Support non-streaming WeNet CTC models. (#426)

sherpa-onnx/csrc/offline-recognizer-ctc-impl.h

@@ -75,6 +75,12 @@ class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
 #endif
 
   void Init() {
+    if (!config_.model_config.wenet_ctc.model.empty()) {
+      // WeNet CTC models assume input samples are in the range
+      // [-32768, 32767], so we set normalize_samples to false
+      config_.feat_config.normalize_samples = false;
+    }
+
     config_.feat_config.nemo_normalize_type =
         model_->FeatureNormalizationMethod();
 
@@ -85,10 +91,11 @@ class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
           config_.ctc_fst_decoder_config);
     } else if (config_.decoding_method == "greedy_search") {
       if (!symbol_table_.contains("<blk>") &&
-          !symbol_table_.contains("<eps>")) {
+          !symbol_table_.contains("<eps>") &&
+          !symbol_table_.contains("<blank>")) {
         SHERPA_ONNX_LOGE(
             "We expect that tokens.txt contains "
-            "the symbol <blk> or <eps> and its ID.");
+            "the symbol <blk> or <eps> or <blank> and its ID.");
         exit(-1);
       }
 
@@ -98,6 +105,9 @@ class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
       } else if (symbol_table_.contains("<eps>")) {
         // for tdnn models of the yesno recipe from icefall
         blank_id = symbol_table_["<eps>"];
+      } else if (symbol_table_.contains("<blank>")) {
+        // for Wenet CTC models
+        blank_id = symbol_table_["<blank>"];
       }
 
       decoder_ = std::make_unique<OfflineCtcGreedySearchDecoder>(blank_id);
@@ -113,6 +123,15 @@ class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
   }
 
   void DecodeStreams(OfflineStream **ss, int32_t n) const override {
+    if (!model_->SupportBatchProcessing()) {
+      // If the model does not support batch process,
+      // we process each stream independently.
+      for (int32_t i = 0; i != n; ++i) {
+        DecodeStream(ss[i]);
+      }
+      return;
+    }
+
     auto memory_info =
         Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
 
@@ -164,6 +183,38 @@ class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
     }
   }
 
+ private:
+  // Decode a single stream.
+  // Some models do not support batch size > 1, e.g., WeNet CTC models.
+  void DecodeStream(OfflineStream *s) const {
+    auto memory_info =
+        Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
+
+    int32_t feat_dim = config_.feat_config.feature_dim;
+    std::vector<float> f = s->GetFrames();
+
+    int32_t num_frames = f.size() / feat_dim;
+
+    std::array<int64_t, 3> shape = {1, num_frames, feat_dim};
+
+    Ort::Value x = Ort::Value::CreateTensor(memory_info, f.data(), f.size(),
+                                            shape.data(), shape.size());
+
+    int64_t x_length_scalar = num_frames;
+    std::array<int64_t, 1> x_length_shape = {1};
+    Ort::Value x_length =
+        Ort::Value::CreateTensor(memory_info, &x_length_scalar, 1,
+                                 x_length_shape.data(), x_length_shape.size());
+
+    auto t = model_->Forward(std::move(x), std::move(x_length));
+    auto results = decoder_->Decode(std::move(t[0]), std::move(t[1]));
+    int32_t frame_shift_ms = 10;
+
+    auto r = Convert(results[0], symbol_table_, frame_shift_ms,
+                     model_->SubsamplingFactor());
+    s->SetResult(r);
+  }
+
  private:
   OfflineRecognizerConfig config_;
   SymbolTable symbol_table_;