add whisper-cpp-server with server

litongjava · Mar 11, 2024 · a58bd39 · a58bd39
1 parent 0d17105
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diff --git a/distribute/docker/pure/Dockerfile b/distribute/docker/pure/Dockerfile
@@ -6,6 +6,4 @@ COPY cmake-build-release/ggml-metal.metal /app/
 COPY cmake-build-release/simplest /app/
 COPY cmake-build-release/whisper_http_server_base_httplib /app/
 COPY cmake-build-release/whisper_server_base_on_uwebsockets /app/
-COPY cmake-build-release/thirdparty/whisper.cpp/libwhisper.so /lib
-
-RUN chmod +x /app/*
+COPY cmake-build-release/thirdparty/whisper.cpp/libwhisper.so /lib
diff --git a/distribute/docker/pure/readme.md b/distribute/docker/pure/readme.md
@@ -1,23 +1,32 @@
+## build
 ```shell
 docker build -t litongjava/whisper-cpp-server:1.0.0 -f distribute/docker/pure/Dockerfile .
 ```
-
-test
+## test
+### 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 "$(pwd)/models":/models \
-  -v "$(pwd)/samples/zh.wav":/zh.wav \
-  litongjava/whisper-cpp-server:1.0.0 /app/simplest -m /models/ggml-base.en.bin /zh.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
+```
 
-log
+### test with server
+```
+docker run -dit --name=whisper-server --net=host -v "$(pwd)/models/ggml-base.en.bin":/models/ggml-base.en.bin litongjava/whisper-cpp-server:1.0.0 /app/whisper_http_server_base_httplib -m /models/ggml-base.en.bin
+```
+
+### test outout
+#### English
 ```
 root@ping-Inspiron-3458:~/code/whisper-cpp-server# docker run --rm \
 >   -v "$(pwd)/models":/models \
@@ -69,3 +78,52 @@ 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.
 ```
+#### Chinese
+```
+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
+
+```