Fix flake8 errors

faster_whisper/transcribe.py

@@ -91,23 +91,24 @@ def __init__(
         """Initializes the Whisper model.
 
         Args:
-          model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en, small, small.en, medium,
-            medium.en, large-v1, large-v2 (same as large), or large-v3), a path to a converted model directory, or a
-            CTranslate2-converted Whisper model ID from the Hugging Face Hub.  When a size or a model ID is configured,
-            the converted model is downloaded from the Hugging Face Hub.
+          model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en, small,
+            small.en, medium, medium.en, large-v1, large-v2 (same as large), or large-v3), a path
+            to a converted model directory, or a CTranslate2-converted Whisper model ID from the
+            Hugging Face Hub.  When a size or a model ID is configured, the converted model is
+            downloaded from the Hugging Face Hub.
           device: Device to use for computation ("cpu", "cuda", "auto").
           device_index: Device ID to use.
-            The model can also be loaded on multiple GPUs by passing a list of IDs (e.g. [0, 1, 2, 3]). In that case,
-            multiple transcriptions can run in parallel when transcribe() is called from multiple Python threads (see
-            also num_workers).
+            The model can also be loaded on multiple GPUs by passing a list of IDs (e.g. [0, 1, 2,
+            3]). In that case, multiple transcriptions can run in parallel when transcribe() is
+            called from multiple Python threads (see also num_workers).
           compute_type: Type to use for computation.
             See https://opennmt.net/CTranslate2/quantization.html.
           cpu_threads: Number of threads to use when running on CPU (4 by default).
             A non zero value overrides the OMP_NUM_THREADS environment variable.
           num_workers: When transcribe() is called from multiple Python threads,
-            having multiple workers enables true parallelism when running the model (concurrent calls to
-            self.model.generate() will run in parallel).  This can improve the global throughput at the cost of
-            increased memory usage.
+            having multiple workers enables true parallelism when running the model (concurrent
+            calls to self.model.generate() will run in parallel).  This can improve the global
+            throughput at the cost of increased memory usage.
           download_root: Directory where the models should be saved. If not set, the models
             are saved in the standard Hugging Face cache directory.
           local_files_only:  If True, avoid downloading the file and return the path to the
@@ -132,25 +133,33 @@ def __init__(
             intra_threads=cpu_threads,
             inter_threads=num_workers,
         )
-        is_large_v3 = "large-v3" in model_size_or_path  # TODO: check by inspecting `self.model` instead?
+        # TODO: check if it's large-v3 by inspecting `self.model` instead?
+        is_large_v3 = "large-v3" in model_size_or_path
 
         tokenizer_file = os.path.join(model_path, "tokenizer.json")
         if os.path.isfile(tokenizer_file):
             self.hf_tokenizer = tokenizers.Tokenizer.from_file(tokenizer_file)
         else:
             if is_large_v3:
-                # Tokenizer for large-v3 is different, so we need to load as in whisper-large-v3 and monkey patch it to
-                # have the `token_to_id` method.
+                # Tokenizer for large-v3 is different, so we need to load as in whisper-large-v3
+                # and monkey patch it to have the `token_to_id` method.
+                # TODO: load the new tokenizer without requiring `transformers`
                 from transformers import AutoProcessor
 
-                self.hf_tokenizer = AutoProcessor.from_pretrained("openai/whisper-large-v3").tokenizer
-                self.hf_tokenizer.token_to_id = lambda token: self.hf_tokenizer.convert_tokens_to_ids(token)
+                processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")
+                self.hf_tokenizer = processor.tokenizer
+                self.hf_tokenizer.token_to_id = (
+                    lambda token: self.hf_tokenizer.convert_tokens_to_ids(token)
+                )
             else:
                 self.hf_tokenizer = tokenizers.Tokenizer.from_pretrained(
-                    "openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en")
+                    "openai/whisper-tiny"
+                    + ("" if self.model.is_multilingual else ".en")
                 )
 
-        self.feature_extractor = FeatureExtractor(feature_size=80 if not is_large_v3 else 128)
+        self.feature_extractor = FeatureExtractor(
+            feature_size=80 if not is_large_v3 else 128
+        )
         self.num_samples_per_token = self.feature_extractor.hop_length * 2
         self.frames_per_second = (
             self.feature_extractor.sampling_rate // self.feature_extractor.hop_length