Implement context biasing with a Aho Corasick automata (#145)

* Implement context graph

* Modify the interface to support context biasing

* Support context biasing in modified beam search; add python wrapper

* Support context biasing in python api example

* Minor fixes

* Fix context graph

* Minor fixes

* Fix tests

* Fix style

* Fix style

* Fix comments

* Minor fixes

* Add missing header

* Replace std::shared_ptr with std::unique_ptr for effciency

* Build graph in constructor

* Fix comments

* Minor fixes

* Fix docs

sherpa-onnx/csrc/hypothesis.h

@@ -14,6 +14,7 @@
 #include <vector>
 
 #include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/context-graph.h"
 #include "sherpa-onnx/csrc/math.h"
 #include "sherpa-onnx/csrc/onnx-utils.h"
 
@@ -39,11 +40,18 @@ struct Hypothesis {
   // the nn lm states
   std::vector<CopyableOrtValue> nn_lm_states;
 
+  const ContextState *context_state;
+
+  // TODO(fangjun): Make it configurable
+  // the minimum of tokens in a chunk for streaming RNN LM
+  int32_t lm_rescore_min_chunk = 2;  // a const
+
   int32_t num_trailing_blanks = 0;
 
   Hypothesis() = default;
-  Hypothesis(const std::vector<int64_t> &ys, double log_prob)
-      : ys(ys), log_prob(log_prob) {}
+  Hypothesis(const std::vector<int64_t> &ys, double log_prob,
+             const ContextState *context_state = nullptr)
+      : ys(ys), log_prob(log_prob), context_state(context_state) {}
 
   double TotalLogProb() const { return log_prob + lm_log_prob; }