Update whisper-large-v3-turbo-coreml-fp16/README.md

whisper-large-v3-turbo-coreml-fp16/README.md

@@ -6,7 +6,7 @@ Core ML export of `openai/whisper-large-v3-turbo` tuned for Apple Silicon. This
 - `DecoderFull.mlpackage` – full-context fallback
 - `DecoderStateful.mlpackage` – experimental MLState variant (requires macOS 15+)
 
-Tokenizers, mel filters, and metadata are included so the bundle can be dropped directly into a Tauri, Swift, or whisper.cpp/Core ML integration.
+Tokenizers, mel filters, and metadata are included so the bundle can be dropped directly into any Core ML-driven Whisper integration.
 
 ## Contents
 
@@ -32,22 +32,23 @@ preprocessor_config.json
 ## Quick Start (Swift)
 
 ```swift
-let bundleURL = URL(fileURLWithPath: "../Models/whisper-large-v3-turbo-coreml-fp16")
-let whisper = try WhisperCoreML(
-    encoderURL: bundleURL.appendingPathComponent("Encoder.mlpackage"),
-    decoderURL: bundleURL.appendingPathComponent("DecoderWithCache.mlpackage"),
-    tokenizerDirectory: bundleURL
-)
-let result = try await whisper.transcribe(audioURL: inputURL)
-print(result.text)
+let bundleURL = URL(fileURLWithPath: "/path/to/whisper-large-v3-turbo-coreml-fp16")
+let encoderURL = bundleURL.appendingPathComponent("Encoder.mlpackage")
+let decoderURL = bundleURL.appendingPathComponent("DecoderWithCache.mlpackage")
+
+let encoder = try MLModel(contentsOf: encoderURL)
+let decoder = try MLModel(contentsOf: decoderURL)
+// Plug these into your transcription pipeline together with the tokenizer assets
+// located alongside the mlpackages.
 ```
 
-## Quick Start (CLI sandbox)
+## Quick Start (Python parity check)
 
 ```bash
-./tauri-coreml-sandbox/src-tauri/target/release/coreml_sandbox \
-  artifacts/hf-upload/whisper-large-v3-turbo-coreml-fp16 \
-  tests/audio/2p_argument.wav
+python3 scripts/verify_coreml_whisper.py \
+  --audio /path/to/audio.wav \
+  --coreml-dir /path/to/whisper-large-v3-turbo-coreml-fp16 \
+  --max-new-tokens 64
 ```
 
 ## Performance Snapshot