Add prefix parameter

faster_whisper/transcribe.py

@@ -36,6 +36,7 @@ class TranscriptionOptions(
             "condition_on_previous_text",
             "temperatures",
             "initial_prompt",
+            "prefix",
             "without_timestamps",
         ),
     )
@@ -112,6 +113,7 @@ class WhisperModel:
         no_speech_threshold: Optional[float] = 0.6,
         condition_on_previous_text: bool = True,
         initial_prompt: Optional[str] = None,
+        prefix: Optional[str] = None,
         without_timestamps: bool = False,
     ):
         """Transcribes an input file.
@@ -141,6 +143,7 @@ class WhisperModel:
             windows, but the model becomes less prone to getting stuck in a failure loop,
             such as repetition looping or timestamps going out of sync.
           initial_prompt: Optional text to provide as a prompt for the first window.
+          prefix: Optional text to provide as a prefix for the first window.
           without_timestamps: Only sample text tokens.
 
         Returns:
@@ -183,6 +186,7 @@ class WhisperModel:
                 temperature if isinstance(temperature, (list, tuple)) else [temperature]
             ),
             initial_prompt=initial_prompt,
+            prefix=prefix,
             without_timestamps=without_timestamps,
         )
 
@@ -219,10 +223,8 @@ class WhisperModel:
 
         if options.initial_prompt is not None:
             initial_prompt = " " + options.initial_prompt.strip()
-            initial_prompt_tokens = self.tokenizer.encode(
-                initial_prompt, add_special_tokens=False
-            )
-            all_tokens.extend(initial_prompt_tokens.ids)
+            initial_prompt_tokens = self.encode_text(initial_prompt)
+            all_tokens.extend(initial_prompt_tokens)
 
         while offset < num_frames:
             time_offset = offset * self.feature_extractor.time_per_frame
@@ -235,6 +237,7 @@ class WhisperModel:
                 previous_tokens,
                 task=options.task,
                 without_timestamps=options.without_timestamps,
+                prefix=options.prefix,
             )
 
             result, avg_log_prob, temperature = self.generate_with_fallback(
@@ -314,6 +317,9 @@ class WhisperModel:
             if not options.condition_on_previous_text or temperature > 0.5:
                 prompt_reset_since = len(all_tokens)
 
+    def encode_text(self, text):
+        return self.tokenizer.encode(text, add_special_tokens=False).ids
+
     def decode_text_tokens(self, tokens):
         text_tokens = [token for token in tokens if token < self.eot_id]
         return self.tokenizer.decode(text_tokens)
@@ -384,6 +390,7 @@ class WhisperModel:
         previous_tokens,
         task="transcribe",
         without_timestamps=False,
+        prefix=None,
     ):
         prompt = []
 
@@ -404,6 +411,12 @@ class WhisperModel:
         if without_timestamps:
             prompt.append(self.tokenizer.token_to_id("<|notimestamps|>"))
 
+        if prefix:
+            prefix_tokens = self.encode_text(" " + prefix.strip())
+            if len(prefix_tokens) >= self.max_length // 2:
+                prefix_tokens = prefix_tokens[: self.max_length // 2 - 1]
+            prompt.extend(prefix_tokens)
+
         return prompt
 
     def get_segment(self, features, offset=0):