refactor: improve type hints and clean up imports

whisperx/alignment.py

@@ -1,4 +1,4 @@
-""""
+"""
 Forced Alignment with Whisper
 C. Max Bain
 """
@@ -14,7 +14,6 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 from .audio import SAMPLE_RATE, load_audio
 from .utils import interpolate_nans
 from .types import AlignedTranscriptionResult, SingleSegment, SingleAlignedSegment, SingleWordSegment
-import nltk
 from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktParameters
 
 PUNKT_ABBREVIATIONS = ['dr', 'vs', 'mr', 'mrs', 'prof']
@@ -194,6 +193,7 @@ def align(
             "end": t2,
             "text": text,
             "words": [],
+            "chars": None,
         }
 
         if return_char_alignments:

whisperx/asr.py

@@ -1,6 +1,5 @@
 import os
-import warnings
-from typing import List, NamedTuple, Optional, Union
+from typing import List, Optional, Union
 from dataclasses import replace
 
 import ctranslate2
@@ -304,8 +303,8 @@ def load_model(
     compute_type="float16",
     asr_options: Optional[dict] = None,
     language: Optional[str] = None,
-    vad_model = None,
-    vad_method = None,
+    vad_model: Optional[Vad]= None,
+    vad_method: Optional[str] = None,
     vad_options: Optional[dict] = None,
     model: Optional[WhisperModel] = None,
     task="transcribe",
@@ -318,7 +317,7 @@ def load_model(
         whisper_arch - The name of the Whisper model to load.
         device - The device to load the model on.
         compute_type - The compute type to use for the model.
-        vad_method: str - The vad method to use. vad_model has higher priority if is not None.
+        vad_method - The vad method to use. vad_model has higher priority if is not None.
         options - A dictionary of options to use for the model.
         language - The language of the model. (use English for now)
         model - The WhisperModel instance to use.

whisperx/diarize.py

@@ -79,7 +79,7 @@ def assign_word_speakers(
 
 
 class Segment:
-    def __init__(self, start, end, speaker=None):
+    def __init__(self, start:int, end:int, speaker:Optional[str]=None):
         self.start = start
         self.end = end
         self.speaker = speaker

whisperx/utils.py

@@ -241,7 +241,7 @@ class SubtitlesWriter(ResultWriter):
             line_count = 1
             # the next subtitle to yield (a list of word timings with whitespace)
             subtitle: list[dict] = []
-            times = []
+            times: list[tuple] = []
             last = result["segments"][0]["start"]
             for segment in result["segments"]:
                 for i, original_timing in enumerate(segment["words"]):

whisperx/vads/vad.py

@@ -26,8 +26,8 @@ class Vad:
          """
         curr_end = 0
         merged_segments = []
-        seg_idxs = []
-        speaker_idxs = []
+        seg_idxs: list[tuple]= []
+        speaker_idxs: list[Optional[str]] = []
 
         curr_start = segments[0].start
         for seg in segments: