add language to words

_collate_word_timestamps uses the return_language flag to determine whether the language of the chunk should be added to the word's information

src/transformers/models/whisper/tokenization_whisper.py

@@ -1013,7 +1013,7 @@ def new_chunk():
                         chunk["text"] = resolved_text
                         if return_timestamps == "word":
                             chunk["words"] = _collate_word_timestamps(
-                                tokenizer, resolved_tokens, resolved_token_timestamps, last_language
+                                tokenizer, resolved_tokens, resolved_token_timestamps, last_language, return_language
                             )
                         chunks.append(chunk)
 
@@ -1065,7 +1065,7 @@ def new_chunk():
         chunk["text"] = resolved_text
         if return_timestamps == "word":
             chunk["words"] = _collate_word_timestamps(
-                tokenizer, resolved_tokens, resolved_token_timestamps, last_language
+                tokenizer, resolved_tokens, resolved_token_timestamps, last_language, return_language
             )
         chunks.append(chunk)
 
@@ -1197,12 +1197,16 @@ def _find_longest_common_sequence(sequences, token_timestamp_sequences=None):
         return total_sequence, []
 
 
-def _collate_word_timestamps(tokenizer, tokens, token_timestamps, language):
+def _collate_word_timestamps(tokenizer, tokens, token_timestamps, language, return_language):
     words, _, token_indices = _combine_tokens_into_words(tokenizer, tokens, language)
+
+    optional_language_field = {"language": language} if return_language else {}
+
     timings = [
         {
             "text": word,
             "timestamp": (token_timestamps[indices[0]][0], token_timestamps[indices[-1]][1]),
+            **optional_language_field
         }
         for word, indices in zip(words, token_indices)
     ]