refactor: update import statements to use explicit module paths across multiple files

whisperx/SubtitlesProcessor.py

@@ -1,6 +1,5 @@
 import math
-from .conjunctions import get_conjunctions, get_comma
-from typing import TextIO
+from whisperx.conjunctions import get_conjunctions, get_comma
 
 def normal_round(n):
     if n - math.floor(n) < 0.5:

whisperx/__init__.py

@@ -1,4 +1,7 @@
-from .alignment import load_align_model, align
-from .audio import load_audio
-from .diarize import assign_word_speakers, DiarizationPipeline
-from .asr import load_model
+from whisperx.alignment import load_align_model as load_align_model, align as align
+from whisperx.asr import load_model as load_model
+from whisperx.audio import load_audio as load_audio
+from whisperx.diarize import (
+    assign_word_speakers as assign_word_speakers,
+    DiarizationPipeline as DiarizationPipeline,
+)

whisperx/__main__.py

@@ -1,4 +1,4 @@
-from .transcribe import cli
+from whisperx.transcribe import cli
 
 
 cli()

whisperx/alignment.py

@@ -13,9 +13,9 @@ import torch
 import torchaudio
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-from .audio import SAMPLE_RATE, load_audio
-from .utils import interpolate_nans
-from .types import (
+from whisperx.audio import SAMPLE_RATE, load_audio
+from whisperx.utils import interpolate_nans
+from whisperx.types import (
     AlignedTranscriptionResult,
     SingleSegment,
     SingleAlignedSegment,

whisperx/asr.py

@@ -11,9 +11,10 @@ from faster_whisper.transcribe import TranscriptionOptions, get_ctranslate2_stor
 from transformers import Pipeline
 from transformers.pipelines.pt_utils import PipelineIterator
 
-from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
-from .types import SingleSegment, TranscriptionResult
-from .vads import Vad, Silero, Pyannote
+from whisperx.audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
+from whisperx.types import SingleSegment, TranscriptionResult
+from whisperx.vads import Vad, Silero, Pyannote
+
 
 def find_numeral_symbol_tokens(tokenizer):
     numeral_symbol_tokens = []

whisperx/audio.py

@@ -7,7 +7,7 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 
-from .utils import exact_div
+from whisperx.utils import exact_div
 
 # hard-coded audio hyperparameters
 SAMPLE_RATE = 16000

whisperx/diarize.py

@@ -4,8 +4,8 @@ from pyannote.audio import Pipeline
 from typing import Optional, Union
 import torch
 
-from .audio import load_audio, SAMPLE_RATE
-from .types import TranscriptionResult, AlignedTranscriptionResult
+from whisperx.audio import load_audio, SAMPLE_RATE
+from whisperx.types import TranscriptionResult, AlignedTranscriptionResult
 
 
 class DiarizationPipeline:

whisperx/transcribe.py

@@ -6,12 +6,12 @@ import warnings
 import numpy as np
 import torch
 
-from .alignment import align, load_align_model
-from .asr import load_model
-from .audio import load_audio
-from .diarize import DiarizationPipeline, assign_word_speakers
-from .types import AlignedTranscriptionResult, TranscriptionResult
-from .utils import (
+from whisperx.alignment import align, load_align_model
+from whisperx.asr import load_model
+from whisperx.audio import load_audio
+from whisperx.diarize import DiarizationPipeline, assign_word_speakers
+from whisperx.types import AlignedTranscriptionResult, TranscriptionResult
+from whisperx.utils import (
     LANGUAGES,
     TO_LANGUAGE_CODE,
     get_writer,

whisperx/vads/__init__.py

@@ -1,3 +1,3 @@
-from whisperx.vads.pyannote import Pyannote
-from whisperx.vads.silero import Silero
-from whisperx.vads.vad import Vad
+from whisperx.vads.pyannote import Pyannote as Pyannote
+from whisperx.vads.silero import Silero as Silero
+from whisperx.vads.vad import Vad as Vad

whisperx/vads/pyannote.py

@@ -1,6 +1,4 @@
-import hashlib
 import os
-import urllib
 from typing import Callable, Text, Union
 from typing import Optional
 
@@ -12,11 +10,11 @@ from pyannote.audio.pipelines import VoiceActivityDetection
 from pyannote.audio.pipelines.utils import PipelineModel
 from pyannote.core import Annotation, SlidingWindowFeature
 from pyannote.core import Segment
-from tqdm import tqdm
 
 from whisperx.diarize import Segment as SegmentX
 from whisperx.vads.vad import Vad
 
+
 def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, use_auth_token=None, model_fp=None):
     model_dir = torch.hub._get_torch_home()