Fix code style issues (#774)

@@ -26,8 +26,7 @@ void FeatureExtractorConfig::Register(ParseOptions *po) {
   po->Register("feat-dim", &feature_dim,
                "Feature dimension. Must match the one expected by the model.");
 
-  po->Register("low-freq", &low_freq,
-               "Low cutoff frequency for mel bins");
+  po->Register("low-freq", &low_freq, "Low cutoff frequency for mel bins");
 
   po->Register("high-freq", &high_freq,
                "High cutoff frequency for mel bins "
@@ -67,7 +66,7 @@ class FeatureExtractor::Impl {
     opts_.mel_opts.num_bins = config.feature_dim;
 
     opts_.mel_opts.high_freq = config.high_freq;
-    opts_.mel_opts.low_freq  = config.low_freq;
+    opts_.mel_opts.low_freq = config.low_freq;
 
     opts_.mel_opts.is_librosa = config.is_librosa;
 

@@ -15,7 +15,7 @@ void OfflineLMConfig::Register(ParseOptions *po) {
   po->Register("lm", &model, "Path to LM model.");
   po->Register("lm-scale", &scale, "LM scale.");
   po->Register("lm-num-threads", &lm_num_threads,
-              "Number of threads to run the neural network of LM model");
+               "Number of threads to run the neural network of LM model");
   po->Register("lm-provider", &lm_provider,
                "Specify a provider to LM model use: cpu, cuda, coreml");
 }

@@ -80,9 +80,8 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
       InitHotwords();
     }
     if (config_.decoding_method == "greedy_search") {
-      decoder_ =
-          std::make_unique<OfflineTransducerGreedySearchDecoder>(
-            model_.get(), config_.blank_penalty);
+      decoder_ = std::make_unique<OfflineTransducerGreedySearchDecoder>(
+          model_.get(), config_.blank_penalty);
     } else if (config_.decoding_method == "modified_beam_search") {
       if (!config_.lm_config.model.empty()) {
         lm_ = OfflineLM::Create(config.lm_config);
@@ -106,9 +105,8 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
         model_(std::make_unique<OfflineTransducerModel>(mgr,
                                                         config_.model_config)) {
     if (config_.decoding_method == "greedy_search") {
-      decoder_ =
-          std::make_unique<OfflineTransducerGreedySearchDecoder>(
-            model_.get(), config_.blank_penalty);
+      decoder_ = std::make_unique<OfflineTransducerGreedySearchDecoder>(
+          model_.get(), config_.blank_penalty);
     } else if (config_.decoding_method == "modified_beam_search") {
       if (!config_.lm_config.model.empty()) {
         lm_ = OfflineLM::Create(mgr, config.lm_config);

@@ -16,8 +16,7 @@ class OfflineTransducerGreedySearchDecoder : public OfflineTransducerDecoder {
  public:
   explicit OfflineTransducerGreedySearchDecoder(OfflineTransducerModel *model,
                                                 float blank_penalty)
-      : model_(model),
-        blank_penalty_(blank_penalty) {}
+      : model_(model), blank_penalty_(blank_penalty) {}
 
   std::vector<OfflineTransducerDecoderResult> Decode(
       Ort::Value encoder_out, Ort::Value encoder_out_length,

@@ -102,9 +102,9 @@ void OfflineWebsocketDecoder::Decode() {
     asio::post(server_->GetConnectionContext(),
                [this, hdl, result = ss[i]->GetResult()]() {
                  websocketpp::lib::error_code ec;
-                 server_->GetServer().send(
-                     hdl, result.AsJsonString(),
-                     websocketpp::frame::opcode::text, ec);
+                 server_->GetServer().send(hdl, result.AsJsonString(),
+                                           websocketpp::frame::opcode::text,
+                                           ec);
                  if (ec) {
                    server_->GetServer().get_alog().write(
                        websocketpp::log::alevel::app, ec.message());

@@ -15,7 +15,7 @@ void OnlineLMConfig::Register(ParseOptions *po) {
   po->Register("lm", &model, "Path to LM model.");
   po->Register("lm-scale", &scale, "LM scale.");
   po->Register("lm-num-threads", &lm_num_threads,
-              "Number of threads to run the neural network of LM model");
+               "Number of threads to run the neural network of LM model");
   po->Register("lm-provider", &lm_provider,
                "Specify a provider to LM model use: cpu, cuda, coreml");
 }

@@ -22,7 +22,7 @@ struct OnlineLMConfig {
   OnlineLMConfig() = default;
 
   OnlineLMConfig(const std::string &model, float scale, int32_t lm_num_threads,
-                  const std::string &lm_provider)
+                 const std::string &lm_provider)
       : model(model),
         scale(scale),
         lm_num_threads(lm_num_threads),

@@ -40,8 +40,7 @@ struct OnlineModelConfig {
                     const OnlineWenetCtcModelConfig &wenet_ctc,
                     const OnlineZipformer2CtcModelConfig &zipformer2_ctc,
                     const std::string &tokens, int32_t num_threads,
-                    int32_t warm_up, bool debug,
-                    const std::string &provider,
+                    int32_t warm_up, bool debug, const std::string &provider,
                     const std::string &model_type)
       : transducer(transducer),
         paraformer(paraformer),

@@ -30,9 +30,9 @@
 #include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
 #include "sherpa-onnx/csrc/online-transducer-model.h"
 #include "sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
 #include "sherpa-onnx/csrc/symbol-table.h"
 #include "sherpa-onnx/csrc/utils.h"
-#include "sherpa-onnx/csrc/onnx-utils.h"
 
 namespace sherpa_onnx {
 
@@ -185,7 +185,7 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
   }
 
   // Warmping up engine with wp: warm_up count and max-batch-size
-  void WarmpUpRecognizer(int32_t warmup, int32_t mbs) const {
+  void WarmpUpRecognizer(int32_t warmup, int32_t mbs) const override {
     auto max_batch_size = mbs;
     if (warmup <= 0 || warmup > 100) {
       return;
@@ -210,8 +210,8 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
     for (int32_t i = 0; i != warmup; ++i) {
       auto states = model_->StackStates(states_vec);
       Ort::Value x = Ort::Value::CreateTensor(memory_info, features_vec.data(),
-                                        features_vec.size(), x_shape.data(),
-                                        x_shape.size());
+                                              features_vec.size(),
+                                              x_shape.data(), x_shape.size());
       auto x_copy = Clone(model_->Allocator(), &x);
       auto pair = model_->RunEncoder(std::move(x), std::move(states),
                                      std::move(x_copy));

@@ -168,7 +168,7 @@ class OnlineRecognizer {
    *
    * @param warmup Number of warmups.
    * @param mbs : max-batch-size Max batch size for the models
-  */
+   */
   void WarmpUpRecognizer(int32_t warmup, int32_t mbs) const;
 
   /** Decode multiple streams in parallel

@@ -12,8 +12,8 @@
 #include "onnxruntime_cxx_api.h"  // NOLINT
 #include "sherpa-onnx/csrc/macros.h"
 #include "sherpa-onnx/csrc/onnx-utils.h"
-#include "sherpa-onnx/csrc/text-utils.h"
 #include "sherpa-onnx/csrc/session.h"
+#include "sherpa-onnx/csrc/text-utils.h"
 
 namespace sherpa_onnx {
 
@@ -42,10 +42,9 @@ class OnlineRnnLM::Impl {
     // nn_lm_scores
     std::array<int64_t, 2> x_shape{1, 1};
     Ort::Value x = Ort::Value::CreateTensor<int64_t>(allocator_, x_shape.data(),
-                                                    x_shape.size());
+                                                     x_shape.size());
     *x.GetTensorMutableData<int64_t>() = hyp->ys.back();
-    auto lm_out =
-        ScoreToken(std::move(x), Convert(hyp->nn_lm_states));
+    auto lm_out = ScoreToken(std::move(x), Convert(hyp->nn_lm_states));
     hyp->nn_lm_scores.value = std::move(lm_out.first);
     hyp->nn_lm_states = Convert(std::move(lm_out.second));
   }

@@ -71,11 +71,9 @@ void OnlineTransducerGreedySearchDecoder::StripLeadingBlanks(
   r->tokens = std::vector<int64_t>(start, end);
 }
 
-
 void OnlineTransducerGreedySearchDecoder::Decode(
     Ort::Value encoder_out,
     std::vector<OnlineTransducerDecoderResult> *result) {
-
   std::vector<int64_t> encoder_out_shape =
       encoder_out.GetTensorTypeAndShapeInfo().GetShape();
 
@@ -106,7 +104,8 @@ void OnlineTransducerGreedySearchDecoder::Decode(
         r.decoder_out.GetTensorTypeAndShapeInfo().GetShape();
     decoder_out_shape[0] = batch_size;
     decoder_out = Ort::Value::CreateTensor<float>(model_->Allocator(),
-        decoder_out_shape.data(), decoder_out_shape.size());
+                                                  decoder_out_shape.data(),
+                                                  decoder_out_shape.size());
     UseCachedDecoderOut(*result, &decoder_out);
   } else {
     Ort::Value decoder_input = model_->BuildDecoderInput(*result);
@@ -116,8 +115,8 @@ void OnlineTransducerGreedySearchDecoder::Decode(
   for (int32_t t = 0; t != num_frames; ++t) {
     Ort::Value cur_encoder_out =
         GetEncoderOutFrame(model_->Allocator(), &encoder_out, t);
-    Ort::Value logit = model_->RunJoiner(
-        std::move(cur_encoder_out), View(&decoder_out));
+    Ort::Value logit =
+        model_->RunJoiner(std::move(cur_encoder_out), View(&decoder_out));
 
     float *p_logit = logit.GetTensorMutableData<float>();
 
@@ -145,9 +144,9 @@ void OnlineTransducerGreedySearchDecoder::Decode(
 
       // export the per-token log scores
       if (y != 0 && y != unk_id_) {
-        LogSoftmax(p_logit, vocab_size);  // renormalize probabilities,
-                                          // save time by doing it only for
-                                          // emitted symbols
+        LogSoftmax(p_logit, vocab_size);   // renormalize probabilities,
+                                           // save time by doing it only for
+                                           // emitted symbols
         const float *p_logprob = p_logit;  // rename p_logit as p_logprob,
                                            // now it contains normalized
                                            // probability

@@ -15,8 +15,7 @@ namespace sherpa_onnx {
 class OnlineTransducerGreedySearchDecoder : public OnlineTransducerDecoder {
  public:
   OnlineTransducerGreedySearchDecoder(OnlineTransducerModel *model,
-                                      int32_t unk_id,
-                                      float blank_penalty)
+                                      int32_t unk_id, float blank_penalty)
       : model_(model), unk_id_(unk_id), blank_penalty_(blank_penalty) {}
 
   OnlineTransducerDecoderResult GetEmptyResult() const override;

@@ -69,7 +69,7 @@ class OnlineTransducerModel {
    * 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) { }
+  virtual void SetFeatureDim(int32_t feature_dim) {}
 
   /** Run the encoder.
    *

@@ -188,7 +188,7 @@ void OnlineTransducerModifiedBeamSearchDecoder::Decode(
                                               // score of the transducer
         // export the per-token log scores
         if (new_token != 0 && new_token != unk_id_) {
-          const Hypothesis& prev_i = prev[hyp_index];
+          const Hypothesis &prev_i = prev[hyp_index];
           // subtract 'prev[i]' path scores, which were added before
           // getting topk tokens
           float y_prob = p_logprob[k] - prev_i.log_prob - prev_i.lm_log_prob;

@@ -16,10 +16,10 @@ TEST(Stack, Test1DTensors) {
   std::array<int64_t, 1> b_shape{3};
 
   Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
-    a_shape.size());
+                                                 a_shape.size());
 
   Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
-    b_shape.size());
+                                                 b_shape.size());
   float *pa = a.GetTensorMutableData<float>();
   float *pb = b.GetTensorMutableData<float>();
   for (int32_t i = 0; i != static_cast<int32_t>(a_shape[0]); ++i) {
@@ -51,11 +51,11 @@ TEST(Stack, Test2DTensorsDim0) {
   std::array<int64_t, 2> a_shape{2, 3};
   std::array<int64_t, 2> b_shape{2, 3};
 
-  Ort::Value a = Ort::Value::CreateTensor<float>(
-    allocator, a_shape.data(), a_shape.size());
+  Ort::Value a = Ort::Value::CreateTensor<float>(allocator, a_shape.data(),
+                                                 a_shape.size());
 
-  Ort::Value b = Ort::Value::CreateTensor<float>(
-    allocator, b_shape.data(), b_shape.size());
+  Ort::Value b = Ort::Value::CreateTensor<float>(allocator, b_shape.data(),
+                                                 b_shape.size());
 
   float *pa = a.GetTensorMutableData<float>();
   float *pb = b.GetTensorMutableData<float>();

@@ -12,10 +12,8 @@ static void PybindFeatureExtractorConfig(py::module *m) {
   using PyClass = FeatureExtractorConfig;
   py::class_<PyClass>(*m, "FeatureExtractorConfig")
       .def(py::init<int32_t, int32_t, float, float, float>(),
-           py::arg("sampling_rate") = 16000,
-           py::arg("feature_dim") = 80,
-           py::arg("low_freq") = 20.0f,
-           py::arg("high_freq") = -400.0f,
+           py::arg("sampling_rate") = 16000, py::arg("feature_dim") = 80,
+           py::arg("low_freq") = 20.0f, py::arg("high_freq") = -400.0f,
            py::arg("dither") = 0.0f)
       .def_readwrite("sampling_rate", &PyClass::sampling_rate)
       .def_readwrite("feature_dim", &PyClass::feature_dim)

@@ -23,8 +23,7 @@ static void PybindOfflineRecognizerConfig(py::module *m) {
            py::arg("ctc_fst_decoder_config") = OfflineCtcFstDecoderConfig(),
            py::arg("decoding_method") = "greedy_search",
            py::arg("max_active_paths") = 4, py::arg("hotwords_file") = "",
-           py::arg("hotwords_score") = 1.5,
-           py::arg("blank_penalty") = 0.0)
+           py::arg("hotwords_score") = 1.5, py::arg("blank_penalty") = 0.0)
       .def_readwrite("feat_config", &PyClass::feat_config)
       .def_readwrite("model_config", &PyClass::model_config)
       .def_readwrite("lm_config", &PyClass::lm_config)

@@ -4,7 +4,6 @@
 
 #include "sherpa-onnx/python/csrc/offline-transducer-model-config.h"
 
-
 #include <string>
 #include <vector>
 
@@ -16,7 +15,7 @@ void PybindOfflineTransducerModelConfig(py::module *m) {
   using PyClass = OfflineTransducerModelConfig;
   py::class_<PyClass>(*m, "OfflineTransducerModelConfig")
       .def(py::init<const std::string &, const std::string &,
-               const std::string &>(),
+                    const std::string &>(),
            py::arg("encoder_filename"), py::arg("decoder_filename"),
            py::arg("joiner_filename"))
       .def_readwrite("encoder_filename", &PyClass::encoder_filename)

@@ -27,9 +27,9 @@ void PybindOnlineModelConfig(py::module *m) {
       .def(py::init<const OnlineTransducerModelConfig &,
                     const OnlineParaformerModelConfig &,
                     const OnlineWenetCtcModelConfig &,
-                    const OnlineZipformer2CtcModelConfig &,
-                    const std::string &, int32_t, int32_t,
-                    bool, const std::string &, const std::string &>(),
+                    const OnlineZipformer2CtcModelConfig &, const std::string &,
+                    int32_t, int32_t, bool, const std::string &,
+                    const std::string &>(),
            py::arg("transducer") = OnlineTransducerModelConfig(),
            py::arg("paraformer") = OnlineParaformerModelConfig(),
            py::arg("wenet_ctc") = OnlineWenetCtcModelConfig(),