fix the encoder states dimension mismatch

- we use less features 80->56
- we pass `feature_dim` to `OnlineZipformer2TransducerModel::GetEncoderInitStates()` via new method `OnlineTransducerModel::SetFeatureDim(.)`
- the value 19 corresponding to 80-dim features was previously hard-coded, in this PR it is imported from the `FeatureExtractorConfig`

sherpa-onnx/csrc/features.cc

@@ -26,11 +26,11 @@ void FeatureExtractorConfig::Register(ParseOptions *po) {
   po->Register("feat-dim", &feature_dim,
                "Feature dimension. Must match the one expected by the model.");
 
-  po->Regiseter("low-freq", &low_freq,
-                "Low cutoff frequency for mel bins");
+  po->Register("low-freq", &low_freq,
+               "Low cutoff frequency for mel bins");
 
-  po->Regiseter("high-freq", &high_freq,
-                "High cutoff frequency for mel bins (if <= 0, offset from Nyquist)");
+  po->Register("high-freq", &high_freq,
+               "High cutoff frequency for mel bins (if <= 0, offset from Nyquist)");
 }
 
 std::string FeatureExtractorConfig::ToString() const {

sherpa-onnx/csrc/online-recognizer-transducer-impl.h

@@ -86,6 +86,9 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
         model_(OnlineTransducerModel::Create(config.model_config)),
         sym_(config.model_config.tokens),
         endpoint_(config_.endpoint_config) {
+
+    model_->SetFeatureDim(config.feat_config.feature_dim);
+
     if (sym_.contains("<unk>")) {
       unk_id_ = sym_["<unk>"];
     }

sherpa-onnx/csrc/online-transducer-model.h

@@ -61,6 +61,16 @@ class OnlineTransducerModel {
    */
   virtual std::vector<Ort::Value> GetEncoderInitStates() = 0;
 
+  /** Set feature dim.
+   *
+   * This is used in `OnlineZipformer2TransducerModel`,
+   * to pass `feature_dim` for `GetEncoderInitStates()`.
+   *
+   * This has to be called before GetEncoderInitStates(), so the `encoder_embed`
+   * init state has the correct `embed_dim` of its output.
+   */
+  virtual void SetFeatureDim(int32_t feature_dim) { }
+
   /** Run the encoder.
    *
    * @param features  A tensor of shape (N, T, C). It is changed in-place.

sherpa-onnx/csrc/online-zipformer2-transducer-model.cc

@@ -403,7 +403,10 @@ OnlineZipformer2TransducerModel::GetEncoderInitStates() {
   }
 
   {
-    std::array<int64_t, 4> s{1, 128, 3, 19};
+    SHERPA_ONNX_CHECK_NE(feature_dim_, 0);
+    int32_t embed_dim = (((feature_dim_ - 1) / 2) - 1) / 2;
+    std::array<int64_t, 4> s{1, 128, 3, embed_dim};
+
     auto v = Ort::Value::CreateTensor<float>(allocator_, s.data(), s.size());
     Fill(&v, 0);
     ans.push_back(std::move(v));

sherpa-onnx/csrc/online-zipformer2-transducer-model.h

@@ -37,6 +37,8 @@ class OnlineZipformer2TransducerModel : public OnlineTransducerModel {
 
   std::vector<Ort::Value> GetEncoderInitStates() override;
 
+  void SetFeatureDim(int32_t feature_dim) override { feature_dim_ = feature_dim; }
+
   std::pair<Ort::Value, std::vector<Ort::Value>> RunEncoder(
       Ort::Value features, std::vector<Ort::Value> states,
       Ort::Value processed_frames) override;
@@ -101,6 +103,7 @@ class OnlineZipformer2TransducerModel : public OnlineTransducerModel {
 
   int32_t context_size_ = 0;
   int32_t vocab_size_ = 0;
+  int32_t feature_dim_ = 0;
 };
 
 }  // namespace sherpa_onnx