Merge pull request #9 from ryback123/nemo-v2

Added support for calculating confidence scores with multilingual models

nemo/collections/asr/parts/submodules/rnnt_decoding.py

@@ -502,7 +502,7 @@ def rnnt_decoder_predictions_tensor(
                 if self.preserve_frame_confidence and (
                     self.preserve_word_confidence or self.preserve_token_confidence
                 ):
-                    hypotheses = self.compute_confidence(hypotheses)
+                    hypotheses = self.compute_confidence(hypotheses, lang_ids)
                 return hypotheses, None
 
             best_hyp_text = [h.text for h in hypotheses]
@@ -561,7 +561,7 @@ def decode_hypothesis(self, hypotheses_list: List[Hypothesis], lang_ids: List[st
 
         return hypotheses_list
 
-    def compute_confidence(self, hypotheses_list: List[Hypothesis]) -> List[Hypothesis]:
+    def compute_confidence(self, hypotheses_list: List[Hypothesis], lang_ids: List[str] = None) -> List[Hypothesis]:
         """
         Computes high-level (per-token and/or per-word) confidence scores for a list of hypotheses.
         Assumes that `frame_confidence` is present in the hypotheses.
@@ -595,8 +595,11 @@ def compute_confidence(self, hypotheses_list: List[Hypothesis]) -> List[Hypothes
                             offset += 1
                 hyp.token_confidence = token_confidence
         if self.preserve_word_confidence:
-            for hyp in hypotheses_list:
-                hyp.word_confidence = self._aggregate_token_confidence(hyp)
+            for idx, hyp in enumerate(hypotheses_list):
+                if lang_ids:
+                    hyp.word_confidence = self._aggregate_token_confidence(hyp, lang_ids[idx])
+                else:
+                    hyp.word_confidence = self._aggregate_token_confidence(hyp)
         return hypotheses_list
 
     @abstractmethod
@@ -1401,7 +1404,7 @@ def __init__(self, decoding_cfg, decoder, joint, tokenizer: TokenizerSpec, blank
         if isinstance(self.decoding, rnnt_beam_decoding.BeamRNNTInfer):
             self.decoding.set_decoding_type('subword')
 
-    def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]:
+    def _aggregate_token_confidence(self, hypothesis: Hypothesis, lang_id: str = None) -> List[float]:
         """
         Implemented by subclass in order to reduce token confidence to a word-level confidence.
 
@@ -1414,7 +1417,7 @@ def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]:
             A list of word-level confidence scores.
         """
         return self._aggregate_token_confidence_subwords_sentencepiece(
-            hypothesis.words, hypothesis.token_confidence, hypothesis.y_sequence
+            hypothesis.words, hypothesis.token_confidence, hypothesis.y_sequence, lang_id
         )
 
     def decode_tokens_to_str(self, tokens: List[int], lang: str = None) -> str:
@@ -1431,9 +1434,10 @@ def decode_tokens_to_str(self, tokens: List[int], lang: str = None) -> str:
             hypothesis = self.tokenizer.ids_to_text(tokens, lang)
         else:
             hypothesis = self.tokenizer.ids_to_text(tokens)
+
         return hypothesis
 
-    def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]:
+    def decode_ids_to_tokens(self, tokens: List[int], lang: str = None) -> List[str]:
         """
         Implemented by subclass in order to decode a token id list into a token list.
         A token list is the string representation of each token id.
@@ -1444,7 +1448,10 @@ def decode_ids_to_tokens(self, tokens: List[int]) -> List[str]:
         Returns:
             A list of decoded tokens.
         """
-        token_list = self.tokenizer.ids_to_tokens(tokens)
+        if lang is not None:
+            token_list = self.tokenizer.ids_to_tokens(tokens, lang)
+        else:
+            token_list = self.tokenizer.ids_to_tokens(tokens)
         return token_list
 
     def decode_tokens_to_lang(self, tokens: List[int]) -> str:

nemo/collections/asr/parts/utils/asr_confidence_utils.py

@@ -424,7 +424,7 @@ def _aggregate_token_confidence_chars(self, words: List[str], token_confidence:
         return word_confidence
 
     def _aggregate_token_confidence_subwords_sentencepiece(
-        self, words: List[str], token_confidence: List[float], token_ids: List[int]
+        self, words: List[str], token_confidence: List[float], token_ids: List[int], lang_id: str = None
     ) -> List[float]:
         """Implementation of token confidence aggregation for subword-based models.
 
@@ -445,8 +445,8 @@ def _aggregate_token_confidence_subwords_sentencepiece(
             prev_unk = False
             prev_underline = False
             for i, token_id in enumerate(token_ids):
-                token = self.decode_ids_to_tokens([int(token_id)])[0]
-                token_text = self.decode_tokens_to_str([int(token_id)])
+                token = self.decode_ids_to_tokens([int(token_id)], lang_id)[0]
+                token_text = self.decode_tokens_to_str([int(token_id)], lang_id)
                 # treat `<unk>` as a separate word regardless of the next token
                 # to match the result of `tokenizer.ids_to_text`
                 if (token != token_text or prev_unk) and i > j:

nemo/collections/common/tokenizers/multilingual_tokenizer.py

@@ -117,10 +117,10 @@ def ids_to_text(self, ids, lang):
             ids = ids.tolist()
 
         tokens = []
+        tokenizer = self.tokenizers_dict[lang]
         for id in ids:
             # offset_id = self.offset_token_ids_by_token_id[id]
             # tokenizer = self.tokenizers_by_token_id[id]
-            tokenizer = self.tokenizers_dict[lang]
             # tokens.extend(tokenizer.ids_to_tokens([offset_id]))
             tokens.extend(tokenizer.ids_to_tokens([id]))
         text = ''.join(tokens).replace('▁', ' ')
@@ -131,14 +131,9 @@ def token_to_id(self, token, lang_id):
         tokenizer = self.tokenizers_dict[lang_id]
         return tokenizer.token_to_id(token) + self.token_id_offset[lang_id]
 
-    def ids_to_tokens(self, ids):
-        tokens = []
-
-        for id in ids:
-            offset_id = self.offset_token_ids_by_token_id[id]
-            tokenizer = self.tokenizers_by_token_id[id]
-            token = tokenizer.ids_to_tokens([offset_id])[0]
-            tokens.append(token)
+    def ids_to_tokens(self, ids, lang_id):
+        tokenizer = self.tokenizers_dict[lang_id]
+        tokens = [tokenizer.ids_to_tokens([id])[0] for id in ids]
 
         return tokens