天宁寺网站建设,seo优化外包,ui设计培训收费标准,东营城乡规划网20240202在Ubuntu20.04.6下使用whisper.cpp的显卡模式 2024/2/2 19:43 【结论#xff1a;在Ubuntu20.04.6下#xff0c;确认large模式识别7分钟中文视频#xff0c;需要356447.78 ms#xff0c;也就是356.5秒#xff0c;需要大概5分钟#xff01;效率太差#xff01;】 …20240202在Ubuntu20.04.6下使用whisper.cpp的显卡模式 2024/2/2 19:43 【结论在Ubuntu20.04.6下确认large模式识别7分钟中文视频需要356447.78 ms也就是356.5秒需要大概5分钟效率太差】 前提条件可以通过技术手段上外网^_ 首先你要有一张NVIDIA的显卡比如我用的PDD拼多多的二手GTX1080显卡。【并且极其可能是矿卡】800 2、请正确安装好NVIDIA最新的545版本的驱动程序和CUDA、cuDNN。 2、安装Torch 3、配置whisper https://github.com/ggerganov/whisper.cpp https://www.toutiao.com/article/7276732434920653312/?appnews_articletimestamp1706802934use_new_style1req_id2024020123553463D3509B1706BC79D479group_id7276732434920653312tt_frommobile_qqutm_sourcemobile_qqutm_mediumtoutiao_androidutm_campaignclient_shareshare_token7bcb7488-a03d-4291-96fb-d0835ac76ccasourcem_redirect https://www.toutiao.com/article/7276732434920653312/ OpenAI的whisper的c/c 版本体验
首先下载代码,注我的OS环境是Ubuntu20.04.6。 git clone https://github.com/ggerganov/whisper.cpp
下载成功后进入项目目录 cd whisper.cpp
执行如下脚本命令下载模型这里选择的base 版本我们先来测试英语识别 bash ./models/download-ggml-model.sh base.en 但是尝试了几次都无法下载成功报错消息如下
网上search 了一下找到可提供下载的链接 https://github.com/ggerganov/whisper.cpp/tree/master/models https://huggingface.co/ggerganov/whisper.cpp/tree/main 我选择下载全部35个文件 下载成功后将模型文件copy 到项目中的models目录 cp ~/Downloads/ggml-base.en.gin /home/havelet/ai/whisper.cpp/models
接下来执行如下编译命令rootrootrootroot-X99-Turbo:~/whisper.cpp$ make clean rootrootrootroot-X99-Turbo:~/whisper.cpp$ WHISPER_CLBLAST1 make -j16 执行结果如下 rootrootrootroot-X99-Turbo:~/whisper.cpp$ WHISPER_CUBLAS1 make I whisper.cpp build info: I UNAME_S: Linux I UNAME_P: x86_64 I UNAME_M: x86_64 I CFLAGS: -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I LDFLAGS: -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib I CC: cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 I CXX: g (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
nvcc --forward-unknown-to-host-compiler -archnative -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -Wno-pedantic -c ggml-cuda.cu -o ggml-cuda.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml.c -o ggml.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml-alloc.c -o ggml-alloc.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml-backend.c -o ggml-backend.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml-quants.c -o ggml-quants.o g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c whisper.cpp -o whisper.o g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/main/main.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o main -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib ./main -h
usage: ./main [options] file0.wav file1.wav ...
options: -h, --help [default] show this help message and exit -t N, --threads N [4 ] number of threads to use during computation -p N, --processors N [1 ] number of processors to use during computation -ot N, --offset-t N [0 ] time offset in milliseconds -on N, --offset-n N [0 ] segment index offset -d N, --duration N [0 ] duration of audio to process in milliseconds -mc N, --max-context N [-1 ] maximum number of text context tokens to store -ml N, --max-len N [0 ] maximum segment length in characters -sow, --split-on-word [false ] split on word rather than on token -bo N, --best-of N [5 ] number of best candidates to keep -bs N, --beam-size N [5 ] beam size for beam search -wt N, --word-thold N [0.01 ] word timestamp probability threshold -et N, --entropy-thold N [2.40 ] entropy threshold for decoder fail -lpt N, --logprob-thold N [-1.00 ] log probability threshold for decoder fail -debug, --debug-mode [false ] enable debug mode (eg. dump log_mel) -tr, --translate [false ] translate from source language to english -di, --diarize [false ] stereo audio diarization -tdrz, --tinydiarize [false ] enable tinydiarize (requires a tdrz model) -nf, --no-fallback [false ] do not use temperature fallback while decoding -otxt, --output-txt [false ] output result in a text file -ovtt, --output-vtt [false ] output result in a vtt file -osrt, --output-srt [false ] output result in a srt file -olrc, --output-lrc [false ] output result in a lrc file -owts, --output-words [false ] output script for generating karaoke video -fp, --font-path [/System/Library/Fonts/Supplemental/Courier New Bold.ttf] path to a monospace font for karaoke video -ocsv, --output-csv [false ] output result in a CSV file -oj, --output-json [false ] output result in a JSON file -ojf, --output-json-full [false ] include more information in the JSON file -of FNAME, --output-file FNAME [ ] output file path (without file extension) -np, --no-prints [false ] do not print anything other than the results -ps, --print-special [false ] print special tokens -pc, --print-colors [false ] print colors -pp, --print-progress [false ] print progress -nt, --no-timestamps [false ] do not print timestamps -l LANG, --language LANG [en ] spoken language (auto for auto-detect) -dl, --detect-language [false ] exit after automatically detecting language --prompt PROMPT [ ] initial prompt -m FNAME, --model FNAME [models/ggml-base.en.bin] model path -f FNAME, --file FNAME [ ] input WAV file path -oved D, --ov-e-device DNAME [CPU ] the OpenVINO device used for encode inference -ls, --log-score [false ] log best decoder scores of tokens -ng, --no-gpu [false ] disable GPU
g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/bench/bench.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o bench -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/quantize/quantize.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o quantize -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/server/server.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o server -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll 编译成功后则可以执行测试程序首先执行自带测试音频【英文】 ./main -f samples/jfk.wav 执行结果如下我们可看到识别结果正确
rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/g generate-coreml-interface.sh generate-coreml-model.sh ggml-base.en.bin ggml-large-v3.bin ggml-medium.bin ggml_to_pt.py rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/ggml ggml-base.en.bin ggml-large-v3.bin ggml-medium.bin ggml_to_pt.py rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/ggml-large-v3.bin chs.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 ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce GTX 1080, compute capability 6.1, VMM: yes whisper_backend_init: using CUDA backend whisper_model_load: CUDA0 total size 3094.86 MB (3 buffers) whisper_model_load: model size 3094.36 MB whisper_backend_init: using CUDA backend whisper_init_state: kv self size 220.20 MB whisper_init_state: kv cross size 245.76 MB whisper_init_state: compute buffer (conv) 35.50 MB whisper_init_state: compute buffer (encode) 233.50 MB whisper_init_state: compute buffer (cross) 10.15 MB whisper_init_state: compute buffer (decode) 108.99 MB
system_info: n_threads 4 / 36 | AVX 1 | AVX2 1 | AVX512 0 | FMA 1 | NEON 0 | ARM_FMA 0 | METAL 0 | F16C 1 | FP16_VA 0 | WASM_SIMD 0 | BLAS 1 | SSE3 1 | SSSE3 1 | VSX 0 | CUDA 1 | COREML 0 | OPENVINO 0 |
main: processing chs.wav (6748501 samples, 421.8 sec), 4 threads, 1 processors, 5 beams best of 5, lang zh, task transcribe, timestamps 1 ... [00:00:00.040 -- 00:00:01.460] 前段时间有个巨石横火 [00:00:01.460 -- 00:00:02.860] 某某是男人最好的衣媒 [00:00:02.860 -- 00:00:04.800] 这里的某某可以替换为减肥 [00:00:04.800 -- 00:00:07.620] 长发 西装 考研 书唱 永结无间等等等等 [00:00:07.620 -- 00:00:09.320] 我听到最新的一个说法是 [00:00:09.320 -- 00:00:11.940] 微分碎盖加口罩加半框眼镜加冲锋衣 [00:00:11.940 -- 00:00:13.440] 等于男人最好的衣媒 [00:00:13.440 -- 00:00:14.420] 大概也就前几年 [00:00:14.420 -- 00:00:17.560] 冲锋衣还和格子衬衫并列为程序员穿搭精华 [00:00:17.560 -- 00:00:19.940] 紫红色冲锋衣还被誉为广场舞达妈标配 [00:00:19.940 -- 00:00:22.700] 骆驼牌还是我爹这个年纪的人才会愿意买的牌子 [00:00:22.700 -- 00:00:24.380] 不知道风向为啥变得这么快 [00:00:24.380 -- 00:00:26.680] 为啥这东西突然变成男生逆袭神器 [00:00:26.680 -- 00:00:27.660] 时尚潮流单品 [00:00:27.660 -- 00:00:29.580] 后来我翻了一下小红书就懂了 [00:00:29.580 -- 00:00:30.460] 时尚这个时期 [00:00:30.460 -- 00:00:31.620] 重点不在于衣服 [00:00:31.620 -- 00:00:32.160] 在于人 [00:00:32.160 -- 00:00:34.500] 现在小红书上面和冲锋衣相关的笔记 [00:00:34.500 -- 00:00:36.220] 照片里的男生都是这样的 [00:00:36.220 -- 00:00:36.880] 这样的 [00:00:36.880 -- 00:00:38.140] 还有这样的 [00:00:38.140 -- 00:00:39.460] 你们哪里是看穿搭的 [00:00:39.460 -- 00:00:40.540] 你们明明是看脸 [00:00:40.540 -- 00:00:41.780] 就这个造型这个年龄 [00:00:41.780 -- 00:00:43.920] 你换上老头衫也能穿出氛围感好吗 [00:00:43.920 -- 00:00:46.560] 我又想起了当年郭德纲老师穿计繁西的残剧 [00:00:46.560 -- 00:00:48.560] 这个世界对我们这些长得不好看的人 [00:00:48.560 -- 00:00:49.480] 还真是苛刻呢 [00:00:49.480 -- 00:00:52.100] 所以说我总结了一下冲锋衣传达的要领 [00:00:52.100 -- 00:00:54.200] 大概就是一张白净且人畜无汉的脸 [00:00:54.200 -- 00:00:55.120] 充足的发量 [00:00:55.120 -- 00:00:55.980] 纤细的体型 [00:00:55.980 -- 00:00:58.160] 当然身上的冲锋衣还得是骆驼的 [00:00:58.160 -- 00:00:59.320] 去年在户外用品界 [00:00:59.320 -- 00:01:01.100] 最顶流的既不是鸟像书 [00:01:01.100 -- 00:01:02.560] 也不是有校服之称的北面 [00:01:02.560 -- 00:01:04.120] 或者老台顶流哥伦比亚 [00:01:04.120 -- 00:01:04.800] 而是骆驼 [00:01:04.800 -- 00:01:06.980] 双十一骆驼在天猫户外服饰品类 [00:01:06.980 -- 00:01:08.860] 拿下销售额和销量双料冠军 [00:01:08.860 -- 00:01:09.980] 销量达到百万级 [00:01:09.980 -- 00:01:10.620] 在抖音 [00:01:10.620 -- 00:01:13.200] 骆驼销售同比增幅高达百分之296 [00:01:13.200 -- 00:01:15.920] 旗下主打的三合一高性价比冲锋衣成为爆品 [00:01:15.920 -- 00:01:17.260] 哪怕不看双十一 [00:01:17.260 -- 00:01:18.020] 随手一搜 [00:01:18.020 -- 00:01:21.040] 骆驼在冲锋衣的七日销售榜上都是图榜的存在 [00:01:21.040 -- 00:01:22.480] 这是线上的销售表现 [00:01:22.480 -- 00:01:24.200] 至于线下还是网友总结的好 [00:01:24.200 -- 00:01:26.740] 如今在南方街头的骆驼比沙漠里的都多 [00:01:26.740 -- 00:01:27.540] 爬个华山 [00:01:27.540 -- 00:01:28.320] 满山的骆驼 [00:01:28.320 -- 00:01:29.840] 随便逛个街撞山了 [00:01:29.840 -- 00:01:31.060] 至于骆驼为啥这么火 [00:01:31.060 -- 00:01:31.800] 便宜啊 [00:01:31.800 -- 00:01:33.400] 拿卖的最好的丁真同款 [00:01:33.400 -- 00:01:35.500] 幻影黑三合一冲锋衣举个例子 [00:01:35.500 -- 00:01:36.000] 线下买 [00:01:36.000 -- 00:01:37.440] 标牌价格2198 [00:01:37.440 -- 00:01:38.940] 但是跑到网上看一下 [00:01:38.940 -- 00:01:40.460] 标价就变成了699 [00:01:40.460 -- 00:01:41.220] 至于折扣 [00:01:41.220 -- 00:01:42.360] 日常也都是有的 [00:01:42.360 -- 00:01:43.440] 400出头就能买到 [00:01:43.440 -- 00:01:44.960] 甚至有时候能低到300价 [00:01:44.960 -- 00:01:46.140] 要是你还嫌贵 [00:01:46.140 -- 00:01:48.200] 路头还有200块出头的单层冲锋衣 [00:01:48.200 -- 00:01:49.080] 就这个价格 [00:01:49.080 -- 00:01:51.520] 搁上海恐怕还不够两次CityWalk的报名费 [00:01:51.520 -- 00:01:52.560] 看了这个价格 [00:01:52.560 -- 00:01:53.560] 再对比一下北面 [00:01:53.560 -- 00:01:54.640] 1000块钱起步 [00:01:54.640 -- 00:01:56.000] 你就能理解为啥北面 [00:01:56.000 -- 00:01:58.120] 这么快就被大学生踢出了校服序列了 [00:01:58.120 -- 00:02:00.380] 我不知道现在大学生每个月生活费多少 [00:02:00.380 -- 00:02:02.160] 反正按照我上学时候的生活费 [00:02:02.160 -- 00:02:03.200] 一个月不吃不喝 [00:02:03.200 -- 00:02:05.080] 也就买得起俩袖子加一个帽子 [00:02:05.080 -- 00:02:06.420] 难怪当年全是假北面 [00:02:06.420 -- 00:02:07.400] 现在都是真路头 [00:02:07.400 -- 00:02:08.640] 至少人家是正品啊 [00:02:08.640 -- 00:02:10.080] 我翻了一下社交媒体 [00:02:10.080 -- 00:02:12.060] 发现对路头的吐槽和买了路头的 [00:02:12.060 -- 00:02:13.340] 基本上是1比1的比例 [00:02:13.340 -- 00:02:15.040] 吐槽最多的就是衣服会掉色 [00:02:15.040 -- 00:02:15.960] 还会串色 [00:02:15.960 -- 00:02:17.100] 比如图增洗个几次 [00:02:17.100 -- 00:02:18.240] 穿个两天就掉光了 [00:02:18.240 -- 00:02:19.600] 比如不同仓库发的货 [00:02:19.600 -- 00:02:20.600] 质量参差不齐 [00:02:20.600 -- 00:02:22.300] 买衣服还得看户口拼出身 [00:02:22.300 -- 00:02:23.660] 至于什么做工比较差 [00:02:23.660 -- 00:02:24.300] 内胆多 [00:02:24.300 -- 00:02:24.880] 走线糙 [00:02:24.880 -- 00:02:26.380] 不防水之类的就更多了 [00:02:26.380 -- 00:02:27.360] 但是这些吐槽 [00:02:27.360 -- 00:02:29.160] 并不意味着会影响路头的销量 [00:02:29.160 -- 00:02:30.820] 甚至还会有不少自来水表示 [00:02:30.820 -- 00:02:32.680] 就这价格要啥自行车啊 [00:02:32.680 -- 00:02:34.080] 所谓性价比性价比 [00:02:34.080 -- 00:02:35.340] 脱离价位谈性能 [00:02:35.340 -- 00:02:36.980] 这就不符合消费者的需求嘛 [00:02:36.980 -- 00:02:38.480] 无数次价格战告诉我们 [00:02:38.480 -- 00:02:39.500] 只要肯降价 [00:02:39.500 -- 00:02:40.960] 就没有卖不出去的产品 [00:02:40.960 -- 00:02:41.820] 一件冲锋衣 [00:02:41.820 -- 00:02:43.500] 1000多你觉得平平无奇 [00:02:43.500 -- 00:02:44.900] 500多你觉得差点意思 [00:02:44.900 -- 00:02:46.480] 200块你就要秒下单了 [00:02:46.480 -- 00:02:48.520] 到99恐怕就要拼点手速了 [00:02:48.520 -- 00:02:49.560] 像冲锋衣这个品类 [00:02:49.560 -- 00:02:50.720] 本来价格跨度就大 [00:02:50.720 -- 00:02:52.660] 北面最便宜的Gortex冲锋衣 [00:02:52.660 -- 00:02:53.740] 价格3000起步 [00:02:53.740 -- 00:02:56.360] 大概是同品牌最便宜冲锋衣的三倍价格 [00:02:56.360 -- 00:02:57.060] 至于十足鸟 [00:02:57.060 -- 00:02:59.020] 搭载了Gortex的硬壳起步价 [00:02:59.020 -- 00:02:59.780] 就要到4500 [00:02:59.780 -- 00:03:01.080] 而且同样是Gortex [00:03:01.080 -- 00:03:02.860] 内部也有不同的系列和档次 [00:03:02.860 -- 00:03:03.520] 做成衣服 [00:03:03.520 -- 00:03:05.780] 中间的差价恐怕就够买两件骆驼了 [00:03:05.780 -- 00:03:06.620] 至于智能控温 [00:03:06.620 -- 00:03:07.320] 防水拉链 [00:03:07.320 -- 00:03:07.900] 全压胶 [00:03:07.900 -- 00:03:09.760] 更加不可能出现在骆驼这里了 [00:03:09.760 -- 00:03:11.780] 至少不会是三四百的骆驼身上会有的 [00:03:11.780 -- 00:03:12.660] 有的价外的衣服 [00:03:12.660 -- 00:03:14.040] 买的就是一个放弃幻想 [00:03:14.040 -- 00:03:15.660] 吃到肚子里的科技鱼很活 [00:03:15.660 -- 00:03:16.840] 是能给你省钱的 [00:03:16.840 -- 00:03:18.320] 穿在身上的科技鱼很活 [00:03:18.320 -- 00:03:20.040] 装装件件都是要加钱的 [00:03:20.040 -- 00:03:21.440] 所以正如罗曼罗兰所说 [00:03:21.440 -- 00:03:23.040] 这世界上只有一种英雄主义 [00:03:23.040 -- 00:03:24.860] 就是在认清了骆驼的本质以后 [00:03:24.860 -- 00:03:26.060] 依然选择买骆驼 [00:03:26.060 -- 00:03:26.900] 关于骆驼的火爆 [00:03:26.900 -- 00:03:28.180] 我有一些小小的看法 [00:03:28.180 -- 00:03:28.960] 骆驼这个东西 [00:03:28.960 -- 00:03:30.220] 它其实就是个潮牌 [00:03:30.220 -- 00:03:31.940] 看看它的营销方式就知道了 [00:03:31.940 -- 00:03:32.920] 现在打开小红书 [00:03:32.920 -- 00:03:35.120] 日常可以看到骆驼穿搭是这样的 [00:03:35.120 -- 00:03:36.900] 加一点氛围感是这样的 [00:03:36.900 -- 00:03:37.400] 对比一下 [00:03:37.400 -- 00:03:39.240] 其他品牌的风格是这样的 [00:03:39.240 -- 00:03:40.020] 这样的 [00:03:40.020 -- 00:03:41.280] 其实对比一下就知道了 [00:03:41.280 -- 00:03:42.600] 其他品牌突出一个时程 [00:03:42.600 -- 00:03:44.240] 能防风就一定要讲防风 [00:03:44.240 -- 00:03:45.960] 能扛冻就一定要讲扛冻 [00:03:45.960 -- 00:03:47.340] 但骆驼在营销的时候 [00:03:47.340 -- 00:03:49.080] 主打的就是一个城市户外风 [00:03:49.080 -- 00:03:50.440] 虽然造型是春风衣 [00:03:50.440 -- 00:03:52.180] 但场景往往是在城市里 [00:03:52.180 -- 00:03:54.220] 哪怕在野外也要突出一个风和日丽 [00:03:54.220 -- 00:03:54.940] 阳光敏媚 [00:03:54.940 -- 00:03:56.500] 至少不会在明显的严寒 [00:03:56.500 -- 00:03:58.020] 高海拔或是恶劣气候下 [00:03:58.020 -- 00:04:00.160] 如果用一个词形容骆驼的营销风格 [00:04:00.160 -- 00:04:00.920] 那就是清洗 [00:04:00.920 -- 00:04:03.060] 或者说他很理解自己的消费者是谁 [00:04:03.060 -- 00:04:03.920] 需要什么产品 [00:04:03.920 -- 00:04:05.260] 从使用场景来说 [00:04:05.260 -- 00:04:06.600] 骆驼的消费者买春风衣 [00:04:06.600 -- 00:04:08.640] 不是真的有什么大风大雨要去应对 [00:04:08.640 -- 00:04:10.880] 春风衣的作用是下雨没带伞的时候 [00:04:10.880 -- 00:04:12.160] 临时顶个几分钟 [00:04:12.160 -- 00:04:13.700] 让你能图书馆跑回宿舍 [00:04:13.700 -- 00:04:14.940] 或者是冬天骑电动车 [00:04:14.940 -- 00:04:16.220] 被风吹得不行的时候 [00:04:16.220 -- 00:04:17.200] 稍微扛一下风 [00:04:17.200 -- 00:04:18.340] 不至于体感太冷 [00:04:18.340 -- 00:04:19.700] 当然他们也会出门 [00:04:19.700 -- 00:04:21.780] 但大部分时候也都是去别的城市 [00:04:21.780 -- 00:04:23.860] 或者在城市周边搞搞简单的徒步 [00:04:23.860 -- 00:04:24.920] 这种情况下 [00:04:24.920 -- 00:04:25.920] 穿个骆驼也就够了 [00:04:25.920 -- 00:04:27.220] 从购买动机来说 [00:04:27.220 -- 00:04:29.260] 骆驼就更没有必要上那些硬核科技了 [00:04:29.260 -- 00:04:30.920] 消费者买骆驼买的是个什么呢 [00:04:30.920 -- 00:04:32.240] 不是春风衣的功能性 [00:04:32.240 -- 00:04:33.380] 而是春风衣的造型 [00:04:33.380 -- 00:04:34.340] 宽松的版型 [00:04:34.340 -- 00:04:36.380] 能精准遮住微微隆起的小肚子 [00:04:36.380 -- 00:04:37.440] 棱角分明的质感 [00:04:37.440 -- 00:04:39.420] 能隐藏一切不完美的整体线条 [00:04:39.420 -- 00:04:41.260] 显瘦的副作用就是显年轻 [00:04:41.260 -- 00:04:42.600] 再配上一条牛仔裤 [00:04:42.600 -- 00:04:43.680] 配上一双大黄靴 [00:04:43.680 -- 00:04:45.100] 大学生的气质就出来了 [00:04:45.100 -- 00:04:47.700] 要是自拍的时候再配上大学宿舍洗漱台 [00:04:47.700 -- 00:04:49.380] 那永远擦不干净的镜子 [00:04:49.380 -- 00:04:50.840] 瞬间青春无敌了 [00:04:50.840 -- 00:04:51.700] 说的更直白一点 [00:04:51.700 -- 00:04:53.060] 人家买的是个锦铃神器 [00:04:53.060 -- 00:04:53.820] 所以说 [00:04:53.820 -- 00:04:55.860] 吐槽穿骆驼都是假户外爱好者的人 [00:04:55.860 -- 00:04:57.460] 其实并没有理解骆驼的定位 [00:04:57.460 -- 00:04:59.780] 骆驼其实是给了想要入门山系穿搭 [00:04:59.780 -- 00:05:01.740] 想要追逐流行的人一个最平价 [00:05:01.740 -- 00:05:02.980] 决策成本最低的选择 [00:05:02.980 -- 00:05:04.880] 至于那些真正的硬核户外爱好者 [00:05:04.880 -- 00:05:05.800] 骆驼既没有能力 [00:05:05.800 -- 00:05:07.080] 也没有打算触打他们 [00:05:07.080 -- 00:05:07.980] 反过来说 [00:05:07.980 -- 00:05:09.460] 那些自驾穿越边疆国道 [00:05:09.460 -- 00:05:11.680] 或者去阿尔卑斯山区登山探险的人 [00:05:11.680 -- 00:05:13.540] 也不太可能在户外服饰上省钱 [00:05:13.540 -- 00:05:14.900] 毕竟光是交通住宿 [00:05:14.900 -- 00:05:15.600] 请假出行 [00:05:15.600 -- 00:05:16.560] 成本就不低了 [00:05:16.560 -- 00:05:17.320] 对他们来说 [00:05:17.320 -- 00:05:19.140] 户外装备很多时候是保命用的 [00:05:19.140 -- 00:05:21.180] 也就不存在跟风凹造型的必要了 [00:05:21.180 -- 00:05:22.300] 最后我再说个题外话 [00:05:22.300 -- 00:05:23.320] 年轻人追捧骆驼 [00:05:23.320 -- 00:05:24.240] 一个隐藏的原因 [00:05:24.240 -- 00:05:25.940] 其实是羽绒服越来越贵了 [00:05:25.940 -- 00:05:26.620] 有媒体统计 [00:05:26.620 -- 00:05:28.440] 现在国产羽绒服的平均售价 [00:05:28.440 -- 00:05:29.880] 已经高达881元 [00:05:29.880 -- 00:05:31.140] 波斯灯均价最高 [00:05:31.140 -- 00:05:31.900] 接近2000元 [00:05:31.900 -- 00:05:32.880] 而且过去几年 [00:05:32.880 -- 00:05:34.800] 国产羽绒服品牌都在转向高端化 [00:05:34.800 -- 00:05:37.060] 羽绒服市场分为8000元以上的奢侈级 [00:05:37.060 -- 00:05:38.440] 2000元以下的大众级 [00:05:38.440 -- 00:05:39.740] 而在中间的高端级 [00:05:39.740 -- 00:05:41.220] 国产品牌一直没有存在感 [00:05:41.220 -- 00:05:42.140] 所以过去几年 [00:05:42.140 -- 00:05:43.520] 波斯灯天空人这些品牌 [00:05:43.520 -- 00:05:45.260] 都把2000元到8000元这个市场 [00:05:45.260 -- 00:05:46.560] 当成未来的发展趋势 [00:05:46.560 -- 00:05:47.980] 东芯证券研报显示 [00:05:47.980 -- 00:05:49.600] 从2018到2021年 [00:05:49.600 -- 00:05:52.080] 波斯灯均价4年涨幅达到60%以上 [00:05:52.080 -- 00:05:53.080] 过去5个财年 [00:05:53.080 -- 00:05:54.300] 这个品牌的营销开支 [00:05:54.300 -- 00:05:56.020] 从20多亿涨到了60多亿 [00:05:56.020 -- 00:05:57.240] 羽绒服价格往上走 [00:05:57.240 -- 00:05:59.160] 年轻消费者就开始抛弃羽绒服 [00:05:59.160 -- 00:06:00.300] 购买平价春风衣 [00:06:00.300 -- 00:06:02.240] 里面再穿个普通价位的摇篱绒 [00:06:02.240 -- 00:06:03.280] 或者羽绒小夹克 [00:06:03.280 -- 00:06:05.100] 也不比大几千的羽绒服差多少 [00:06:05.100 -- 00:06:05.740] 说到底 [00:06:05.740 -- 00:06:07.120] 现在消费社会发达了 [00:06:07.120 -- 00:06:08.300] 没有什么需求是一定要 [00:06:08.300 -- 00:06:09.740] 某种特定的解决方案 [00:06:09.740 -- 00:06:11.500] 特定价位的商品才能实现的 [00:06:11.500 -- 00:06:12.080] 要保暖 [00:06:12.080 -- 00:06:13.140] 羽绒服固然很好 [00:06:13.140 -- 00:06:15.320] 但春风衣加一些内搭也很暖和 [00:06:15.320 -- 00:06:15.820] 要时尚 [00:06:15.820 -- 00:06:17.860] 大几千块钱的设计师品牌非常不错 [00:06:17.860 -- 00:06:19.360] 但350的拼多多服饰 [00:06:19.360 -- 00:06:20.520] 搭得好也能出产 [00:06:20.520 -- 00:06:21.620] 要去野外徒步 [00:06:21.620 -- 00:06:22.940] 花五六千买鸟也可以 [00:06:22.940 -- 00:06:25.100] 但迪卡侬也足以应付大多数状况 [00:06:25.100 -- 00:06:25.720] 所以说 [00:06:25.720 -- 00:06:27.420] 花高价买春风衣当然也OK [00:06:27.420 -- 00:06:28.540] 三四百买件骆驼 [00:06:28.540 -- 00:06:29.880] 也是可以介绍的选择 [00:06:29.880 -- 00:06:31.900] 何况骆驼也多多少少有一些功能性 [00:06:31.900 -- 00:06:32.840] 毕竟它再怎么样 [00:06:32.840 -- 00:06:33.920] 还是个春风衣 [00:06:33.920 -- 00:06:34.800] 理解了这个事情 [00:06:34.800 -- 00:06:35.740] 就很容易分辨 [00:06:35.740 -- 00:06:36.900] 什么是智商税的 [00:06:36.900 -- 00:06:38.740] 那些向你灌输非某个品牌不用 [00:06:38.740 -- 00:06:39.880] 告诉你某个需求 [00:06:39.880 -- 00:06:41.380] 只有某个产品才能满足 [00:06:41.380 -- 00:06:42.160] 某个品牌 [00:06:42.160 -- 00:06:44.220] 就是某个品类绝对的鄙视链顶端 [00:06:44.220 -- 00:06:45.900] 这类营销的智商税含量 [00:06:45.900 -- 00:06:46.860] 必然是很高的 [00:06:46.860 -- 00:06:48.780] 它的目的是剥夺你选择的权利 [00:06:48.780 -- 00:06:51.220] 让你主动放弃比价和寻找平梯的想法 [00:06:51.220 -- 00:06:52.920] 从而避免与其他品牌竞争 [00:06:52.920 -- 00:06:54.280] 而没有竞争的市场 [00:06:54.280 -- 00:06:56.020] 才是智商税含量最高的市场 [00:06:56.020 -- 00:06:57.360] 消费商业洞见 [00:06:57.360 -- 00:06:58.420] 近在IC实验室 [00:06:58.420 -- 00:06:59.000] 我是馆长 [00:06:59.000 -- 00:06:59.840] 我们下期再见 [00:06:59.840 -- 00:07:01.840] 谢谢大家!
output_srt: saving output to chs.wav.srt
whisper_print_timings: load time 1232.24 ms whisper_print_timings: fallbacks 1 p / 0 h whisper_print_timings: mel time 507.42 ms whisper_print_timings: sample time 14211.34 ms / 19337 runs ( 0.73 ms per run) whisper_print_timings: encode time 9234.67 ms / 19 runs ( 486.04 ms per run) whisper_print_timings: decode time 41.85 ms / 2 runs ( 20.92 ms per run) whisper_print_timings: batchd time 325320.62 ms / 19329 runs ( 16.83 ms per run) whisper_print_timings: prompt time 5857.69 ms / 3869 runs ( 1.51 ms per run) whisper_print_timings: total time 356447.78 ms rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/ggml-large-v3.bin chs.wav 参考资料 https://blog.csdn.net/qq_43907505/article/details/135048613?spm1001.2101.3001.6650.4utm_mediumdistribute.pc_relevant.none-task-blog-2%7Edefault%7EYuanLiJiHua%7EPosition-4-135048613-blog-127843094.235%5Ev43%5Epc_blog_bottom_relevance_base1depth_1-utm_sourcedistribute.pc_relevant.none-task-blog-2%7Edefault%7EYuanLiJiHua%7EPosition-4-135048613-blog-127843094.235%5Ev43%5Epc_blog_bottom_relevance_base1utm_relevant_index9 https://blog.csdn.net/qq_43907505/article/details/135048613 开源语音识别faster-whisper部署教程 日语源视频【通过hotbox获取】 https://www.bilibili.com/video/BV1fG4y1b74e/?vd_source4a6b675fa22dfa306da59f67b1f22616 「原神」神里绫华日语配音谁能拒绝一只蝴蝶忍呢
中文源视频【通过猫抓获取】 https://www.ixigua.com/7320445308314485283 2024-01-05 11:06国产冲锋衣杀疯了百元骆驼如何营销卖爆-IC实验室 rootrootrootroot-X99-Turbo:~/whisper.cpp$ ffmpeg ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers usage: ffmpeg [options] [[infile options] -i infile]... {[outfile options] outfile}... Use -h to get full help or, even better, run man ffmpeg rootrootrootroot-X99-Turbo:~/whisper.cpp$ ffmpeg -i chi.mp4 -ar 16000 -ac 1 -c:a pcm_s16le chi.wav ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers rootrootrootroot-X99-Turbo:~/whisper.cpp$ ffmpeg -i chs.mp4 -ar 16000 -ac 1 -c:a pcm_s16le chs.wav LOG如下 rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ make clean I whisper.cpp build info: I UNAME_S: Linux I UNAME_P: x86_64 I UNAME_M: x86_64 I CFLAGS: -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 I LDFLAGS: I CC: cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 I CXX: g (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
rm -f *.o main stream command talk talk-llama bench quantize server lsp libwhisper.a libwhisper.so rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll total 19196 drwxrwxr-x 17 rootroot rootroot 4096 2月 2 17:46 ./ drwxr-xr-x 30 rootroot rootroot 4096 2月 2 16:49 ../ drwxrwxr-x 7 rootroot rootroot 4096 2月 2 16:49 bindings/ -rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4* -rw-rw-r-- 1 rootroot rootroot 13497126 2月 2 17:26 chs.wav -rw-rw-r-- 1 rootroot rootroot 11821 2月 2 17:41 chs.wav使用CPU.srt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 cmake/ -rw-rw-r-- 1 rootroot rootroot 19150 2月 2 16:49 CMakeLists.txt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 coreml/ drwx------ 2 rootroot rootroot 4096 2月 2 17:45 CPU/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 .devops/ drwxrwxr-x 24 rootroot rootroot 4096 2月 2 16:49 examples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 extra/ -rw-rw-r-- 1 rootroot rootroot 31647 2月 2 16:49 ggml-alloc.c -rw-rw-r-- 1 rootroot rootroot 4055 2月 2 16:49 ggml-alloc.h -rw-rw-r-- 1 rootroot rootroot 67212 2月 2 16:49 ggml-backend.c -rw-rw-r-- 1 rootroot rootroot 11720 2月 2 16:49 ggml-backend.h -rw-rw-r-- 1 rootroot rootroot 5874 2月 2 16:49 ggml-backend-impl.h -rw-rw-r-- 1 rootroot rootroot 676115 2月 2 16:49 ggml.c -rw-rw-r-- 1 rootroot rootroot 440093 2月 2 16:49 ggml-cuda.cu -rw-rw-r-- 1 rootroot rootroot 2104 2月 2 16:49 ggml-cuda.h -rw-rw-r-- 1 rootroot rootroot 85094 2月 2 16:49 ggml.h -rw-rw-r-- 1 rootroot rootroot 7567 2月 2 16:49 ggml-impl.h -rw-rw-r-- 1 rootroot rootroot 2358 2月 2 16:49 ggml-metal.h -rw-rw-r-- 1 rootroot rootroot 150160 2月 2 16:49 ggml-metal.m -rw-rw-r-- 1 rootroot rootroot 225659 2月 2 16:49 ggml-metal.metal -rw-rw-r-- 1 rootroot rootroot 85693 2月 2 16:49 ggml-opencl.cpp -rw-rw-r-- 1 rootroot rootroot 1386 2月 2 16:49 ggml-opencl.h -rw-rw-r-- 1 rootroot rootroot 401791 2月 2 16:49 ggml-quants.c -rw-rw-r-- 1 rootroot rootroot 13705 2月 2 16:49 ggml-quants.h drwxrwxr-x 8 rootroot rootroot 4096 2月 2 16:49 .git/ drwxrwxr-x 3 rootroot rootroot 4096 2月 2 16:49 .github/ -rw-rw-r-- 1 rootroot rootroot 803 2月 2 16:49 .gitignore -rw-rw-r-- 1 rootroot rootroot 96 2月 2 16:49 .gitmodules drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 grammars/ -rw-rw-r-- 1 rootroot rootroot 1072 2月 2 16:49 LICENSE -rw-rw-r-- 1 rootroot rootroot 14883 2月 2 16:49 Makefile drwxrwxr-x 2 rootroot rootroot 4096 2月 2 17:24 models/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 openvino/ -rw-rw-r-- 1 rootroot rootroot 1776 2月 2 16:49 Package.swift -rw-rw-r-- 1 rootroot rootroot 39115 2月 2 16:49 README.md drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 samples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 spm-headers/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 tests/ -rw-rw-r-- 1 rootroot rootroot 232975 2月 2 16:49 whisper.cpp -rw-rw-r-- 1 rootroot rootroot 30248 2月 2 16:49 whisper.h rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll main ls: cannot access main: No such file or directory rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ WHISPER_CLBLAST1 make -j16 I whisper.cpp build info: I UNAME_S: Linux I UNAME_P: x86_64 I UNAME_M: x86_64 I CFLAGS: -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST I LDFLAGS: -lclblast -lOpenCL I CC: cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 I CXX: g (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST -c ggml-opencl.cpp -o ggml-opencl.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST -c ggml.c -o ggml.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST -c ggml-alloc.c -o ggml-alloc.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST -c ggml-backend.c -o ggml-backend.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST -c ggml-quants.c -o ggml-quants.o g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CLBLAST -c whisper.cpp -o whisper.o ggml-opencl.cpp:15:10: fatal error: clblast.h: No such file or directory 15 | #include clblast.h | ^~~~~~~~~~~ compilation terminated. make: *** [Makefile:255: ggml-opencl.o] Error 1 make: *** Waiting for unfinished jobs....
rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ sidp aptg-et install openblas
Command sidp not found, did you mean: command ssdp from snap ssdp (0.0.1) command sipp from deb sip-tester (1:3.6.0-1build1) command sip from deb sip-dev (4.19.21dfsg-1build1) command sfdp from deb graphviz (2.42.2-3build2)
See snap info snapname for additional versions.
rootrootrootroot-X99-Turbo:~/whisper.cpp$ sidp apt-get install openblas
Command sidp not found, did you mean: command ssdp from snap ssdp (0.0.1) command sfdp from deb graphviz (2.42.2-3build2) command sip from deb sip-dev (4.19.21dfsg-1build1) command sipp from deb sip-tester (1:3.6.0-1build1)
See snap info snapname for additional versions.
rootrootrootroot-X99-Turbo:~/whisper.cpp$ sudo apt-get install openblas [sudo] password for rootroot: Reading package lists... Done Building dependency tree Reading state information... Done E: Unable to locate package openblas rootrootrootroot-X99-Turbo:~/whisper.cpp$ sudo apt install openblas Reading package lists... Done Building dependency tree Reading state information... Done E: Unable to locate package openblas rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ sudo apt-get install libopenblas-dev Reading package lists... Done Building dependency tree Reading state information... Done The following additional packages will be installed: libopenblas-pthread-dev libopenblas0 libopenblas0-pthread The following NEW packages will be installed: libopenblas-dev libopenblas-pthread-dev libopenblas0 libopenblas0-pthread 0 upgraded, 4 newly installed, 0 to remove and 11 not upgraded. Need to get 13.7 MB of archives. After this operation, 153 MB of additional disk space will be used. Do you want to continue? [Y/n] y Get:1 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal-updates/universe amd64 libopenblas0-pthread amd64 0.3.8ds-1ubuntu0.20.04.1 [9,127 kB] Get:2 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal-updates/universe amd64 libopenblas0 amd64 0.3.8ds-1ubuntu0.20.04.1 [5,892 B] Get:3 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal-updates/universe amd64 libopenblas-pthread-dev amd64 0.3.8ds-1ubuntu0.20.04.1 [4,526 kB] Get:4 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal-updates/universe amd64 libopenblas-dev amd64 0.3.8ds-1ubuntu0.20.04.1 [16.4 kB] Fetched 13.7 MB in 2s (8,470 kB/s) Selecting previously unselected package libopenblas0-pthread:amd64. (Reading database ... 207405 files and directories currently installed.) Preparing to unpack .../libopenblas0-pthread_0.3.8ds-1ubuntu0.20.04.1_amd64.deb ... Unpacking libopenblas0-pthread:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... Selecting previously unselected package libopenblas0:amd64. Preparing to unpack .../libopenblas0_0.3.8ds-1ubuntu0.20.04.1_amd64.deb ... Unpacking libopenblas0:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... Selecting previously unselected package libopenblas-pthread-dev:amd64. Preparing to unpack .../libopenblas-pthread-dev_0.3.8ds-1ubuntu0.20.04.1_amd64.deb ... Unpacking libopenblas-pthread-dev:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... Selecting previously unselected package libopenblas-dev:amd64. Preparing to unpack .../libopenblas-dev_0.3.8ds-1ubuntu0.20.04.1_amd64.deb ... Unpacking libopenblas-dev:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... Setting up libopenblas0-pthread:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 to provide /usr/lib/x86_64-linux-gnu/libblas.so.3 (libblas.so.3-x86_64-linux-gnu) in auto mode update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3 to provide /usr/lib/x86_64-linux-gnu/liblapack.so.3 (liblapack.so.3-x86_64-linux-gnu) in auto mode update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblas.so.0 to provide /usr/lib/x86_64-linux-gnu/libopenblas.so.0 (libopenblas.so.0-x86_64-linux-gnu) in auto mode Setting up libopenblas0:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... Setting up libopenblas-pthread-dev:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so to provide /usr/lib/x86_64-linux-gnu/libblas.so (libblas.so-x86_64-linux-gnu) in auto mode update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so to provide /usr/lib/x86_64-linux-gnu/liblapack.so (liblapack.so-x86_64-linux-gnu) in auto mode update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblas.so to provide /usr/lib/x86_64-linux-gnu/libopenblas.so (libopenblas.so-x86_64-linux-gnu) in auto mode Setting up libopenblas-dev:amd64 (0.3.8ds-1ubuntu0.20.04.1) ... Processing triggers for libc-bin (2.31-0ubuntu9.14) ... rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ WHISPER_CUBLAS1 make -j16 expr: syntax error: unexpected argument ‘11.6’ I whisper.cpp build info: I UNAME_S: Linux I UNAME_P: x86_64 I UNAME_M: x86_64 I CFLAGS: -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I LDFLAGS: -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib I CC: cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 I CXX: g (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
nvcc --forward-unknown-to-host-compiler -archall -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -Wno-pedantic -c ggml-cuda.cu -o ggml-cuda.o make: nvcc: Command not found make: *** [Makefile:225: ggml-cuda.o] Error 127 rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ nvcc -v
Command nvcc not found, but can be installed with:
sudo apt install nvidia-cuda-toolkit
rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ sudo apt install nvidia-cuda-toolkit Reading package lists... Done Building dependency tree Reading state information... Done The following additional packages will be installed: g-8 javascript-common libaccinj64-10.1 libcublas10 libcublaslt10 libcudart10.1 libcufft10 libcufftw10 libcuinj64-10.1 libcupti-dev libcupti-doc libcupti10.1 libcurand10 libcusolver10 libcusolvermg10 libcusparse10 libjs-jquery libnppc10 libnppial10 libnppicc10 libnppicom10 libnppidei10 libnppif10 libnppig10 libnppim10 libnppist10 libnppisu10 libnppitc10 libnpps10 libnvblas10 libnvgraph10 libnvidia-compute-545 libnvidia-ml-dev libnvjpeg10 libnvrtc10.1 libnvtoolsext1 libnvvm3 libstdc-8-dev libthrust-dev libvdpau-dev node-html5shiv nvidia-cuda-dev nvidia-cuda-doc nvidia-cuda-gdb nvidia-opencl-dev nvidia-profiler nvidia-visual-profiler ocl-icd-opencl-dev opencl-c-headers Suggested packages: g-8-multilib gcc-8-doc apache2 | lighttpd | httpd libstdc-8-doc libvdpau-doc nodejs nvidia-driver | nvidia-tesla-440-driver | nvidia-tesla-418-driver libpoclu-dev Recommended packages: libnvcuvid1 nsight-compute nsight-systems The following NEW packages will be installed: g-8 javascript-common libaccinj64-10.1 libcublas10 libcublaslt10 libcudart10.1 libcufft10 libcufftw10 libcuinj64-10.1 libcupti-dev libcupti-doc libcupti10.1 libcurand10 libcusolver10 libcusolvermg10 libcusparse10 libjs-jquery libnppc10 libnppial10 libnppicc10 libnppicom10 libnppidei10 libnppif10 libnppig10 libnppim10 libnppist10 libnppisu10 libnppitc10 libnpps10 libnvblas10 libnvgraph10 libnvidia-compute-545 libnvidia-ml-dev libnvjpeg10 libnvrtc10.1 libnvtoolsext1 libnvvm3 libstdc-8-dev libthrust-dev libvdpau-dev node-html5shiv nvidia-cuda-dev nvidia-cuda-doc nvidia-cuda-gdb nvidia-cuda-toolkit nvidia-opencl-dev nvidia-profiler nvidia-visual-profiler ocl-icd-opencl-dev opencl-c-headers 0 upgraded, 50 newly installed, 0 to remove and 11 not upgraded. Need to get 1,111 MB/1,160 MB of archives. After this operation, 3,056 MB of additional disk space will be used. Do you want to continue? [Y/n] y Get:1 file:/var/cuda-repo-ubuntu2004-12-3-local libnvidia-compute-545 545.23.08-0ubuntu1 [48.8 MB] Err:1 file:/var/cuda-repo-ubuntu2004-12-3-local libnvidia-compute-545 545.23.08-0ubuntu1 File not found - /var/cuda-repo-ubuntu2004-12-3-local/./libnvidia-compute-545_545.23.08-0ubuntu1_amd64.deb (2: No such file or directory) Get:2 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/universe amd64 libstdc-8-dev amd64 8.4.0-3ubuntu2 [1,537 kB] Get:3 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/universe amd64 g-8 amd64 8.4.0-3ubuntu2 [10.1 MB] Get:4 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/main amd64 javascript-common all 11 [6,066 B] Get:5 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libaccinj64-10.1 amd64 10.1.243-3 [1,893 kB] Get:6 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcublaslt10 amd64 10.1.243-3 [9,249 kB] Get:7 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcublas10 amd64 10.1.243-3 [29.7 MB] Get:8 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcudart10.1 amd64 10.1.243-3 [125 kB] Get:9 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcufft10 amd64 10.1.243-3 [85.3 MB] Get:10 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcufftw10 amd64 10.1.243-3 [124 kB] Get:11 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcuinj64-10.1 amd64 10.1.243-3 [2,030 kB] Get:12 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcupti10.1 amd64 10.1.243-3 [4,311 kB] Get:13 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcurand10 amd64 10.1.243-3 [39.0 MB] Get:14 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcusolver10 amd64 10.1.243-3 [44.5 MB] Get:15 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcusolvermg10 amd64 10.1.243-3 [28.1 MB] Get:16 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcusparse10 amd64 10.1.243-3 [56.8 MB] Get:17 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/main amd64 libjs-jquery all 3.3.1~dfsg-3 [329 kB] Get:18 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppc10 amd64 10.1.243-3 [123 kB] Get:19 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppial10 amd64 10.1.243-3 [3,667 kB] Get:20 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppicc10 amd64 10.1.243-3 [1,621 kB] Get:21 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppicom10 amd64 10.1.243-3 [539 kB] Get:22 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppidei10 amd64 10.1.243-3 [2,001 kB] Get:23 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppif10 amd64 10.1.243-3 [22.0 MB] Get:24 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppig10 amd64 10.1.243-3 [12.0 MB] Get:25 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppim10 amd64 10.1.243-3 [2,694 kB] Get:26 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppist10 amd64 10.1.243-3 [7,313 kB] Get:27 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppisu10 amd64 10.1.243-3 [116 kB] Get:28 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnppitc10 amd64 10.1.243-3 [802 kB] Get:29 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnpps10 amd64 10.1.243-3 [2,970 kB] Get:30 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnvblas10 amd64 10.1.243-3 [129 kB] Get:31 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnvgraph10 amd64 10.1.243-3 [44.5 MB] Get:32 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnvidia-ml-dev amd64 10.1.243-3 [58.1 kB] Get:33 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnvjpeg10 amd64 10.1.243-3 [1,227 kB] Get:34 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnvrtc10.1 amd64 10.1.243-3 [6,307 kB] Get:35 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/main amd64 libvdpau-dev amd64 1.3-1ubuntu2 [37.3 kB] Get:36 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/universe amd64 node-html5shiv all 3.7.3dfsg-3 [12.9 kB] Get:37 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcupti-dev amd64 10.1.243-3 [4,779 kB] Get:38 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libcupti-doc all 10.1.243-3 [2,117 kB] Get:39 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnvtoolsext1 amd64 10.1.243-3 [25.1 kB] Get:40 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libnvvm3 amd64 10.1.243-3 [4,436 kB] Get:41 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 libthrust-dev all 1.9.5-1 [526 kB] Get:42 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 nvidia-cuda-dev amd64 10.1.243-3 [420 MB] Get:43 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 nvidia-cuda-doc all 10.1.243-3 [102 MB] Get:44 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 nvidia-cuda-gdb amd64 10.1.243-3 [2,722 kB] Get:45 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 nvidia-profiler amd64 10.1.243-3 [2,673 kB] Get:46 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/main amd64 opencl-c-headers all 2.2~2019.08.06-g0d5f18c-1 [29.9 kB] Get:47 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/main amd64 ocl-icd-opencl-dev amd64 2.2.11-1ubuntu1 [2,512 B] Get:48 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 nvidia-opencl-dev amd64 10.1.243-3 [16.5 kB] Get:49 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 nvidia-cuda-toolkit amd64 10.1.243-3 [35.0 MB] Get:50 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal/multiverse amd64 nvidia-visual-profiler amd64 10.1.243-3 [115 MB] Fetched 1,111 MB in 29s (38.0 MB/s) E: Failed to fetch file:/var/cuda-repo-ubuntu2004-12-3-local/./libnvidia-compute-545_545.23.08-0ubuntu1_amd64.deb File not found - /var/cuda-repo-ubuntu2004-12-3-local/./libnvidia-compute-545_545.23.08-0ubuntu1_amd64.deb (2: No such file or directory) E: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing? rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ sudo apt install nvidia-cuda-toolkit Reading package lists... Done Building dependency tree Reading state information... Done The following additional packages will be installed: g-8 javascript-common libaccinj64-10.1 libcublas10 libcublaslt10 libcudart10.1 libcufft10 libcufftw10 libcuinj64-10.1 libcupti-dev libcupti-doc libcupti10.1 libcurand10 libcusolver10 libcusolvermg10 libcusparse10 libjs-jquery libnppc10 libnppial10 libnppicc10 libnppicom10 libnppidei10 libnppif10 libnppig10 libnppim10 libnppist10 libnppisu10 libnppitc10 libnpps10 libnvblas10 libnvgraph10 libnvidia-compute-545 libnvidia-ml-dev libnvjpeg10 libnvrtc10.1 libnvtoolsext1 libnvvm3 libstdc-8-dev libthrust-dev libvdpau-dev node-html5shiv nvidia-cuda-dev nvidia-cuda-doc nvidia-cuda-gdb nvidia-opencl-dev nvidia-profiler nvidia-visual-profiler ocl-icd-opencl-dev opencl-c-headers Suggested packages: g-8-multilib gcc-8-doc apache2 | lighttpd | httpd libstdc-8-doc libvdpau-doc nodejs nvidia-driver | nvidia-tesla-440-driver | nvidia-tesla-418-driver libpoclu-dev Recommended packages: libnvcuvid1 nsight-compute nsight-systems The following NEW packages will be installed: g-8 javascript-common libaccinj64-10.1 libcublas10 libcublaslt10 libcudart10.1 libcufft10 libcufftw10 libcuinj64-10.1 libcupti-dev libcupti-doc libcupti10.1 libcurand10 libcusolver10 libcusolvermg10 libcusparse10 libjs-jquery libnppc10 libnppial10 libnppicc10 libnppicom10 libnppidei10 libnppif10 libnppig10 libnppim10 libnppist10 libnppisu10 libnppitc10 libnpps10 libnvblas10 libnvgraph10 libnvidia-compute-545 libnvidia-ml-dev libnvjpeg10 libnvrtc10.1 libnvtoolsext1 libnvvm3 libstdc-8-dev libthrust-dev libvdpau-dev node-html5shiv nvidia-cuda-dev nvidia-cuda-doc nvidia-cuda-gdb nvidia-cuda-toolkit nvidia-opencl-dev nvidia-profiler nvidia-visual-profiler ocl-icd-opencl-dev opencl-c-headers 0 upgraded, 50 newly installed, 0 to remove and 11 not upgraded. Need to get 0 B/1,160 MB of archives. After this operation, 3,056 MB of additional disk space will be used. Do you want to continue? [Y/n] y Get:1 file:/var/cuda-repo-ubuntu2004-12-3-local libnvidia-compute-545 545.23.08-0ubuntu1 [48.8 MB] Err:1 file:/var/cuda-repo-ubuntu2004-12-3-local libnvidia-compute-545 545.23.08-0ubuntu1 File not found - /var/cuda-repo-ubuntu2004-12-3-local/./libnvidia-compute-545_545.23.08-0ubuntu1_amd64.deb (2: No such file or directory) E: Failed to fetch file:/var/cuda-repo-ubuntu2004-12-3-local/./libnvidia-compute-545_545.23.08-0ubuntu1_amd64.deb File not found - /var/cuda-repo-ubuntu2004-12-3-local/./libnvidia-compute-545_545.23.08-0ubuntu1_amd64.deb (2: No such file or directory) E: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing? rootrootrootroot-X99-Turbo:~/whisper.cpp$ nvcc -v
Command nvcc not found, but can be installed with:
sudo apt install nvidia-cuda-toolkit
rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ cd /usr/local/ rootrootrootroot-X99-Turbo:/usr/local$ ll total 44 drwxr-xr-x 11 root root 4096 1月 15 17:10 ./ drwxr-xr-x 14 root root 4096 3月 16 2023 ../ drwxr-xr-x 2 root root 4096 1月 15 17:10 bin/ lrwxrwxrwx 1 root root 22 1月 15 17:10 cuda - /etc/alternatives/cuda/ lrwxrwxrwx 1 root root 25 1月 15 17:10 cuda-12 - /etc/alternatives/cuda-12/ drwxr-xr-x 15 root root 4096 1月 15 17:10 cuda-12.3/ drwxr-xr-x 2 root root 4096 3月 16 2023 etc/ drwxr-xr-x 2 root root 4096 3月 16 2023 games/ drwxr-xr-x 2 root root 4096 3月 16 2023 include/ drwxr-xr-x 4 root root 4096 12月 16 19:57 lib/ lrwxrwxrwx 1 root root 9 12月 16 18:23 man - share/man/ drwxr-xr-x 2 root root 4096 3月 16 2023 sbin/ drwxr-xr-x 7 root root 4096 3月 16 2023 share/ drwxr-xr-x 2 root root 4096 3月 16 2023 src/ rootrootrootroot-X99-Turbo:/usr/local$ cd cuda rootrootrootroot-X99-Turbo:/usr/local/cuda$ ll total 136 drwxr-xr-x 15 root root 4096 1月 15 17:10 ./ drwxr-xr-x 11 root root 4096 1月 15 17:10 ../ drwxr-xr-x 3 root root 4096 1月 15 17:09 bin/ drwxr-xr-x 5 root root 4096 1月 15 17:07 compute-sanitizer/ drwxr-xr-x 3 root root 4096 1月 15 17:09 doc/ -rw-r--r-- 1 root root 160 10月 31 17:24 DOCS -rw-r--r-- 1 root root 61498 10月 31 17:24 EULA.txt drwxr-xr-x 4 root root 4096 1月 16 10:39 extras/ drwxr-xr-x 4 root root 4096 1月 15 17:09 gds/ lrwxrwxrwx 1 root root 28 10月 31 17:20 include - targets/x86_64-linux/include/ lrwxrwxrwx 1 root root 24 10月 31 17:20 lib64 - targets/x86_64-linux/lib/ drwxr-xr-x 7 root root 4096 1月 15 17:09 libnvvp/ drwxr-xr-x 2 root root 4096 1月 15 17:09 nsightee_plugins/ drwxr-xr-x 3 root root 4096 1月 15 17:09 nvml/ drwxr-xr-x 6 root root 4096 1月 15 17:07 nvvm/ -rw-r--r-- 1 root root 524 10月 31 17:24 README drwxr-xr-x 3 root root 4096 1月 15 17:07 share/ drwxr-xr-x 2 root root 4096 1月 15 17:09 src/ drwxr-xr-x 3 root root 4096 1月 15 17:07 targets/ drwxr-xr-x 2 root root 4096 1月 15 17:07 tools/ -rw-r--r-- 1 root root 3037 11月 30 02:48 version.json rootrootrootroot-X99-Turbo:/usr/local/cuda$ rootrootrootroot-X99-Turbo:/usr/local/cuda$ cd bin/ rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ ll total 159484 drwxr-xr-x 3 root root 4096 1月 15 17:09 ./ drwxr-xr-x 15 root root 4096 1月 15 17:10 ../ -rwxr-xr-x 1 root root 88848 11月 23 03:32 bin2c* lrwxrwxrwx 1 root root 4 10月 31 21:25 computeprof - nvvp* -rwxr-xr-x 1 root root 112 10月 31 17:41 compute-sanitizer* drwxr-xr-x 2 root root 4096 1月 15 17:07 crt/ -rwxr-xr-x 1 root root 7336920 11月 23 03:32 cudafe* -rwxr-xr-x 1 root root 15812648 10月 31 18:46 cuda-gdb* -rwxr-xr-x 1 root root 812256 10月 31 18:46 cuda-gdbserver* -rwxr-xr-x 1 root root 75928 10月 31 17:49 cufilt* -rwxr-xr-x 1 root root 536064 10月 31 17:46 cuobjdump* -rwxr-xr-x 1 root root 802968 11月 23 03:32 fatbinary* -rwxr-xr-x 1 root root 3826 11月 30 02:48 ncu* -rwxr-xr-x 1 root root 3616 11月 30 02:48 ncu-ui* -rwxr-xr-x 1 root root 1580 10月 31 17:36 nsight_ee_plugins_manage.sh* -rwxr-xr-x 1 root root 197 11月 30 02:48 nsight-sys* -rwxr-xr-x 1 root root 743 11月 30 02:48 nsys* -rwxr-xr-x 1 root root 833 11月 30 02:48 nsys-ui* -rwxr-xr-x 1 root root 21784968 11月 23 03:32 nvcc* -rwxr-xr-x 1 root root 10456 11月 23 03:32 __nvcc_device_query* -rw-r--r-- 1 root root 417 11月 23 03:32 nvcc.profile -rwxr-xr-x 1 root root 50674712 10月 31 17:45 nvdisasm* -rwxr-xr-x 1 root root 29746536 11月 23 03:32 nvlink* -rwxr-xr-x 1 root root 6022464 10月 31 21:16 nvprof* -rwxr-xr-x 1 root root 109536 10月 31 17:44 nvprune* -rwxr-xr-x 1 root root 285 10月 31 21:25 nvvp* -rwxr-xr-x 1 root root 29421152 11月 23 03:32 ptxas* rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ nvcc -v
Command nvcc not found, but can be installed with:
sudo apt install nvidia-cuda-toolkit
rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ ./nvcc -v nvcc fatal : No input files specified; use option --help for more information rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ ll nvcc -rwxr-xr-x 1 root root 21784968 11月 23 03:32 nvcc* rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ ./nvcc bin2c cuda-gdb ncu nsys-ui nvlink computeprof cuda-gdbserver ncu-ui nvcc nvprof compute-sanitizer cufilt nsight_ee_plugins_manage.sh __nvcc_device_query nvprune crt/ cuobjdump nsight-sys nvcc.profile nvvp cudafe fatbinary nsys nvdisasm ptxas rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ ./nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2023 NVIDIA Corporation Built on Wed_Nov_22_10:17:15_PST_2023 Cuda compilation tools, release 12.3, V12.3.107 Build cuda_12.3.r12.3/compiler.33567101_0 rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ rootrootrootroot-X99-Turbo:/usr/local/cuda/bin$ cd .. rootrootrootroot-X99-Turbo:/usr/local/cuda$ ll total 136 drwxr-xr-x 15 root root 4096 1月 15 17:10 ./ drwxr-xr-x 11 root root 4096 1月 15 17:10 ../ drwxr-xr-x 3 root root 4096 1月 15 17:09 bin/ drwxr-xr-x 5 root root 4096 1月 15 17:07 compute-sanitizer/ drwxr-xr-x 3 root root 4096 1月 15 17:09 doc/ -rw-r--r-- 1 root root 160 10月 31 17:24 DOCS -rw-r--r-- 1 root root 61498 10月 31 17:24 EULA.txt drwxr-xr-x 4 root root 4096 1月 16 10:39 extras/ drwxr-xr-x 4 root root 4096 1月 15 17:09 gds/ lrwxrwxrwx 1 root root 28 10月 31 17:20 include - targets/x86_64-linux/include/ lrwxrwxrwx 1 root root 24 10月 31 17:20 lib64 - targets/x86_64-linux/lib/ drwxr-xr-x 7 root root 4096 1月 15 17:09 libnvvp/ drwxr-xr-x 2 root root 4096 1月 15 17:09 nsightee_plugins/ drwxr-xr-x 3 root root 4096 1月 15 17:09 nvml/ drwxr-xr-x 6 root root 4096 1月 15 17:07 nvvm/ -rw-r--r-- 1 root root 524 10月 31 17:24 README drwxr-xr-x 3 root root 4096 1月 15 17:07 share/ drwxr-xr-x 2 root root 4096 1月 15 17:09 src/ drwxr-xr-x 3 root root 4096 1月 15 17:07 targets/ drwxr-xr-x 2 root root 4096 1月 15 17:07 tools/ -rw-r--r-- 1 root root 3037 11月 30 02:48 version.json rootrootrootroot-X99-Turbo:/usr/local/cuda$ cd lib64/ rootrootrootroot-X99-Turbo:/usr/local/cuda/lib64$ ll total 4137208 drwxr-xr-x 4 root root 4096 1月 15 17:09 ./ drwxr-xr-x 4 root root 4096 1月 15 17:07 ../ drwxr-xr-x 6 root root 4096 1月 15 17:07 cmake/ lrwxrwxrwx 1 root root 19 10月 31 21:16 libaccinj64.so - libaccinj64.so.12.3 lrwxrwxrwx 1 root root 23 10月 31 21:16 libaccinj64.so.12.3 - libaccinj64.so.12.3.101 -rw-r--r-- 1 root root 2412184 10月 31 21:16 libaccinj64.so.12.3.101 -rw-r--r-- 1 root root 1493144 10月 31 20:51 libcheckpoint.so lrwxrwxrwx 1 root root 17 10月 31 17:51 libcublasLt.so - libcublasLt.so.12 lrwxrwxrwx 1 root root 23 10月 31 17:51 libcublasLt.so.12 - libcublasLt.so.12.3.4.1 -rw-r--r-- 1 root root 518358624 10月 31 17:51 libcublasLt.so.12.3.4.1 -rw-r--r-- 1 root root 781766258 10月 31 17:51 libcublasLt_static.a lrwxrwxrwx 1 root root 15 10月 31 17:51 libcublas.so - libcublas.so.12 lrwxrwxrwx 1 root root 21 10月 31 17:51 libcublas.so.12 - libcublas.so.12.3.4.1 -rw-r--r-- 1 root root 106679344 10月 31 17:51 libcublas.so.12.3.4.1 -rw-r--r-- 1 root root 168603496 10月 31 17:51 libcublas_static.a -rw-r--r-- 1 root root 1647010 10月 31 17:48 libcudadevrt.a lrwxrwxrwx 1 root root 15 10月 31 17:48 libcudart.so - libcudart.so.12 lrwxrwxrwx 1 root root 21 10月 31 17:48 libcudart.so.12 - libcudart.so.12.3.101 -rw-r--r-- 1 root root 703808 10月 31 17:48 libcudart.so.12.3.101 -rw-r--r-- 1 root root 1417724 10月 31 17:48 libcudart_static.a lrwxrwxrwx 1 root root 14 10月 31 17:57 libcufft.so - libcufft.so.11 lrwxrwxrwx 1 root root 21 10月 31 17:57 libcufft.so.11 - libcufft.so.11.0.12.1 -rw-r--r-- 1 root root 177827520 10月 31 17:57 libcufft.so.11.0.12.1 -rw-r--r-- 1 root root 199432168 10月 31 17:57 libcufft_static.a -rw-r--r-- 1 root root 199334148 10月 31 17:57 libcufft_static_nocallback.a lrwxrwxrwx 1 root root 15 10月 31 17:57 libcufftw.so - libcufftw.so.11 lrwxrwxrwx 1 root root 22 10月 31 17:57 libcufftw.so.11 - libcufftw.so.11.0.12.1 -rw-r--r-- 1 root root 966600 10月 31 17:57 libcufftw.so.11.0.12.1 -rw-r--r-- 1 root root 79566 10月 31 17:57 libcufftw_static.a lrwxrwxrwx 1 root root 19 10月 26 07:36 libcufile_rdma.so - libcufile_rdma.so.1 lrwxrwxrwx 1 root root 23 10月 26 07:36 libcufile_rdma.so.1 - libcufile_rdma.so.1.8.1 -rw-r--r-- 1 root root 43320 10月 26 07:36 libcufile_rdma.so.1.8.1 -rw-r--r-- 1 root root 65206 10月 26 07:36 libcufile_rdma_static.a lrwxrwxrwx 1 root root 14 10月 26 07:36 libcufile.so - libcufile.so.0 lrwxrwxrwx 1 root root 18 10月 26 07:36 libcufile.so.0 - libcufile.so.1.8.1 -rw-r--r-- 1 root root 2993680 10月 26 07:36 libcufile.so.1.8.1 -rw-r--r-- 1 root root 24282190 10月 26 07:36 libcufile_static.a -rw-r--r-- 1 root root 948952 10月 31 17:49 libcufilt.a lrwxrwxrwx 1 root root 18 10月 31 21:16 libcuinj64.so - libcuinj64.so.12.3 lrwxrwxrwx 1 root root 22 10月 31 21:16 libcuinj64.so.12.3 - libcuinj64.so.12.3.101 -rw-r--r-- 1 root root 2832640 10月 31 21:16 libcuinj64.so.12.3.101 -rw-r--r-- 1 root root 30922 10月 31 17:48 libculibos.a lrwxrwxrwx 1 root root 14 10月 31 20:51 libcupti.so - libcupti.so.12 lrwxrwxrwx 1 root root 20 10月 31 20:51 libcupti.so.12 - libcupti.so.2023.3.1 -rw-r--r-- 1 root root 7683440 10月 31 20:51 libcupti.so.2023.3.1 -rw-r--r-- 1 root root 19214978 10月 31 20:51 libcupti_static.a lrwxrwxrwx 1 root root 15 11月 23 03:55 libcurand.so - libcurand.so.10 lrwxrwxrwx 1 root root 23 11月 23 03:55 libcurand.so.10 - libcurand.so.10.3.4.107 -rw-r--r-- 1 root root 96259504 11月 23 03:55 libcurand.so.10.3.4.107 -rw-r--r-- 1 root root 96328614 11月 23 03:55 libcurand_static.a -rw-r--r-- 1 root root 16788330 10月 31 18:36 libcusolver_lapack_static.a -rw-r--r-- 1 root root 1005514 10月 31 18:36 libcusolver_metis_static.a lrwxrwxrwx 1 root root 19 10月 31 18:36 libcusolverMg.so - libcusolverMg.so.11 lrwxrwxrwx 1 root root 27 10月 31 18:36 libcusolverMg.so.11 - libcusolverMg.so.11.5.4.101 -rw-r--r-- 1 root root 83040368 10月 31 18:36 libcusolverMg.so.11.5.4.101 lrwxrwxrwx 1 root root 17 10月 31 18:36 libcusolver.so - libcusolver.so.11 lrwxrwxrwx 1 root root 25 10月 31 18:36 libcusolver.so.11 - libcusolver.so.11.5.4.101 -rw-r--r-- 1 root root 115640600 10月 31 18:36 libcusolver.so.11.5.4.101 -rw-r--r-- 1 root root 133576956 10月 31 18:36 libcusolver_static.a lrwxrwxrwx 1 root root 17 10月 31 18:09 libcusparse.so - libcusparse.so.12 lrwxrwxrwx 1 root root 25 10月 31 18:09 libcusparse.so.12 - libcusparse.so.12.2.0.103 -rw-r--r-- 1 root root 267184960 10月 31 18:09 libcusparse.so.12.2.0.103 -rw-r--r-- 1 root root 299914796 10月 31 18:09 libcusparse_static.a -rw-r--r-- 1 root root 1005514 10月 31 18:36 libmetis_static.a lrwxrwxrwx 1 root root 13 10月 31 18:19 libnppc.so - libnppc.so.12 lrwxrwxrwx 1 root root 19 10月 31 18:19 libnppc.so.12 - libnppc.so.12.2.3.2 -rw-r--r-- 1 root root 1642992 10月 31 18:19 libnppc.so.12.2.3.2 -rw-r--r-- 1 root root 30686 10月 31 18:19 libnppc_static.a lrwxrwxrwx 1 root root 15 10月 31 18:19 libnppial.so - libnppial.so.12 lrwxrwxrwx 1 root root 21 10月 31 18:19 libnppial.so.12 - libnppial.so.12.2.3.2 -rw-r--r-- 1 root root 17568560 10月 31 18:19 libnppial.so.12.2.3.2 -rw-r--r-- 1 root root 19071940 10月 31 18:19 libnppial_static.a lrwxrwxrwx 1 root root 15 10月 31 18:19 libnppicc.so - libnppicc.so.12 lrwxrwxrwx 1 root root 21 10月 31 18:19 libnppicc.so.12 - libnppicc.so.12.2.3.2 -rw-r--r-- 1 root root 7500616 10月 31 18:19 libnppicc.so.12.2.3.2 -rw-r--r-- 1 root root 7041694 10月 31 18:19 libnppicc_static.a lrwxrwxrwx 1 root root 16 10月 31 18:19 libnppidei.so - libnppidei.so.12 lrwxrwxrwx 1 root root 22 10月 31 18:19 libnppidei.so.12 - libnppidei.so.12.2.3.2 -rw-r--r-- 1 root root 11134104 10月 31 18:19 libnppidei.so.12.2.3.2 -rw-r--r-- 1 root root 11875304 10月 31 18:19 libnppidei_static.a lrwxrwxrwx 1 root root 14 10月 31 18:19 libnppif.so - libnppif.so.12 lrwxrwxrwx 1 root root 20 10月 31 18:19 libnppif.so.12 - libnppif.so.12.2.3.2 -rw-r--r-- 1 root root 101066824 10月 31 18:19 libnppif.so.12.2.3.2 -rw-r--r-- 1 root root 103942380 10月 31 18:19 libnppif_static.a lrwxrwxrwx 1 root root 14 10月 31 18:19 libnppig.so - libnppig.so.12 lrwxrwxrwx 1 root root 20 10月 31 18:19 libnppig.so.12 - libnppig.so.12.2.3.2 -rw-r--r-- 1 root root 41137040 10月 31 18:19 libnppig.so.12.2.3.2 -rw-r--r-- 1 root root 41987560 10月 31 18:19 libnppig_static.a lrwxrwxrwx 1 root root 14 10月 31 18:19 libnppim.so - libnppim.so.12 lrwxrwxrwx 1 root root 20 10月 31 18:19 libnppim.so.12 - libnppim.so.12.2.3.2 -rw-r--r-- 1 root root 10322760 10月 31 18:19 libnppim.so.12.2.3.2 -rw-r--r-- 1 root root 9259562 10月 31 18:19 libnppim_static.a lrwxrwxrwx 1 root root 15 10月 31 18:19 libnppist.so - libnppist.so.12 lrwxrwxrwx 1 root root 21 10月 31 18:19 libnppist.so.12 - libnppist.so.12.2.3.2 -rw-r--r-- 1 root root 38171728 10月 31 18:19 libnppist.so.12.2.3.2 -rw-r--r-- 1 root root 39228112 10月 31 18:19 libnppist_static.a lrwxrwxrwx 1 root root 15 10月 31 18:19 libnppisu.so - libnppisu.so.12 lrwxrwxrwx 1 root root 21 10月 31 18:19 libnppisu.so.12 - libnppisu.so.12.2.3.2 -rw-r--r-- 1 root root 716168 10月 31 18:19 libnppisu.so.12.2.3.2 -rw-r--r-- 1 root root 11266 10月 31 18:19 libnppisu_static.a lrwxrwxrwx 1 root root 15 10月 31 18:19 libnppitc.so - libnppitc.so.12 lrwxrwxrwx 1 root root 21 10月 31 18:19 libnppitc.so.12 - libnppitc.so.12.2.3.2 -rw-r--r-- 1 root root 5530224 10月 31 18:19 libnppitc.so.12.2.3.2 -rw-r--r-- 1 root root 4503836 10月 31 18:19 libnppitc_static.a lrwxrwxrwx 1 root root 13 10月 31 18:19 libnpps.so - libnpps.so.12 lrwxrwxrwx 1 root root 19 10月 31 18:19 libnpps.so.12 - libnpps.so.12.2.3.2 -rw-r--r-- 1 root root 18105592 10月 31 18:19 libnpps.so.12.2.3.2 -rw-r--r-- 1 root root 17960158 10月 31 18:19 libnpps_static.a lrwxrwxrwx 1 root root 15 10月 31 17:51 libnvblas.so - libnvblas.so.12 lrwxrwxrwx 1 root root 21 10月 31 17:51 libnvblas.so.12 - libnvblas.so.12.3.4.1 -rw-r--r-- 1 root root 728856 10月 31 17:51 libnvblas.so.12.3.4.1 lrwxrwxrwx 1 root root 18 10月 31 18:11 libnvJitLink.so - libnvJitLink.so.12 lrwxrwxrwx 1 root root 24 10月 31 18:11 libnvJitLink.so.12 - libnvJitLink.so.12.3.101 -rw-r--r-- 1 root root 52190720 10月 31 18:11 libnvJitLink.so.12.3.101 -rw-r--r-- 1 root root 63530708 10月 31 18:11 libnvJitLink_static.a lrwxrwxrwx 1 root root 15 10月 31 17:49 libnvjpeg.so - libnvjpeg.so.12 lrwxrwxrwx 1 root root 22 10月 31 17:49 libnvjpeg.so.12 - libnvjpeg.so.12.3.0.81 -rw-r--r-- 1 root root 6705968 10月 31 17:49 libnvjpeg.so.12.3.0.81 -rw-r--r-- 1 root root 6828780 10月 31 17:49 libnvjpeg_static.a -rw-r--r-- 1 root root 28538488 10月 31 20:51 libnvperf_host.so -rw-r--r-- 1 root root 36274804 10月 31 20:51 libnvperf_host_static.a -rw-r--r-- 1 root root 6018384 10月 31 20:51 libnvperf_target.so -rw-r--r-- 1 root root 47925582 11月 23 03:32 libnvptxcompiler_static.a lrwxrwxrwx 1 root root 25 11月 23 03:49 libnvrtc-builtins.so - libnvrtc-builtins.so.12.3 lrwxrwxrwx 1 root root 29 11月 23 03:49 libnvrtc-builtins.so.12.3 - libnvrtc-builtins.so.12.3.107 -rw-r--r-- 1 root root 6662024 11月 23 03:49 libnvrtc-builtins.so.12.3.107 -rw-r--r-- 1 root root 6681284 11月 23 03:49 libnvrtc-builtins_static.a lrwxrwxrwx 1 root root 14 11月 23 03:49 libnvrtc.so - libnvrtc.so.12 lrwxrwxrwx 1 root root 20 11月 23 03:49 libnvrtc.so.12 - libnvrtc.so.12.3.107 -rw-r--r-- 1 root root 60792048 11月 23 03:49 libnvrtc.so.12.3.107 -rw-r--r-- 1 root root 75105270 11月 23 03:49 libnvrtc_static.a lrwxrwxrwx 1 root root 18 10月 31 17:52 libnvToolsExt.so - libnvToolsExt.so.1 lrwxrwxrwx 1 root root 22 10月 31 17:52 libnvToolsExt.so.1 - libnvToolsExt.so.1.0.0 -rw-r--r-- 1 root root 40136 10月 31 17:52 libnvToolsExt.so.1.0.0 lrwxrwxrwx 1 root root 14 10月 31 17:37 libOpenCL.so - libOpenCL.so.1 lrwxrwxrwx 1 root root 16 10月 31 17:37 libOpenCL.so.1 - libOpenCL.so.1.0 lrwxrwxrwx 1 root root 18 10月 31 17:37 libOpenCL.so.1.0 - libOpenCL.so.1.0.0 -rw-r--r-- 1 root root 30856 10月 31 17:37 libOpenCL.so.1.0.0 -rw-r--r-- 1 root root 912728 10月 31 20:51 libpcsamplingutil.so drwxr-xr-x 2 root root 4096 1月 15 17:09 stubs/ rootrootrootroot-X99-Turbo:/usr/local/cuda/lib64$ cd - /usr/local/cuda rootrootrootroot-X99-Turbo:/usr/local/cuda$ cd ~/whisper.cpp/ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll total 20728 drwxrwxr-x 17 rootroot rootroot 4096 2月 2 17:46 ./ drwxr-xr-x 30 rootroot rootroot 4096 2月 2 16:49 ../ drwxrwxr-x 7 rootroot rootroot 4096 2月 2 16:49 bindings/ -rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4* -rw-rw-r-- 1 rootroot rootroot 13497126 2月 2 17:26 chs.wav -rw-rw-r-- 1 rootroot rootroot 11821 2月 2 17:41 chs.wav使用CPU.srt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 cmake/ -rw-rw-r-- 1 rootroot rootroot 19150 2月 2 16:49 CMakeLists.txt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 coreml/ drwx------ 2 rootroot rootroot 4096 2月 2 17:45 CPU/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 .devops/ drwxrwxr-x 24 rootroot rootroot 4096 2月 2 16:49 examples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 extra/ -rw-rw-r-- 1 rootroot rootroot 31647 2月 2 16:49 ggml-alloc.c -rw-rw-r-- 1 rootroot rootroot 4055 2月 2 16:49 ggml-alloc.h -rw-rw-r-- 1 rootroot rootroot 20504 2月 2 17:46 ggml-alloc.o -rw-rw-r-- 1 rootroot rootroot 67212 2月 2 16:49 ggml-backend.c -rw-rw-r-- 1 rootroot rootroot 11720 2月 2 16:49 ggml-backend.h -rw-rw-r-- 1 rootroot rootroot 5874 2月 2 16:49 ggml-backend-impl.h -rw-rw-r-- 1 rootroot rootroot 58464 2月 2 17:46 ggml-backend.o -rw-rw-r-- 1 rootroot rootroot 676115 2月 2 16:49 ggml.c -rw-rw-r-- 1 rootroot rootroot 440093 2月 2 16:49 ggml-cuda.cu -rw-rw-r-- 1 rootroot rootroot 2104 2月 2 16:49 ggml-cuda.h -rw-rw-r-- 1 rootroot rootroot 85094 2月 2 16:49 ggml.h -rw-rw-r-- 1 rootroot rootroot 7567 2月 2 16:49 ggml-impl.h -rw-rw-r-- 1 rootroot rootroot 2358 2月 2 16:49 ggml-metal.h -rw-rw-r-- 1 rootroot rootroot 150160 2月 2 16:49 ggml-metal.m -rw-rw-r-- 1 rootroot rootroot 225659 2月 2 16:49 ggml-metal.metal -rw-rw-r-- 1 rootroot rootroot 550040 2月 2 17:46 ggml.o -rw-rw-r-- 1 rootroot rootroot 85693 2月 2 16:49 ggml-opencl.cpp -rw-rw-r-- 1 rootroot rootroot 1386 2月 2 16:49 ggml-opencl.h -rw-rw-r-- 1 rootroot rootroot 401791 2月 2 16:49 ggml-quants.c -rw-rw-r-- 1 rootroot rootroot 13705 2月 2 16:49 ggml-quants.h -rw-rw-r-- 1 rootroot rootroot 198024 2月 2 17:46 ggml-quants.o drwxrwxr-x 8 rootroot rootroot 4096 2月 2 16:49 .git/ drwxrwxr-x 3 rootroot rootroot 4096 2月 2 16:49 .github/ -rw-rw-r-- 1 rootroot rootroot 803 2月 2 16:49 .gitignore -rw-rw-r-- 1 rootroot rootroot 96 2月 2 16:49 .gitmodules drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 grammars/ -rw-rw-r-- 1 rootroot rootroot 1072 2月 2 16:49 LICENSE -rw-rw-r-- 1 rootroot rootroot 14883 2月 2 16:49 Makefile drwxrwxr-x 2 rootroot rootroot 4096 2月 2 17:24 models/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 openvino/ -rw-rw-r-- 1 rootroot rootroot 1776 2月 2 16:49 Package.swift -rw-rw-r-- 1 rootroot rootroot 39115 2月 2 16:49 README.md drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 samples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 spm-headers/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 tests/ -rw-rw-r-- 1 rootroot rootroot 232975 2月 2 16:49 whisper.cpp -rw-rw-r-- 1 rootroot rootroot 30248 2月 2 16:49 whisper.h -rw-rw-r-- 1 rootroot rootroot 728384 2月 2 17:46 whisper.o rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ cd .. rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ cp .bashrc bak1.bashrc rootrootrootroot-X99-Turbo:~$ cd - /home/rootroot/whisper.cpp rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll total 20728 drwxrwxr-x 17 rootroot rootroot 4096 2月 2 17:46 ./ drwxr-xr-x 30 rootroot rootroot 4096 2月 2 17:55 ../ drwxrwxr-x 7 rootroot rootroot 4096 2月 2 16:49 bindings/ -rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4* -rw-rw-r-- 1 rootroot rootroot 13497126 2月 2 17:26 chs.wav -rw-rw-r-- 1 rootroot rootroot 11821 2月 2 17:41 chs.wav使用CPU.srt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 cmake/ -rw-rw-r-- 1 rootroot rootroot 19150 2月 2 16:49 CMakeLists.txt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 coreml/ drwx------ 2 rootroot rootroot 4096 2月 2 17:45 CPU/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 .devops/ drwxrwxr-x 24 rootroot rootroot 4096 2月 2 16:49 examples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 extra/ -rw-rw-r-- 1 rootroot rootroot 31647 2月 2 16:49 ggml-alloc.c -rw-rw-r-- 1 rootroot rootroot 4055 2月 2 16:49 ggml-alloc.h -rw-rw-r-- 1 rootroot rootroot 20504 2月 2 17:46 ggml-alloc.o -rw-rw-r-- 1 rootroot rootroot 67212 2月 2 16:49 ggml-backend.c -rw-rw-r-- 1 rootroot rootroot 11720 2月 2 16:49 ggml-backend.h -rw-rw-r-- 1 rootroot rootroot 5874 2月 2 16:49 ggml-backend-impl.h -rw-rw-r-- 1 rootroot rootroot 58464 2月 2 17:46 ggml-backend.o -rw-rw-r-- 1 rootroot rootroot 676115 2月 2 16:49 ggml.c -rw-rw-r-- 1 rootroot rootroot 440093 2月 2 16:49 ggml-cuda.cu -rw-rw-r-- 1 rootroot rootroot 2104 2月 2 16:49 ggml-cuda.h -rw-rw-r-- 1 rootroot rootroot 85094 2月 2 16:49 ggml.h -rw-rw-r-- 1 rootroot rootroot 7567 2月 2 16:49 ggml-impl.h -rw-rw-r-- 1 rootroot rootroot 2358 2月 2 16:49 ggml-metal.h -rw-rw-r-- 1 rootroot rootroot 150160 2月 2 16:49 ggml-metal.m -rw-rw-r-- 1 rootroot rootroot 225659 2月 2 16:49 ggml-metal.metal -rw-rw-r-- 1 rootroot rootroot 550040 2月 2 17:46 ggml.o -rw-rw-r-- 1 rootroot rootroot 85693 2月 2 16:49 ggml-opencl.cpp -rw-rw-r-- 1 rootroot rootroot 1386 2月 2 16:49 ggml-opencl.h -rw-rw-r-- 1 rootroot rootroot 401791 2月 2 16:49 ggml-quants.c -rw-rw-r-- 1 rootroot rootroot 13705 2月 2 16:49 ggml-quants.h -rw-rw-r-- 1 rootroot rootroot 198024 2月 2 17:46 ggml-quants.o drwxrwxr-x 8 rootroot rootroot 4096 2月 2 16:49 .git/ drwxrwxr-x 3 rootroot rootroot 4096 2月 2 16:49 .github/ -rw-rw-r-- 1 rootroot rootroot 803 2月 2 16:49 .gitignore -rw-rw-r-- 1 rootroot rootroot 96 2月 2 16:49 .gitmodules drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 grammars/ -rw-rw-r-- 1 rootroot rootroot 1072 2月 2 16:49 LICENSE -rw-rw-r-- 1 rootroot rootroot 14883 2月 2 16:49 Makefile drwxrwxr-x 2 rootroot rootroot 4096 2月 2 17:24 models/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 openvino/ -rw-rw-r-- 1 rootroot rootroot 1776 2月 2 16:49 Package.swift -rw-rw-r-- 1 rootroot rootroot 39115 2月 2 16:49 README.md drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 samples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 spm-headers/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 tests/ -rw-rw-r-- 1 rootroot rootroot 232975 2月 2 16:49 whisper.cpp -rw-rw-r-- 1 rootroot rootroot 30248 2月 2 16:49 whisper.h -rw-rw-r-- 1 rootroot rootroot 728384 2月 2 17:46 whisper.o rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ source ~/.bashrc rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ echo $P $PATH $PIPESTATUS $PPID $PS1 $PS2 $PS4 $PWD rootrootrootroot-X99-Turbo:~/whisper.cpp$ echo $PATH /usr/local/cuda/bin:/home/rootroot/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2023 NVIDIA Corporation Built on Wed_Nov_22_10:17:15_PST_2023 Cuda compilation tools, release 12.3, V12.3.107 Build cuda_12.3.r12.3/compiler.33567101_0 rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ echo $LD_LIBRARY_PATH /usr/local/cuda/lib64: rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ WHISPER_CUBLAS1 make -j16 I whisper.cpp build info: I UNAME_S: Linux I UNAME_P: x86_64 I UNAME_M: x86_64 I CFLAGS: -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I LDFLAGS: -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib I CC: cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 I CXX: g (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
nvcc --forward-unknown-to-host-compiler -archnative -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -Wno-pedantic -c ggml-cuda.cu -o ggml-cuda.o g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/main/main.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o main -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/bench/bench.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o bench -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/quantize/quantize.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o quantize -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/server/server.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o server -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul_mat: ggml.c:(.text0x178a3): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x17e01): undefined reference to ggml_cl_mul_mat /usr/bin/ld: ggml.o: in function ggml_init: ggml.c:(.text0x23942): undefined reference to ggml_cl_init /usr/bin/ld: ggml.o: in function ggml_graph_plan: ggml.c:(.text0x38346): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x386a4): undefined reference to ggml_cl_mul_mat_get_wsize /usr/bin/ld: ggml.o: in function ggml_compute_forward_add: ggml.c:(.text0x1afdc): undefined reference to ggml_cl_add /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul: ggml.c:(.text0x1d60c): undefined reference to ggml_cl_mul collect2: error: ld returned 1 exit status make: *** [Makefile:367: bench] Error 1 make: *** Waiting for unfinished jobs.... /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul_mat: ggml.c:(.text0x178a3): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x17e01): undefined reference to ggml_cl_mul_mat /usr/bin/ld: ggml.o: in function ggml_init: ggml.c:(.text0x23942): undefined reference to ggml_cl_init /usr/bin/ld: ggml.o: in function ggml_graph_plan: ggml.c:(.text0x38346): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x386a4): undefined reference to ggml_cl_mul_mat_get_wsize /usr/bin/ld: ggml.o: in function ggml_compute_forward_add: ggml.c:(.text0x1afdc): undefined reference to ggml_cl_add /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul: ggml.c:(.text0x1d60c): undefined reference to ggml_cl_mul collect2: error: ld returned 1 exit status make: *** [Makefile:370: quantize] Error 1 /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul_mat: ggml.c:(.text0x178a3): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x17e01): undefined reference to ggml_cl_mul_mat /usr/bin/ld: ggml.o: in function ggml_init: ggml.c:(.text0x23942): undefined reference to ggml_cl_init /usr/bin/ld: ggml.o: in function ggml_graph_plan: ggml.c:(.text0x38346): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x386a4): undefined reference to ggml_cl_mul_mat_get_wsize /usr/bin/ld: ggml.o: in function ggml_compute_forward_add: ggml.c:(.text0x1afdc): undefined reference to ggml_cl_add /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul: ggml.c:(.text0x1d60c): undefined reference to ggml_cl_mul collect2: error: ld returned 1 exit status make: *** [Makefile:363: main] Error 1 /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul_mat: ggml.c:(.text0x178a3): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x17e01): undefined reference to ggml_cl_mul_mat /usr/bin/ld: ggml.o: in function ggml_init: ggml.c:(.text0x23942): undefined reference to ggml_cl_init /usr/bin/ld: ggml.o: in function ggml_graph_plan: ggml.c:(.text0x38346): undefined reference to ggml_cl_can_mul_mat /usr/bin/ld: ggml.c:(.text0x386a4): undefined reference to ggml_cl_mul_mat_get_wsize /usr/bin/ld: ggml.o: in function ggml_compute_forward_add: ggml.c:(.text0x1afdc): undefined reference to ggml_cl_add /usr/bin/ld: ggml.o: in function ggml_compute_forward_mul: ggml.c:(.text0x1d60c): undefined reference to ggml_cl_mul collect2: error: ld returned 1 exit status make: *** [Makefile:373: server] Error 1 rootrootrootroot-X99-Turbo:~/whisper.cpp$ make clean I whisper.cpp build info: I UNAME_S: Linux I UNAME_P: x86_64 I UNAME_M: x86_64 I CFLAGS: -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 I LDFLAGS: I CC: cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 I CXX: g (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
rm -f *.o main stream command talk talk-llama bench quantize server lsp libwhisper.a libwhisper.so rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ WHISPER_CUBLAS1 make I whisper.cpp build info: I UNAME_S: Linux I UNAME_P: x86_64 I UNAME_M: x86_64 I CFLAGS: -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include I LDFLAGS: -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib I CC: cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 I CXX: g (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
nvcc --forward-unknown-to-host-compiler -archnative -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -Wno-pedantic -c ggml-cuda.cu -o ggml-cuda.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml.c -o ggml.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml-alloc.c -o ggml-alloc.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml-backend.c -o ggml-backend.o cc -I. -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c ggml-quants.c -o ggml-quants.o g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include -c whisper.cpp -o whisper.o g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/main/main.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o main -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib ./main -h
usage: ./main [options] file0.wav file1.wav ...
options: -h, --help [default] show this help message and exit -t N, --threads N [4 ] number of threads to use during computation -p N, --processors N [1 ] number of processors to use during computation -ot N, --offset-t N [0 ] time offset in milliseconds -on N, --offset-n N [0 ] segment index offset -d N, --duration N [0 ] duration of audio to process in milliseconds -mc N, --max-context N [-1 ] maximum number of text context tokens to store -ml N, --max-len N [0 ] maximum segment length in characters -sow, --split-on-word [false ] split on word rather than on token -bo N, --best-of N [5 ] number of best candidates to keep -bs N, --beam-size N [5 ] beam size for beam search -wt N, --word-thold N [0.01 ] word timestamp probability threshold -et N, --entropy-thold N [2.40 ] entropy threshold for decoder fail -lpt N, --logprob-thold N [-1.00 ] log probability threshold for decoder fail -debug, --debug-mode [false ] enable debug mode (eg. dump log_mel) -tr, --translate [false ] translate from source language to english -di, --diarize [false ] stereo audio diarization -tdrz, --tinydiarize [false ] enable tinydiarize (requires a tdrz model) -nf, --no-fallback [false ] do not use temperature fallback while decoding -otxt, --output-txt [false ] output result in a text file -ovtt, --output-vtt [false ] output result in a vtt file -osrt, --output-srt [false ] output result in a srt file -olrc, --output-lrc [false ] output result in a lrc file -owts, --output-words [false ] output script for generating karaoke video -fp, --font-path [/System/Library/Fonts/Supplemental/Courier New Bold.ttf] path to a monospace font for karaoke video -ocsv, --output-csv [false ] output result in a CSV file -oj, --output-json [false ] output result in a JSON file -ojf, --output-json-full [false ] include more information in the JSON file -of FNAME, --output-file FNAME [ ] output file path (without file extension) -np, --no-prints [false ] do not print anything other than the results -ps, --print-special [false ] print special tokens -pc, --print-colors [false ] print colors -pp, --print-progress [false ] print progress -nt, --no-timestamps [false ] do not print timestamps -l LANG, --language LANG [en ] spoken language (auto for auto-detect) -dl, --detect-language [false ] exit after automatically detecting language --prompt PROMPT [ ] initial prompt -m FNAME, --model FNAME [models/ggml-base.en.bin] model path -f FNAME, --file FNAME [ ] input WAV file path -oved D, --ov-e-device DNAME [CPU ] the OpenVINO device used for encode inference -ls, --log-score [false ] log best decoder scores of tokens -ng, --no-gpu [false ] disable GPU
g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/bench/bench.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o bench -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/quantize/quantize.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o quantize -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib g -I. -I./examples -O3 -DNDEBUG -stdc11 -fPIC -D_XOPEN_SOURCE600 -D_GNU_SOURCE -pthread -mavx -mavx2 -mfma -mf16c -msse3 -mssse3 -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/targets/x86_64-linux/include examples/server/server.cpp examples/common.cpp examples/common-ggml.cpp ggml-cuda.o ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o whisper.o -o server -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L/targets/x86_64-linux/lib rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll total 33624 drwxrwxr-x 17 rootroot rootroot 4096 2月 2 18:00 ./ drwxr-xr-x 30 rootroot rootroot 4096 2月 2 17:55 ../ -rwxrwxr-x 1 rootroot rootroot 2632736 2月 2 18:00 bench* drwxrwxr-x 7 rootroot rootroot 4096 2月 2 16:49 bindings/ -rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4* -rw-rw-r-- 1 rootroot rootroot 13497126 2月 2 17:26 chs.wav -rw-rw-r-- 1 rootroot rootroot 11821 2月 2 17:41 chs.wav使用CPU.srt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 cmake/ -rw-rw-r-- 1 rootroot rootroot 19150 2月 2 16:49 CMakeLists.txt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 coreml/ drwx------ 2 rootroot rootroot 4096 2月 2 17:45 CPU/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 .devops/ drwxrwxr-x 24 rootroot rootroot 4096 2月 2 16:49 examples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 extra/ -rw-rw-r-- 1 rootroot rootroot 31647 2月 2 16:49 ggml-alloc.c -rw-rw-r-- 1 rootroot rootroot 4055 2月 2 16:49 ggml-alloc.h -rw-rw-r-- 1 rootroot rootroot 20504 2月 2 17:59 ggml-alloc.o -rw-rw-r-- 1 rootroot rootroot 67212 2月 2 16:49 ggml-backend.c -rw-rw-r-- 1 rootroot rootroot 11720 2月 2 16:49 ggml-backend.h -rw-rw-r-- 1 rootroot rootroot 5874 2月 2 16:49 ggml-backend-impl.h -rw-rw-r-- 1 rootroot rootroot 58712 2月 2 17:59 ggml-backend.o -rw-rw-r-- 1 rootroot rootroot 676115 2月 2 16:49 ggml.c -rw-rw-r-- 1 rootroot rootroot 440093 2月 2 16:49 ggml-cuda.cu -rw-rw-r-- 1 rootroot rootroot 2104 2月 2 16:49 ggml-cuda.h -rw-rw-r-- 1 rootroot rootroot 1741536 2月 2 17:59 ggml-cuda.o -rw-rw-r-- 1 rootroot rootroot 85094 2月 2 16:49 ggml.h -rw-rw-r-- 1 rootroot rootroot 7567 2月 2 16:49 ggml-impl.h -rw-rw-r-- 1 rootroot rootroot 2358 2月 2 16:49 ggml-metal.h -rw-rw-r-- 1 rootroot rootroot 150160 2月 2 16:49 ggml-metal.m -rw-rw-r-- 1 rootroot rootroot 225659 2月 2 16:49 ggml-metal.metal -rw-rw-r-- 1 rootroot rootroot 548304 2月 2 17:59 ggml.o -rw-rw-r-- 1 rootroot rootroot 85693 2月 2 16:49 ggml-opencl.cpp -rw-rw-r-- 1 rootroot rootroot 1386 2月 2 16:49 ggml-opencl.h -rw-rw-r-- 1 rootroot rootroot 401791 2月 2 16:49 ggml-quants.c -rw-rw-r-- 1 rootroot rootroot 13705 2月 2 16:49 ggml-quants.h -rw-rw-r-- 1 rootroot rootroot 198024 2月 2 17:59 ggml-quants.o drwxrwxr-x 8 rootroot rootroot 4096 2月 2 16:49 .git/ drwxrwxr-x 3 rootroot rootroot 4096 2月 2 16:49 .github/ -rw-rw-r-- 1 rootroot rootroot 803 2月 2 16:49 .gitignore -rw-rw-r-- 1 rootroot rootroot 96 2月 2 16:49 .gitmodules drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 grammars/ -rw-rw-r-- 1 rootroot rootroot 1072 2月 2 16:49 LICENSE -rwxrwxr-x 1 rootroot rootroot 2858480 2月 2 18:00 main* -rw-rw-r-- 1 rootroot rootroot 14883 2月 2 16:49 Makefile drwxrwxr-x 2 rootroot rootroot 4096 2月 2 17:24 models/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 openvino/ -rw-rw-r-- 1 rootroot rootroot 1776 2月 2 16:49 Package.swift -rwxrwxr-x 1 rootroot rootroot 2805104 2月 2 18:00 quantize* -rw-rw-r-- 1 rootroot rootroot 39115 2月 2 16:49 README.md drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 samples/ -rwxrwxr-x 1 rootroot rootroot 3161376 2月 2 18:00 server* drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 spm-headers/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 tests/ -rw-rw-r-- 1 rootroot rootroot 232975 2月 2 16:49 whisper.cpp -rw-rw-r-- 1 rootroot rootroot 30248 2月 2 16:49 whisper.h -rw-rw-r-- 1 rootroot rootroot 729136 2月 2 18:00 whisper.o rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll main -rwxrwxr-x 1 rootroot rootroot 2858480 2月 2 18:00 main* rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ll total 33624 drwxrwxr-x 17 rootroot rootroot 4096 2月 2 18:00 ./ drwxr-xr-x 30 rootroot rootroot 4096 2月 2 17:55 ../ -rwxrwxr-x 1 rootroot rootroot 2632736 2月 2 18:00 bench* drwxrwxr-x 7 rootroot rootroot 4096 2月 2 16:49 bindings/ -rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4* -rw-rw-r-- 1 rootroot rootroot 13497126 2月 2 17:26 chs.wav -rw-rw-r-- 1 rootroot rootroot 11821 2月 2 17:41 chs.wav使用CPU.srt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 cmake/ -rw-rw-r-- 1 rootroot rootroot 19150 2月 2 16:49 CMakeLists.txt drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 coreml/ drwx------ 2 rootroot rootroot 4096 2月 2 17:45 CPU/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 .devops/ drwxrwxr-x 24 rootroot rootroot 4096 2月 2 16:49 examples/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 extra/ -rw-rw-r-- 1 rootroot rootroot 31647 2月 2 16:49 ggml-alloc.c -rw-rw-r-- 1 rootroot rootroot 4055 2月 2 16:49 ggml-alloc.h -rw-rw-r-- 1 rootroot rootroot 20504 2月 2 17:59 ggml-alloc.o -rw-rw-r-- 1 rootroot rootroot 67212 2月 2 16:49 ggml-backend.c -rw-rw-r-- 1 rootroot rootroot 11720 2月 2 16:49 ggml-backend.h -rw-rw-r-- 1 rootroot rootroot 5874 2月 2 16:49 ggml-backend-impl.h -rw-rw-r-- 1 rootroot rootroot 58712 2月 2 17:59 ggml-backend.o -rw-rw-r-- 1 rootroot rootroot 676115 2月 2 16:49 ggml.c -rw-rw-r-- 1 rootroot rootroot 440093 2月 2 16:49 ggml-cuda.cu -rw-rw-r-- 1 rootroot rootroot 2104 2月 2 16:49 ggml-cuda.h -rw-rw-r-- 1 rootroot rootroot 1741536 2月 2 17:59 ggml-cuda.o -rw-rw-r-- 1 rootroot rootroot 85094 2月 2 16:49 ggml.h -rw-rw-r-- 1 rootroot rootroot 7567 2月 2 16:49 ggml-impl.h -rw-rw-r-- 1 rootroot rootroot 2358 2月 2 16:49 ggml-metal.h -rw-rw-r-- 1 rootroot rootroot 150160 2月 2 16:49 ggml-metal.m -rw-rw-r-- 1 rootroot rootroot 225659 2月 2 16:49 ggml-metal.metal -rw-rw-r-- 1 rootroot rootroot 548304 2月 2 17:59 ggml.o -rw-rw-r-- 1 rootroot rootroot 85693 2月 2 16:49 ggml-opencl.cpp -rw-rw-r-- 1 rootroot rootroot 1386 2月 2 16:49 ggml-opencl.h -rw-rw-r-- 1 rootroot rootroot 401791 2月 2 16:49 ggml-quants.c -rw-rw-r-- 1 rootroot rootroot 13705 2月 2 16:49 ggml-quants.h -rw-rw-r-- 1 rootroot rootroot 198024 2月 2 17:59 ggml-quants.o drwxrwxr-x 8 rootroot rootroot 4096 2月 2 16:49 .git/ drwxrwxr-x 3 rootroot rootroot 4096 2月 2 16:49 .github/ -rw-rw-r-- 1 rootroot rootroot 803 2月 2 16:49 .gitignore -rw-rw-r-- 1 rootroot rootroot 96 2月 2 16:49 .gitmodules drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 grammars/ -rw-rw-r-- 1 rootroot rootroot 1072 2月 2 16:49 LICENSE -rwxrwxr-x 1 rootroot rootroot 2858480 2月 2 18:00 main* -rw-rw-r-- 1 rootroot rootroot 14883 2月 2 16:49 Makefile drwxrwxr-x 2 rootroot rootroot 4096 2月 2 17:24 models/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 openvino/ -rw-rw-r-- 1 rootroot rootroot 1776 2月 2 16:49 Package.swift -rwxrwxr-x 1 rootroot rootroot 2805104 2月 2 18:00 quantize* -rw-rw-r-- 1 rootroot rootroot 39115 2月 2 16:49 README.md drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 samples/ -rwxrwxr-x 1 rootroot rootroot 3161376 2月 2 18:00 server* drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 spm-headers/ drwxrwxr-x 2 rootroot rootroot 4096 2月 2 16:49 tests/ -rw-rw-r-- 1 rootroot rootroot 232975 2月 2 16:49 whisper.cpp -rw-rw-r-- 1 rootroot rootroot 30248 2月 2 16:49 whisper.h -rw-rw-r-- 1 rootroot rootroot 729136 2月 2 18:00 whisper.o rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/ggml-medium.bin chs.wav whisper_init_from_file_with_params_no_state: loading model from models/ggml-medium.bin whisper_model_load: loading model whisper_model_load: n_vocab 51865 whisper_model_load: n_audio_ctx 1500 whisper_model_load: n_audio_state 1024 whisper_model_load: n_audio_head 16 whisper_model_load: n_audio_layer 24 whisper_model_load: n_text_ctx 448 whisper_model_load: n_text_state 1024 whisper_model_load: n_text_head 16 whisper_model_load: n_text_layer 24 whisper_model_load: n_mels 80 whisper_model_load: ftype 1 whisper_model_load: qntvr 0 whisper_model_load: type 4 (medium) whisper_model_load: adding 1608 extra tokens whisper_model_load: n_langs 99 ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce GTX 1080, compute capability 6.1, VMM: yes whisper_backend_init: using CUDA backend whisper_model_load: CUDA0 total size 1533.52 MB (2 buffers) whisper_model_load: model size 1533.14 MB whisper_backend_init: using CUDA backend whisper_init_state: kv self size 132.12 MB whisper_init_state: kv cross size 147.46 MB whisper_init_state: compute buffer (conv) 28.00 MB whisper_init_state: compute buffer (encode) 187.14 MB whisper_init_state: compute buffer (cross) 8.46 MB whisper_init_state: compute buffer (decode) 107.98 MB
system_info: n_threads 4 / 36 | AVX 1 | AVX2 1 | AVX512 0 | FMA 1 | NEON 0 | ARM_FMA 0 | METAL 0 | F16C 1 | FP16_VA 0 | WASM_SIMD 0 | BLAS 1 | SSE3 1 | SSSE3 1 | VSX 0 | CUDA 1 | COREML 0 | OPENVINO 0 |
main: processing chs.wav (6748501 samples, 421.8 sec), 4 threads, 1 processors, 5 beams best of 5, lang zh, task transcribe, timestamps 1 ... [00:00:00.000 -- 00:00:01.400] 前段時間有個巨石恒火 [00:00:01.400 -- 00:00:03.000] 某某是男人最好的醫妹 [00:00:03.000 -- 00:00:04.760] 這裡的某某可以替換為減肥 [00:00:04.760 -- 00:00:07.720] 長髮 西裝 考研 速唱 永潔無間等等等等 [00:00:07.720 -- 00:00:09.280] 我聽到最新的一個說法是 [00:00:09.280 -- 00:00:11.960] 微分碎蓋加口罩加半框眼鏡加春風衣 [00:00:11.960 -- 00:00:13.320] 等於男人最好的醫妹 [00:00:13.320 -- 00:00:14.400] 大概也就前幾年 [00:00:14.400 -- 00:00:17.400] 春風衣還和格子襯衫並列為程序員穿搭精華 [00:00:17.400 -- 00:00:20.000] 紫紅色春風衣還被譽為廣場5大媽標配 [00:00:20.000 -- 00:00:21.600] 路透牌還是我爹這個年紀的人 [00:00:21.600 -- 00:00:22.800] 才會願意買的牌子 [00:00:22.800 -- 00:00:24.400] 不知道風向為啥變得這麼快 [00:00:24.400 -- 00:00:29.600] 為啥這東西突然變成男生逆襲神器 時尚潮流單品了後來我翻了一下小紅書就懂了 [00:00:29.600 -- 00:00:32.400] 時尚這個時期重點不在於衣服在於人 [00:00:32.400 -- 00:00:34.600] 現在小紅書上面和春風衣相關的筆記 [00:00:34.600 -- 00:00:36.200] 照片裡的男生都是這樣的 [00:00:36.200 -- 00:00:37.000] 這樣的 [00:00:37.000 -- 00:00:38.000] 還有這樣的 [00:00:38.000 -- 00:00:39.400] 你們哪裡是看穿搭的 [00:00:39.400 -- 00:00:40.600] 你們明明是看臉 [00:00:40.600 -- 00:00:41.800] 就這個造型這個年齡 [00:00:41.800 -- 00:00:44.000] 你換上老頭衫也能穿出氛圍感好嗎 [00:00:44.000 -- 00:00:46.600] 我又想起了當年郭德綱老師穿季凡西的殘劇 [00:00:46.600 -- 00:00:49.600] 這個世界對我們這些長得不好看的人還真是苛刻的 [00:00:49.600 -- 00:00:52.000] 所以說我總結了一下春風衣傳達的要領 [00:00:52.000 -- 00:00:54.200] 大概就是一張白鏡且人畜無憾的臉 [00:00:54.200 -- 00:00:56.000] 充足的發亮 纖細的體型 [00:00:56.000 -- 00:00:58.000] 當然身上的春風衣還得是駱駝的 [00:00:58.000 -- 00:01:00.000] 去年在戶外用品界最頂流的 [00:01:00.000 -- 00:01:01.000] 既不是鳥像樹 [00:01:01.000 -- 00:01:02.600] 也不是有校服之稱的北面 [00:01:02.600 -- 00:01:04.800] 或者老臺頂流哥倫比亞而是駱駝 [00:01:04.800 -- 00:01:07.000] 雙11 駱駝在天貓戶外服飾品類 [00:01:07.000 -- 00:01:08.800] 拿下銷售額和銷量雙料冠軍 [00:01:08.800 -- 00:01:10.000] 銷量達到百萬幾 [00:01:10.000 -- 00:01:10.600] 再抖音 [00:01:10.600 -- 00:01:13.200] 駱駝銷售同比增幅高達296% [00:01:13.200 -- 00:01:16.000] 旗下主打的三合一高性價比春風衣成為爆品 [00:01:16.000 -- 00:01:18.000] 哪怕不看雙11 隨手一搜 [00:01:18.000 -- 00:01:21.000] 駱駝在春風衣的7日銷售榜上都是圖榜的存在 [00:01:21.000 -- 00:01:22.400] 這是線上的銷售表現 [00:01:22.400 -- 00:01:24.200] 至於線下還是網友總覺得好 [00:01:24.200 -- 00:01:26.800] 如今在南方街頭的駱駝比沙漠里的都多 [00:01:26.800 -- 00:01:28.400] 塔克華山 滿山的駱駝 [00:01:28.400 -- 00:01:29.800] 隨便逛個街撞山了 [00:01:29.800 -- 00:01:31.800] 至於駱駝為啥這麼火 便宜啊 [00:01:31.800 -- 00:01:33.400] 拿賣得最好的丁珍銅款 [00:01:33.400 -- 00:01:35.400] 幻影黑三合一春風衣舉個例子 [00:01:35.400 -- 00:01:37.600] 線下買標牌價格2198 [00:01:37.600 -- 00:01:39.000] 但是跑到網上看一下 [00:01:39.000 -- 00:01:40.600] 標價就變成了699 [00:01:40.600 -- 00:01:42.200] 至於折扣 日常也都是有的 [00:01:42.200 -- 00:01:45.000] 400出頭就能買到 甚至有時候能递到300價 [00:01:45.000 -- 00:01:48.200] 要是你還顯貴 駱駝還有200塊出頭的單層春風衣 [00:01:48.200 -- 00:01:49.000] 就這個價格 [00:01:49.000 -- 00:01:51.600] 哥上海恐怕還不夠兩次City Walk的報名費 [00:01:51.600 -- 00:01:54.600] 看來這個價格再對比一下北面1000塊錢起步 [00:01:54.600 -- 00:01:58.200] 你就能理解為啥北面這麼快就被大學生踢出了校服序列了 [00:01:58.200 -- 00:02:00.400] 我不知道現在大學生每個月生活費多少 [00:02:00.400 -- 00:02:02.200] 反正按照我上學時候的生活費 [00:02:02.200 -- 00:02:05.000] 一個月不吃不喝也就買得起倆袖子加一個帽子 [00:02:05.000 -- 00:02:07.400] 難怪當年全是假北面 現在都是真駱駝 [00:02:07.400 -- 00:02:08.600] 至少人家是正品啊 [00:02:08.600 -- 00:02:10.000] 我翻了一下社交媒體 [00:02:10.000 -- 00:02:13.400] 發現對駱駝的吐槽和買了駱駝的 基本上是1比1的比例 [00:02:13.400 -- 00:02:15.800] 吐槽最多的就是衣服會掉色 還會串色 [00:02:15.800 -- 00:02:18.200] 比如吐樽洗個幾次 穿個兩天就掉光了 [00:02:18.200 -- 00:02:20.600] 比如不同倉庫發的貨 質量參差不齊 [00:02:20.600 -- 00:02:22.400] 買衣服還得看戶口 聽出聲 [00:02:22.400 -- 00:02:26.400] 至於什麼做工比較差 內膽多 走線操 不防水之類的就更多 [00:02:26.400 -- 00:02:29.200] 但是這些吐槽 並不意味著會影響駱駝的銷量 [00:02:29.200 -- 00:02:31.000] 甚至還會有不少自來水表示 [00:02:31.000 -- 00:02:32.600] 就這價格 要啥子行車啊 [00:02:32.600 -- 00:02:35.400] 所謂性價比性價比 脫離價位談性能 [00:02:35.400 -- 00:02:38.600] 這就不符合消費者的需求嘛 無數次價格戰告訴我們 [00:02:38.600 -- 00:02:41.000] 只要肯降價 就沒有賣不出去的產品 [00:02:41.000 -- 00:02:43.600] 一件衝鋒衣1000多 你覺得平平無奇 [00:02:43.600 -- 00:02:46.400] 500多你覺得差點意思 200塊你就秒下單了 [00:02:46.400 -- 00:02:48.400] 到99 恐怕就要聘點手速了 [00:02:48.400 -- 00:02:50.800] 像衝鋒衣這個品類 本來價格跨度就大 [00:02:50.800 -- 00:02:53.800] 北面最便宜的GORTEX衝鋒衣 價格3000起步 [00:02:53.800 -- 00:02:56.200] 大概是同品牌最便宜衝鋒衣的三倍價格 [00:02:56.200 -- 00:03:00.000] 至於十足鳥搭載了GORTEX的硬殼起步價就要到4500 [00:03:00.000 -- 00:03:03.000] 而且同樣是GORTEX 內部也有不同的系列和檔次 [00:03:03.000 -- 00:03:05.800] 做成衣服 中間的差價恐怕就夠買兩件駱駝了 [00:03:05.800 -- 00:03:08.000] 至於智能控溫 防水拉鍊 全壓膠 [00:03:08.000 -- 00:03:09.800] 更加不可能出現在駱駝這裏了 [00:03:09.800 -- 00:03:11.800] 至少不會是三四百的駱駝身上會有的 [00:03:11.800 -- 00:03:14.200] 有的價外的衣服 買的就是一個放棄幻想 [00:03:14.200 -- 00:03:17.000] 吃到肚子裏的科技魚很活 是能給你省錢的 [00:03:17.000 -- 00:03:20.000] 穿在身上的科技魚很活 裝裝件件都是要加錢的 [00:03:20.000 -- 00:03:21.600] 所以正如羅曼羅蘭所說 [00:03:21.600 -- 00:03:23.200] 這世界上只有一種英雄主義 [00:03:23.200 -- 00:03:26.000] 就是在認清了駱駝的本質以後 依然選擇買駱駝 [00:03:26.000 -- 00:03:29.000] 關於駱駝的火爆 我有一些小小的看法 駱駝這東西 [00:03:29.000 -- 00:03:31.800] 它其實就是個潮牌 看看它的營銷方式就知道了 [00:03:31.800 -- 00:03:35.000] 現在打開小黃書 日常可以看到駱駝穿搭是這樣的 [00:03:35.000 -- 00:03:36.800] 加一點氛圍感是這樣的 [00:03:36.800 -- 00:03:40.000] 對比一下 其他品牌的風格是這樣的 這樣的 [00:03:40.000 -- 00:03:42.600] 其實對比一下就知道了 其他品牌突出一個時程 [00:03:42.600 -- 00:03:46.000] 能防風就一定要講防風 能扛洞就一定要講扛洞 [00:03:46.000 -- 00:03:49.200] 但駱駝在營銷的時候 主打的就是一個城市戶外風 [00:03:49.200 -- 00:03:52.200] 雖然造型是春風衣 但場景往往是在城市裏 [00:03:52.200 -- 00:03:55.000] 哪怕在野外也要突出一個風和日麗 陽光明媚 [00:03:55.000 -- 00:03:58.000] 至少不會在明顯的炎寒 高海拔或是惡劣氣候下 [00:03:58.000 -- 00:04:01.000] 如果用一個詞形容駱駝的營銷風格 那就是清洗 [00:04:01.000 -- 00:04:04.000] 或者說他很理解自己的消費者是誰 需要什麼產品 [00:04:04.000 -- 00:04:06.600] 從使用場景來說 駱駝的消費者買春風衣 [00:04:06.600 -- 00:04:08.800] 不是真的有什麼大風大雨要去應對 [00:04:08.800 -- 00:04:12.000] 春風衣的作用是下雨沒帶傘的時候 臨時頂個幾分鐘 [00:04:12.000 -- 00:04:13.600] 讓你能圖書館跑回宿舍 [00:04:13.600 -- 00:04:16.200] 或者是冬天騎電動車 被風吹得不行的時候 [00:04:16.200 -- 00:04:18.400] 稍微扛一下風 不至於體感太冷 [00:04:18.400 -- 00:04:21.800] 當然他們也會出門 但大部分時候也都是去別的城市 [00:04:21.800 -- 00:04:26.000] 或者在城市周邊搞搞簡單的徒步 這種情況下穿個駱駝已經夠了 [00:04:26.000 -- 00:04:29.400] 從購買動機來說 駱駝就更沒有必要上那些應和科技了 [00:04:29.400 -- 00:04:31.000] 消費者買駱駝買的是個什麼呢 [00:04:31.000 -- 00:04:33.400] 不是春風衣的功能性 而是春風衣的造型 [00:04:33.400 -- 00:04:36.400] 寬鬆的版型 能精準遮住微微隆起的小肚子 [00:04:36.400 -- 00:04:39.600] 棱角分明的質感 能隱藏一切不完美的身體線條 [00:04:39.600 -- 00:04:41.400] 顯瘦的副作用就是顯年輕 [00:04:41.400 -- 00:04:43.800] 再配上一條牛仔褲 配上一雙大黃靴 [00:04:43.800 -- 00:04:45.200] 大學生的氣質就出來了 [00:04:45.200 -- 00:04:47.800] 要是自拍的時候再配上大學宿舍洗素臺 [00:04:47.800 -- 00:04:51.800] 那永遠擦不乾淨的鏡子 瞬間青春無敵了 說的更直白一點 [00:04:51.800 -- 00:04:53.400] 人家買的是個剪輪神器 [00:04:53.400 -- 00:04:56.000] 所以說 吐槽穿駱駝都是假戶外愛好者的人 [00:04:56.000 -- 00:04:57.600] 其實並沒有理解駱駝的定位 [00:04:57.600 -- 00:04:59.900] 駱駝其實是給了想要入門山系穿搭 [00:04:59.900 -- 00:05:03.100] 想要追逐流行的人一個最平價 決策成本最低的選擇 [00:05:03.100 -- 00:05:04.900] 至於那些真正的應和戶外愛好者 [00:05:04.900 -- 00:05:07.300] 駱駝既沒有能力 也沒有打算觸打他們 [00:05:07.300 -- 00:05:09.600] 反過來說 那些自駕穿越邊疆國道 [00:05:09.600 -- 00:05:11.800] 或者去奧爾卑斯山區登山探險的人 [00:05:11.800 -- 00:05:16.600] 也不太可能在戶外服飾上省錢 畢竟光是交通住宿 請假出行 成本就不低了 [00:05:16.600 -- 00:05:19.100] 對他們來說 戶外裝備很多時候是保命用的 [00:05:19.100 -- 00:05:21.100] 也就不存在跟風奧造型的必要了 [00:05:21.100 -- 00:05:23.400] 最後我再說個題外話 年輕人追捧駱駝 [00:05:23.400 -- 00:05:25.900] 一個隱藏的原因 其實是羽絨服越來越貴了 [00:05:25.900 -- 00:05:30.000] 有媒體統計 現在國產羽絨服的平均售價已經高達881元 [00:05:30.000 -- 00:05:32.000] 波斯登軍價最高 接近2000元 [00:05:32.000 -- 00:05:34.900] 而且過去幾年 國產羽絨服品牌都在轉向高端化 [00:05:34.900 -- 00:05:37.100] 羽絨服市場分為8000元以上的奢侈級 [00:05:37.100 -- 00:05:41.300] 2000元以下的大重級 而在中間的高端級 國產品牌一直沒有存在感 [00:05:41.300 -- 00:05:43.600] 所以過去幾年 波斯登 天工人這些品牌 [00:05:43.600 -- 00:05:46.700] 都把2000元到8000元這個市場當成未來的發展趨勢 [00:05:46.700 -- 00:05:49.600] 東新證券研報顯示 從2018到2021年 [00:05:49.600 -- 00:05:52.200] 波斯登軍價四年漲幅達到60%以上 [00:05:52.200 -- 00:05:56.000] 過去五個菜年 這個品牌的營銷開支從20多億漲到了60多億 [00:05:56.000 -- 00:06:00.400] 羽絨服價格往上走 年輕消費者就開始拋棄羽絨服 購買平價衝鋒衣 [00:06:00.400 -- 00:06:03.400] 裡面再穿個普通價外的瑤麗絨或者羽絨小夾克 [00:06:03.400 -- 00:06:07.000] 也不比大幾千的羽絨服差多少 說到底 現在消費是會發達的 [00:06:07.000 -- 00:06:09.700] 沒有什麼需求是一定要某種特定的解決方案 [00:06:09.700 -- 00:06:11.600] 特定價位的商品才能實現的 [00:06:11.600 -- 00:06:15.200] 要保暖 羽絨服固然很好 但春風衣加一些內搭也很暖和 [00:06:15.200 -- 00:06:18.000] 要時尚 大幾千塊錢的設計師品牌非常不錯 [00:06:18.000 -- 00:06:20.700] 但350的拼多多服飾搭的好也能出彩 [00:06:20.700 -- 00:06:23.100] 要去野外徒步 花五六千買鳥也可以 [00:06:23.100 -- 00:06:25.200] 但迪卡儂也足以應付大多數狀況 [00:06:25.200 -- 00:06:27.600] 所以說 花高價買春風衣當然也OK [00:06:27.600 -- 00:06:29.800] 三四百買件駱駝也是可以接受的選擇 [00:06:29.800 -- 00:06:33.800] 駱駝也多多少少有一些功能性 畢竟它再怎麼樣還是個春風衣 [00:06:33.800 -- 00:06:36.800] 理解了這個事情就很容易分辨什麼是智商稅的 [00:06:36.800 -- 00:06:38.900] 那些向你灌輸非某個品牌不用 [00:06:38.900 -- 00:06:41.500] 告訴你某個需求只有某個產品才能滿足 [00:06:41.500 -- 00:06:44.400] 某個品牌就是某個品類絕對的鄙視鏈頂端 [00:06:44.400 -- 00:06:46.900] 這類營銷的智商稅含量必然是很高的 [00:06:46.900 -- 00:06:48.900] 它的目的是剝奪你選擇的權利 [00:06:48.900 -- 00:06:51.300] 讓你主動放棄比價和尋找平梯的想法 [00:06:51.300 -- 00:06:53.100] 從而避免與其他品牌競爭 [00:06:53.100 -- 00:06:56.300] 而沒有競爭的市場才是智商稅含量最高的市場 [00:06:56.300 -- 00:06:59.900] 消費商業洞穴禁在IC實驗室 我是館長 我們下期再見 [00:06:59.900 -- 00:07:01.900] 謝謝收看!
output_srt: saving output to chs.wav.srt
whisper_print_timings: load time 841.23 ms whisper_print_timings: fallbacks 1 p / 0 h whisper_print_timings: mel time 440.91 ms whisper_print_timings: sample time 13100.71 ms / 17724 runs ( 0.74 ms per run) whisper_print_timings: encode time 4078.38 ms / 18 runs ( 226.58 ms per run) whisper_print_timings: decode time 40.70 ms / 2 runs ( 20.35 ms per run) whisper_print_timings: batchd time 155882.95 ms / 17702 runs ( 8.81 ms per run) whisper_print_timings: prompt time 3419.58 ms / 3632 runs ( 0.94 ms per run) whisper_print_timings: total time 177848.30 ms rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/g generate-coreml-interface.sh generate-coreml-model.sh ggml-base.en.bin ggml-large-v3.bin ggml-medium.bin ggml_to_pt.py rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/ggml ggml-base.en.bin ggml-large-v3.bin ggml-medium.bin ggml_to_pt.py rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/ggml-large-v3.bin chs.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 ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce GTX 1080, compute capability 6.1, VMM: yes whisper_backend_init: using CUDA backend whisper_model_load: CUDA0 total size 3094.86 MB (3 buffers) whisper_model_load: model size 3094.36 MB whisper_backend_init: using CUDA backend whisper_init_state: kv self size 220.20 MB whisper_init_state: kv cross size 245.76 MB whisper_init_state: compute buffer (conv) 35.50 MB whisper_init_state: compute buffer (encode) 233.50 MB whisper_init_state: compute buffer (cross) 10.15 MB whisper_init_state: compute buffer (decode) 108.99 MB
system_info: n_threads 4 / 36 | AVX 1 | AVX2 1 | AVX512 0 | FMA 1 | NEON 0 | ARM_FMA 0 | METAL 0 | F16C 1 | FP16_VA 0 | WASM_SIMD 0 | BLAS 1 | SSE3 1 | SSSE3 1 | VSX 0 | CUDA 1 | COREML 0 | OPENVINO 0 |
main: processing chs.wav (6748501 samples, 421.8 sec), 4 threads, 1 processors, 5 beams best of 5, lang zh, task transcribe, timestamps 1 ... [00:00:00.040 -- 00:00:01.460] 前段时间有个巨石横火 [00:00:01.460 -- 00:00:02.860] 某某是男人最好的衣媒 [00:00:02.860 -- 00:00:04.800] 这里的某某可以替换为减肥 [00:00:04.800 -- 00:00:07.620] 长发 西装 考研 书唱 永结无间等等等等 [00:00:07.620 -- 00:00:09.320] 我听到最新的一个说法是 [00:00:09.320 -- 00:00:11.940] 微分碎盖加口罩加半框眼镜加冲锋衣 [00:00:11.940 -- 00:00:13.440] 等于男人最好的衣媒 [00:00:13.440 -- 00:00:14.420] 大概也就前几年 [00:00:14.420 -- 00:00:17.560] 冲锋衣还和格子衬衫并列为程序员穿搭精华 [00:00:17.560 -- 00:00:19.940] 紫红色冲锋衣还被誉为广场舞达妈标配 [00:00:19.940 -- 00:00:22.700] 骆驼牌还是我爹这个年纪的人才会愿意买的牌子 [00:00:22.700 -- 00:00:24.380] 不知道风向为啥变得这么快 [00:00:24.380 -- 00:00:26.680] 为啥这东西突然变成男生逆袭神器 [00:00:26.680 -- 00:00:27.660] 时尚潮流单品 [00:00:27.660 -- 00:00:29.580] 后来我翻了一下小红书就懂了 [00:00:29.580 -- 00:00:30.460] 时尚这个时期 [00:00:30.460 -- 00:00:31.620] 重点不在于衣服 [00:00:31.620 -- 00:00:32.160] 在于人 [00:00:32.160 -- 00:00:34.500] 现在小红书上面和冲锋衣相关的笔记 [00:00:34.500 -- 00:00:36.220] 照片里的男生都是这样的 [00:00:36.220 -- 00:00:36.880] 这样的 [00:00:36.880 -- 00:00:38.140] 还有这样的 [00:00:38.140 -- 00:00:39.460] 你们哪里是看穿搭的 [00:00:39.460 -- 00:00:40.540] 你们明明是看脸 [00:00:40.540 -- 00:00:41.780] 就这个造型这个年龄 [00:00:41.780 -- 00:00:43.920] 你换上老头衫也能穿出氛围感好吗 [00:00:43.920 -- 00:00:46.560] 我又想起了当年郭德纲老师穿计繁西的残剧 [00:00:46.560 -- 00:00:48.560] 这个世界对我们这些长得不好看的人 [00:00:48.560 -- 00:00:49.480] 还真是苛刻呢 [00:00:49.480 -- 00:00:52.100] 所以说我总结了一下冲锋衣传达的要领 [00:00:52.100 -- 00:00:54.200] 大概就是一张白净且人畜无汉的脸 [00:00:54.200 -- 00:00:55.120] 充足的发量 [00:00:55.120 -- 00:00:55.980] 纤细的体型 [00:00:55.980 -- 00:00:58.160] 当然身上的冲锋衣还得是骆驼的 [00:00:58.160 -- 00:00:59.320] 去年在户外用品界 [00:00:59.320 -- 00:01:01.100] 最顶流的既不是鸟像书 [00:01:01.100 -- 00:01:02.560] 也不是有校服之称的北面 [00:01:02.560 -- 00:01:04.120] 或者老台顶流哥伦比亚 [00:01:04.120 -- 00:01:04.800] 而是骆驼 [00:01:04.800 -- 00:01:06.980] 双十一骆驼在天猫户外服饰品类 [00:01:06.980 -- 00:01:08.860] 拿下销售额和销量双料冠军 [00:01:08.860 -- 00:01:09.980] 销量达到百万级 [00:01:09.980 -- 00:01:10.620] 在抖音 [00:01:10.620 -- 00:01:13.200] 骆驼销售同比增幅高达百分之296 [00:01:13.200 -- 00:01:15.920] 旗下主打的三合一高性价比冲锋衣成为爆品 [00:01:15.920 -- 00:01:17.260] 哪怕不看双十一 [00:01:17.260 -- 00:01:18.020] 随手一搜 [00:01:18.020 -- 00:01:21.040] 骆驼在冲锋衣的七日销售榜上都是图榜的存在 [00:01:21.040 -- 00:01:22.480] 这是线上的销售表现 [00:01:22.480 -- 00:01:24.200] 至于线下还是网友总结的好 [00:01:24.200 -- 00:01:26.740] 如今在南方街头的骆驼比沙漠里的都多 [00:01:26.740 -- 00:01:27.540] 爬个华山 [00:01:27.540 -- 00:01:28.320] 满山的骆驼 [00:01:28.320 -- 00:01:29.840] 随便逛个街撞山了 [00:01:29.840 -- 00:01:31.060] 至于骆驼为啥这么火 [00:01:31.060 -- 00:01:31.800] 便宜啊 [00:01:31.800 -- 00:01:33.400] 拿卖的最好的丁真同款 [00:01:33.400 -- 00:01:35.500] 幻影黑三合一冲锋衣举个例子 [00:01:35.500 -- 00:01:36.000] 线下买 [00:01:36.000 -- 00:01:37.440] 标牌价格2198 [00:01:37.440 -- 00:01:38.940] 但是跑到网上看一下 [00:01:38.940 -- 00:01:40.460] 标价就变成了699 [00:01:40.460 -- 00:01:41.220] 至于折扣 [00:01:41.220 -- 00:01:42.360] 日常也都是有的 [00:01:42.360 -- 00:01:43.440] 400出头就能买到 [00:01:43.440 -- 00:01:44.960] 甚至有时候能低到300价 [00:01:44.960 -- 00:01:46.140] 要是你还嫌贵 [00:01:46.140 -- 00:01:48.200] 路头还有200块出头的单层冲锋衣 [00:01:48.200 -- 00:01:49.080] 就这个价格 [00:01:49.080 -- 00:01:51.520] 搁上海恐怕还不够两次CityWalk的报名费 [00:01:51.520 -- 00:01:52.560] 看了这个价格 [00:01:52.560 -- 00:01:53.560] 再对比一下北面 [00:01:53.560 -- 00:01:54.640] 1000块钱起步 [00:01:54.640 -- 00:01:56.000] 你就能理解为啥北面 [00:01:56.000 -- 00:01:58.120] 这么快就被大学生踢出了校服序列了 [00:01:58.120 -- 00:02:00.380] 我不知道现在大学生每个月生活费多少 [00:02:00.380 -- 00:02:02.160] 反正按照我上学时候的生活费 [00:02:02.160 -- 00:02:03.200] 一个月不吃不喝 [00:02:03.200 -- 00:02:05.080] 也就买得起俩袖子加一个帽子 [00:02:05.080 -- 00:02:06.420] 难怪当年全是假北面 [00:02:06.420 -- 00:02:07.400] 现在都是真路头 [00:02:07.400 -- 00:02:08.640] 至少人家是正品啊 [00:02:08.640 -- 00:02:10.080] 我翻了一下社交媒体 [00:02:10.080 -- 00:02:12.060] 发现对路头的吐槽和买了路头的 [00:02:12.060 -- 00:02:13.340] 基本上是1比1的比例 [00:02:13.340 -- 00:02:15.040] 吐槽最多的就是衣服会掉色 [00:02:15.040 -- 00:02:15.960] 还会串色 [00:02:15.960 -- 00:02:17.100] 比如图增洗个几次 [00:02:17.100 -- 00:02:18.240] 穿个两天就掉光了 [00:02:18.240 -- 00:02:19.600] 比如不同仓库发的货 [00:02:19.600 -- 00:02:20.600] 质量参差不齐 [00:02:20.600 -- 00:02:22.300] 买衣服还得看户口拼出身 [00:02:22.300 -- 00:02:23.660] 至于什么做工比较差 [00:02:23.660 -- 00:02:24.300] 内胆多 [00:02:24.300 -- 00:02:24.880] 走线糙 [00:02:24.880 -- 00:02:26.380] 不防水之类的就更多了 [00:02:26.380 -- 00:02:27.360] 但是这些吐槽 [00:02:27.360 -- 00:02:29.160] 并不意味着会影响路头的销量 [00:02:29.160 -- 00:02:30.820] 甚至还会有不少自来水表示 [00:02:30.820 -- 00:02:32.680] 就这价格要啥自行车啊 [00:02:32.680 -- 00:02:34.080] 所谓性价比性价比 [00:02:34.080 -- 00:02:35.340] 脱离价位谈性能 [00:02:35.340 -- 00:02:36.980] 这就不符合消费者的需求嘛 [00:02:36.980 -- 00:02:38.480] 无数次价格战告诉我们 [00:02:38.480 -- 00:02:39.500] 只要肯降价 [00:02:39.500 -- 00:02:40.960] 就没有卖不出去的产品 [00:02:40.960 -- 00:02:41.820] 一件冲锋衣 [00:02:41.820 -- 00:02:43.500] 1000多你觉得平平无奇 [00:02:43.500 -- 00:02:44.900] 500多你觉得差点意思 [00:02:44.900 -- 00:02:46.480] 200块你就要秒下单了 [00:02:46.480 -- 00:02:48.520] 到99恐怕就要拼点手速了 [00:02:48.520 -- 00:02:49.560] 像冲锋衣这个品类 [00:02:49.560 -- 00:02:50.720] 本来价格跨度就大 [00:02:50.720 -- 00:02:52.660] 北面最便宜的Gortex冲锋衣 [00:02:52.660 -- 00:02:53.740] 价格3000起步 [00:02:53.740 -- 00:02:56.360] 大概是同品牌最便宜冲锋衣的三倍价格 [00:02:56.360 -- 00:02:57.060] 至于十足鸟 [00:02:57.060 -- 00:02:59.020] 搭载了Gortex的硬壳起步价 [00:02:59.020 -- 00:02:59.780] 就要到4500 [00:02:59.780 -- 00:03:01.080] 而且同样是Gortex [00:03:01.080 -- 00:03:02.860] 内部也有不同的系列和档次 [00:03:02.860 -- 00:03:03.520] 做成衣服 [00:03:03.520 -- 00:03:05.780] 中间的差价恐怕就够买两件骆驼了 [00:03:05.780 -- 00:03:06.620] 至于智能控温 [00:03:06.620 -- 00:03:07.320] 防水拉链 [00:03:07.320 -- 00:03:07.900] 全压胶 [00:03:07.900 -- 00:03:09.760] 更加不可能出现在骆驼这里了 [00:03:09.760 -- 00:03:11.780] 至少不会是三四百的骆驼身上会有的 [00:03:11.780 -- 00:03:12.660] 有的价外的衣服 [00:03:12.660 -- 00:03:14.040] 买的就是一个放弃幻想 [00:03:14.040 -- 00:03:15.660] 吃到肚子里的科技鱼很活 [00:03:15.660 -- 00:03:16.840] 是能给你省钱的 [00:03:16.840 -- 00:03:18.320] 穿在身上的科技鱼很活 [00:03:18.320 -- 00:03:20.040] 装装件件都是要加钱的 [00:03:20.040 -- 00:03:21.440] 所以正如罗曼罗兰所说 [00:03:21.440 -- 00:03:23.040] 这世界上只有一种英雄主义 [00:03:23.040 -- 00:03:24.860] 就是在认清了骆驼的本质以后 [00:03:24.860 -- 00:03:26.060] 依然选择买骆驼 [00:03:26.060 -- 00:03:26.900] 关于骆驼的火爆 [00:03:26.900 -- 00:03:28.180] 我有一些小小的看法 [00:03:28.180 -- 00:03:28.960] 骆驼这个东西 [00:03:28.960 -- 00:03:30.220] 它其实就是个潮牌 [00:03:30.220 -- 00:03:31.940] 看看它的营销方式就知道了 [00:03:31.940 -- 00:03:32.920] 现在打开小红书 [00:03:32.920 -- 00:03:35.120] 日常可以看到骆驼穿搭是这样的 [00:03:35.120 -- 00:03:36.900] 加一点氛围感是这样的 [00:03:36.900 -- 00:03:37.400] 对比一下 [00:03:37.400 -- 00:03:39.240] 其他品牌的风格是这样的 [00:03:39.240 -- 00:03:40.020] 这样的 [00:03:40.020 -- 00:03:41.280] 其实对比一下就知道了 [00:03:41.280 -- 00:03:42.600] 其他品牌突出一个时程 [00:03:42.600 -- 00:03:44.240] 能防风就一定要讲防风 [00:03:44.240 -- 00:03:45.960] 能扛冻就一定要讲扛冻 [00:03:45.960 -- 00:03:47.340] 但骆驼在营销的时候 [00:03:47.340 -- 00:03:49.080] 主打的就是一个城市户外风 [00:03:49.080 -- 00:03:50.440] 虽然造型是春风衣 [00:03:50.440 -- 00:03:52.180] 但场景往往是在城市里 [00:03:52.180 -- 00:03:54.220] 哪怕在野外也要突出一个风和日丽 [00:03:54.220 -- 00:03:54.940] 阳光敏媚 [00:03:54.940 -- 00:03:56.500] 至少不会在明显的严寒 [00:03:56.500 -- 00:03:58.020] 高海拔或是恶劣气候下 [00:03:58.020 -- 00:04:00.160] 如果用一个词形容骆驼的营销风格 [00:04:00.160 -- 00:04:00.920] 那就是清洗 [00:04:00.920 -- 00:04:03.060] 或者说他很理解自己的消费者是谁 [00:04:03.060 -- 00:04:03.920] 需要什么产品 [00:04:03.920 -- 00:04:05.260] 从使用场景来说 [00:04:05.260 -- 00:04:06.600] 骆驼的消费者买春风衣 [00:04:06.600 -- 00:04:08.640] 不是真的有什么大风大雨要去应对 [00:04:08.640 -- 00:04:10.880] 春风衣的作用是下雨没带伞的时候 [00:04:10.880 -- 00:04:12.160] 临时顶个几分钟 [00:04:12.160 -- 00:04:13.700] 让你能图书馆跑回宿舍 [00:04:13.700 -- 00:04:14.940] 或者是冬天骑电动车 [00:04:14.940 -- 00:04:16.220] 被风吹得不行的时候 [00:04:16.220 -- 00:04:17.200] 稍微扛一下风 [00:04:17.200 -- 00:04:18.340] 不至于体感太冷 [00:04:18.340 -- 00:04:19.700] 当然他们也会出门 [00:04:19.700 -- 00:04:21.780] 但大部分时候也都是去别的城市 [00:04:21.780 -- 00:04:23.860] 或者在城市周边搞搞简单的徒步 [00:04:23.860 -- 00:04:24.920] 这种情况下 [00:04:24.920 -- 00:04:25.920] 穿个骆驼也就够了 [00:04:25.920 -- 00:04:27.220] 从购买动机来说 [00:04:27.220 -- 00:04:29.260] 骆驼就更没有必要上那些硬核科技了 [00:04:29.260 -- 00:04:30.920] 消费者买骆驼买的是个什么呢 [00:04:30.920 -- 00:04:32.240] 不是春风衣的功能性 [00:04:32.240 -- 00:04:33.380] 而是春风衣的造型 [00:04:33.380 -- 00:04:34.340] 宽松的版型 [00:04:34.340 -- 00:04:36.380] 能精准遮住微微隆起的小肚子 [00:04:36.380 -- 00:04:37.440] 棱角分明的质感 [00:04:37.440 -- 00:04:39.420] 能隐藏一切不完美的整体线条 [00:04:39.420 -- 00:04:41.260] 显瘦的副作用就是显年轻 [00:04:41.260 -- 00:04:42.600] 再配上一条牛仔裤 [00:04:42.600 -- 00:04:43.680] 配上一双大黄靴 [00:04:43.680 -- 00:04:45.100] 大学生的气质就出来了 [00:04:45.100 -- 00:04:47.700] 要是自拍的时候再配上大学宿舍洗漱台 [00:04:47.700 -- 00:04:49.380] 那永远擦不干净的镜子 [00:04:49.380 -- 00:04:50.840] 瞬间青春无敌了 [00:04:50.840 -- 00:04:51.700] 说的更直白一点 [00:04:51.700 -- 00:04:53.060] 人家买的是个锦铃神器 [00:04:53.060 -- 00:04:53.820] 所以说 [00:04:53.820 -- 00:04:55.860] 吐槽穿骆驼都是假户外爱好者的人 [00:04:55.860 -- 00:04:57.460] 其实并没有理解骆驼的定位 [00:04:57.460 -- 00:04:59.780] 骆驼其实是给了想要入门山系穿搭 [00:04:59.780 -- 00:05:01.740] 想要追逐流行的人一个最平价 [00:05:01.740 -- 00:05:02.980] 决策成本最低的选择 [00:05:02.980 -- 00:05:04.880] 至于那些真正的硬核户外爱好者 [00:05:04.880 -- 00:05:05.800] 骆驼既没有能力 [00:05:05.800 -- 00:05:07.080] 也没有打算触打他们 [00:05:07.080 -- 00:05:07.980] 反过来说 [00:05:07.980 -- 00:05:09.460] 那些自驾穿越边疆国道 [00:05:09.460 -- 00:05:11.680] 或者去阿尔卑斯山区登山探险的人 [00:05:11.680 -- 00:05:13.540] 也不太可能在户外服饰上省钱 [00:05:13.540 -- 00:05:14.900] 毕竟光是交通住宿 [00:05:14.900 -- 00:05:15.600] 请假出行 [00:05:15.600 -- 00:05:16.560] 成本就不低了 [00:05:16.560 -- 00:05:17.320] 对他们来说 [00:05:17.320 -- 00:05:19.140] 户外装备很多时候是保命用的 [00:05:19.140 -- 00:05:21.180] 也就不存在跟风凹造型的必要了 [00:05:21.180 -- 00:05:22.300] 最后我再说个题外话 [00:05:22.300 -- 00:05:23.320] 年轻人追捧骆驼 [00:05:23.320 -- 00:05:24.240] 一个隐藏的原因 [00:05:24.240 -- 00:05:25.940] 其实是羽绒服越来越贵了 [00:05:25.940 -- 00:05:26.620] 有媒体统计 [00:05:26.620 -- 00:05:28.440] 现在国产羽绒服的平均售价 [00:05:28.440 -- 00:05:29.880] 已经高达881元 [00:05:29.880 -- 00:05:31.140] 波斯灯均价最高 [00:05:31.140 -- 00:05:31.900] 接近2000元 [00:05:31.900 -- 00:05:32.880] 而且过去几年 [00:05:32.880 -- 00:05:34.800] 国产羽绒服品牌都在转向高端化 [00:05:34.800 -- 00:05:37.060] 羽绒服市场分为8000元以上的奢侈级 [00:05:37.060 -- 00:05:38.440] 2000元以下的大众级 [00:05:38.440 -- 00:05:39.740] 而在中间的高端级 [00:05:39.740 -- 00:05:41.220] 国产品牌一直没有存在感 [00:05:41.220 -- 00:05:42.140] 所以过去几年 [00:05:42.140 -- 00:05:43.520] 波斯灯天空人这些品牌 [00:05:43.520 -- 00:05:45.260] 都把2000元到8000元这个市场 [00:05:45.260 -- 00:05:46.560] 当成未来的发展趋势 [00:05:46.560 -- 00:05:47.980] 东芯证券研报显示 [00:05:47.980 -- 00:05:49.600] 从2018到2021年 [00:05:49.600 -- 00:05:52.080] 波斯灯均价4年涨幅达到60%以上 [00:05:52.080 -- 00:05:53.080] 过去5个财年 [00:05:53.080 -- 00:05:54.300] 这个品牌的营销开支 [00:05:54.300 -- 00:05:56.020] 从20多亿涨到了60多亿 [00:05:56.020 -- 00:05:57.240] 羽绒服价格往上走 [00:05:57.240 -- 00:05:59.160] 年轻消费者就开始抛弃羽绒服 [00:05:59.160 -- 00:06:00.300] 购买平价春风衣 [00:06:00.300 -- 00:06:02.240] 里面再穿个普通价位的摇篱绒 [00:06:02.240 -- 00:06:03.280] 或者羽绒小夹克 [00:06:03.280 -- 00:06:05.100] 也不比大几千的羽绒服差多少 [00:06:05.100 -- 00:06:05.740] 说到底 [00:06:05.740 -- 00:06:07.120] 现在消费社会发达了 [00:06:07.120 -- 00:06:08.300] 没有什么需求是一定要 [00:06:08.300 -- 00:06:09.740] 某种特定的解决方案 [00:06:09.740 -- 00:06:11.500] 特定价位的商品才能实现的 [00:06:11.500 -- 00:06:12.080] 要保暖 [00:06:12.080 -- 00:06:13.140] 羽绒服固然很好 [00:06:13.140 -- 00:06:15.320] 但春风衣加一些内搭也很暖和 [00:06:15.320 -- 00:06:15.820] 要时尚 [00:06:15.820 -- 00:06:17.860] 大几千块钱的设计师品牌非常不错 [00:06:17.860 -- 00:06:19.360] 但350的拼多多服饰 [00:06:19.360 -- 00:06:20.520] 搭得好也能出产 [00:06:20.520 -- 00:06:21.620] 要去野外徒步 [00:06:21.620 -- 00:06:22.940] 花五六千买鸟也可以 [00:06:22.940 -- 00:06:25.100] 但迪卡侬也足以应付大多数状况 [00:06:25.100 -- 00:06:25.720] 所以说 [00:06:25.720 -- 00:06:27.420] 花高价买春风衣当然也OK [00:06:27.420 -- 00:06:28.540] 三四百买件骆驼 [00:06:28.540 -- 00:06:29.880] 也是可以介绍的选择 [00:06:29.880 -- 00:06:31.900] 何况骆驼也多多少少有一些功能性 [00:06:31.900 -- 00:06:32.840] 毕竟它再怎么样 [00:06:32.840 -- 00:06:33.920] 还是个春风衣 [00:06:33.920 -- 00:06:34.800] 理解了这个事情 [00:06:34.800 -- 00:06:35.740] 就很容易分辨 [00:06:35.740 -- 00:06:36.900] 什么是智商税的 [00:06:36.900 -- 00:06:38.740] 那些向你灌输非某个品牌不用 [00:06:38.740 -- 00:06:39.880] 告诉你某个需求 [00:06:39.880 -- 00:06:41.380] 只有某个产品才能满足 [00:06:41.380 -- 00:06:42.160] 某个品牌 [00:06:42.160 -- 00:06:44.220] 就是某个品类绝对的鄙视链顶端 [00:06:44.220 -- 00:06:45.900] 这类营销的智商税含量 [00:06:45.900 -- 00:06:46.860] 必然是很高的 [00:06:46.860 -- 00:06:48.780] 它的目的是剥夺你选择的权利 [00:06:48.780 -- 00:06:51.220] 让你主动放弃比价和寻找平梯的想法 [00:06:51.220 -- 00:06:52.920] 从而避免与其他品牌竞争 [00:06:52.920 -- 00:06:54.280] 而没有竞争的市场 [00:06:54.280 -- 00:06:56.020] 才是智商税含量最高的市场 [00:06:56.020 -- 00:06:57.360] 消费商业洞见 [00:06:57.360 -- 00:06:58.420] 近在IC实验室 [00:06:58.420 -- 00:06:59.000] 我是馆长 [00:06:59.000 -- 00:06:59.840] 我们下期再见 [00:06:59.840 -- 00:07:01.840] 谢谢大家!
output_srt: saving output to chs.wav.srt
whisper_print_timings: load time 1232.24 ms whisper_print_timings: fallbacks 1 p / 0 h whisper_print_timings: mel time 507.42 ms whisper_print_timings: sample time 14211.34 ms / 19337 runs ( 0.73 ms per run) whisper_print_timings: encode time 9234.67 ms / 19 runs ( 486.04 ms per run) whisper_print_timings: decode time 41.85 ms / 2 runs ( 20.92 ms per run) whisper_print_timings: batchd time 325320.62 ms / 19329 runs ( 16.83 ms per run) whisper_print_timings: prompt time 5857.69 ms / 3869 runs ( 1.51 ms per run) whisper_print_timings: total time 356447.78 ms rootrootrootroot-X99-Turbo:~/whisper.cpp$ rootrootrootroot-X99-Turbo:~/whisper.cpp$ ./main -l zh -osrt -m models/ggml-large-v3.bin chs.wavConnection closing...Socket close.
Connection closed by foreign host.
Disconnected from remote host(rootroot192.168.186.230) at 18:34:03.
Type help to learn how to use Xshell prompt.
[END] 2024/2/2 19:43:47
