[Whisper Tokenizer] Make decoding faster after adding timestamps (#26299)

make decoding faster

src/transformers/models/whisper/tokenization_whisper.py

@@ -314,6 +314,7 @@ class WhisperTokenizer(PreTrainedTokenizer):
 
         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+        self.timestamp_pat = re.compile(r"<\|(\d+\.\d+)\|>")
 
         self.language = language
         super().__init__(
@@ -560,10 +561,12 @@ class WhisperTokenizer(PreTrainedTokenizer):
                 start_timestamp_position = sliced_tokens[0].item() - timestamp_begin
                 end_timestamp_position = sliced_tokens[-1].item() - timestamp_begin
                 # strip timestamp tokens from the text output
-                sliced_tokens = self._preprocess_token_ids(sliced_tokens, decode_with_timestamps=False)
+                sliced_tokens = self._preprocess_token_ids(sliced_tokens)
+                text = self._decode(sliced_tokens)
+                text = self._filter_timestamp_ids(text)
                 offsets.append(
                     {
-                        "text": self._decode(sliced_tokens),
+                        "text": text,
                         "timestamp": (
                             start_timestamp_position * time_precision,
                             end_timestamp_position * time_precision,
@@ -585,9 +588,7 @@ class WhisperTokenizer(PreTrainedTokenizer):
         """
         return self.convert_tokens_to_ids([("<|%.2f|>" % (i * time_precision)) for i in range(1500 + 1)])
 
-    def _preprocess_token_ids(
-        self, token_ids, skip_special_tokens: bool = False, decode_with_timestamps: bool = False, time_precision=0.02
-    ):
+    def _preprocess_token_ids(self, token_ids, skip_special_tokens: bool = False):
         """
         Pre-process the token ids for decoding by removing the prompt tokens ids and timestamp token ids.
 
@@ -597,24 +598,17 @@ class WhisperTokenizer(PreTrainedTokenizer):
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens from the token ids. If `True`, the prompt token ids will be
                 removed.
-            decode_with_timestamps (`bool`, *optional*, defaults to `False`):
-                Whether or not to decode with timestamps included in the raw text. If `False`, timestamps will be
-                filtered out from the token ids.
-            time_precision (`float`, `optional`, defaults to 0.02):
-                The time ratio to convert from token to time.
         """
         if skip_special_tokens:
             prompt_token_id = self.convert_tokens_to_ids("<|startofprev|>")
             decoder_start_token_id = self.convert_tokens_to_ids("<|startoftranscript|>")
             token_ids = self._strip_prompt(token_ids, prompt_token_id, decoder_start_token_id)
 
-        if not decode_with_timestamps:
-            # filter timestamp tokens if they are contained in the vocab
-            timestamp_ids = self.timestamp_ids(time_precision=time_precision)
-            token_ids = [token for token in token_ids if token not in timestamp_ids]
-
         return token_ids
 
+    def _filter_timestamp_ids(self, token_ids):
+        return re.sub(self.timestamp_pat, "", token_ids)
+
     def decode(
         self,
         token_ids,
@@ -644,6 +638,8 @@ class WhisperTokenizer(PreTrainedTokenizer):
             output_offsets (`bool`, *optional*, defaults to `False`):
                 Whether or not to output the offsets of the tokens. This should only be set if the model predicted
                 timestamps.
+            time_precision (`float`, `optional`, defaults to 0.02):
+                The time ratio to convert from token to time.
             decode_with_timestamps (`bool`, *optional*, defaults to `False`):
                 Whether or not to decode with timestamps included in the raw text.
         Returns:
@@ -652,8 +648,6 @@ class WhisperTokenizer(PreTrainedTokenizer):
         filtered_ids = self._preprocess_token_ids(
             token_ids,
             skip_special_tokens=skip_special_tokens,
-            decode_with_timestamps=decode_with_timestamps,
-            time_precision=time_precision,
         )
 
         text = super().decode(
@@ -668,6 +662,9 @@ class WhisperTokenizer(PreTrainedTokenizer):
             text = self._decode_with_timestamps(
                 filtered_ids, time_precision=time_precision, skip_special_tokens=skip_special_tokens
             )
+        else:
+            text = self._filter_timestamp_ids(text)
+
         # retrieve offsets
         if output_offsets:
             offsets = self._compute_offsets(token_ids, time_precision=time_precision)

src/transformers/models/whisper/tokenization_whisper_fast.py

@@ -15,6 +15,7 @@
 """Tokenization classes for Whisper."""
 import json
 import os
+import re
 from functools import lru_cache
 from typing import List, Optional, Tuple
 
@@ -190,6 +191,7 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
             self.english_spelling_normalizer = None
 
         self.add_prefix_space = add_prefix_space
+        self.timestamp_pat = re.compile(r"<\|(\d+\.\d+)\|>")
 
         self.language = language
         self.task = task
@@ -269,10 +271,12 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
                 start_timestamp_position = sliced_tokens[0].item() - timestamp_begin
                 end_timestamp_position = sliced_tokens[-1].item() - timestamp_begin
                 # strip timestamp tokens from the text output
-                sliced_tokens = self._preprocess_token_ids(sliced_tokens, decode_with_timestamps=False)
+                sliced_tokens = self._preprocess_token_ids(sliced_tokens)
+                text = self._decode(sliced_tokens)
+                text = self._filter_timestamp_ids(text)
                 offsets.append(
                     {
-                        "text": self._decode(sliced_tokens),
+                        "text": text,
                         "timestamp": (
                             start_timestamp_position * time_precision,
                             end_timestamp_position * time_precision,
@@ -296,9 +300,7 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
         return self.convert_tokens_to_ids([("<|%.2f|>" % (i * time_precision)) for i in range(1500 + 1)])
 
     # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._preprocess_token_ids
-    def _preprocess_token_ids(
-        self, token_ids, skip_special_tokens: bool = False, decode_with_timestamps: bool = False, time_precision=0.02
-    ):
+    def _preprocess_token_ids(self, token_ids, skip_special_tokens: bool = False):
         """
         Pre-process the token ids for decoding by removing the prompt tokens ids and timestamp token ids.
 
@@ -308,24 +310,18 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
             skip_special_tokens (`bool`, *optional*, defaults to `False`):
                 Whether or not to remove special tokens from the token ids. If `True`, the prompt token ids will be
                 removed.
-            decode_with_timestamps (`bool`, *optional*, defaults to `False`):
-                Whether or not to decode with timestamps included in the raw text. If `False`, timestamps will be
-                filtered out from the token ids.
-            time_precision (`float`, `optional`, defaults to 0.02):
-                The time ratio to convert from token to time.
         """
         if skip_special_tokens:
             prompt_token_id = self.convert_tokens_to_ids("<|startofprev|>")
             decoder_start_token_id = self.convert_tokens_to_ids("<|startoftranscript|>")
             token_ids = self._strip_prompt(token_ids, prompt_token_id, decoder_start_token_id)
 
-        if not decode_with_timestamps:
-            # filter timestamp tokens if they are contained in the vocab
-            timestamp_ids = self.timestamp_ids(time_precision=time_precision)
-            token_ids = [token for token in token_ids if token not in timestamp_ids]
-
         return token_ids
 
+    # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._filter_timestamp_ids
+    def _filter_timestamp_ids(self, token_ids):
+        return re.sub(self.timestamp_pat, "", token_ids)
+
     # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer.decode
     def decode(
         self,
@@ -356,6 +352,8 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
             output_offsets (`bool`, *optional*, defaults to `False`):
                 Whether or not to output the offsets of the tokens. This should only be set if the model predicted
                 timestamps.
+            time_precision (`float`, `optional`, defaults to 0.02):
+                The time ratio to convert from token to time.
             decode_with_timestamps (`bool`, *optional*, defaults to `False`):
                 Whether or not to decode with timestamps included in the raw text.
         Returns:
@@ -364,8 +362,6 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
         filtered_ids = self._preprocess_token_ids(
             token_ids,
             skip_special_tokens=skip_special_tokens,
-            decode_with_timestamps=decode_with_timestamps,
-            time_precision=time_precision,
         )
 
         text = super().decode(
@@ -380,6 +376,9 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
             text = self._decode_with_timestamps(
                 filtered_ids, time_precision=time_precision, skip_special_tokens=skip_special_tokens
             )
+        else:
+            text = self._filter_timestamp_ids(text)
+
         # retrieve offsets
         if output_offsets:
             offsets = self._compute_offsets(token_ids, time_precision=time_precision)