[pre-commit.ci] pre-commit suggestions

.pre-commit-config.yaml

@@ -56,6 +56,6 @@ repos:
         name: Flake8 linting
 
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.7.7
+    rev: v1.7.8-rc1
     hooks:
       - id: docformatter

README.md

@@ -7,7 +7,7 @@
 </div>
     <a href="https://trendshift.io/repositories/6007" target="_blank"><img src="https://trendshift.io/api/badge/repositories/6007" alt="kadirnar%2Fwhisper-plus | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
-<div>    
+<div>
     <a href="https://pypi.org/project/whisperplus" target="_blank">
         <img src="https://img.shields.io/pypi/pyversions/whisperplus.svg?color=%2334D058" alt="Supported Python versions">
     </a>

whisperplus/app.py

@@ -105,7 +105,6 @@ def speaker_diarization(url, model_id, device, num_speakers, min_speaker, max_sp
         transcript (str): The transcript of the speech-to-text conversion.
         video_path (str): The path of the downloaded video.
     """
-
     pipeline = ASRDiarizationPipeline.from_pretrained(
         asr_model=model_id,
         diarizer_model="pyannote/speaker-diarization",

whisperplus/pipelines/lightning_whisper_mlx/audio.py

@@ -37,7 +37,6 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
     -------
     A NumPy array containing the audio waveform, in float32 dtype.
     """
-
     # This launches a subprocess to decode audio while down-mixing
     # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
     # fmt: off
@@ -79,8 +78,8 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
 @lru_cache(maxsize=None)
 def mel_filters(n_mels: int) -> mx.array:
     """
-    Load the mel filterbank matrix for projecting STFT into a Mel spectrogram. Allows decoupling librosa
-    dependency; saved using:
+    Load the mel filterbank matrix for projecting STFT into a Mel spectrogram. dependency; saved using: Allows
+    decoupling librosa.
 
     np.savez_compressed(     "mel_filters.npz",     mel_80=librosa.filters.mel(sr=16000, n_fft=400,
     n_mels=80),     mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128), )

whisperplus/pipelines/lightning_whisper_mlx/tokenizer.py

@@ -356,7 +356,9 @@ def get_encoding(name: str = "gpt2", num_languages: int = 99):
     return tiktoken.Encoding(
         name=os.path.basename(vocab_path),
         explicit_n_vocab=n_vocab,
-        pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        pat_str=\
+                r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+                                                                                                 ,
         mergeable_ranks=ranks,
         special_tokens=special_tokens,
     )

whisperplus/pipelines/lightning_whisper_mlx/transcribe.py

@@ -144,7 +144,6 @@ def transcribe_audio(
     A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
     the spoken language ("language"), which is detected when `decode_options["language"]` is None.
     """
-
     dtype = mx.float16 if decode_options.get("fp16", True) else mx.float32
     model = ModelHolder.get_model(path_or_hf_repo, dtype)
 

whisperplus/pipelines/mlx_whisper/audio.py

@@ -37,7 +37,6 @@ def load_audio(file: str, sr: int = SAMPLE_RATE):
     -------
     A NumPy array containing the audio waveform, in float32 dtype.
     """
-
     # This launches a subprocess to decode audio while down-mixing
     # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
     # fmt: off
@@ -79,8 +78,8 @@ def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
 @lru_cache(maxsize=None)
 def mel_filters(n_mels: int) -> mx.array:
     """
-    Load the mel filterbank matrix for projecting STFT into a Mel spectrogram. Allows decoupling librosa
-    dependency; saved using:
+    Load the mel filterbank matrix for projecting STFT into a Mel spectrogram. dependency; saved using: Allows
+    decoupling librosa.
 
     np.savez_compressed(     "mel_filters.npz",     mel_80=librosa.filters.mel(sr=16000, n_fft=400,
     n_mels=80),     mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128), )

whisperplus/pipelines/mlx_whisper/convert.py

@@ -135,7 +135,6 @@ def load_torch_model(
     model : Whisper
         The Whisper ASR model instance
     """
-
     if download_root is None:
         download_root = os.path.join(os.path.expanduser("~"), ".cache/whisper")
 

whisperplus/pipelines/mlx_whisper/tokenizer.py

@@ -356,7 +356,9 @@ def get_encoding(name: str = "gpt2", num_languages: int = 99):
     return tiktoken.Encoding(
         name=os.path.basename(vocab_path),
         explicit_n_vocab=n_vocab,
-        pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        pat_str=\
+                r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+                                                                                                 ,
         mergeable_ranks=ranks,
         special_tokens=special_tokens,
     )

whisperplus/pipelines/mlx_whisper/transcribe.py

@@ -142,7 +142,6 @@ def transcribe(
     A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
     the spoken language ("language"), which is detected when `decode_options["language"]` is None.
     """
-
     dtype = mx.float16 if decode_options.get("fp16", True) else mx.float32
     model = ModelHolder.get_model(path_or_hf_repo, dtype)
 

whisperplus/pipelines/whisper.py

@@ -63,17 +63,17 @@ def hqq_compile_model(self, model_id, quant_config, device):
         prepare_for_inference(model.model.decoder, backend="torchao_int4")
 
         model = self.compile_model(model)
-        
+
         return model, processor
 
     def load_model_whisper(
-        self,
-        model_id: str = "distil-whisper/distil-large-v3",
-        quant_config=None,
-        hqq_compile: bool = False,
-        flash_attention_2: bool = False,
-        device=None):
-        
+            self,
+            model_id: str = "distil-whisper/distil-large-v3",
+            quant_config=None,
+            hqq_compile: bool = False,
+            flash_attention_2: bool = False,
+            device=None):
+
         if hqq_compile:
             return self.hqq_compile_model(model_id, quant_config, device)
         else:
@@ -134,7 +134,6 @@ def __call__(
         Returns:
             str: Transcribed text from the audio.
         """
-
         pipe = pipeline(
             "automatic-speech-recognition",
             model=self.model,

whisperplus/pipelines/whisper_diarize.py

@@ -50,8 +50,8 @@ def __call__(
         **kwargs,
     ):
         """
-        Transcribe the audio sequence(s) given as inputs to text and label with speaker information. The input
-        audio is first passed to the speaker diarization pipeline, which returns timestamps for 'who spoke
+        Transcribe the audio sequence(s) given as inputs to text and label with speaker information. audio is
+        first passed to the speaker diarization pipeline, which returns timestamps for 'who spoke The input
         when'. The audio is then passed to the ASR pipeline, which returns utterance-level transcriptions and
         their corresponding timestamps. The speaker diarizer timestamps are aligned with the ASR transcription
         timestamps to give speaker-labelled transcriptions. We cannot use the speaker diarization timestamps