Adding temperature scaling on Joiner logits: (#789)

* Adding temperature scaling on Joiner logits: - T hard-coded to 2.0 - so far best result NCE 0.122 (still not so high) - the BPE scores were rescaled with 0.2 (but then also incorrect words get high confidence, visually reasonable histograms are for 0.5 scale) - BPE->WORD score merging done by min(.) function (tried also prob-product, and also arithmetic, geometric, harmonic mean) - without temperature scaling (i.e. scale 1.0), the best NCE was 0.032 (here product merging was best) Results seem consistent with: https://arxiv.org/abs/2110.15222 Everything tuned on a very-small set of 100 sentences with 813 words and 10.2% WER, a Czech model. I also experimented with blank posteriors mixed into the BPE confidences, but no NCE improvement found, so not pushing that. Temperature scling added also to the Greedy search confidences. * making `temperature_scale` configurable from outside

Adding temperature scaling on Joiner logits: (#789)
* Adding temperature scaling on Joiner logits: - T hard-coded to 2.0 - so far best result NCE 0.122 (still not so high) - the BPE scores were rescaled with 0.2 (but then also incorrect words get high confidence, visually reasonable histograms are for 0.5 scale) - BPE->WORD score merging done by min(.) function (tried also prob-product, and also arithmetic, geometric, harmonic mean) - without temperature scaling (i.e. scale 1.0), the best NCE was 0.032 (here product merging was best) Results seem consistent with: https://arxiv.org/abs/2110.15222 Everything tuned on a very-small set of 100 sentences with 813 words and 10.2% WER, a Czech model. I also experimented with blank posteriors mixed into the BPE confidences, but no NCE improvement found, so not pushing that. Temperature scling added also to the Greedy search confidences. * making `temperature_scale` configurable from outside
Karel Vesely · GitHub
Commit 2e45d327a50ef0ae16c2619a028ced0e872c5361 2e45d327 1 parent 15772d21
sherpa-onnx/csrc/online-recognizer-transducer-impl.h
sherpa-onnx/csrc/online-recognizer.cc
sherpa-onnx/csrc/online-recognizer.h
sherpa-onnx/csrc/online-transducer-greedy-search-decoder.cc
sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h
sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.cc
sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.h
sherpa-onnx/python/csrc/online-recognizer.cc
sherpa-onnx/python/sherpa_onnx/online_recognizer.py
--- a/sherpa-onnx/csrc/online-recognizer-transducer-impl.h
查看文件 @2e45d32
+++ b/sherpa-onnx/csrc/online-recognizer-transducer-impl.h
查看文件 @2e45d32
@@ -103,11 +103,21 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
       }
       decoder_ = std::make_unique<OnlineTransducerModifiedBeamSearchDecoder>(
-          model_.get(), lm_.get(), config_.max_active_paths,
-          config_.lm_config.scale, unk_id_, config_.blank_penalty);
+          model_.get(),
+          lm_.get(),
+          config_.max_active_paths,
+          config_.lm_config.scale,
+          unk_id_,
+          config_.blank_penalty,
+          config_.temperature_scale);
+
     } else if (config.decoding_method == "greedy_search") {
       decoder_ = std::make_unique<OnlineTransducerGreedySearchDecoder>(
-          model_.get(), unk_id_, config_.blank_penalty);
+          model_.get(),
+          unk_id_,
+          config_.blank_penalty,
+          config_.temperature_scale);
+
     } else {
       SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
                        config.decoding_method.c_str());
@@ -141,11 +151,21 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
       }
       decoder_ = std::make_unique<OnlineTransducerModifiedBeamSearchDecoder>(
-          model_.get(), lm_.get(), config_.max_active_paths,
-          config_.lm_config.scale, unk_id_, config_.blank_penalty);
+          model_.get(),
+          lm_.get(),
+          config_.max_active_paths,
+          config_.lm_config.scale,
+          unk_id_,
+          config_.blank_penalty,
+          config_.temperature_scale);
+
     } else if (config.decoding_method == "greedy_search") {
       decoder_ = std::make_unique<OnlineTransducerGreedySearchDecoder>(
-          model_.get(), unk_id_, config_.blank_penalty);
+          model_.get(),
+          unk_id_,
+          config_.blank_penalty,
+          config_.temperature_scale);
+
     } else {
       SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
                        config.decoding_method.c_str());
--- a/sherpa-onnx/csrc/online-recognizer.cc
查看文件 @2e45d32
+++ b/sherpa-onnx/csrc/online-recognizer.cc
查看文件 @2e45d32
@@ -96,6 +96,8 @@ void OnlineRecognizerConfig::Register(ParseOptions *po) {
   po->Register("decoding-method", &decoding_method,
                "decoding method,"
                "now support greedy_search and modified_beam_search.");
+  po->Register("temperature-scale", &temperature_scale,
+               "Temperature scale for confidence computation in decoding.");
 }
 bool OnlineRecognizerConfig::Validate() const {
@@ -142,7 +144,8 @@ std::string OnlineRecognizerConfig::ToString() const {
   os << "hotwords_score=" << hotwords_score << ", ";
   os << "hotwords_file=\"" << hotwords_file << "\", ";
   os << "decoding_method=\"" << decoding_method << "\", ";
-  os << "blank_penalty=" << blank_penalty << ")";
+  os << "blank_penalty=" << blank_penalty << ", ";
+  os << "temperature_scale=" << temperature_scale << ")";
   return os.str();
 }
--- a/sherpa-onnx/csrc/online-recognizer.h
查看文件 @2e45d32
+++ b/sherpa-onnx/csrc/online-recognizer.h
查看文件 @2e45d32
@@ -96,16 +96,23 @@ struct OnlineRecognizerConfig {
   float blank_penalty = 0.0;
+  float temperature_scale = 2.0;
+
   OnlineRecognizerConfig() = default;
   OnlineRecognizerConfig(
       const FeatureExtractorConfig &feat_config,
-      const OnlineModelConfig &model_config, const OnlineLMConfig &lm_config,
+      const OnlineModelConfig &model_config,
+      const OnlineLMConfig &lm_config,
       const EndpointConfig &endpoint_config,
       const OnlineCtcFstDecoderConfig &ctc_fst_decoder_config,
-      bool enable_endpoint, const std::string &decoding_method,
-      int32_t max_active_paths, const std::string &hotwords_file,
-      float hotwords_score, float blank_penalty)
+      bool enable_endpoint,
+      const std::string &decoding_method,
+      int32_t max_active_paths,
+      const std::string &hotwords_file,
+      float hotwords_score,
+      float blank_penalty,
+      float temperature_scale)
       : feat_config(feat_config),
         model_config(model_config),
         lm_config(lm_config),
@@ -114,9 +121,10 @@ struct OnlineRecognizerConfig {
         enable_endpoint(enable_endpoint),
         decoding_method(decoding_method),
         max_active_paths(max_active_paths),
-        hotwords_score(hotwords_score),
         hotwords_file(hotwords_file),
-        blank_penalty(blank_penalty) {}
+        hotwords_score(hotwords_score),
+        blank_penalty(blank_penalty),
+        temperature_scale(temperature_scale) {}
   void Register(ParseOptions *po);
   bool Validate() const;
--- a/sherpa-onnx/csrc/online-transducer-greedy-search-decoder.cc
查看文件 @2e45d32
+++ b/sherpa-onnx/csrc/online-transducer-greedy-search-decoder.cc
查看文件 @2e45d32
@@ -144,6 +144,10 @@ void OnlineTransducerGreedySearchDecoder::Decode(
       // export the per-token log scores
       if (y != 0 && y != unk_id_) {
+        // apply temperature-scaling
+        for (int32_t n = 0; n < vocab_size; ++n) {
+          p_logit[n] /= temperature_scale_;
+        }
         LogSoftmax(p_logit, vocab_size);   // renormalize probabilities,
                                            // save time by doing it only for
                                            // emitted symbols
--- a/sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h
查看文件 @2e45d32
+++ b/sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h
查看文件 @2e45d32
@@ -15,8 +15,13 @@ namespace sherpa_onnx {
 class OnlineTransducerGreedySearchDecoder : public OnlineTransducerDecoder {
  public:
   OnlineTransducerGreedySearchDecoder(OnlineTransducerModel *model,
-                                      int32_t unk_id, float blank_penalty)
-      : model_(model), unk_id_(unk_id), blank_penalty_(blank_penalty) {}
+                                      int32_t unk_id,
+                                      float blank_penalty,
+                                      float temperature_scale)
+      : model_(model),
+      unk_id_(unk_id),
+      blank_penalty_(blank_penalty),
+      temperature_scale_(temperature_scale) {}
   OnlineTransducerDecoderResult GetEmptyResult() const override;
@@ -29,6 +34,7 @@ class OnlineTransducerGreedySearchDecoder : public OnlineTransducerDecoder {
   OnlineTransducerModel *model_;  // Not owned
   int32_t unk_id_;
   float blank_penalty_;
+  float temperature_scale_;
 };
 }  // namespace sherpa_onnx
--- a/sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.cc
查看文件 @2e45d32
+++ b/sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.cc
查看文件 @2e45d32
@@ -129,6 +129,22 @@ void OnlineTransducerModifiedBeamSearchDecoder::Decode(
         model_->RunJoiner(std::move(cur_encoder_out), View(&decoder_out));
     float *p_logit = logit.GetTensorMutableData<float>();
+
+    // copy raw logits, apply temperature-scaling  (for confidences)
+    // Note: temperature scaling is used only for the confidences,
+    //       the decoding algorithm uses the original logits
+    int32_t p_logit_items = vocab_size * num_hyps;
+    std::vector<float> logit_with_temperature(p_logit_items);
+    {
+      std::copy(p_logit,
+                p_logit + p_logit_items,
+                logit_with_temperature.begin());
+      for (float& elem : logit_with_temperature) {
+        elem /= temperature_scale_;
+      }
+      LogSoftmax(logit_with_temperature.data(), vocab_size, num_hyps);
+    }
+
     if (blank_penalty_ > 0.0) {
       // assuming blank id is 0
       SubtractBlank(p_logit, vocab_size, num_hyps, 0, blank_penalty_);
@@ -188,10 +204,7 @@ 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];
-          // 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;
+          float y_prob = logit_with_temperature[start * vocab_size + k];
           new_hyp.ys_probs.push_back(y_prob);
           if (lm_) {  // export only when LM is used
@@ -213,7 +226,7 @@ void OnlineTransducerModifiedBeamSearchDecoder::Decode(
       cur.push_back(std::move(hyps));
       p_logprob += (end - start) * vocab_size;
     }  // for (int32_t b = 0; b != batch_size; ++b)
-  }
+  }  // for (int32_t t = 0; t != num_frames; ++t)
   for (int32_t b = 0; b != batch_size; ++b) {
     auto &hyps = cur[b];
--- a/sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.h
查看文件 @2e45d32
+++ b/sherpa-onnx/csrc/online-transducer-modified-beam-search-decoder.h
查看文件 @2e45d32
@@ -22,13 +22,15 @@ class OnlineTransducerModifiedBeamSearchDecoder
                                             OnlineLM *lm,
                                             int32_t max_active_paths,
                                             float lm_scale, int32_t unk_id,
-                                            float blank_penalty)
+                                            float blank_penalty,
+                                            float temperature_scale)
       : model_(model),
         lm_(lm),
         max_active_paths_(max_active_paths),
         lm_scale_(lm_scale),
         unk_id_(unk_id),
-        blank_penalty_(blank_penalty) {}
+        blank_penalty_(blank_penalty),
+        temperature_scale_(temperature_scale) {}
   OnlineTransducerDecoderResult GetEmptyResult() const override;
@@ -50,6 +52,7 @@ class OnlineTransducerModifiedBeamSearchDecoder
   float lm_scale_;  // used only when lm_ is not nullptr
   int32_t unk_id_;
   float blank_penalty_;
+  float temperature_scale_;
 };
 }  // namespace sherpa_onnx
--- a/sherpa-onnx/python/csrc/online-recognizer.cc
查看文件 @2e45d32
+++ b/sherpa-onnx/python/csrc/online-recognizer.cc
查看文件 @2e45d32
@@ -50,17 +50,30 @@ static void PybindOnlineRecognizerConfig(py::module *m) {
   using PyClass = OnlineRecognizerConfig;
   py::class_<PyClass>(*m, "OnlineRecognizerConfig")
       .def(
-          py::init<const FeatureExtractorConfig &, const OnlineModelConfig &,
-                   const OnlineLMConfig &, const EndpointConfig &,
-                   const OnlineCtcFstDecoderConfig &, bool, const std::string &,
-                   int32_t, const std::string &, float, float>(),
-          py::arg("feat_config"), py::arg("model_config"),
+          py::init<const FeatureExtractorConfig &,
+                   const OnlineModelConfig &,
+                   const OnlineLMConfig &,
+                   const EndpointConfig &,
+                   const OnlineCtcFstDecoderConfig &,
+                   bool,
+                   const std::string &,
+                   int32_t,
+                   const std::string &,
+                   float,
+                   float,
+                   float>(),
+          py::arg("feat_config"),
+          py::arg("model_config"),
           py::arg("lm_config") = OnlineLMConfig(),
           py::arg("endpoint_config") = EndpointConfig(),
           py::arg("ctc_fst_decoder_config") = OnlineCtcFstDecoderConfig(),
-          py::arg("enable_endpoint"), py::arg("decoding_method"),
-          py::arg("max_active_paths") = 4, py::arg("hotwords_file") = "",
-          py::arg("hotwords_score") = 0, py::arg("blank_penalty") = 0.0)
+          py::arg("enable_endpoint"),
+          py::arg("decoding_method"),
+          py::arg("max_active_paths") = 4,
+          py::arg("hotwords_file") = "",
+          py::arg("hotwords_score") = 0,
+          py::arg("blank_penalty") = 0.0,
+          py::arg("temperature_scale") = 2.0)
       .def_readwrite("feat_config", &PyClass::feat_config)
       .def_readwrite("model_config", &PyClass::model_config)
       .def_readwrite("lm_config", &PyClass::lm_config)
@@ -72,6 +85,7 @@ static void PybindOnlineRecognizerConfig(py::module *m) {
       .def_readwrite("hotwords_file", &PyClass::hotwords_file)
       .def_readwrite("hotwords_score", &PyClass::hotwords_score)
       .def_readwrite("blank_penalty", &PyClass::blank_penalty)
+      .def_readwrite("temperature_scale", &PyClass::temperature_scale)
       .def("__str__", &PyClass::ToString);
 }
--- a/sherpa-onnx/python/sherpa_onnx/online_recognizer.py
查看文件 @2e45d32
+++ b/sherpa-onnx/python/sherpa_onnx/online_recognizer.py
查看文件 @2e45d32
@@ -58,6 +58,7 @@ class OnlineRecognizer(object):
         model_type: str = "",
         lm: str = "",
         lm_scale: float = 0.1,
+        temperature_scale: float = 2.0,
     ):
         """
         Please refer to
@@ -123,6 +124,10 @@ class OnlineRecognizer(object):
           hotwords_score:
             The hotword score of each token for biasing word/phrase. Used only if
             hotwords_file is given with modified_beam_search as decoding method.
+          temperature_scale:
+            Temperature scaling for output symbol confidence estiamation.
+            It affects only confidence values, the decoding uses the original
+            logits without temperature.
           provider:
             onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
           model_type:
@@ -193,6 +198,7 @@ class OnlineRecognizer(object):
             hotwords_score=hotwords_score,
             hotwords_file=hotwords_file,
             blank_penalty=blank_penalty,
+            temperature_scale=temperature_scale,
         )
         self.recognizer = _Recognizer(recognizer_config)