Reformat the code and organize the imports.

app/faster_whisper/core.py

+import gc
 import os
+import time
 from io import StringIO
 from threading import Lock, Thread
 from typing import BinaryIO, Union
-import time
-import gc
 
 import torch
 import whisper
@@ -16,15 +16,13 @@ model_path = os.getenv("ASR_MODEL_PATH", os.path.join(os.path.expanduser("~"), "
 model = None
 model_lock = Lock()
 
-# More about available quantization levels is here:
-#   https://opennmt.net/CTranslate2/quantization.html
-
 last_activity_time = time.time()
 idle_timeout = int(os.getenv("IDLE_TIMEOUT", 0))  # default to being disabled
 
+
 def monitor_idleness():
     global model
-    if(idle_timeout <= 0): return
+    if idle_timeout <= 0: return
     while True:
         time.sleep(15)
         if time.time() - last_activity_time > idle_timeout:
@@ -32,9 +30,12 @@ def monitor_idleness():
                 release_model()
                 break
 
+
 def load_model():
     global model, device, model_quantization
 
+    # More about available quantization levels is here:
+    #   https://opennmt.net/CTranslate2/quantization.html
     if torch.cuda.is_available():
         device = "cuda"
         model_quantization = os.getenv("ASR_QUANTIZATION", "float32")
@@ -48,8 +49,10 @@ def load_model():
 
     Thread(target=monitor_idleness, daemon=True).start()
 
+
 load_model()
 
+
 def release_model():
     global model
     del model
@@ -58,6 +61,7 @@ def release_model():
     model = None
     print("Model unloaded due to timeout")
 
+
 def transcribe(
         audio,
         task: Union[str, None],
@@ -71,7 +75,7 @@ def transcribe(
     last_activity_time = time.time()
 
     with model_lock:
-        if(model is None): load_model()
+        if model is None: load_model()
 
     options_dict = {"task": task}
     if language:
@@ -103,7 +107,7 @@ def language_detection(audio):
     last_activity_time = time.time()
 
     with model_lock:
-        if(model is None): load_model()
+        if model is None: load_model()
 
     # load audio and pad/trim it to fit 30 seconds
     audio = whisper.pad_or_trim(audio)

app/openai_whisper/core.py

+import gc
 import os
+import time
 from io import StringIO
 from threading import Lock, Thread
 from typing import BinaryIO, Union
-import time
-import gc
 
 import torch
 import whisper
@@ -17,16 +17,18 @@ model_lock = Lock()
 last_activity_time = time.time()
 idle_timeout = int(os.getenv("IDLE_TIMEOUT", 0))  # default to being disabled
 
+
 def monitor_idleness():
     global model
-    if(idle_timeout <= 0): return
+    if idle_timeout <= 0: return
     while True:
-        time.sleep(15)  # check every minute
+        time.sleep(15)
         if time.time() - last_activity_time > idle_timeout:
             with model_lock:
                 release_model()
                 break
 
+
 def load_model():
     global model
 
@@ -37,8 +39,10 @@ def load_model():
 
     Thread(target=monitor_idleness, daemon=True).start()
 
+
 load_model()
 
+
 def release_model():
     global model
     del model
@@ -47,6 +51,7 @@ def release_model():
     model = None
     print("Model unloaded due to timeout")
 
+
 def transcribe(
         audio,
         task: Union[str, None],
@@ -60,7 +65,7 @@ def transcribe(
     last_activity_time = time.time()
 
     with model_lock:
-        if(model is None): load_model()
+        if model is None: load_model()
 
     options_dict = {"task": task}
     if language:
@@ -84,7 +89,7 @@ def language_detection(audio):
     last_activity_time = time.time()
 
     with model_lock:
-        if(model is None): load_model()
+        if model is None: load_model()
 
     # load audio and pad/trim it to fit 30 seconds
     audio = whisper.pad_or_trim(audio)

app/webservice.py

@@ -37,6 +37,7 @@ assets_path = os.getcwd() + "/swagger-ui-assets"
 if path.exists(assets_path + "/swagger-ui.css") and path.exists(assets_path + "/swagger-ui-bundle.js"):
     app.mount("/assets", StaticFiles(directory=assets_path), name="static")
 
+
     def swagger_monkey_patch(*args, **kwargs):
         return get_swagger_ui_html(
             *args,
@@ -46,6 +47,7 @@ if path.exists(assets_path + "/swagger-ui.css") and path.exists(assets_path + "/
             swagger_js_url="/assets/swagger-ui-bundle.js",
         )
 
+
     applications.get_swagger_ui_html = swagger_monkey_patch
 
 
@@ -90,7 +92,8 @@ async def detect_language(
         encode: bool = Query(default=True, description="Encode audio first through FFmpeg"),
 ):
     detected_lang_code, confidence = language_detection(load_audio(audio_file.file, encode))
-    return {"detected_language": tokenizer.LANGUAGES[detected_lang_code], "language_code": detected_lang_code, "confidence": confidence}
+    return {"detected_language": tokenizer.LANGUAGES[detected_lang_code], "language_code": detected_lang_code,
+            "confidence": confidence}
 
 
 def load_audio(file: BinaryIO, encode=True, sr: int = SAMPLE_RATE):
@@ -125,6 +128,7 @@ def load_audio(file: BinaryIO, encode=True, sr: int = SAMPLE_RATE):
 
     return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
 
+
 @click.command()
 @click.option(
     "-h",
@@ -147,5 +151,6 @@ def start(
 ):
     uvicorn.run(app, host=host, port=port)
 
+
 if __name__ == "__main__":
     start()