Feature/add hotwords

faster_whisper/transcribe.py

@@ -69,6 +69,7 @@ class TranscriptionOptions(NamedTuple):
     max_new_tokens: Optional[int]
     clip_timestamps: Union[str, List[float]]
     hallucination_silence_threshold: Optional[float]
+    hotwords: Optional[str]
 
 
 class TranscriptionInfo(NamedTuple):
@@ -220,6 +221,7 @@ def transcribe(
         chunk_length: Optional[int] = None,
         clip_timestamps: Union[str, List[float]] = "0",
         hallucination_silence_threshold: Optional[float] = None,
+        hotwords: Optional[str] = None,
         language_detection_threshold: Optional[float] = None,
         language_detection_segments: int = 1,
     ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
@@ -283,10 +285,11 @@ def transcribe(
           hallucination_silence_threshold: Optional[float]
             When word_timestamps is True, skip silent periods longer than this threshold
              (in seconds) when a possible hallucination is detected
+          hotwords:Optional text
+            add hotwords if set prefix it invalid
           language_detection_threshold: If the maximum probability of the language tokens is higher
            than this value, the language is detected.
           language_detection_segments: Number of segments to consider for the language detection.
-
         Returns:
           A tuple with:
 
@@ -440,6 +443,7 @@ def transcribe(
             max_new_tokens=max_new_tokens,
             clip_timestamps=clip_timestamps,
             hallucination_silence_threshold=hallucination_silence_threshold,
+            hotwords=hotwords,
         )
 
         segments = self.generate_segments(features, tokenizer, options, encoder_output)
@@ -546,6 +550,7 @@ def generate_segments(
                 previous_tokens,
                 without_timestamps=options.without_timestamps,
                 prefix=options.prefix if seek == 0 else None,
+                hotwords=options.hotwords,
             )
 
             if seek > 0 or encoder_output is None:
@@ -938,12 +943,19 @@ def get_prompt(
         previous_tokens: List[int],
         without_timestamps: bool = False,
         prefix: Optional[str] = None,
+        hotwords: Optional[str] = None,
     ) -> List[int]:
         prompt = []
 
-        if previous_tokens:
+        if previous_tokens or (hotwords and not prefix):
             prompt.append(tokenizer.sot_prev)
-            prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
+            if hotwords and not prefix:
+                hotwords_tokens = tokenizer.encode(" " + hotwords.strip())
+                if len(hotwords_tokens) >= self.max_length // 2:
+                    hotwords_tokens = hotwords_tokens[: self.max_length // 2 - 1]
+                prompt.extend(hotwords_tokens)
+            if previous_tokens:
+                prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
 
         prompt.extend(tokenizer.sot_sequence)