Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.
2026-07-09 22:38:58 +08:00
commit 6116b1f3c6
3683 changed files with 990984 additions and 0 deletions
@@ -0,0 +1,77 @@
+// lat/confidence.h
+
+// Copyright 2013  Johns Hopkins University (Author: Daniel Povey)
+
+// See ../../COPYING for clarification regarding multiple authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//  http://www.apache.org/licenses/LICENSE-2.0
+//
+// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+// MERCHANTABLITY OR NON-INFRINGEMENT.
+// See the Apache 2 License for the specific language governing permissions and
+// limitations under the License.
+
+
+#ifndef KALDI_LAT_CONFIDENCE_H_
+#define KALDI_LAT_CONFIDENCE_H_
+
+#include <vector>
+#include <map>
+
+#include "base/kaldi-common.h"
+#include "util/common-utils.h"
+#include "fstext/fstext-lib.h"
+#include "lat/kaldi-lattice.h"
+
+namespace kaldi {
+
+/// Caution: this function is not the only way to get confidences in Kaldi.
+/// This only gives you sentence-level (utterance-level) confidence.  You can
+/// get word-by-word confidence within a sentence, along with Minimum Bayes Risk
+/// decoding, by looking at sausages.h.
+/// Caution: confidences estimated using this type of method are not very
+/// accurate.
+/// This function will return the difference between the best path in clat and
+/// the second-best path in clat (a positive number), or zero if clat was
+/// equivalent to the empty FST (no successful paths), or infinity if there
+/// was only one path in "clat".  It will output to "num_paths" (if non-NULL)
+/// a number n = 0, 1 or 2 saying how many n-best paths (up to two) were found.
+/// If n >= 1 it outputs to "best_sentence" (if non-NULL) the best word-sequence;
+/// if n == 2 it outputs to "second_best_sentence" (if non-NULL) the second best
+/// word-sequence (this may be useful for testing whether the two best word
+/// sequences are somehow equivalent for the task at hand).  If you need more
+/// information than this or want to look deeper inside the n-best list, then
+/// look at the implementation of this function.
+/// This function requires that distinct paths in "lat" have distinct word
+/// sequences; this will automatically be the case if you generated "clat"
+/// in any normal way, such as from a decoder, because a deterministic FST
+/// has this property.
+/// This function assumes that any acoustic scaling you want to apply,
+/// has already been applied.
+BaseFloat SentenceLevelConfidence(const CompactLattice &clat,
+                                  int32 *num_paths,
+                                  std::vector<int32> *best_sentence,
+                                  std::vector<int32> *second_best_sentence);
+
+
+/// This version of SentenceLevelConfidence takes as input a state-level lattice.
+/// It needs to determinize it first, but it does so in a "smart" way that only generates
+/// about as many output paths as it needs.
+BaseFloat SentenceLevelConfidence(const Lattice &lat,
+                                  int32 *num_paths,
+                                  std::vector<int32> *best_sentence,
+                                  std::vector<int32> *second_best_sentence);
+
+
+
+
+
+}  // namespace kaldi
+
+#endif  // KALDI_LAT_CONFIDENCE_H_