extern/whisper.cpp
1
0
Fork 1
mirror of https://github.com/ggerganov/whisper.cpp.git synced 2024-11-07 08:34:37 +01:00
Code Issues Packages Projects Releases Wiki Activity

whisper : make large version explicit + fix data size units (#1493)

Browse Source
This commit is contained in:
Georgi Gerganov 2023-11-15 19:42:25 +02:00 committed by GitHub
parent 1d79e78402
commit bfbaa4dce5
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
16 changed files with 69 additions and 69 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
Makefile
View File

@ -418,9 +418,9 @@ samples:
.PHONY: medium
.PHONY: large-v1
.PHONY: large-v2
.PHONY: large
.PHONY: large-v3
tiny.en tiny base.en base small.en small medium.en medium large-v1 large-v2 large: main
tiny.en tiny base.en base small.en small medium.en medium large-v1 large-v2 large-v3: main
bash ./models/download-ggml-model.sh $@
@echo ""
@echo "==============================================="

16
README.md
View File

@ -231,18 +231,18 @@ make medium.en
make medium
make large-v1
make large-v2
make large
make large-v3
```
## Memory usage
| Model | Disk | Mem | SHA |
| --- | --- | --- | --- |
| tiny | 75 MB | ~125 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
| base | 142 MB | ~210 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
| small | 466 MB | ~600 MB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
| medium | 1.5 GB | ~1.7 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
| large | 2.9 GB | ~3.3 GB | `ad82bf6a9043ceed055076d0fd39f5f186ff8062` |
| Model | Disk | Mem |
| --- | --- | --- |
| tiny | 75 MiB | ~273 MB |
| base | 142 MiB | ~388 MB |
| small | 466 MiB | ~852 MB |
| medium | 1.5 GiB | ~2.1 GB |
| large | 2.9 GiB | ~3.9 GB |
## Quantization

2
bindings/go/examples/go-model-download/main.go
View File

@ -24,7 +24,7 @@ const (
var (
// The models which will be downloaded, if no model is specified as an argument
modelNames = []string{"ggml-tiny.en", "ggml-tiny", "ggml-base.en", "ggml-base", "ggml-small.en", "ggml-small", "ggml-medium.en", "ggml-medium", "ggml-large-v1", "ggml-large-v2", "ggml-large"}
modelNames = []string{"ggml-tiny.en", "ggml-tiny", "ggml-base.en", "ggml-base", "ggml-small.en", "ggml-small", "ggml-medium.en", "ggml-medium", "ggml-large-v1", "ggml-large-v2", "ggml-large-v3"}
)
var (

2
examples/livestream.sh
View File

@ -48,7 +48,7 @@ if [ -n "$3" ]; then
fi
# Whisper models
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large-v3" )
# list available models
function list_models {

2
examples/twitch.sh
View File

@ -21,7 +21,7 @@ help()
echo "Usage: ./twitch.sh -s [step] -m [model] -t [threads] [url]"
echo "options:"
echo "-s Step in seconds (default is $step)."
echo "-m Choose model, options are: 'tiny.en' 'tiny' 'base.en' 'base' 'small.en' 'small' 'medium.en' 'medium' 'large-v1' 'large-v2' 'large' (default is '$model')."
echo "-m Choose model, options are: 'tiny.en' 'tiny' 'base.en' 'base' 'small.en' 'small' 'medium.en' 'medium' 'large-v1' 'large-v2' 'large-v3' (default is '$model')."
echo "-t Number of threads to use."
echo "-h Print this help page."
echo

2
extra/convert-all.sh
View File

@ -1,6 +1,6 @@
#!/bin/bash
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large-v3" )
for model in "${models[@]}"; do
python3 models/convert-pt-to-ggml.py ~/.cache/whisper/$model.pt ../whisper models/

18
ggml-metal.m
View File

@ -346,9 +346,9 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
}
GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6);
if (ctx->device.maxTransferRate != 0) {
GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6);
} else {
GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__);
}
@ -541,11 +541,11 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1e6);
return false;
}
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1e6);
++ctx->n_buffers;
} else {
@ -565,11 +565,11 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1e6);
return false;
}
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1e6, i);
if (i + size_step < size) {
GGML_METAL_LOG_INFO("\n");
}
@ -580,8 +580,8 @@ bool ggml_metal_add_buffer(
#if TARGET_OS_OSX
GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
ctx->device.currentAllocatedSize / 1e6,
ctx->device.recommendedMaxWorkingSetSize / 1e6);
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
@ -589,7 +589,7 @@ bool ggml_metal_add_buffer(
GGML_METAL_LOG_INFO("\n");
}
#else
GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1e6);
#endif
}

26
models/README.md
View File

@ -39,19 +39,19 @@ https://huggingface.co/ggerganov/whisper.cpp/tree/main
## Available models
| Model | Disk | Mem | SHA |
| --- | --- | --- | --- |
| tiny | 75 MB | ~390 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
| tiny.en | 75 MB | ~390 MB | `c78c86eb1a8faa21b369bcd33207cc90d64ae9df` |
| base | 142 MB | ~500 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
| base.en | 142 MB | ~500 MB | `137c40403d78fd54d454da0f9bd998f78703390c` |
| small | 466 MB | ~1.0 GB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
| small.en | 466 MB | ~1.0 GB | `db8a495a91d927739e50b3fc1cc4c6b8f6c2d022` |
| medium | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
| medium.en | 1.5 GB | ~2.6 GB | `8c30f0e44ce9560643ebd10bbe50cd20eafd3723` |
| large-v1 | 2.9 GB | ~4.7 GB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
| large-v2 | 2.9 GB | ~4.7 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
| large | 2.9 GB | ~4.7 GB | `ad82bf6a9043ceed055076d0fd39f5f186ff8062` |
| Model | Disk | SHA |
| --- | --- | --- |
| tiny | 75 MiB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
| tiny.en | 75 MiB | `c78c86eb1a8faa21b369bcd33207cc90d64ae9df` |
| base | 142 MiB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
| base.en | 142 MiB | `137c40403d78fd54d454da0f9bd998f78703390c` |
| small | 466 MiB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
| small.en | 466 MiB | `db8a495a91d927739e50b3fc1cc4c6b8f6c2d022` |
| medium | 1.5 GiB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
| medium.en | 1.5 GiB | `8c30f0e44ce9560643ebd10bbe50cd20eafd3723` |
| large-v1 | 2.9 GiB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
| large-v2 | 2.9 GiB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
| large-v3 | 2.9 GiB | `ad82bf6a9043ceed055076d0fd39f5f186ff8062` |
## Model files for testing purposes

4
models/convert-h5-to-coreml.py
View File

@ -78,14 +78,14 @@ def convert_hf_whisper(hf_model_name_or_path: str, whisper_state_path: str):
# Ported from models/convert-whisper-to-coreml.py
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-name", type=str, help="name of model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1, large-v2)", required=True)
parser.add_argument("--model-name", type=str, help="name of model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large-v1, large-v2, large-v3)", required=True)
parser.add_argument("--model-path", type=str, help="path to the model (e.g. if published on HuggingFace: Oblivion208/whisper-tiny-cantonese)", required=True)
parser.add_argument("--encoder-only", type=bool, help="only convert encoder", default=False)
parser.add_argument("--quantize", type=bool, help="quantize weights to F16", default=False)
parser.add_argument("--optimize-ane", type=bool, help="optimize for ANE execution (currently broken)", default=False)
args = parser.parse_args()
if args.model_name not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1", "large-v2"]:
if args.model_name not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v1", "large-v2", "large-v3"]:
raise ValueError("Invalid model name")
pt_target_path = f"models/hf-{args.model_name}.pt"

4
models/convert-whisper-to-coreml.py
View File

@ -296,13 +296,13 @@ def convert_decoder(hparams, model, quantize=False):
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1, large-v2)", required=True)
parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large-v1, large-v2, large-v3)", required=True)
parser.add_argument("--encoder-only", type=bool, help="only convert encoder", default=False)
parser.add_argument("--quantize", type=bool, help="quantize weights to F16", default=False)
parser.add_argument("--optimize-ane", type=bool, help="optimize for ANE execution (currently broken)", default=False)
args = parser.parse_args()
if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "small.en-tdrz", "medium", "medium.en", "large", "large-v1", "large-v2"]:
if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "small.en-tdrz", "medium", "medium.en", "large-v1", "large-v2", "large-v3"]:
raise ValueError("Invalid model name")
whisper = load_model(args.model).cpu()

4
models/convert-whisper-to-openvino.py
View File

@ -38,10 +38,10 @@ def convert_encoder(hparams, encoder, mname):
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1, large-v2)", required=True)
parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large-v1, large-v2, large-v3)", required=True)
args = parser.parse_args()
if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1", "large-v2"]:
if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v1", "large-v2", "large-v3"]:
raise ValueError("Invalid model name")
whisper = load_model(args.model).cpu()

2
models/download-coreml-model.sh
View File

@ -19,7 +19,7 @@ function get_script_path() {
models_path="$(get_script_path)"
# Whisper models
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large-v3" )
# list available models
function list_models {

2
models/download-ggml-model.cmd
View File

@ -8,7 +8,7 @@ popd
set argc=0
for %%x in (%*) do set /A argc+=1
set models=tiny.en tiny base.en base small.en small medium.en medium large-v1 large-v2 large
set models=tiny.en tiny base.en base small.en small medium.en medium large-v1 large-v2 large-v3
if %argc% neq 1 (
echo.

4
models/download-ggml-model.sh
View File

@ -22,7 +22,7 @@ function get_script_path() {
models_path="$(get_script_path)"
# Whisper models
models=(
models=(
"tiny.en"
"tiny"
"tiny-q5_1"
@ -42,7 +42,7 @@ models=(
"medium.en-q5_0"
"large-v1"
"large-v2"
"large"
"large-v3"
"large-q5_0"
)

2
tests/run-tests.sh
View File

@ -19,7 +19,7 @@
cd `dirname $0`
# Whisper models
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large-v3" )
# list available models
function list_models {

44
whisper.cpp
View File

@ -1522,7 +1522,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
model.buffer = ggml_backend_alloc_buffer(wctx.backend, size_main);
WHISPER_LOG_INFO("%s: %8s buffer size = %8.2f MB\n", __func__, ggml_backend_name(wctx.backend), size_main / 1024.0 / 1024.0);
WHISPER_LOG_INFO("%s: %8s buffer size = %8.2f MB\n", __func__, ggml_backend_name(wctx.backend), size_main / 1e6);
}
ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.buffer);
@ -1637,12 +1637,12 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
}
//printf("%48s - [%5d, %5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ne[2], ggml_type_name((ggml_type) ttype), ggml_nbytes(tensor)/1024.0/1024.0);
//printf("%48s - [%5d, %5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ne[2], ggml_type_name((ggml_type) ttype), ggml_nbytes(tensor)/1e6);
total_size += ggml_nbytes(tensor);
model.n_loaded++;
}
WHISPER_LOG_INFO("%s: model size = %7.2f MB\n", __func__, total_size/1024.0/1024.0);
WHISPER_LOG_INFO("%s: model size = %7.2f MB\n", __func__, total_size/1e6);
if (model.n_loaded == 0) {
WHISPER_LOG_WARN("%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
@ -2027,11 +2027,11 @@ static struct ggml_cgraph * whisper_build_graph_encoder(
////////////////////////////////////////////////////////////////////////////
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
// ggml_used_mem(ctx0)/1024.0/1024.0,
// wstate.get_buf_max_mem(0)/1024.0/1024.0,
// wstate.get_buf_max_mem(1)/1024.0/1024.0,
// wstate.get_buf_max_mem(2)/1024.0/1024.0,
// wstate.get_buf_max_mem(3)/1024.0/1024.0);
// ggml_used_mem(ctx0)/1e6,
// wstate.get_buf_max_mem(0)/1e6,
// wstate.get_buf_max_mem(1)/1e6,
// wstate.get_buf_max_mem(2)/1e6,
// wstate.get_buf_max_mem(3)/1e6);
ggml_free(ctx0);
@ -2613,11 +2613,11 @@ static bool whisper_decode_internal(
if (batch.n_tokens > 1) {
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
// ggml_used_mem(ctx0)/1024.0/1024.0,
// wstate.get_buf_max_mem(0)/1024.0/1024.0,
// wstate.get_buf_max_mem(1)/1024.0/1024.0,
// wstate.get_buf_max_mem(2)/1024.0/1024.0,
// wstate.get_buf_max_mem(3)/1024.0/1024.0);
// ggml_used_mem(ctx0)/1e6,
// wstate.get_buf_max_mem(0)/1e6,
// wstate.get_buf_max_mem(1)/1e6,
// wstate.get_buf_max_mem(2)/1e6,
// wstate.get_buf_max_mem(3)/1e6);
}
if (batch.n_tokens == 1) {
@ -3057,7 +3057,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
{
const size_t memory_size = ggml_nbytes(state->kv_self.k) + ggml_nbytes(state->kv_self.v);
WHISPER_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
WHISPER_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1e6);
}
if (!kv_cache_init(ctx->model.hparams, state->kv_cross, ctx->backend, ctx->itype, ctx->model.hparams.n_audio_ctx)) {
@ -3068,7 +3068,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
{
const size_t memory_size = ggml_nbytes(state->kv_cross.k) + ggml_nbytes(state->kv_cross.v);
WHISPER_LOG_INFO("%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
WHISPER_LOG_INFO("%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1e6);
}
#ifdef WHISPER_USE_COREML
@ -3110,7 +3110,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
return whisper_build_graph_conv(*ctx, *state, 0);
});
WHISPER_LOG_INFO("%s: compute buffer (conv) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_conv) / 1024.0 / 1024.0);
WHISPER_LOG_INFO("%s: compute buffer (conv) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_conv) / 1e6);
}
// encoder allocator
@ -3120,7 +3120,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
return whisper_build_graph_encoder(*ctx, *state);
});
WHISPER_LOG_INFO("%s: compute buffer (encode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_encode) / 1024.0 / 1024.0);
WHISPER_LOG_INFO("%s: compute buffer (encode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_encode) / 1e6);
}
// cross allocator
@ -3130,7 +3130,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
return whisper_build_graph_cross(*ctx, *state);
});
WHISPER_LOG_INFO("%s: compute buffer (cross) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_cross) / 1024.0 / 1024.0);
WHISPER_LOG_INFO("%s: compute buffer (cross) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_cross) / 1e6);
}
// decoder allocator
@ -3148,7 +3148,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
return whisper_build_graph_decoder(*ctx, *state, state->batch);
});
WHISPER_LOG_INFO("%s: compute buffer (decode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_decode) / 1024.0 / 1024.0);
WHISPER_LOG_INFO("%s: compute buffer (decode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_decode) / 1e6);
}
whisper_allocr_graph_realloc(state->alloc_conv, ctx->backend);
@ -6072,8 +6072,8 @@ WHISPER_API const char * whisper_bench_memcpy_str(int n_threads) {
size_t n = 20;
size_t arr = n_threads > 0 ? 1024llu : n_threads; // trick to avoid compiler optimizations
// 1GB MB array
const size_t size = arr*1024llu*1024llu;
// 1GB array
const size_t size = arr*1e9;
// single-thread
{
@ -6099,7 +6099,7 @@ WHISPER_API const char * whisper_bench_memcpy_str(int n_threads) {
src[rand() % size] = rand() % 256;
}
snprintf(strbuf, sizeof(strbuf), "memcpy: %.2f GB/s (1 thread)\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
snprintf(strbuf, sizeof(strbuf), "memcpy: %.2f GB/s (1 thread)\n", (double) (n*size)/(tsum*1e9));
s += strbuf;
// needed to prevent the compiler from optimizing the memcpy away