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Hi there I am following instructions to get CoreML working on Apple Silicon M1.
after I get everything going and trying to transcribe the jfk sample, I only get a wrong transcription:
[00:00:00.000 --> 00:00:30.000] " in "
while the correct output is:
[00:00:00.300 --> 00:00:09.180] And so, my fellow Americans, ask not what your country can do for you, ask what you
[00:00:09.180 --> 00:00:11.000] can do for your country.
I think I did everything as instructed (python 3.10, miniconda, installed the packages) but I am very new to all this AI thing.
The regular (not CoreML) model is working perfectly for me, I am just trying to see if I get a better performance out of my M1 chip.
Thank you in advance, Nicolas.
(here's an excerpt of the output)
./main -m models/ggml-large-v3.bin -f samples/jfk.wav
...
whisper_init_state: loading Core ML model from 'models/ggml-large-v3-encoder.mlmodelc'
whisper_init_state: first run on a device may take a while ...
whisper_init_state: Core ML model loaded
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 8.80 MiB, ( 3412.97 / 10922.67)
whisper_init_state: compute buffer (conv) = 10.92 MB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 7.33 MiB, ( 3420.30 / 10922.67)
whisper_init_state: compute buffer (cross) = 9.38 MB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 197.95 MiB, ( 3618.25 / 10922.67)
whisper_init_state: compute buffer (decode) = 209.26 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 |
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 ...
[00:00:00.000 --> 00:00:30.000] " in "
whisper_print_timings: load time = 1128.05 ms
whisper_print_timings: fallbacks = 1 p / 0 h
whisper_print_timings: mel time = 7.88 ms
whisper_print_timings: sample time = 53.50 ms / 45 runs ( 1.19 ms per run)
whisper_print_timings: encode time = 1311.38 ms / 1 runs ( 1311.38 ms per run)
whisper_print_timings: decode time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: batchd time = 798.65 ms / 41 runs ( 19.48 ms per run)
whisper_print_timings: prompt time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: total time = 8631.10 ms
ggml_metal_free: deallocating
The text was updated successfully, but these errors were encountered:
Hi there I am following instructions to get CoreML working on Apple Silicon M1.
after I get everything going and trying to transcribe the jfk sample, I only get a wrong transcription:
while the correct output is:
I think I did everything as instructed (python 3.10, miniconda, installed the packages) but I am very new to all this AI thing.
The regular (not CoreML) model is working perfectly for me, I am just trying to see if I get a better performance out of my M1 chip.
Thank you in advance, Nicolas.
(here's an excerpt of the output)
The text was updated successfully, but these errors were encountered: