fix: apply ruff fixes to ray_serve package

[ray-serve only]

- Fix whitespace in docstrings
- Add strict=True to zip() calls
- Use ternary operators where appropriate
- Rename unused loop variables

ray-serve/ray_serve/serve_whisper.py

@@ -3,12 +3,10 @@ Ray Serve deployment for faster-whisper STT.
 Runs on: elminster (RTX 2070 8GB, CUDA)
 """
 
-import os
-import io
-import time
-import uuid
 import base64
-from typing import Any, Dict, Optional
+import io
+import os
+from typing import Any
 
 from ray import serve
 
@@ -16,11 +14,11 @@ from ray import serve
 @serve.deployment(name="WhisperDeployment", num_replicas=1)
 class WhisperDeployment:
     def __init__(self):
-        from faster_whisper import WhisperModel
         import torch
-        
+        from faster_whisper import WhisperModel
+
         self.model_size = os.environ.get("MODEL_SIZE", "large-v3")
-        
+
         # Detect device and compute type
         if torch.cuda.is_available():
             self.device = "cuda"
@@ -28,22 +26,22 @@ class WhisperDeployment:
         else:
             self.device = "cpu"
             self.compute_type = "int8"
-        
+
         print(f"Loading Whisper model: {self.model_size}")
         print(f"Using device: {self.device}, compute_type: {self.compute_type}")
-        
+
         self.model = WhisperModel(
             self.model_size,
             device=self.device,
             compute_type=self.compute_type,
         )
-        
-        print(f"Whisper model loaded successfully")
 
-    async def __call__(self, request: Dict[str, Any]) -> Dict[str, Any]:
+        print("Whisper model loaded successfully")
+
+    async def __call__(self, request: dict[str, Any]) -> dict[str, Any]:
         """
         Handle transcription requests.
-        
+
         Expected request format:
         {
             "audio": "base64_encoded_audio_data",
@@ -53,24 +51,22 @@ class WhisperDeployment:
             "response_format": "json",
             "word_timestamps": false
         }
-        
+
         Alternative with file path:
         {
             "file": "/path/to/audio.wav",
             ...
         }
         """
-        import numpy as np
-        from scipy.io import wavfile
-        
-        language = request.get("language", None)
+
+        language = request.get("language")
         task = request.get("task", "transcribe")  # transcribe or translate
         response_format = request.get("response_format", "json")
         word_timestamps = request.get("word_timestamps", False)
-        
+
         # Get audio data
         audio_input = None
-        
+
         if "audio" in request:
             # Base64 encoded audio
             audio_bytes = base64.b64decode(request["audio"])
@@ -85,7 +81,7 @@ class WhisperDeployment:
             return {
                 "error": "No audio data provided. Use 'audio' (base64), 'file' (path), or 'audio_bytes'",
             }
-        
+
         # Transcribe
         segments, info = self.model.transcribe(
             audio_input,
@@ -94,11 +90,11 @@ class WhisperDeployment:
             word_timestamps=word_timestamps,
             vad_filter=True,
         )
-        
+
         # Collect segments
         segment_list = []
         full_text = ""
-        
+
         for segment in segments:
             seg_data = {
                 "id": segment.id,
@@ -106,7 +102,7 @@ class WhisperDeployment:
                 "end": segment.end,
                 "text": segment.text,
             }
-            
+
             if word_timestamps and segment.words:
                 seg_data["words"] = [
                     {
@@ -117,14 +113,14 @@ class WhisperDeployment:
                     }
                     for word in segment.words
                 ]
-            
+
             segment_list.append(seg_data)
             full_text += segment.text
-        
+
         # Build response based on format
         if response_format == "text":
             return {"text": full_text.strip()}
-        
+
         if response_format == "verbose_json":
             return {
                 "task": task,
@@ -133,7 +129,7 @@ class WhisperDeployment:
                 "text": full_text.strip(),
                 "segments": segment_list,
             }
-        
+
         # Default JSON format (OpenAI-compatible)
         return {
             "text": full_text.strip(),