Track token scores (#571)

* add export of per-token scores (ys, lm, context)

- for best path of the modified-beam-search decoding of transducer

* refactoring JSON export of OnlineRecognitionResult, extending pybind11 API of OnlineRecognitionResult

* export per-token scores also for greedy-search (online-transducer)

- export un-scaled lm_probs (modified-beam search, online-transducer)
- polishing

* fill lm_probs/context_scores only if LM/ContextGraph is present (make Result smaller)

sherpa-onnx/python/sherpa_onnx/online_recognizer.py

@@ -503,6 +503,9 @@ class OnlineRecognizer(object):
     def get_result(self, s: OnlineStream) -> str:
         return self.recognizer.get_result(s).text.strip()
 
+    def get_result_as_json_string(self, s: OnlineStream) -> str:
+        return self.recognizer.get_result(s).as_json_string()
+
     def tokens(self, s: OnlineStream) -> List[str]:
         return self.recognizer.get_result(s).tokens
 
@@ -512,6 +515,15 @@ class OnlineRecognizer(object):
     def start_time(self, s: OnlineStream) -> float:
         return self.recognizer.get_result(s).start_time
 
+    def ys_probs(self, s: OnlineStream) -> List[float]:
+        return self.recognizer.get_result(s).ys_probs
+
+    def lm_probs(self, s: OnlineStream) -> List[float]:
+        return self.recognizer.get_result(s).lm_probs
+
+    def context_scores(self, s: OnlineStream) -> List[float]:
+        return self.recognizer.get_result(s).context_scores
+
     def is_endpoint(self, s: OnlineStream) -> bool:
         return self.recognizer.is_endpoint(s)