diff --git a/faster_whisper/transcribe.py b/faster_whisper/transcribe.py
index 7d56c5a0..a4ab58cb 100644
--- a/faster_whisper/transcribe.py
+++ b/faster_whisper/transcribe.py
@@ -7,7 +7,7 @@
 from dataclasses import asdict, dataclass
 from inspect import signature
 from math import ceil
-from typing import BinaryIO, Iterable, List, Optional, Tuple, Union, Any
+from typing import Any, BinaryIO, Iterable, List, Optional, Tuple, Union
 from warnings import warn
 
 import ctranslate2
@@ -231,7 +231,7 @@ def transcribe(
         clip_timestamps: Optional[List[dict]] = None,
         batch_size: int = 16,
         hotwords: Optional[str] = None,
-        language_detection_threshold: float= 0.5,
+        language_detection_threshold: float = 0.5,
         language_detection_segments: int = 1,
     ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
         """transcribe audio in chunks in batched fashion and return with language info.
@@ -1782,16 +1782,22 @@ def detect_language(
                 speech_chunks = get_speech_timestamps(audio, vad_parameters)
                 audio_chunks, _ = collect_chunks(audio, speech_chunks)
                 audio = np.concatenate(audio_chunks, axis=0)
-                assert audio is not None, "Audio have a problem while concatanating the audio_chunks; return None"
+                assert (
+                    audio is not None
+                ), "Audio have a problem while concatanating the audio_chunks; return None"
             audio = audio[
                 : language_detection_segments * self.feature_extractor.n_samples
             ]
             features = self.feature_extractor(audio)
-        assert features is not None, "No features extracted from audio file; return None"
+        assert (
+            features is not None
+        ), "No features extracted from audio file; return None"
         features = features[
             ..., : language_detection_segments * self.feature_extractor.nb_max_frames
         ]
-        assert features is not None, "No features extracted when detectting language in audio segments; return None"
+        assert (
+            features is not None
+        ), "No features extracted when detectting language in audio segments; return None"
         detected_language_info = {}
         for i in range(0, features.shape[-1], self.feature_extractor.nb_max_frames):
             encoder_output = self.encode(
@@ -1862,7 +1868,7 @@ def get_compression_ratio(text: str) -> float:
 def get_suppressed_tokens(
     tokenizer: Tokenizer,
     suppress_tokens: Optional[List[int]],
-) -> tuple[int, ...]:
+) -> Tuple[int, ...]:
     if suppress_tokens is None or len(suppress_tokens) == 0:
         suppress_tokens = []  # interpret empty string as an empty list
     elif -1 in suppress_tokens: