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/csrc/online-transducer-greedy-search-decoder.cc

@@ -71,9 +71,11 @@ 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();
 
@@ -97,6 +99,7 @@ void OnlineTransducerGreedySearchDecoder::Decode(
       break;
     }
   }
+
   if (is_batch_decoder_out_cached) {
     auto &r = result->front();
     std::vector<int64_t> decoder_out_shape =
@@ -124,6 +127,7 @@ void OnlineTransducerGreedySearchDecoder::Decode(
       if (blank_penalty_ > 0.0) {
         p_logit[0] -= blank_penalty_;  // assuming blank id is 0
       }
+
       auto y = static_cast<int32_t>(std::distance(
           static_cast<const float *>(p_logit),
           std::max_element(static_cast<const float *>(p_logit),
@@ -138,6 +142,17 @@ void OnlineTransducerGreedySearchDecoder::Decode(
       } else {
         ++r.num_trailing_blanks;
       }
+
+      // 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
+        const float *p_logprob = p_logit;  // rename p_logit as p_logprob,
+                                           // now it contains normalized
+                                           // probability
+        r.ys_probs.push_back(p_logprob[y]);
+      }
     }
     if (emitted) {
       Ort::Value decoder_input = model_->BuildDecoderInput(*result);