docker build -t litongjava/whisper-cpp-server:1.0.0 -f distribute/docker/pure/Dockerfile . test with ggml-base.en.bin docker run --rm \
-v "$(pwd)/models":/models \
-v "$(pwd)/samples/jfk.wav":/jfk.wav \
litongjava/whisper-cpp-server:1.0.0 /app/simplest -m /models/ggml-base.en.bin /jfk.wav
docker run --rm -v "$(pwd)/models/ggml-base.en.bin":/models/ggml-base.en.bin -v "$(pwd)/samples/zh.wav":/samples/zh.wav litongjava/whisper-cpp-server:1.0.0 /app/simplest -m /models/ggml-base.en.bin /samples/zh.wav
test with ggml-large-v3.bin docker run --rm -v "$(pwd)/models/ggml-large-v3.bin":/models/ggml-large-v3.bin -v "$(pwd)/samples/zh.wav":/samples/zh.wav litongjava/whisper-cpp-server:1.0.0 /app/simplest -m /models/ggml-large-v3.bin /samples/zh.wav
docker run -dit --name=whisper-server --net=host -v "$(pwd)/models/ggml-base.en.bin":/models/ggml-base.en.bin litongjava/whisper-cpp-server:1.0.0 /app/whisper_http_server_base_httplib -m /models/ggml-base.en.bin
root@ping-Inspiron-3458:~/code/whisper-cpp-server# docker run --rm \
> -v "$(pwd)/models":/models \
> -v "$(pwd)/jfk.wav":/jfk.wav \
> litongjava/whisper-cpp-server:1.0.0 /app/simplest -m /models/ggml-base.en.bin /jfk.wav
whisper_init_from_file_with_params_no_state: loading model from '/models/ggml-base.en.bin'
whisper_model_load: loading model
whisper_model_load: n_vocab = 51864
whisper_model_load: n_audio_ctx = 1500
whisper_model_load: n_audio_state = 512
whisper_model_load: n_audio_head = 8
whisper_model_load: n_audio_layer = 6
whisper_model_load: n_text_ctx = 448
whisper_model_load: n_text_state = 512
whisper_model_load: n_text_head = 8
whisper_model_load: n_text_layer = 6
whisper_model_load: n_mels = 80
whisper_model_load: ftype = 1
whisper_model_load: qntvr = 0
whisper_model_load: type = 2 (base)
whisper_model_load: adding 1607 extra tokens
whisper_model_load: n_langs = 99
whisper_model_load: CPU total size = 147.37 MB
whisper_model_load: model size = 147.37 MB
whisper_init_state: kv self size = 16.52 MB
whisper_init_state: kv cross size = 18.43 MB
whisper_init_state: compute buffer (conv) = 16.39 MB
whisper_init_state: compute buffer (encode) = 132.07 MB
whisper_init_state: compute buffer (cross) = 4.78 MB
whisper_init_state: compute buffer (decode) = 96.48 MB
system_info: n_threads = 4 / 4 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | METAL = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | CUDA = 0 | COREML = 0 | OPENVINO = 0 |
main: WARNING: model is not multilingual, ignoring language and translation options
main: processing '/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
whisper_print_timings: load time = 169.51 ms
whisper_print_timings: fallbacks = 0 p / 0 h
whisper_print_timings: mel time = 59.75 ms
whisper_print_timings: sample time = 25.05 ms / 1 runs ( 25.05 ms per run)
whisper_print_timings: encode time = 6384.86 ms / 1 runs ( 6384.86 ms per run)
whisper_print_timings: decode time = 236.91 ms / 27 runs ( 8.77 ms per run)
whisper_print_timings: batchd time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: prompt time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: total time = 6885.22 ms
start
[00:00:00.000 --> 00:00:11.000] And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
root@ping-Inspiron-3458:~/code/whisper-cpp-server# docker run --rm -v "$(pwd)/models/ggml-large-v3.bin":/models/ggml-large-v3.bin -v "$(pwd)/samples/zh.wav":/samples/zh.wav litongjava/whisper-cpp-server:1.0.0 /app/simplest -m /models/ggml-large-v3.bin /samples/zh.wav
whisper_init_from_file_with_params_no_state: loading model from '/models/ggml-large-v3.bin'
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 = 32
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: CPU total size = 3094.36 MB
whisper_model_load: model size = 3094.36 MB
whisper_init_state: kv self size = 220.20 MB
whisper_init_state: kv cross size = 245.76 MB
whisper_init_state: compute buffer (conv) = 36.26 MB
whisper_init_state: compute buffer (encode) = 926.66 MB
whisper_init_state: compute buffer (cross) = 9.38 MB
whisper_init_state: compute buffer (decode) = 209.26 MB
system_info: n_threads = 4 / 4 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | METAL = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | CUDA = 0 | COREML = 0 | OPENVINO = 0 |
main: processing '/samples/zh.wav' (79949 samples, 5.0 sec), 4 threads, 1 processors, lang = auto, task = transcribe, timestamps = 1 ...
whisper_full_with_state: auto-detected language: zh (p = 0.998135)
start
[00:00:00.000 --> 00:00:05.000] 我认为跑步最重要的就是给我带来了身体健康
whisper_print_timings: load time = 5730.34 ms
whisper_print_timings: fallbacks = 0 p / 0 h
whisper_print_timings: mel time = 27.16 ms
whisper_print_timings: sample time = 27.23 ms / 1 runs ( 27.23 ms per run)
whisper_print_timings: encode time = 253393.73 ms / 2 runs (126696.87 ms per run)
whisper_print_timings: decode time = 11884.19 ms / 21 runs ( 565.91 ms per run)
whisper_print_timings: batchd time = 260.31 ms / 3 runs ( 86.77 ms per run)
whisper_print_timings: prompt time = 0.00 ms / 1 runs ( 0.00 ms per run)
whisper_print_timings: total time = 271354.41 ms