refactor: add type hints

whisperx/alignment.py

@@ -3,7 +3,7 @@ Forced Alignment with Whisper
 C. Max Bain
 """
 from dataclasses import dataclass
-from typing import Iterable, Union, List
+from typing import Iterable, Optional, Union, List
 
 import numpy as np
 import pandas as pd
@@ -65,7 +65,7 @@ DEFAULT_ALIGN_MODELS_HF = {
 }
 
 
-def load_align_model(language_code, device, model_name=None, model_dir=None):
+def load_align_model(language_code: str, device: str, model_name: Optional[str] = None, model_dir=None):
     if model_name is None:
         # use default model
         if language_code in DEFAULT_ALIGN_MODELS_TORCH:

whisperx/asr.py

@@ -1,20 +1,20 @@
 import os
 import warnings
-from typing import List, Union, Optional, NamedTuple
+from typing import List, NamedTuple, Optional, Union
 
 import ctranslate2
 import faster_whisper
 import numpy as np
 import torch
 from faster_whisper.tokenizer import Tokenizer
-from faster_whisper.transcribe import (TranscriptionOptions,
+from faster_whisper.transcribe import TranscriptionOptions, get_ctranslate2_storage
-                                       get_ctranslate2_storage)
 from transformers import Pipeline
 from transformers.pipelines.pt_utils import PipelineIterator
 
 from .audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
-from .vad import load_vad_model, merge_chunks
+from .types import SingleSegment, TranscriptionResult
-from .types import TranscriptionResult, SingleSegment
+from .vad import VoiceActivitySegmentation, load_vad_model, merge_chunks
+
 
 def find_numeral_symbol_tokens(tokenizer):
     numeral_symbol_tokens = []
@@ -104,16 +104,16 @@ class FasterWhisperPipeline(Pipeline):
 
     def __init__(
         self,
-            model,
+        model: WhisperModel,
-            vad,
+        vad: VoiceActivitySegmentation,
         vad_params: dict,
         options: NamedTuple,
-            tokenizer=None,
+        tokenizer: Optional[Tokenizer] = None,
         device: Union[int, str, "torch.device"] = -1,
         framework="pt",
         language: Optional[str] = None,
         suppress_numerals: bool = False,
-            **kwargs
+        **kwargs,
     ):
         self.model = model
         self.tokenizer = tokenizer
@@ -165,7 +165,13 @@ class FasterWhisperPipeline(Pipeline):
         return model_outputs
 
     def get_iterator(
-        self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params
+        self,
+        inputs,
+        num_workers: int,
+        batch_size: int,
+        preprocess_params: dict,
+        forward_params: dict,
+        postprocess_params: dict,
     ):
         dataset = PipelineIterator(inputs, self.preprocess, preprocess_params)
         if "TOKENIZERS_PARALLELISM" not in os.environ:
@@ -180,7 +186,16 @@ class FasterWhisperPipeline(Pipeline):
         return final_iterator
 
     def transcribe(
-        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False, verbose=False
+        self,
+        audio: Union[str, np.ndarray],
+        batch_size: Optional[int] = None,
+        num_workers=0,
+        language: Optional[str] = None,
+        task: Optional[str] = None,
+        chunk_size=30,
+        print_progress=False,
+        combined_progress=False,
+        verbose=False,
     ) -> TranscriptionResult:
         if isinstance(audio, str):
             audio = load_audio(audio)
@@ -258,8 +273,7 @@ class FasterWhisperPipeline(Pipeline):
 
         return {"segments": segments, "language": language}
 
-
+    def detect_language(self, audio: np.ndarray) -> str:
-    def detect_language(self, audio: np.ndarray):
         if audio.shape[0] < N_SAMPLES:
             print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
         model_n_mels = self.model.feat_kwargs.get("feature_size")
@@ -273,33 +287,36 @@ class FasterWhisperPipeline(Pipeline):
         print(f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio...")
         return language
 
-def load_model(whisper_arch,
+
-               device,
+def load_model(
+    whisper_arch: str,
+    device: str,
     device_index=0,
     compute_type="float16",
-               asr_options=None,
+    asr_options: Optional[dict] = None,
     language: Optional[str] = None,
-               vad_model=None,
+    vad_model: Optional[VoiceActivitySegmentation] = None,
-               vad_options=None,
+    vad_options: Optional[dict] = None,
     model: Optional[WhisperModel] = None,
     task="transcribe",
-               download_root=None,
+    download_root: Optional[str] = None,
     local_files_only=False,
-               threads=4):
+    threads=4,
-    '''Load a Whisper model for inference.
+) -> FasterWhisperPipeline:
+    """Load a Whisper model for inference.
     Args:
-        whisper_arch: str - The name of the Whisper model to load.
+        whisper_arch - The name of the Whisper model to load.
-        device: str - The device to load the model on.
+        device - The device to load the model on.
-        compute_type: str - The compute type to use for the model.
+        compute_type - The compute type to use for the model.
-        options: dict - A dictionary of options to use for the model.
+        options - A dictionary of options to use for the model.
-        language: str - The language of the model. (use English for now)
+        language - The language of the model. (use English for now)
-        model: Optional[WhisperModel] - The WhisperModel instance to use.
+        model - The WhisperModel instance to use.
-        download_root: Optional[str] - The root directory to download the model to.
+        download_root - The root directory to download the model to.
-        local_files_only: bool - If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+        local_files_only - If `True`, avoid downloading the file and return the path to the local cached file if it exists.
-        threads: int - The number of cpu threads to use per worker, e.g. will be multiplied by num workers.
+        threads - The number of cpu threads to use per worker, e.g. will be multiplied by num workers.
     Returns:
         A Whisper pipeline.
-    '''
+    """
 
     if whisper_arch.endswith(".en"):
         language = "en"

whisperx/audio.py

@@ -22,7 +22,7 @@ FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH)  # 10ms per audio frame
 TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audio token
 
 
-def load_audio(file: str, sr: int = SAMPLE_RATE):
+def load_audio(file: str, sr: int = SAMPLE_RATE) -> np.ndarray:
     """
     Open an audio file and read as mono waveform, resampling as necessary
 

whisperx/conjunctions.py

@@ -1,5 +1,8 @@
 # conjunctions.py
 
+from typing import Set
+
+
 conjunctions_by_language = {
     'en': {'and', 'whether', 'or', 'as', 'but', 'so', 'for', 'nor', 'which', 'yet', 'although', 'since', 'unless', 'when', 'while', 'because', 'if', 'how', 'that', 'than', 'who', 'where', 'what', 'near', 'before', 'after', 'across', 'through', 'until', 'once', 'whereas', 'even', 'both', 'either', 'neither', 'though'},
     'fr': {'et', 'ou', 'mais', 'parce', 'bien', 'pendant', 'quand', 'où', 'comme', 'si', 'que', 'avant', 'après', 'aussitôt', 'jusqu’à', 'à', 'malgré', 'donc', 'tant', 'puisque', 'ni', 'soit', 'bien', 'encore', 'dès', 'lorsque'},
@@ -36,8 +39,9 @@ commas_by_language = {
     'ur': '،'  
 }
 
-def get_conjunctions(lang_code):
+def get_conjunctions(lang_code: str) -> Set[str]:
     return conjunctions_by_language.get(lang_code, set())
 
-def get_comma(lang_code):
+
-    return commas_by_language.get(lang_code, ',')
+def get_comma(lang_code: str) -> str:
+    return commas_by_language.get(lang_code, ",")

whisperx/diarize.py

@@ -5,6 +5,7 @@ from typing import Optional, Union
 import torch
 
 from .audio import load_audio, SAMPLE_RATE
+from .types import TranscriptionResult, AlignedTranscriptionResult
 
 
 class DiarizationPipeline:
@@ -18,7 +19,13 @@ class DiarizationPipeline:
             device = torch.device(device)
         self.model = Pipeline.from_pretrained(model_name, use_auth_token=use_auth_token).to(device)
 
-    def __call__(self, audio: Union[str, np.ndarray], num_speakers=None, min_speakers=None, max_speakers=None):
+    def __call__(
+        self,
+        audio: Union[str, np.ndarray],
+        num_speakers: Optional[int] = None,
+        min_speakers: Optional[int] = None,
+        max_speakers: Optional[int] = None,
+    ):
         if isinstance(audio, str):
             audio = load_audio(audio)
         audio_data = {
@@ -32,7 +39,11 @@ class DiarizationPipeline:
         return diarize_df
 
 
-def assign_word_speakers(diarize_df, transcript_result, fill_nearest=False):
+def assign_word_speakers(
+    diarize_df: pd.DataFrame,
+    transcript_result: Union[AlignedTranscriptionResult, TranscriptionResult],
+    fill_nearest=False,
+) -> dict:
     transcript_segments = transcript_result["segments"]
     for seg in transcript_segments:
         # assign speaker to segment (if any)

whisperx/transcribe.py

@@ -10,8 +10,15 @@ 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 .utils import (LANGUAGES, TO_LANGUAGE_CODE, get_writer, optional_float,
+from .types import AlignedTranscriptionResult, TranscriptionResult
-                    optional_int, str2bool)
+from .utils import (
+    LANGUAGES,
+    TO_LANGUAGE_CODE,
+    get_writer,
+    optional_float,
+    optional_int,
+    str2bool,
+)
 
 
 def cli():
@@ -174,7 +181,13 @@ def cli():
         audio = load_audio(audio_path)
         # >> VAD & ASR
         print(">>Performing transcription...")
-        result = model.transcribe(audio, batch_size=batch_size, chunk_size=chunk_size, print_progress=print_progress, verbose=verbose)
+        result: TranscriptionResult = model.transcribe(
+            audio,
+            batch_size=batch_size,
+            chunk_size=chunk_size,
+            print_progress=print_progress,
+            verbose=verbose,
+        )
         results.append((result, audio_path))
 
     # Unload Whisper and VAD
@@ -201,7 +214,16 @@ def cli():
                     print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
                     align_model, align_metadata = load_align_model(result["language"], device)
                 print(">>Performing alignment...")
-                result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method, return_char_alignments=return_char_alignments, print_progress=print_progress)
+                result: AlignedTranscriptionResult = align(
+                    result["segments"],
+                    align_model,
+                    align_metadata,
+                    input_audio,
+                    device,
+                    interpolate_method=interpolate_method,
+                    return_char_alignments=return_char_alignments,
+                    print_progress=print_progress,
+                )
 
             results.append((result, audio_path))