Filter out non_speech_tokens in suppressed tokens (#898)

* Filter out non_speech_tokens in suppressed tokens

faster_whisper/transcribe.py

@@ -277,7 +277,7 @@ class WhisperModel:
           prefix: Optional text to provide as a prefix for the first window.
           suppress_blank: Suppress blank outputs at the beginning of the sampling.
           suppress_tokens: List of token IDs to suppress. -1 will suppress a default set
-            of symbols as defined in the model config.json file.
+            of symbols as defined in `tokenizer.non_speech_tokens()`
           without_timestamps: Only sample text tokens.
           max_initial_timestamp: The initial timestamp cannot be later than this.
           word_timestamps: Extract word-level timestamps using the cross-attention pattern
@@ -462,7 +462,11 @@ class WhisperModel:
             initial_prompt=initial_prompt,
             prefix=prefix,
             suppress_blank=suppress_blank,
-            suppress_tokens=get_suppressed_tokens(tokenizer, suppress_tokens),
+            suppress_tokens=(
+                get_suppressed_tokens(tokenizer, suppress_tokens)
+                if suppress_tokens
+                else suppress_tokens
+            ),
             without_timestamps=without_timestamps,
             max_initial_timestamp=max_initial_timestamp,
             word_timestamps=word_timestamps,
@@ -488,7 +492,6 @@ class WhisperModel:
             vad_options=vad_parameters,
             all_language_probs=all_language_probs,
         )
-
         return segments, info
 
     def generate_segments(
@@ -1227,15 +1230,16 @@ def get_compression_ratio(text: str) -> float:
 
 def get_suppressed_tokens(
     tokenizer: Tokenizer,
-    suppress_tokens: Optional[List[int]],
+    suppress_tokens: Tuple[int],
 ) -> Optional[List[int]]:
-    if not suppress_tokens or -1 in suppress_tokens:
-        return suppress_tokens
+    if -1 in suppress_tokens:
+        suppress_tokens = [t for t in suppress_tokens if t >= 0]
+        suppress_tokens.extend(tokenizer.non_speech_tokens)
+    elif suppress_tokens is None or len(suppress_tokens) == 0:
+        suppress_tokens = []  # interpret empty string as an empty list
+    else:
+        assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
 
-    suppress_tokens = list(suppress_tokens)
-
-    # Ensure the following special tokens are suppressed when the user does
-    # not use the default set (-1).
     suppress_tokens.extend(
         [
             tokenizer.transcribe,
@@ -1246,7 +1250,7 @@ def get_suppressed_tokens(
         ]
     )
 
-    return sorted(set(suppress_tokens))
+    return tuple(sorted(set(suppress_tokens)))
 
 
 def merge_punctuations(alignment: List[dict], prepended: str, appended: str) -> None: