mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-24 06:46:37 +00:00
Reduce memory usage even more + better sampling
- The encode/decode memory buffers are now reused - If the 30-sec segment goes for too long without a timestamp token, we force one. Improves transcription for large model - Stereo support - Add "micro-machines.wav" sample
This commit is contained in:
Georgi Gerganov
parent
310f4883d1
commit
3bcdbdfc32
3
Makefile
3
Makefile
@ -20,10 +20,13 @@ samples:
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@wget --quiet --show-progress -O samples/gb0.ogg https://upload.wikimedia.org/wikipedia/commons/2/22/George_W._Bush%27s_weekly_radio_address_%28November_1%2C_2008%29.oga
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@wget --quiet --show-progress -O samples/gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
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@wget --quiet --show-progress -O samples/hp0.ogg https://upload.wikimedia.org/wikipedia/en/d/d4/En.henryfphillips.ogg
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@wget --quiet --show-progress -O samples/mm1.wav https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav
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@echo "Converting to 16-bit WAV ..."
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@ffmpeg -loglevel -0 -y -i samples/gb0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb0.wav
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@ffmpeg -loglevel -0 -y -i samples/gb1.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/gb1.wav
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@ffmpeg -loglevel -0 -y -i samples/hp0.ogg -ar 16000 -ac 1 -c:a pcm_s16le samples/hp0.wav
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@ffmpeg -loglevel -0 -y -i samples/mm1.wav -ar 16000 -ac 1 -c:a pcm_s16le samples/mm0.wav
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@rm samples/mm1.wav
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# if not already downloaded, the following targets download the specified model and
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56
README.md
56
README.md
@ -1,12 +1,13 @@
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# whisper.cpp
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C/C++ port of [OpenAI's Whisper](https://github.com/openai/whisper) speech-to-text model
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High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
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- Plain C/C++ implementation without dependencies
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- ARM_NEON and AVX intrinsics support
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- Mixed F16 / F32 support
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- Low memory usage (Flash Attention + Flash Forward)
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- Zero memory allocations at runtime
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- Runs on the CPU (Mac and Linux support)
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## Usage
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@ -50,7 +51,12 @@ options:
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bash ./download-ggml-model.sh base.en
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Downloading ggml model base.en ...
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Model base.en already exists. Skipping download.
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models/ggml-base.en.bin 100%[=====================================>] 141.11M 8.58MB/s in 22s
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Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
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You can now use it like this:
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$ ./main -m models/ggml-base.en.bin -f samples/jfk.wav
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===============================================
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Running base.en on all samples in ./samples ...
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@ -73,7 +79,7 @@ whisper_model_load: n_text_layer = 6
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whisper_model_load: n_mels = 80
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whisper_model_load: f16 = 1
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whisper_model_load: type = 2
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whisper_model_load: mem_required = 611.00 MB
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whisper_model_load: mem_required = 377.00 MB
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whisper_model_load: adding 1607 extra tokens
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whisper_model_load: ggml ctx size = 163.43 MB
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whisper_model_load: memory size = 22.83 MB
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@ -86,12 +92,12 @@ main: processing 176000 samples (11.0 sec), 4 threads, lang = english, task = tr
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[00:00.000 --> 00:11.000] And so my fellow Americans ask not what your country can do for you. Ask what you can do for your country.
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main: load time = 61.78 ms
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main: mel time = 41.74 ms
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main: sample time = 2.10 ms
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main: encode time = 718.60 ms / 119.77 ms per layer
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main: decode time = 83.55 ms
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main: total time = 908.15 ms
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main: load time = 82.05 ms
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main: mel time = 44.15 ms
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main: sample time = 1.98 ms
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main: encode time = 674.77 ms / 112.46 ms per layer
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main: decode time = 82.91 ms
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main: total time = 886.29 ms
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```
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The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
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@ -131,10 +137,12 @@ make large
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## Another example
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Here is another example of transcribing a [3:24 min speech](https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg) in less than a minute, using `medium.en` model:
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Here is another example of transcribing a [3:24 min speech](https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg)
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in less than a minute on a MacBook M1 Pro, using `medium.en` model:
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```java
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$ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8
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whisper_model_load: loading model from 'models/ggml-medium.en.bin'
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whisper_model_load: n_vocab = 51864
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whisper_model_load: n_audio_ctx = 1500
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@ -148,7 +156,7 @@ whisper_model_load: n_text_layer = 24
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whisper_model_load: n_mels = 80
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whisper_model_load: f16 = 1
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whisper_model_load: type = 4
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whisper_model_load: mem_required = 2786.00 MB
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whisper_model_load: mem_required = 2502.00 MB
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whisper_model_load: adding 1607 extra tokens
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whisper_model_load: ggml ctx size = 1644.97 MB
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whisper_model_load: memory size = 182.62 MB
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@ -187,30 +195,30 @@ main: processing 3179750 samples (198.7 sec), 8 threads, lang = english, task =
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[03:14.000 --> 03:24.000] [Music]
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main: load time = 438.55 ms
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main: mel time = 440.22 ms
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main: sample time = 32.23 ms
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main: encode time = 42329.63 ms / 1763.73 ms per layer
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main: decode time = 15190.00 ms
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main: total time = 58444.63 ms
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main: load time = 522.18 ms
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main: mel time = 423.43 ms
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main: sample time = 31.42 ms
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main: encode time = 41518.51 ms / 1729.94 ms per layer
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main: decode time = 14907.22 ms
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main: total time = 57416.63 ms
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```
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## Limitations
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- Very basic greedy sampling scheme - always pick up the top token
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- Only 16-bit WAV at 16 kHz is supported
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- Inference only
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- Runs on the CPU
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- Only mono-channel 16-bit WAV is supported
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- No GPU support
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## Memory usage
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| Model | Disk | Mem |
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| --- | --- | --- |
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| tiny | 75 MB | ~460 MB |
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| base | 142 MB | ~620 MB |
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| small | 466 MB | ~1.3 GB |
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| medium | 1.5 GB | ~2.8 GB |
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| large | 2.9 GB | ~4.9 GB |
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| tiny | 75 MB | ~240 MB |
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| base | 142 MB | ~380 MB |
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| small | 466 MB | ~970 MB |
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| medium | 1.5 GB | ~2.5 GB |
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| large | 2.9 GB | ~4.6 GB |
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## ggml format
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110
main.cpp
110
main.cpp
@ -158,11 +158,11 @@ const std::map<e_model, size_t> MEM_REQ_ENCODE_LAYER = {
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};
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const std::map<e_model, size_t> MEM_REQ_DECODE = {
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{ MODEL_TINY, 190ull*MB },
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{ MODEL_BASE, 190ull*MB },
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{ MODEL_SMALL, 190ull*MB },
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{ MODEL_MEDIUM, 200ull*MB },
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{ MODEL_LARGE, 200ull*MB },
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{ MODEL_TINY, 94ull*MB },
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{ MODEL_BASE, 96ull*MB },
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{ MODEL_SMALL, 98ull*MB },
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{ MODEL_MEDIUM, 100ull*MB },
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{ MODEL_LARGE, 102ull*MB },
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};
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const std::map<e_model, size_t> MEM_REQ_DECODE_LAYER = {
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@ -173,6 +173,11 @@ const std::map<e_model, size_t> MEM_REQ_DECODE_LAYER = {
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{ MODEL_LARGE, 110ull*MB },
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};
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// the memory buffers used to store the model in memory and perform the inference computations
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std::vector<uint8_t> g_buf_model;
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std::vector<uint8_t> g_buf_compute;
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std::vector<uint8_t> g_buf_compute_layer;
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const int SAMPLE_RATE = 16000;
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const int N_FFT = 400;
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const int N_MEL = 80;
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@ -542,13 +547,15 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
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printf("%s: f16 = %d\n", __func__, hparams.f16);
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printf("%s: type = %d\n", __func__, model.type);
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g_buf_model.resize(MEM_REQ_MODEL.at(model.type));
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g_buf_compute.resize(std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
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g_buf_compute_layer.resize(std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type)));
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// this is the total memory required to run the inference
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const size_t mem_required =
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MEM_REQ_MODEL.at(model.type) +
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MEM_REQ_ENCODE.at(model.type) +
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MEM_REQ_ENCODE_LAYER.at(model.type) +
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MEM_REQ_DECODE.at(model.type) +
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MEM_REQ_DECODE_LAYER.at(model.type);
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g_buf_model.size() +
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g_buf_compute.size() +
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g_buf_compute_layer.size();
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printf("%s: mem_required = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
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}
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@ -752,8 +759,8 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
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// create the ggml context
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{
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struct ggml_init_params params = {
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.mem_size = ctx_size,
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.mem_buffer = NULL,
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.mem_size = g_buf_model.size(),
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.mem_buffer = g_buf_model.data(),
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};
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model.ctx = ggml_init(params);
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@ -1089,17 +1096,10 @@ bool whisper_encode(
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const int n_mels = hparams.n_mels;
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assert(mel_inp.n_mel == n_mels);
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struct ggml_init_params params;
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{
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static size_t buf_size = MEM_REQ_ENCODE.at(model.type);
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static void * buf = malloc(buf_size);
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params = {
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.mem_size = buf_size,
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.mem_buffer = buf,
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struct ggml_init_params params = {
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.mem_size = g_buf_compute.size(),
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.mem_buffer = g_buf_compute.data(),
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};
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}
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struct ggml_context * ctx0 = ggml_init(params);
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@ -1151,16 +1151,10 @@ bool whisper_encode(
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// create separate context for each layer to reduce memory usage
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struct ggml_init_params paramsL;
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{
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static size_t buf_size = MEM_REQ_ENCODE_LAYER.at(model.type);
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static void * buf = malloc(buf_size);
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paramsL = {
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.mem_size = buf_size,
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.mem_buffer = buf,
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struct ggml_init_params paramsL = {
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.mem_size = g_buf_compute_layer.size(),
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.mem_buffer = g_buf_compute_layer.data(),
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};
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}
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struct ggml_context * ctxL = ggml_init(paramsL);
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@ -1492,17 +1486,10 @@ bool whisper_decode(
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const int N = prompt.size();
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const int M = hparams.n_audio_ctx;
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struct ggml_init_params params;
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{
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static size_t buf_size = MEM_REQ_DECODE.at(model.type);
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static void * buf = malloc(buf_size);
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params = {
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.mem_size = buf_size,
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.mem_buffer = buf,
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struct ggml_init_params params = {
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.mem_size = g_buf_compute.size(),
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.mem_buffer = g_buf_compute.data(),
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};
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}
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struct ggml_context * ctx0 = ggml_init(params);
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@ -1525,17 +1512,10 @@ bool whisper_decode(
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for (int il = 0; il < n_layer; ++il) {
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const auto & layer = model.layers_decoder[il];
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struct ggml_init_params paramsL;
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{
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static size_t buf_size = MEM_REQ_DECODE_LAYER.at(model.type);
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static void * buf = malloc(buf_size);
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paramsL = {
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.mem_size = buf_size,
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.mem_buffer = buf,
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struct ggml_init_params paramsL = {
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.mem_size = g_buf_compute_layer.size(),
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.mem_buffer = g_buf_compute_layer.data(),
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};
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}
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struct ggml_context * ctxL = ggml_init(paramsL);
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struct ggml_cgraph gf = { .n_threads = n_threads };
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@ -1849,7 +1829,7 @@ bool whisper_decode(
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// TODO: temperature
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whisper_vocab::id whisper_sample_best(
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const whisper_vocab & vocab,
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const float * probs) {
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const float * probs, bool need_timestamp) {
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int n_logits = vocab.id_to_token.size();
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std::vector<std::pair<double, whisper_vocab::id>> probs_id;
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@ -1859,7 +1839,7 @@ whisper_vocab::id whisper_sample_best(
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probs_id.push_back(std::make_pair(probs[i], i));
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}
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const int top_k = 10;
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const int top_k = 4;
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// find the top K tokens
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std::partial_sort(
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@ -1876,6 +1856,15 @@ whisper_vocab::id whisper_sample_best(
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// printf("%d: '%s' %f, %d\n", i, vocab.id_to_token.at(probs_id[i].second).c_str(), probs_id[i].first, probs_id[i].second);
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//}
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if (need_timestamp) {
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// at the end of the 30-second audio segment, we start giving preference to time tokens
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for (int i = 0; i < top_k; i++) {
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if (probs_id[i].second > vocab.token_beg + 1300 && probs_id[i].first > probs_id[0].first*0.1) {
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return probs_id[i].second;
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}
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}
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}
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int res = 0;
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while ((probs_id[res].second == vocab.token_sot ||
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probs_id[res].second == vocab.token_solm ||
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@ -2136,8 +2125,8 @@ int main(int argc, char ** argv) {
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return 2;
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}
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if (wav.channels != 1) {
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fprintf(stderr, "%s: WAV file '%s' must be mono\n", argv[0], params.fname_inp.c_str());
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if (wav.channels != 1 && wav.channels != 2) {
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fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], params.fname_inp.c_str());
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return 3;
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}
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@ -2158,9 +2147,15 @@ int main(int argc, char ** argv) {
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// convert to float
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pcmf32.resize(pcm16.size());
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if (wav.channels == 1) {
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for (size_t i = 0; i < pcm16.size(); i++) {
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pcmf32[i] = float(pcm16[i])/32768.0f;
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}
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} else {
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for (size_t i = 0; i < pcm16.size(); i++) {
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pcmf32[i] = float(pcm16[i*2 + 0] + pcm16[i*2 + 1])/32768.0f/2.0f;
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}
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}
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}
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// compute log mel spectrogram
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@ -2252,6 +2247,7 @@ int main(int argc, char ** argv) {
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int seek_delta = 100*CHUNK_SIZE;
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whisper_vocab::id last_id = 0;
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// print the prompt
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//printf("\n\n");
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//for (int i = 0; i < prompt.size(); i++) {
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// printf("%s: prompt[%d] = %s\n", __func__, i, vocab.id_to_token[prompt[i]].c_str());
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@ -2294,7 +2290,7 @@ int main(int argc, char ** argv) {
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{
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const int64_t t_start_sample_us = ggml_time_us();
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id = whisper_sample_best(vocab, probs.data() + (probs.size() - n_vocab));
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id = whisper_sample_best(vocab, probs.data() + (probs.size() - n_vocab), result_len == 0);
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if (i > 0) {
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tid = whisper_sample_timestamp(vocab, probs.data() + (probs.size() - n_vocab));
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}
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@ -2313,6 +2309,8 @@ int main(int argc, char ** argv) {
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prompt.push_back(id);
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result_cur.push_back({ id, seek + 2*(tid - vocab.token_beg) });
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//printf("%s: %s\n", __func__, vocab.id_to_token[id].c_str());
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// end of text token
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if (id == vocab.token_eot) {
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break;
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