chore: update GitHub Actions workflow to use dynamic ref for checkout

.github/workflows/build-and-release.yml

@@ -10,6 +10,8 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
+        with:
+          ref: ${{ github.ref_name }}
 
       - name: Set up Python
         uses: actions/setup-python@v5

.github/workflows/tmp.yml

@@ -0,0 +1,35 @@
+name: Python Compatibility Test (PyPi)
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+  workflow_dispatch:  # Allows manual triggering from GitHub UI
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install package
+        run: |
+          pip install whisperx
+
+      - name: Print packages
+        run: |
+          pip list
+
+      - name: Test import
+        run: |
+          python -c "import whisperx; print('Successfully imported whisperx')"

requirements.txt

@@ -1,7 +1,7 @@
 torch>=2
 torchaudio>=2
 faster-whisper==1.1.0
-ctranslate2<4.5.0
+ctranslate2>=4.5.0
 transformers
 pandas
 setuptools>=65

setup.py

@@ -9,7 +9,7 @@ with open("README.md", "r", encoding="utf-8") as f:
 setup(
     name="whisperx",
     py_modules=["whisperx"],
-    version="3.3.0",
+    version="3.3.2",
     description="Time-Accurate Automatic Speech Recognition using Whisper.",
     long_description=long_description,
     long_description_content_type="text/markdown",

whisperx/SubtitlesProcessor.py

@@ -1,5 +1,5 @@
 import math
-from conjunctions import get_conjunctions, get_comma
+from .conjunctions import get_conjunctions, get_comma
 from typing import TextIO
 
 def normal_round(n):

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

@@ -1,17 +1,21 @@
 import os
 import warnings
-from typing import List, Union, Optional, NamedTuple
+from typing import List, NamedTuple, Optional, Union
+from dataclasses import replace
 
 import ctranslate2
 import faster_whisper
 import numpy as np
 import torch
+from faster_whisper.tokenizer import Tokenizer
+from faster_whisper.transcribe import TranscriptionOptions, 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 TranscriptionResult, SingleSegment
+from .types import SingleSegment, TranscriptionResult
+from .vad import VoiceActivitySegmentation, load_vad_model, merge_chunks
+
 
 def find_numeral_symbol_tokens(tokenizer):
     numeral_symbol_tokens = []
@@ -28,7 +32,13 @@ class WhisperModel(faster_whisper.WhisperModel):
     Currently only works in non-timestamp mode and fixed prompt for all samples in batch.
     '''
 
-    def generate_segment_batched(self, features: np.ndarray, tokenizer: faster_whisper.tokenizer.Tokenizer, options: faster_whisper.transcribe.TranscriptionOptions, encoder_output = None):
+    def generate_segment_batched(
+        self,
+        features: np.ndarray,
+        tokenizer: Tokenizer,
+        options: TranscriptionOptions,
+        encoder_output=None,
+    ):
         batch_size = features.shape[0]
         all_tokens = []
         prompt_reset_since = 0
@@ -81,7 +91,7 @@ class WhisperModel(faster_whisper.WhisperModel):
         # unsqueeze if batch size = 1
         if len(features.shape) == 2:
             features = np.expand_dims(features, 0)
-        features = faster_whisper.transcribe.get_ctranslate2_storage(features)
+        features = get_ctranslate2_storage(features)
 
         return self.model.encode(features, to_cpu=to_cpu)
 
@@ -94,17 +104,17 @@ class FasterWhisperPipeline(Pipeline):
     # - add support for custom inference kwargs
 
     def __init__(
-            self,
-            model,
-            vad,
-            vad_params: dict,
-            options : NamedTuple,
-            tokenizer=None,
-            device: Union[int, str, "torch.device"] = -1,
-            framework = "pt",
-            language : Optional[str] = None,
-            suppress_numerals: bool = False,
-            **kwargs
+        self,
+        model: WhisperModel,
+        vad: VoiceActivitySegmentation,
+        vad_params: dict,
+        options: TranscriptionOptions,
+        tokenizer: Optional[Tokenizer] = None,
+        device: Union[int, str, "torch.device"] = -1,
+        framework="pt",
+        language: Optional[str] = None,
+        suppress_numerals: bool = False,
+        **kwargs,
     ):
         self.model = model
         self.tokenizer = tokenizer
@@ -156,7 +166,13 @@ 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:
@@ -171,7 +187,16 @@ 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)
@@ -193,24 +218,30 @@ class FasterWhisperPipeline(Pipeline):
         if self.tokenizer is None:
             language = language or self.detect_language(audio)
             task = task or "transcribe"
-            self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer,
-                                                                self.model.model.is_multilingual, task=task,
-                                                                language=language)
+            self.tokenizer = Tokenizer(
+                self.model.hf_tokenizer,
+                self.model.model.is_multilingual,
+                task=task,
+                language=language,
+            )
         else:
             language = language or self.tokenizer.language_code
             task = task or self.tokenizer.task
             if task != self.tokenizer.task or language != self.tokenizer.language_code:
-                self.tokenizer = faster_whisper.tokenizer.Tokenizer(self.model.hf_tokenizer,
-                                                                    self.model.model.is_multilingual, task=task,
-                                                                    language=language)
-                
+                self.tokenizer = Tokenizer(
+                    self.model.hf_tokenizer,
+                    self.model.model.is_multilingual,
+                    task=task,
+                    language=language,
+                )
+
         if self.suppress_numerals:
             previous_suppress_tokens = self.options.suppress_tokens
             numeral_symbol_tokens = find_numeral_symbol_tokens(self.tokenizer)
             print(f"Suppressing numeral and symbol tokens")
             new_suppressed_tokens = numeral_symbol_tokens + self.options.suppress_tokens
             new_suppressed_tokens = list(set(new_suppressed_tokens))
-            self.options = self.options._replace(suppress_tokens=new_suppressed_tokens)
+            self.options = replace(self.options, suppress_tokens=new_suppressed_tokens)
 
         segments: List[SingleSegment] = []
         batch_size = batch_size or self._batch_size
@@ -239,12 +270,11 @@ class FasterWhisperPipeline(Pipeline):
 
         # revert suppressed tokens if suppress_numerals is enabled
         if self.suppress_numerals:
-            self.options = self.options._replace(suppress_tokens=previous_suppress_tokens)
+            self.options = replace(self.options, suppress_tokens=previous_suppress_tokens)
 
         return {"segments": segments, "language": language}
 
-
-    def detect_language(self, audio: np.ndarray):
+    def detect_language(self, audio: np.ndarray) -> str:
         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")
@@ -258,33 +288,36 @@ class FasterWhisperPipeline(Pipeline):
         print(f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio...")
         return language
 
-def load_model(whisper_arch,
-               device,
-               device_index=0,
-               compute_type="float16",
-               asr_options=None,
-               language : Optional[str] = None,
-               vad_model=None,
-               vad_options=None,
-               model : Optional[WhisperModel] = None,
-               task="transcribe",
-               download_root=None,
-               local_files_only=False,
-               threads=4):
-    '''Load a Whisper model for inference.
+
+def load_model(
+    whisper_arch: str,
+    device: str,
+    device_index=0,
+    compute_type="float16",
+    asr_options: Optional[dict] = None,
+    language: Optional[str] = None,
+    vad_model: Optional[VoiceActivitySegmentation] = None,
+    vad_options: Optional[dict] = None,
+    model: Optional[WhisperModel] = None,
+    task="transcribe",
+    download_root: Optional[str] = None,
+    local_files_only=False,
+    threads=4,
+) -> FasterWhisperPipeline:
+    """Load a Whisper model for inference.
     Args:
-        whisper_arch: str - The name of the Whisper model to load.
-        device: str - The device to load the model on.
-        compute_type: str - The compute type to use for the model.
-        options: dict - A dictionary of options to use for the model.
-        language: str - The language of the model. (use English for now)
-        model: Optional[WhisperModel] - The WhisperModel instance to use.
-        download_root: Optional[str] - 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.
-        threads: int - The number of cpu threads to use per worker, e.g. will be multiplied by num workers.
+        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.
+        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.
+        download_root - The root directory to download the model to.
+        local_files_only - If `True`, avoid downloading the file and return the path to the local cached file if it exists.
+        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"
@@ -297,7 +330,7 @@ def load_model(whisper_arch,
                          local_files_only=local_files_only,
                          cpu_threads=threads)
     if language is not None:
-        tokenizer = faster_whisper.tokenizer.Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
+        tokenizer = Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
     else:
         print("No language specified, language will be first be detected for each audio file (increases inference time).")
         tokenizer = None
@@ -338,7 +371,7 @@ def load_model(whisper_arch,
     suppress_numerals = default_asr_options["suppress_numerals"]
     del default_asr_options["suppress_numerals"]
 
-    default_asr_options = faster_whisper.transcribe.TranscriptionOptions(**default_asr_options)
+    default_asr_options = TranscriptionOptions(**default_asr_options)
 
     default_vad_options = {
         "vad_onset": 0.500,

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,
-                    optional_int, str2bool)
+from .types import AlignedTranscriptionResult, TranscriptionResult
+from .utils import (
+    LANGUAGES,
+    TO_LANGUAGE_CODE,
+    get_writer,
+    optional_float,
+    optional_int,
+    str2bool,
+)
 
 
 def cli():
@@ -95,7 +102,7 @@ def cli():
     align_model: str = args.pop("align_model")
     interpolate_method: str = args.pop("interpolate_method")
     no_align: bool = args.pop("no_align")
-    task : str = args.pop("task")
+    task: str = args.pop("task")
     if task == "translate":
         # translation cannot be aligned
         no_align = True
@@ -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))
 

whisperx/utils.py

@@ -214,7 +214,12 @@ class WriteTXT(ResultWriter):
 
     def write_result(self, result: dict, file: TextIO, options: dict):
         for segment in result["segments"]:
-            print(segment["text"].strip(), file=file, flush=True)
+            speaker = segment.get("speaker")
+            text = segment["text"].strip()
+            if speaker is not None:
+                print(f"[{speaker}]: {text}", file=file, flush=True)
+            else:
+                print(text, file=file, flush=True)
 
 
 class SubtitlesWriter(ResultWriter):