Revert "Enhance MLX batch_size functionality and documentation"

This reverts commit 8b72232.

Author: aj47

README.md

@@ -15,9 +15,7 @@ Over 700+⭐'s because this program this app just works! Works great for windows
 
 ### New in 3.1!
 
-Mac acceleration option using the new [lightning-whisper-mlx](https://github.com/mustafaaljadery/lightning-whisper-mlx) backend. Enable with `--device mlx`. Now supports multiple languages, custom vocabulary via `--initial_prompt`, both transcribe/translate tasks, and **batch processing** for improved throughput. 10x faster than Whisper CPP, 4x faster than previous MLX implementations!
-
-**Batch Processing:** MLX backend supports `--batch_size` parameter (default: 12) for improved throughput. Higher batch sizes provide better performance but require more memory.
+Mac acceleration option using the new [lightning-whisper-mlx](https://github.com/mustafaaljadery/lightning-whisper-mlx) backend. Enable with `--device mlx`. Now supports multiple languages, custom vocabulary via `--initial_prompt`, and both transcribe/translate tasks. 10x faster than Whisper CPP, 4x faster than previous MLX implementations!
 
 **Model Storage:** MLX models are now stored in `~/.cache/whisper/mlx_models/` for consistency with other backends, instead of cluttering your current working directory.
 
@@ -57,8 +55,6 @@ transcribe-anything https://www.youtube.com/watch?v=dQw4w9WgXcQ --device insane
 transcribe-anything https://www.youtube.com/watch?v=dQw4w9WgXcQ --device insane --task translate
 # Mac accelerated back-end
 transcribe-anything https://www.youtube.com/watch?v=dQw4w9WgXcQ --device mlx
-# Mac accelerated with custom batch size for better throughput
-transcribe-anything video.mp4 --device mlx --batch_size 24
 # Use custom prompt for better recognition of specific terms
 transcribe-anything video.mp4 --initial_prompt "The speaker discusses AI, machine learning, and neural networks."
 # Load prompt from file
@@ -178,27 +174,6 @@ Mac:
 
 - Use `--device mlx`
 
-#### MLX Batch Processing
-
-The MLX backend supports batch processing for improved throughput on Apple Silicon:
-
-```bash
-# Default batch size (12)
-transcribe-anything video.mp4 --device mlx
-
-# Custom batch size for better performance
-transcribe-anything video.mp4 --device mlx --batch_size 24
-
-# Lower batch size for memory-constrained systems
-transcribe-anything video.mp4 --device mlx --batch_size 6
-```
-
-**Batch Size Guidelines:**
-- Default: 12 (good balance of speed and memory usage)
-- Higher values (16-24): Better throughput but require more unified memory
-- Lower values (4-8): Use if experiencing memory issues
-- The optimal batch size depends on your model size and available unified memory
-
 # Custom Prompts and Vocabulary
 
 Whisper supports custom prompts to improve transcription accuracy for domain-specific vocabulary, names, or technical terms. This is especially useful when transcribing content with:

src/transcribe_anything/whisper_mac.py

@@ -233,7 +233,6 @@ def run_whisper_mac_mlx(  # pylint: disable=too-many-arguments
     word_timestamps = parsed_args.get("word_timestamps", False)
     verbose = parsed_args.get("verbose", False)
     temperature = parsed_args.get("temperature", 0.0)
-    task_param = parsed_args.get("task", task)  # Use parsed task or fallback to function parameter
 
     # Get the environment and run transcription
     env = get_environment()
@@ -292,12 +291,8 @@ def run_whisper_mac_mlx(  # pylint: disable=too-many-arguments
         audio="{input_wav_abs}",
         path_or_hf_repo=str(model_dir),
         language={repr(parsed_args.get("language"))},
-        task={repr(task_param)},
         batch_size={batch_size},
-        initial_prompt={repr(initial_prompt)},
-        word_timestamps={repr(word_timestamps)},
-        verbose={repr(verbose)},
-        temperature={repr(temperature)}
+        initial_prompt={repr(initial_prompt)}
     )
 
     # Print the result as JSON

tests/test_insanely_fast_whisper_mlx.py

@@ -101,47 +101,6 @@ def test_multilingual_support(self) -> None:
         self.assertTrue((test_dir / "out.srt").exists())
         self.assertTrue((test_dir / "out.json").exists())
 
-    @unittest.skipUnless(CAN_RUN_TEST, "Not mac")
-    def test_batch_size_functionality(self) -> None:
-        """Check that batch_size parameter works correctly."""
-        test_dir = LOCALFILE_DIR / "text_video_batch_size"
-        shutil.rmtree(test_dir, ignore_errors=True)
-
-        # Test with custom batch_size
-        run_whisper_mac_mlx(
-            input_wav=TEST_WAV,
-            model="small",
-            output_dir=test_dir,
-            language="en",
-            task="transcribe",
-            other_args=["--batch_size", "6"]  # Custom batch size
-        )
-
-        # Verify output files were created
-        self.assertTrue((test_dir / "out.txt").exists())
-        self.assertTrue((test_dir / "out.srt").exists())
-        self.assertTrue((test_dir / "out.json").exists())
-        self.assertTrue((test_dir / "out.vtt").exists())
-
-    @unittest.skipUnless(CAN_RUN_TEST, "Not mac")
-    def test_batch_size_parsing(self) -> None:
-        """Check that batch_size argument parsing works correctly."""
-        from transcribe_anything.whisper_mac import _parse_other_args
-
-        # Test valid batch_size
-        result = _parse_other_args(["--batch_size", "24"])
-        self.assertEqual(result["batch_size"], 24)
-
-        # Test with other arguments
-        result = _parse_other_args(["--language", "en", "--batch_size", "8", "--verbose"])
-        self.assertEqual(result["batch_size"], 8)
-        self.assertEqual(result["language"], "en")
-        self.assertTrue(result["verbose"])
-
-        # Test invalid batch_size (should not crash, just use default)
-        result = _parse_other_args(["--batch_size", "invalid"])
-        self.assertNotIn("batch_size", result)  # Should be filtered out due to ValueError
-
 
 if __name__ == "__main__":
     unittest.main()