[android] update units for android (#1222)

runtime/device/android/wenet/README.md

@@ -24,7 +24,7 @@ Or you can train your own model using WeNet training pipeline on your data.
 
 ### 2) Build APK
 
-When your model is ready, put `final.zip` and `words.txt` into Android assets (`app/src/main/assets`) folder,
+When your model is ready, put `final.zip` and `units.txt` into Android assets (`app/src/main/assets`) folder,
 then just build and run the APK. Here is a gif demo, which shows how our on-device streaming e2e ASR runs with low latency.
 Please note the wifi and data has been disabled in the demo so there is no network connection ^\_^.
 

runtime/device/android/wenet/app/src/main/assets/README.md

@@ -1 +1 @@
-put final.zip and words.txt here.
+put final.zip and units.txt here.

runtime/device/android/wenet/app/src/main/cpp/wenet.cc

@@ -38,7 +38,7 @@ void init(JNIEnv* env, jobject, jstring jModelDir) {
   const char* pModelDir = env->GetStringUTFChars(jModelDir, nullptr);
 
   std::string modelPath = std::string(pModelDir) + "/final.zip";
-  std::string dictPath = std::string(pModelDir) + "/words.txt";
+  std::string dictPath = std::string(pModelDir) + "/units.txt";
   auto model = std::make_shared<TorchAsrModel>();
   model->Read(modelPath);
   LOG(INFO) << "model path: " << modelPath;

runtime/device/android/wenet/app/src/main/java/com/mobvoi/wenet/MainActivity.java

@@ -33,7 +33,7 @@ public class MainActivity extends AppCompatActivity {
   private static final int SAMPLE_RATE = 16000;  // The sampling rate
   private static final int MAX_QUEUE_SIZE = 2500;  // 100 seconds audio, 1 / 0.04 * 100
   private static final List<String> resource = Arrays.asList(
-    "final.zip", "words.txt", "ctc.ort", "decoder.ort", "encoder.ort"
+    "final.zip", "units.txt", "ctc.ort", "decoder.ort", "encoder.ort"
   );
 
   private boolean startRecord = false;