducerModelConfig.java
--- a/java-api-examples/modelconfig.cfg
查看文件 @5a54961
+++ b/java-api-examples/modelconfig.cfg
查看文件 @5a54961
@@ -4,16 +4,17 @@ feature_dim=80
 rule1_min_trailing_silence=2.4
 rule2_min_trailing_silence=1.2
 rule3_min_utterance_length=20
-encoder=/sherpa-onnx/build_old/bin/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx
-decoder=/sherpa-onnx/build_old/bin/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx
-joiner=/sherpa-onnx/build_old/bin/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx
-tokens=/sherpa-onnx/build_old/bin/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt
+encoder=/sherpa/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/encoder-epoch-99-avg-1.onnx
+decoder=/sherpa/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/decoder-epoch-99-avg-1.onnx
+joiner=/sherpa/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/joiner-epoch-99-avg-1.onnx
+tokens=/sherpa/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt
 num_threads=4
 enable_endpoint_detection=true
 decoding_method=modified_beam_search
 max_active_paths=4
 lm_model=
 lm_scale=0.5
+model_type=zipformer
 #websocket server config
 port=8890
--- a/java-api-examples/src/DecodeFile.java
查看文件 @5a54961
+++ b/java-api-examples/src/DecodeFile.java
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@@ -49,8 +49,9 @@ public class DecodeFile {
       float rule3MinUtteranceLength = 20F;
       String decodingMethod = "greedy_search";
       int maxActivePaths = 4;
-      String lm_model="";
-	  float lm_scale=0.5F;
+      String lm_model = "";
+      float lm_scale = 0.5F;
+      String modelType = "zipformer";
       rcgOjb =
           new OnlineRecognizer(
               tokens,
@@ -65,9 +66,10 @@ public class DecodeFile {
               rule2MinTrailingSilence,
               rule3MinUtteranceLength,
               decodingMethod,
-			  lm_model,
-			  lm_scale,
-              maxActivePaths);
+              lm_model,
+              lm_scale,
+              maxActivePaths,
+              modelType);
       streamObj = rcgOjb.createStream();
     } catch (Exception e) {
       System.err.println(e);
--- a/sherpa-onnx/java-api/src/com/k2fsa/sherpa/onnx/OnlineRecognizer.java
查看文件 @5a54961
+++ b/sherpa-onnx/java-api/src/com/k2fsa/sherpa/onnx/OnlineRecognizer.java
查看文件 @5a54961
@@ -39,6 +39,7 @@ public class OnlineRecognizer {
   private long ptr = 0; // this is the asr engine ptrss
   private int sampleRate = 16000;
+
   // load config file for OnlineRecognizer
   public OnlineRecognizer(String modelCfgPath) {
     Map<String, String> proMap = this.readProperties(modelCfgPath);
@@ -62,17 +63,20 @@ public class OnlineRecognizer {
               proMap.get("joiner").trim(),
               proMap.get("tokens").trim(),
               Integer.parseInt(proMap.get("num_threads").trim()),
-              false);
+              false,
+              proMap.get("model_type").trim());
       FeatureConfig featConfig =
           new FeatureConfig(sampleRate, Integer.parseInt(proMap.get("feature_dim").trim()));
-      OnlineLMConfig onlineLmConfig=new OnlineLMConfig(proMap.get("lm_model").trim(),Float.parseFloat(proMap.get("lm_scale").trim()));
-	  
-	  OnlineRecognizerConfig rcgCfg =
+      OnlineLMConfig onlineLmConfig =
+          new OnlineLMConfig(
+              proMap.get("lm_model").trim(), Float.parseFloat(proMap.get("lm_scale").trim()));
+
+      OnlineRecognizerConfig rcgCfg =
           new OnlineRecognizerConfig(
               featConfig,
               modelCfg,
               endCfg,
-			  onlineLmConfig,
+              onlineLmConfig,
               Boolean.parseBoolean(proMap.get("enable_endpoint_detection").trim()),
               proMap.get("decoding_method").trim(),
               Integer.parseInt(proMap.get("max_active_paths").trim()));
@@ -107,18 +111,21 @@ public class OnlineRecognizer {
               proMap.get("joiner").trim(),
               proMap.get("tokens").trim(),
               Integer.parseInt(proMap.get("num_threads").trim()),
-              false);
+              false,
+              proMap.get("model_type").trim());
       FeatureConfig featConfig =
           new FeatureConfig(sampleRate, Integer.parseInt(proMap.get("feature_dim").trim()));
-      
-	  OnlineLMConfig onlineLmConfig=new OnlineLMConfig(proMap.get("lm_model").trim(),Float.parseFloat(proMap.get("lm_scale").trim()));
-	  
-	  OnlineRecognizerConfig rcgCfg =
+
+      OnlineLMConfig onlineLmConfig =
+          new OnlineLMConfig(
+              proMap.get("lm_model").trim(), Float.parseFloat(proMap.get("lm_scale").trim()));
+
+      OnlineRecognizerConfig rcgCfg =
           new OnlineRecognizerConfig(
               featConfig,
               modelCfg,
               endCfg,
-			  onlineLmConfig,
+              onlineLmConfig,
               Boolean.parseBoolean(proMap.get("enable_endpoint_detection").trim()),
               proMap.get("decoding_method").trim(),
               Integer.parseInt(proMap.get("max_active_paths").trim()));
@@ -144,21 +151,29 @@ public class OnlineRecognizer {
       float rule2MinTrailingSilence,
       float rule3MinUtteranceLength,
       String decodingMethod,
-	  String lm_model,
-	  float lm_scale,
-      int maxActivePaths) {
+      String lm_model,
+      float lm_scale,
+      int maxActivePaths,
+      String modelType) {
     this.sampleRate = sampleRate;
     EndpointRule rule1 = new EndpointRule(false, rule1MinTrailingSilence, 0.0F);
     EndpointRule rule2 = new EndpointRule(true, rule2MinTrailingSilence, 0.0F);
     EndpointRule rule3 = new EndpointRule(false, 0.0F, rule3MinUtteranceLength);
     EndpointConfig endCfg = new EndpointConfig(rule1, rule2, rule3);
     OnlineTransducerModelConfig modelCfg =
-        new OnlineTransducerModelConfig(encoder, decoder, joiner, tokens, numThreads, false);
+        new OnlineTransducerModelConfig(
+            encoder, decoder, joiner, tokens, numThreads, false, modelType);
     FeatureConfig featConfig = new FeatureConfig(sampleRate, featureDim);
-    OnlineLMConfig onlineLmConfig=new OnlineLMConfig(lm_model,lm_scale);
-	OnlineRecognizerConfig rcgCfg =
+    OnlineLMConfig onlineLmConfig = new OnlineLMConfig(lm_model, lm_scale);
+    OnlineRecognizerConfig rcgCfg =
         new OnlineRecognizerConfig(
-            featConfig, modelCfg, endCfg, onlineLmConfig,enableEndpointDetection, decodingMethod, maxActivePaths);
+            featConfig,
+            modelCfg,
+            endCfg,
+            onlineLmConfig,
+            enableEndpointDetection,
+            decodingMethod,
+            maxActivePaths);
     // create a new Recognizer, first parameter kept for android asset_manager ANDROID_API__ >= 9
     this.ptr = createOnlineRecognizer(new Object(), rcgCfg);
   }
@@ -284,9 +299,10 @@ public class OnlineRecognizer {
   public void releaseStream(OnlineStream s) {
     s.release();
   }
+
   // JNI interface libsherpa-onnx-jni.so
-  private static native Object[] readWave(String fileName);  // static
+  private static native Object[] readWave(String fileName); // static
   private native String getResult(long ptr, long streamPtr);
--- a/sherpa-onnx/java-api/src/com/k2fsa/sherpa/onnx/OnlineTransducerModelConfig.java
查看文件 @5a54961
+++ b/sherpa-onnx/java-api/src/com/k2fsa/sherpa/onnx/OnlineTransducerModelConfig.java
查看文件 @5a54961
@@ -11,15 +11,24 @@ public class OnlineTransducerModelConfig {
   private final String tokens;
   private final int numThreads;
   private final boolean debug;
+  private final String provider = "cpu";
+  private String modelType = "";
   public OnlineTransducerModelConfig(
-      String encoder, String decoder, String joiner, String tokens, int numThreads, boolean debug) {
+      String encoder,
+      String decoder,
+      String joiner,
+      String tokens,
+      int numThreads,
+      boolean debug,
+      String modelType) {
     this.encoder = encoder;
     this.decoder = decoder;
     this.joiner = joiner;
     this.tokens = tokens;
     this.numThreads = numThreads;
     this.debug = debug;
+    this.modelType = modelType;
   }
   public String getEncoder() {