Include compressed version of the CoreML version of large-v3-turbo model.

I have uploaded the updated large-v3-turbo CoreML model.
It seems to be working properly according to the operation check logs.

> whisper_init_state: loading Core ML model from 'models/ggml-large-v3-turbo-encoder.mlmodelc'

```
./main -m models/ggml-large-v3-turbo.bin -f samples/jfk.wav
whisper_init_from_file_with_params_no_state: loading model from 'models/ggml-large-v3-turbo.bin'
whisper_init_with_params_no_state: use gpu = 1
whisper_init_with_params_no_state: flash attn = 0
whisper_init_with_params_no_state: gpu_device = 0
whisper_init_with_params_no_state: dtw = 0
whisper_model_load: loading model
whisper_model_load: n_vocab = 51866
whisper_model_load: n_audio_ctx = 1500
whisper_model_load: n_audio_state = 1280
whisper_model_load: n_audio_head = 20
whisper_model_load: n_audio_layer = 32
whisper_model_load: n_text_ctx = 448
whisper_model_load: n_text_state = 1280
whisper_model_load: n_text_head = 20
whisper_model_load: n_text_layer = 4
whisper_model_load: n_mels = 128
whisper_model_load: ftype = 1
whisper_model_load: qntvr = 0
whisper_model_load: type = 5 (large v3)
whisper_model_load: adding 1609 extra tokens
whisper_model_load: n_langs = 100
whisper_model_load: Metal total size = 1623.92 MB
whisper_model_load: model size = 1623.92 MB
whisper_backend_init_gpu: using Metal backend
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2
ggml_metal_init: picking default device: Apple M2
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name: Apple M2
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 17179.89 MB
whisper_backend_init: using BLAS backend
whisper_init_state: kv self size = 31.46 MB
whisper_init_state: kv cross size = 31.46 MB
whisper_init_state: kv pad size = 7.86 MB
whisper_init_state: loading Core ML model from 'models/ggml-large-v3-turbo-encoder.mlmodelc'
whisper_init_state: first run on a device may take a while ...
whisper_init_state: Core ML model loaded
whisper_init_state: compute buffer (conv) = 10.79 MB
whisper_init_state: compute buffer (cross) = 16.93 MB
whisper_init_state: compute buffer (decode) = 99.57 MB

system_info: n_threads = 4 / 8 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | METAL = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | CUDA = 0 | COREML = 1 | OPENVINO = 0 | CANN = 0

main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, 5 beams + best of 5, lang = en, task = transcribe, timestamps = 1 ...


[00:00:00.300 --> 00:00:09.360] And so, my fellow Americans, ask not what your country can do for you, ask what you can
[00:00:09.360 --> 00:00:11.000] do for your country.


whisper_print_timings: load time = 511.85 ms
whisper_print_timings: fallbacks = 0 p / 0 h
whisper_print_timings: mel time = 9.03 ms
whisper_print_timings: sample time = 34.10 ms / 148 runs ( 0.23 ms per run)
whisper_print_timings: encode time = 748.83 ms / 1 runs ( 748.83 ms per run)
whisper_print_timings: decode time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: batchd time = 233.59 ms / 146 runs ( 1.60 ms per run)
whisper_print_timings: prompt time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: total time = 356773.66 ms
ggml_metal_free: deallocating
```

Looking at the logs above, it might seem like it's taking too long.
However, given that the second log was as follows, I think this is just the normal behavior of the ANE service.

```
./main -m models/ggml-large-v3-turbo.bin -f samples/jfk.wav
whisper_init_from_file_with_params_no_state: loading model from 'models/ggml-large-v3-turbo.bin'
whisper_init_with_params_no_state: use gpu = 1
whisper_init_with_params_no_state: flash attn = 0
whisper_init_with_params_no_state: gpu_device = 0
whisper_init_with_params_no_state: dtw = 0
whisper_model_load: loading model
whisper_model_load: n_vocab = 51866
whisper_model_load: n_audio_ctx = 1500
whisper_model_load: n_audio_state = 1280
whisper_model_load: n_audio_head = 20
whisper_model_load: n_audio_layer = 32
whisper_model_load: n_text_ctx = 448
whisper_model_load: n_text_state = 1280
whisper_model_load: n_text_head = 20
whisper_model_load: n_text_layer = 4
whisper_model_load: n_mels = 128
whisper_model_load: ftype = 1
whisper_model_load: qntvr = 0
whisper_model_load: type = 5 (large v3)
whisper_model_load: adding 1609 extra tokens
whisper_model_load: n_langs = 100
whisper_model_load: Metal total size = 1623.92 MB
whisper_model_load: model size = 1623.92 MB
whisper_backend_init_gpu: using Metal backend
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2
ggml_metal_init: picking default device: Apple M2
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name: Apple M2
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 17179.89 MB
whisper_backend_init: using BLAS backend
whisper_init_state: kv self size = 31.46 MB
whisper_init_state: kv cross size = 31.46 MB
whisper_init_state: kv pad size = 7.86 MB
whisper_init_state: loading Core ML model from 'models/ggml-large-v3-turbo-encoder.mlmodelc'
whisper_init_state: first run on a device may take a while ...
whisper_init_state: Core ML model loaded
whisper_init_state: compute buffer (conv) = 10.79 MB
whisper_init_state: compute buffer (cross) = 16.93 MB
whisper_init_state: compute buffer (decode) = 99.57 MB

system_info: n_threads = 4 / 8 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | METAL = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | CUDA = 0 | COREML = 1 | OPENVINO = 0 | CANN = 0

main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, 5 beams + best of 5, lang = en, task = transcribe, timestamps = 1 ...


[00:00:00.300 --> 00:00:09.360] And so, my fellow Americans, ask not what your country can do for you, ask what you can
[00:00:09.360 --> 00:00:11.000] do for your country.


whisper_print_timings: load time = 643.47 ms
whisper_print_timings: fallbacks = 0 p / 0 h
whisper_print_timings: mel time = 8.74 ms
whisper_print_timings: sample time = 35.67 ms / 148 runs ( 0.24 ms per run)
whisper_print_timings: encode time = 689.74 ms / 1 runs ( 689.74 ms per run)
whisper_print_timings: decode time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: batchd time = 253.82 ms / 146 runs ( 1.74 ms per run)
whisper_print_timings: prompt time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: total time = 3488.60 ms
ggml_metal_free: deallocating
```

ggml-large-v3-turbo-encoder.mlmodelc.zip

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