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Add config for TensorRT and CUDA execution provider (#992)

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Signed-off-by: manickavela1998@gmail.com <manickavela1998@gmail.com>
Signed-off-by: manickavela1998@gmail.com <manickavela.arumugam@uniphore.com>
This commit is contained in:
Manix
2024-07-05 12:48:37 +05:30
committed by GitHub
parent f5e9a162d1
commit 55decb7bee
21 changed files with 622 additions and 49 deletions
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4
sherpa-onnx/c-api/c-api.cc
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@@ -73,7 +73,7 @@ SherpaOnnxOnlineRecognizer *CreateOnlineRecognizer(
SHERPA_ONNX_OR(config->model_config.tokens, "");
recognizer_config.model_config.num_threads =
SHERPA_ONNX_OR(config->model_config.num_threads, 1);
recognizer_config.model_config.provider =
recognizer_config.model_config.provider_config.provider =
SHERPA_ONNX_OR(config->model_config.provider, "cpu");
recognizer_config.model_config.model_type =
SHERPA_ONNX_OR(config->model_config.model_type, "");
@@ -570,7 +570,7 @@ SherpaOnnxKeywordSpotter *CreateKeywordSpotter(
SHERPA_ONNX_OR(config->model_config.tokens, "");
spotter_config.model_config.num_threads =
SHERPA_ONNX_OR(config->model_config.num_threads, 1);
spotter_config.model_config.provider =
spotter_config.model_config.provider_config.provider =
SHERPA_ONNX_OR(config->model_config.provider, "cpu");
spotter_config.model_config.model_type =
SHERPA_ONNX_OR(config->model_config.model_type, "");

1
sherpa-onnx/csrc/CMakeLists.txt
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@@ -87,6 +87,7 @@ set(sources
packed-sequence.cc
pad-sequence.cc
parse-options.cc
provider-config.cc
provider.cc
resample.cc
session.cc

10
sherpa-onnx/csrc/online-model-config.cc
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@@ -16,6 +16,7 @@ void OnlineModelConfig::Register(ParseOptions *po) {
wenet_ctc.Register(po);
zipformer2_ctc.Register(po);
nemo_ctc.Register(po);
provider_config.Register(po);
po->Register("tokens", &tokens, "Path to tokens.txt");
@@ -29,9 +30,6 @@ void OnlineModelConfig::Register(ParseOptions *po) {
po->Register("debug", &debug,
"true to print model information while loading it.");
po->Register("provider", &provider,
"Specify a provider to use: cpu, cuda, coreml");
po->Register("modeling-unit", &modeling_unit,
"The modeling unit of the model, commonly used units are bpe, "
"cjkchar, cjkchar+bpe, etc. Currently, it is needed only when "
@@ -87,6 +85,10 @@ bool OnlineModelConfig::Validate() const {
return nemo_ctc.Validate();
}
if (!provider_config.Validate()) {
return false;
}
return transducer.Validate();
}
@@ -99,11 +101,11 @@ std::string OnlineModelConfig::ToString() const {
os << "wenet_ctc=" << wenet_ctc.ToString() << ", ";
os << "zipformer2_ctc=" << zipformer2_ctc.ToString() << ", ";
os << "nemo_ctc=" << nemo_ctc.ToString() << ", ";
os << "provider_config=" << provider_config.ToString() << ", ";
os << "tokens=\"" << tokens << "\", ";
os << "num_threads=" << num_threads << ", ";
os << "warm_up=" << warm_up << ", ";
os << "debug=" << (debug ? "True" : "False") << ", ";
os << "provider=\"" << provider << "\", ";
os << "model_type=\"" << model_type << "\", ";
os << "modeling_unit=\"" << modeling_unit << "\", ";
os << "bpe_vocab=\"" << bpe_vocab << "\")";

8
sherpa-onnx/csrc/online-model-config.h
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@@ -11,6 +11,7 @@
#include "sherpa-onnx/csrc/online-transducer-model-config.h"
#include "sherpa-onnx/csrc/online-wenet-ctc-model-config.h"
#include "sherpa-onnx/csrc/online-zipformer2-ctc-model-config.h"
#include "sherpa-onnx/csrc/provider-config.h"
namespace sherpa_onnx {
@@ -20,11 +21,11 @@ struct OnlineModelConfig {
OnlineWenetCtcModelConfig wenet_ctc;
OnlineZipformer2CtcModelConfig zipformer2_ctc;
OnlineNeMoCtcModelConfig nemo_ctc;
ProviderConfig provider_config;
std::string tokens;
int32_t num_threads = 1;
int32_t warm_up = 0;
bool debug = false;
std::string provider = "cpu";
// Valid values:
// - conformer, conformer transducer from icefall
@@ -50,8 +51,9 @@ struct OnlineModelConfig {
const OnlineWenetCtcModelConfig &wenet_ctc,
const OnlineZipformer2CtcModelConfig &zipformer2_ctc,
const OnlineNeMoCtcModelConfig &nemo_ctc,
const ProviderConfig &provider_config,
const std::string &tokens, int32_t num_threads,
int32_t warm_up, bool debug, const std::string &provider,
int32_t warm_up, bool debug,
const std::string &model_type,
const std::string &modeling_unit,
const std::string &bpe_vocab)
@@ -60,11 +62,11 @@ struct OnlineModelConfig {
wenet_ctc(wenet_ctc),
zipformer2_ctc(zipformer2_ctc),
nemo_ctc(nemo_ctc),
provider_config(provider_config),
tokens(tokens),
num_threads(num_threads),
warm_up(warm_up),
debug(debug),
provider(provider),
model_type(model_type),
modeling_unit(modeling_unit),
bpe_vocab(bpe_vocab) {}

143
sherpa-onnx/csrc/provider-config.cc Normal file
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@@ -0,0 +1,143 @@
// sherpa-onnx/csrc/provider-config.cc
//
// Copyright (c) 2024 Uniphore (Author: Manickavela)
#include "sherpa-onnx/csrc/provider-config.h"
#include <sstream>
#include "sherpa-onnx/csrc/file-utils.h"
#include "sherpa-onnx/csrc/macros.h"
namespace sherpa_onnx {
void CudaConfig::Register(ParseOptions *po) {
po->Register("cuda-cudnn-conv-algo-search", &cudnn_conv_algo_search,
"CuDNN convolution algrorithm search");
}
bool CudaConfig::Validate() const {
if (cudnn_conv_algo_search < 1 || cudnn_conv_algo_search > 3) {
SHERPA_ONNX_LOGE("cudnn_conv_algo_search: '%d' is not a valid option."
"Options : [1,3]. Check OnnxRT docs",
cudnn_conv_algo_search);
return false;
}
return true;
}
std::string CudaConfig::ToString() const {
std::ostringstream os;
os << "CudaConfig(";
os << "cudnn_conv_algo_search=" << cudnn_conv_algo_search << ")";
return os.str();
}
void TensorrtConfig::Register(ParseOptions *po) {
po->Register("trt-max-workspace-size", &trt_max_workspace_size,
"Set TensorRT EP GPU memory usage limit.");
po->Register("trt-max-partition-iterations", &trt_max_partition_iterations,
"Limit partitioning iterations for model conversion.");
po->Register("trt-min-subgraph-size", &trt_min_subgraph_size,
"Set minimum size for subgraphs in partitioning.");
po->Register("trt-fp16-enable", &trt_fp16_enable,
"Enable FP16 precision for faster performance.");
po->Register("trt-detailed-build-log", &trt_detailed_build_log,
"Enable detailed logging of build steps.");
po->Register("trt-engine-cache-enable", &trt_engine_cache_enable,
"Enable caching of TensorRT engines.");
po->Register("trt-timing-cache-enable", &trt_timing_cache_enable,
"Enable use of timing cache to speed up builds.");
po->Register("trt-engine-cache-path", &trt_engine_cache_path,
"Set path to store cached TensorRT engines.");
po->Register("trt-timing-cache-path", &trt_timing_cache_path,
"Set path for storing timing cache.");
po->Register("trt-dump-subgraphs", &trt_dump_subgraphs,
"Dump optimized subgraphs for debugging.");
}
bool TensorrtConfig::Validate() const {
if (trt_max_workspace_size < 0) {
SHERPA_ONNX_LOGE("trt_max_workspace_size: %d is not valid.",
trt_max_workspace_size);
return false;
}
if (trt_max_partition_iterations < 0) {
SHERPA_ONNX_LOGE("trt_max_partition_iterations: %d is not valid.",
trt_max_partition_iterations);
return false;
}
if (trt_min_subgraph_size < 0) {
SHERPA_ONNX_LOGE("trt_min_subgraph_size: %d is not valid.",
trt_min_subgraph_size);
return false;
}
return true;
}
std::string TensorrtConfig::ToString() const {
std::ostringstream os;
os << "TensorrtConfig(";
os << "trt_max_workspace_size=" << trt_max_workspace_size << ", ";
os << "trt_max_partition_iterations="
<< trt_max_partition_iterations << ", ";
os << "trt_min_subgraph_size=" << trt_min_subgraph_size << ", ";
os << "trt_fp16_enable=\""
<< (trt_fp16_enable? "True" : "False") << "\", ";
os << "trt_detailed_build_log=\""
<< (trt_detailed_build_log? "True" : "False") << "\", ";
os << "trt_engine_cache_enable=\""
<< (trt_engine_cache_enable? "True" : "False") << "\", ";
os << "trt_engine_cache_path=\""
<< trt_engine_cache_path.c_str() << "\", ";
os << "trt_timing_cache_enable=\""
<< (trt_timing_cache_enable? "True" : "False") << "\", ";
os << "trt_timing_cache_path=\""
<< trt_timing_cache_path.c_str() << "\",";
os << "trt_dump_subgraphs=\""
<< (trt_dump_subgraphs? "True" : "False") << "\" )";
return os.str();
}
void ProviderConfig::Register(ParseOptions *po) {
cuda_config.Register(po);
trt_config.Register(po);
po->Register("device", &device, "GPU device index for CUDA and Trt EP");
po->Register("provider", &provider,
"Specify a provider to use: cpu, cuda, coreml");
}
bool ProviderConfig::Validate() const {
if (device < 0) {
SHERPA_ONNX_LOGE("device: '%d' is invalid.", device);
return false;
}
if (provider == "cuda" && !cuda_config.Validate()) {
return false;
}
if (provider == "trt" && !trt_config.Validate()) {
return false;
}
return true;
}
std::string ProviderConfig::ToString() const {
std::ostringstream os;
os << "ProviderConfig(";
os << "device=" << device << ", ";
os << "provider=\"" << provider << "\", ";
os << "cuda_config=" << cuda_config.ToString() << ", ";
os << "trt_config=" << trt_config.ToString() << ")";
return os.str();
}
} // namespace sherpa_onnx

95
sherpa-onnx/csrc/provider-config.h Normal file
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@@ -0,0 +1,95 @@
// sherpa-onnx/csrc/provider-config.h
//
// Copyright (c) 2024 Uniphore (Author: Manickavela)
#ifndef SHERPA_ONNX_CSRC_PROVIDER_CONFIG_H_
#define SHERPA_ONNX_CSRC_PROVIDER_CONFIG_H_
#include <string>
#include "sherpa-onnx/csrc/parse-options.h"
#include "sherpa-onnx/csrc/macros.h"
#include "onnxruntime_cxx_api.h" // NOLINT
namespace sherpa_onnx {
struct CudaConfig {
int32_t cudnn_conv_algo_search = OrtCudnnConvAlgoSearchHeuristic;
CudaConfig() = default;
explicit CudaConfig(int32_t cudnn_conv_algo_search)
: cudnn_conv_algo_search(cudnn_conv_algo_search) {}
void Register(ParseOptions *po);
bool Validate() const;
std::string ToString() const;
};
struct TensorrtConfig {
int32_t trt_max_workspace_size = 2147483647;
int32_t trt_max_partition_iterations = 10;
int32_t trt_min_subgraph_size = 5;
bool trt_fp16_enable = true;
bool trt_detailed_build_log = false;
bool trt_engine_cache_enable = true;
bool trt_timing_cache_enable = true;
std::string trt_engine_cache_path = ".";
std::string trt_timing_cache_path = ".";
bool trt_dump_subgraphs = false;
TensorrtConfig() = default;
TensorrtConfig(int32_t trt_max_workspace_size,
int32_t trt_max_partition_iterations,
int32_t trt_min_subgraph_size,
bool trt_fp16_enable,
bool trt_detailed_build_log,
bool trt_engine_cache_enable,
bool trt_timing_cache_enable,
const std::string &trt_engine_cache_path,
const std::string &trt_timing_cache_path,
bool trt_dump_subgraphs)
: trt_max_workspace_size(trt_max_workspace_size),
trt_max_partition_iterations(trt_max_partition_iterations),
trt_min_subgraph_size(trt_min_subgraph_size),
trt_fp16_enable(trt_fp16_enable),
trt_detailed_build_log(trt_detailed_build_log),
trt_engine_cache_enable(trt_engine_cache_enable),
trt_timing_cache_enable(trt_timing_cache_enable),
trt_engine_cache_path(trt_engine_cache_path),
trt_timing_cache_path(trt_timing_cache_path),
trt_dump_subgraphs(trt_dump_subgraphs) {}
void Register(ParseOptions *po);
bool Validate() const;
std::string ToString() const;
};
struct ProviderConfig {
TensorrtConfig trt_config;
CudaConfig cuda_config;
std::string provider = "cpu";
int32_t device = 0;
// device only used for cuda and trt
ProviderConfig() = default;
ProviderConfig(const std::string &provider,
int32_t device)
: provider(provider), device(device) {}
ProviderConfig(const TensorrtConfig &trt_config,
const CudaConfig &cuda_config,
const std::string &provider,
int32_t device)
: trt_config(trt_config), cuda_config(cuda_config),
provider(provider), device(device) {}
void Register(ParseOptions *po);
bool Validate() const;
std::string ToString() const;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_PROVIDER_CONFIG_H_

1
sherpa-onnx/csrc/provider.h
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@@ -7,6 +7,7 @@
#include <string>
#include "sherpa-onnx/csrc/provider-config.h"
namespace sherpa_onnx {
// Please refer to

72
sherpa-onnx/csrc/session.cc
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@@ -32,11 +32,13 @@ static void OrtStatusFailure(OrtStatus *status, const char *s) {
}
static Ort::SessionOptions GetSessionOptionsImpl(int32_t num_threads,
std::string provider_str) {
Provider p = StringToProvider(std::move(provider_str));
const std::string &provider_str,
const ProviderConfig *provider_config = nullptr) {
Provider p = StringToProvider(provider_str);
Ort::SessionOptions sess_opts;
sess_opts.SetIntraOpNumThreads(num_threads);
sess_opts.SetInterOpNumThreads(num_threads);
std::vector<std::string> available_providers = Ort::GetAvailableProviders();
@@ -64,26 +66,51 @@ static Ort::SessionOptions GetSessionOptionsImpl(int32_t num_threads,
break;
}
case Provider::kTRT: {
if (provider_config == nullptr) {
SHERPA_ONNX_LOGE("Tensorrt support for Online models ony,"
"Must be extended for offline and others");
exit(1);
}
auto trt_config = provider_config->trt_config;
struct TrtPairs {
const char *op_keys;
const char *op_values;
};
auto device_id = std::to_string(provider_config->device);
auto trt_max_workspace_size =
std::to_string(trt_config.trt_max_workspace_size);
auto trt_max_partition_iterations =
std::to_string(trt_config.trt_max_partition_iterations);
auto trt_min_subgraph_size =
std::to_string(trt_config.trt_min_subgraph_size);
auto trt_fp16_enable =
std::to_string(trt_config.trt_fp16_enable);
auto trt_detailed_build_log =
std::to_string(trt_config.trt_detailed_build_log);
auto trt_engine_cache_enable =
std::to_string(trt_config.trt_engine_cache_enable);
auto trt_timing_cache_enable =
std::to_string(trt_config.trt_timing_cache_enable);
auto trt_dump_subgraphs =
std::to_string(trt_config.trt_dump_subgraphs);
std::vector<TrtPairs> trt_options = {
{"device_id", "0"},
{"trt_max_workspace_size", "2147483648"},
{"trt_max_partition_iterations", "10"},
{"trt_min_subgraph_size", "5"},
{"trt_fp16_enable", "0"},
{"trt_detailed_build_log", "0"},
{"trt_engine_cache_enable", "1"},
{"trt_engine_cache_path", "."},
{"trt_timing_cache_enable", "1"},
{"trt_timing_cache_path", "."}};
{"device_id", device_id.c_str()},
{"trt_max_workspace_size", trt_max_workspace_size.c_str()},
{"trt_max_partition_iterations", trt_max_partition_iterations.c_str()},
{"trt_min_subgraph_size", trt_min_subgraph_size.c_str()},
{"trt_fp16_enable", trt_fp16_enable.c_str()},
{"trt_detailed_build_log", trt_detailed_build_log.c_str()},
{"trt_engine_cache_enable", trt_engine_cache_enable.c_str()},
{"trt_engine_cache_path", trt_config.trt_engine_cache_path.c_str()},
{"trt_timing_cache_enable", trt_timing_cache_enable.c_str()},
{"trt_timing_cache_path", trt_config.trt_timing_cache_path.c_str()},
{"trt_dump_subgraphs", trt_dump_subgraphs.c_str()}
};
// ToDo : Trt configs
// "trt_int8_enable"
// "trt_int8_use_native_calibration_table"
// "trt_dump_subgraphs"
std::vector<const char *> option_keys, option_values;
for (const TrtPairs &pair : trt_options) {
@@ -122,10 +149,18 @@ static Ort::SessionOptions GetSessionOptionsImpl(int32_t num_threads,
"CUDAExecutionProvider") != available_providers.end()) {
// The CUDA provider is available, proceed with setting the options
OrtCUDAProviderOptions options;
options.device_id = 0;
// Default OrtCudnnConvAlgoSearchExhaustive is extremely slow
options.cudnn_conv_algo_search = OrtCudnnConvAlgoSearchHeuristic;
// set more options on need
if (provider_config != nullptr) {
options.device_id = provider_config->device;
options.cudnn_conv_algo_search =
OrtCudnnConvAlgoSearch(provider_config->cuda_config
.cudnn_conv_algo_search);
} else {
options.device_id = 0;
// Default OrtCudnnConvAlgoSearchExhaustive is extremely slow
options.cudnn_conv_algo_search = OrtCudnnConvAlgoSearchHeuristic;
// set more options on need
}
sess_opts.AppendExecutionProvider_CUDA(options);
} else {
SHERPA_ONNX_LOGE(
@@ -184,7 +219,8 @@ static Ort::SessionOptions GetSessionOptionsImpl(int32_t num_threads,
}
Ort::SessionOptions GetSessionOptions(const OnlineModelConfig &config) {
return GetSessionOptionsImpl(config.num_threads, config.provider);
return GetSessionOptionsImpl(config.num_threads,
config.provider_config.provider, &config.provider_config);
}
Ort::SessionOptions GetSessionOptions(const OfflineModelConfig &config) {

2
sherpa-onnx/jni/keyword-spotter.cc
View File

@@ -94,7 +94,7 @@ static KeywordSpotterConfig GetKwsConfig(JNIEnv *env, jobject config) {
fid = env->GetFieldID(model_config_cls, "provider", "Ljava/lang/String;");
s = (jstring)env->GetObjectField(model_config, fid);
p = env->GetStringUTFChars(s, nullptr);
ans.model_config.provider = p;
ans.model_config.provider_config.provider = p;
env->ReleaseStringUTFChars(s, p);
fid = env->GetFieldID(model_config_cls, "modelType", "Ljava/lang/String;");

2
sherpa-onnx/jni/online-recognizer.cc
View File

@@ -198,7 +198,7 @@ static OnlineRecognizerConfig GetConfig(JNIEnv *env, jobject config) {
fid = env->GetFieldID(model_config_cls, "provider", "Ljava/lang/String;");
s = (jstring)env->GetObjectField(model_config, fid);
p = env->GetStringUTFChars(s, nullptr);
ans.model_config.provider = p;
ans.model_config.provider_config.provider = p;
env->ReleaseStringUTFChars(s, p);
fid = env->GetFieldID(model_config_cls, "modelType", "Ljava/lang/String;");

3
sherpa-onnx/python/csrc/CMakeLists.txt
View File

@@ -3,6 +3,7 @@ include_directories(${CMAKE_SOURCE_DIR})
set(srcs
audio-tagging.cc
circular-buffer.cc
cuda-config.cc
display.cc
endpoint.cc
features.cc
@@ -30,11 +31,13 @@ set(srcs
online-transducer-model-config.cc
online-wenet-ctc-model-config.cc
online-zipformer2-ctc-model-config.cc
provider-config.cc
sherpa-onnx.cc
silero-vad-model-config.cc
speaker-embedding-extractor.cc
speaker-embedding-manager.cc
spoken-language-identification.cc
tensorrt-config.cc
vad-model-config.cc
vad-model.cc
voice-activity-detector.cc

24
sherpa-onnx/python/csrc/cuda-config.cc Normal file
View File

@@ -0,0 +1,24 @@
// sherpa-onnx/python/csrc/cuda-config.cc
//
// Copyright (c) 2024 Uniphore (Author: Manickavela A)
#include "sherpa-onnx/python/csrc/cuda-config.h"
#include <memory>
#include <string>
#include "sherpa-onnx/csrc/provider-config.h"
namespace sherpa_onnx {
void PybindCudaConfig(py::module *m) {
using PyClass = CudaConfig;
py::class_<PyClass>(*m, "CudaConfig")
.def(py::init<>())
.def(py::init<int32_t>(),
py::arg("cudnn_conv_algo_search") = 1)
.def_readwrite("cudnn_conv_algo_search", &PyClass::cudnn_conv_algo_search)
.def("__str__", &PyClass::ToString);
}
} // namespace sherpa_onnx

16
sherpa-onnx/python/csrc/cuda-config.h Normal file
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@@ -0,0 +1,16 @@
// sherpa-onnx/python/csrc/cuda-config.h
//
// Copyright (c) 2024 Uniphore (Author: Manickavela A)
#ifndef SHERPA_ONNX_PYTHON_CSRC_CUDA_CONFIG_H_
#define SHERPA_ONNX_PYTHON_CSRC_CUDA_CONFIG_H_
#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
namespace sherpa_onnx {
void PybindCudaConfig(py::module *m);
}
#endif // SHERPA_ONNX_PYTHON_CSRC_CUDA_CONFIG_H_

24
sherpa-onnx/python/csrc/online-model-config.cc
View File

@@ -9,11 +9,13 @@
#include "sherpa-onnx/csrc/online-model-config.h"
#include "sherpa-onnx/csrc/online-transducer-model-config.h"
#include "sherpa-onnx/csrc/provider-config.h"
#include "sherpa-onnx/python/csrc/online-nemo-ctc-model-config.h"
#include "sherpa-onnx/python/csrc/online-paraformer-model-config.h"
#include "sherpa-onnx/python/csrc/online-transducer-model-config.h"
#include "sherpa-onnx/python/csrc/online-wenet-ctc-model-config.h"
#include "sherpa-onnx/python/csrc/online-zipformer2-ctc-model-config.h"
#include "sherpa-onnx/python/csrc/provider-config.h"
namespace sherpa_onnx {
@@ -23,6 +25,7 @@ void PybindOnlineModelConfig(py::module *m) {
PybindOnlineWenetCtcModelConfig(m);
PybindOnlineZipformer2CtcModelConfig(m);
PybindOnlineNeMoCtcModelConfig(m);
PybindProviderConfig(m);
using PyClass = OnlineModelConfig;
py::class_<PyClass>(*m, "OnlineModelConfig")
@@ -30,33 +33,34 @@ void PybindOnlineModelConfig(py::module *m) {
const OnlineParaformerModelConfig &,
const OnlineWenetCtcModelConfig &,
const OnlineZipformer2CtcModelConfig &,
const OnlineNeMoCtcModelConfig &, const std::string &,
int32_t, int32_t, bool, const std::string &,
const std::string &, const std::string &,
const OnlineNeMoCtcModelConfig &,
const ProviderConfig &,
const std::string &, int32_t, int32_t,
bool, const std::string &, const std::string &,
const std::string &>(),
py::arg("transducer") = OnlineTransducerModelConfig(),
py::arg("paraformer") = OnlineParaformerModelConfig(),
py::arg("wenet_ctc") = OnlineWenetCtcModelConfig(),
py::arg("zipformer2_ctc") = OnlineZipformer2CtcModelConfig(),
py::arg("nemo_ctc") = OnlineNeMoCtcModelConfig(), py::arg("tokens"),
py::arg("num_threads"), py::arg("warm_up") = 0,
py::arg("debug") = false, py::arg("provider") = "cpu",
py::arg("model_type") = "", py::arg("modeling_unit") = "",
py::arg("bpe_vocab") = "")
py::arg("nemo_ctc") = OnlineNeMoCtcModelConfig(),
py::arg("provider_config") = ProviderConfig(),
py::arg("tokens"), py::arg("num_threads"), py::arg("warm_up") = 0,
py::arg("debug") = false, py::arg("model_type") = "",
py::arg("modeling_unit") = "", py::arg("bpe_vocab") = "")
.def_readwrite("transducer", &PyClass::transducer)
.def_readwrite("paraformer", &PyClass::paraformer)
.def_readwrite("wenet_ctc", &PyClass::wenet_ctc)
.def_readwrite("zipformer2_ctc", &PyClass::zipformer2_ctc)
.def_readwrite("nemo_ctc", &PyClass::nemo_ctc)
.def_readwrite("provider_config", &PyClass::provider_config)
.def_readwrite("tokens", &PyClass::tokens)
.def_readwrite("num_threads", &PyClass::num_threads)
.def_readwrite("warm_up", &PyClass::warm_up)
.def_readwrite("debug", &PyClass::debug)
.def_readwrite("provider", &PyClass::provider)
.def_readwrite("model_type", &PyClass::model_type)
.def_readwrite("modeling_unit", &PyClass::modeling_unit)
.def_readwrite("bpe_vocab", &PyClass::bpe_vocab)
.def("validate", &PyClass::Validate)
.def("__str__", &PyClass::ToString);
}
} // namespace sherpa_onnx

39
sherpa-onnx/python/csrc/provider-config.cc Normal file
View File

@@ -0,0 +1,39 @@
// sherpa-onnx/python/csrc/provider-config.cc
//
// Copyright (c) 2024 Uniphore (Author: Manickavela A)
#include "sherpa-onnx/python/csrc/provider-config.h"
#include <string>
#include "sherpa-onnx/csrc/provider-config.h"
#include "sherpa-onnx/python/csrc/cuda-config.h"
#include "sherpa-onnx/python/csrc/tensorrt-config.h"
namespace sherpa_onnx {
void PybindProviderConfig(py::module *m) {
PybindCudaConfig(m);
PybindTensorrtConfig(m);
using PyClass = ProviderConfig;
py::class_<PyClass>(*m, "ProviderConfig")
.def(py::init<>())
.def(py::init<const std::string &, int32_t>(),
py::arg("provider") = "cpu",
py::arg("device") = 0)
.def(py::init<const TensorrtConfig &, const CudaConfig &,
const std::string &, int32_t>(),
py::arg("trt_config") = TensorrtConfig{},
py::arg("cuda_config") = CudaConfig{},
py::arg("provider") = "cpu",
py::arg("device") = 0)
.def_readwrite("trt_config", &PyClass::trt_config)
.def_readwrite("cuda_config", &PyClass::cuda_config)
.def_readwrite("provider", &PyClass::provider)
.def_readwrite("device", &PyClass::device)
.def("__str__", &PyClass::ToString)
.def("validate", &PyClass::Validate);
}
} // namespace sherpa_onnx

16
sherpa-onnx/python/csrc/provider-config.h Normal file
View File

@@ -0,0 +1,16 @@
// sherpa-onnx/python/csrc/provider-config.h
//
// Copyright (c) 2024 Uniphore (Author: Manickavela A)
#ifndef SHERPA_ONNX_PYTHON_CSRC_PROVIDER_CONFIG_H_
#define SHERPA_ONNX_PYTHON_CSRC_PROVIDER_CONFIG_H_
#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
namespace sherpa_onnx {
void PybindProviderConfig(py::module *m);
}
#endif // SHERPA_ONNX_PYTHON_CSRC_PROVIDER_CONFIG_H_

1
sherpa-onnx/python/csrc/sherpa-onnx.cc
View File

@@ -51,7 +51,6 @@ PYBIND11_MODULE(_sherpa_onnx, m) {
PybindEndpoint(&m);
PybindOnlineRecognizer(&m);
PybindKeywordSpotter(&m);
PybindDisplay(&m);
PybindOfflineStream(&m);

72
sherpa-onnx/python/csrc/tensorrt-config.cc Normal file
View File

@@ -0,0 +1,72 @@
// sherpa-onnx/python/csrc/tensorrt-config.cc
//
// Copyright (c) 2024 Uniphore (Author: Manickavela A)
#include "sherpa-onnx/python/csrc/tensorrt-config.h"
#include <string>
#include <memory>
#include "sherpa-onnx/csrc/provider-config.h"
namespace sherpa_onnx {
void PybindTensorrtConfig(py::module *m) {
using PyClass = TensorrtConfig;
py::class_<PyClass>(*m, "TensorrtConfig")
.def(py::init<>())
.def(py::init([](int32_t trt_max_workspace_size,
int32_t trt_max_partition_iterations,
int32_t trt_min_subgraph_size,
bool trt_fp16_enable,
bool trt_detailed_build_log,
bool trt_engine_cache_enable,
bool trt_timing_cache_enable,
const std::string &trt_engine_cache_path,
const std::string &trt_timing_cache_path,
bool trt_dump_subgraphs) -> std::unique_ptr<PyClass> {
auto ans = std::make_unique<PyClass>();
ans->trt_max_workspace_size = trt_max_workspace_size;
ans->trt_max_partition_iterations = trt_max_partition_iterations;
ans->trt_min_subgraph_size = trt_min_subgraph_size;
ans->trt_fp16_enable = trt_fp16_enable;
ans->trt_detailed_build_log = trt_detailed_build_log;
ans->trt_engine_cache_enable = trt_engine_cache_enable;
ans->trt_timing_cache_enable = trt_timing_cache_enable;
ans->trt_engine_cache_path = trt_engine_cache_path;
ans->trt_timing_cache_path = trt_timing_cache_path;
ans->trt_dump_subgraphs = trt_dump_subgraphs;
return ans;
}),
py::arg("trt_max_workspace_size") = 2147483647,
py::arg("trt_max_partition_iterations") = 10,
py::arg("trt_min_subgraph_size") = 5,
py::arg("trt_fp16_enable") = true,
py::arg("trt_detailed_build_log") = false,
py::arg("trt_engine_cache_enable") = true,
py::arg("trt_timing_cache_enable") = true,
py::arg("trt_engine_cache_path") = ".",
py::arg("trt_timing_cache_path") = ".",
py::arg("trt_dump_subgraphs") = false)
.def_readwrite("trt_max_workspace_size",
&PyClass::trt_max_workspace_size)
.def_readwrite("trt_max_partition_iterations",
&PyClass::trt_max_partition_iterations)
.def_readwrite("trt_min_subgraph_size", &PyClass::trt_min_subgraph_size)
.def_readwrite("trt_fp16_enable", &PyClass::trt_fp16_enable)
.def_readwrite("trt_detailed_build_log",
&PyClass::trt_detailed_build_log)
.def_readwrite("trt_engine_cache_enable",
&PyClass::trt_engine_cache_enable)
.def_readwrite("trt_timing_cache_enable",
&PyClass::trt_timing_cache_enable)
.def_readwrite("trt_engine_cache_path", &PyClass::trt_engine_cache_path)
.def_readwrite("trt_timing_cache_path", &PyClass::trt_timing_cache_path)
.def_readwrite("trt_dump_subgraphs", &PyClass::trt_dump_subgraphs)
.def("__str__", &PyClass::ToString)
.def("validate", &PyClass::Validate);
}
} // namespace sherpa_onnx

16
sherpa-onnx/python/csrc/tensorrt-config.h Normal file
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@@ -0,0 +1,16 @@
// sherpa-onnx/python/csrc/tensorrt-config.h
//
// Copyright (c) 2024 Uniphore (Author: Manickavela A)
#ifndef SHERPA_ONNX_PYTHON_CSRC_TENSORRT_CONFIG_H_
#define SHERPA_ONNX_PYTHON_CSRC_TENSORRT_CONFIG_H_
#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
namespace sherpa_onnx {
void PybindTensorrtConfig(py::module *m);
}
#endif // SHERPA_ONNX_PYTHON_CSRC_TENSORRT_CONFIG_H_

11
sherpa-onnx/python/sherpa_onnx/keyword_spotter.py
View File

@@ -9,6 +9,7 @@ from _sherpa_onnx import (
OnlineModelConfig,
OnlineTransducerModelConfig,
OnlineStream,
ProviderConfig,
)
from _sherpa_onnx import KeywordSpotter as _KeywordSpotter
@@ -41,6 +42,7 @@ class KeywordSpotter(object):
keywords_threshold: float = 0.25,
num_trailing_blanks: int = 1,
provider: str = "cpu",
device: int = 0,
):
"""
Please refer to
@@ -85,6 +87,8 @@ class KeywordSpotter(object):
between each other.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
device:
onnxruntime cuda device index.
"""
_assert_file_exists(tokens)
_assert_file_exists(encoder)
@@ -99,11 +103,16 @@ class KeywordSpotter(object):
joiner=joiner,
)
provider_config = ProviderConfig(
provider=provider,
device = device,
)
model_config = OnlineModelConfig(
transducer=transducer_config,
tokens=tokens,
num_threads=num_threads,
provider=provider,
provider_config=provider_config,
)
feat_config = FeatureExtractorConfig(

111
sherpa-onnx/python/sherpa_onnx/online_recognizer.py
View File

@@ -11,6 +11,9 @@ from _sherpa_onnx import (
)
from _sherpa_onnx import OnlineRecognizer as _Recognizer
from _sherpa_onnx import (
CudaConfig,
TensorrtConfig,
ProviderConfig,
OnlineRecognizerConfig,
OnlineRecognizerResult,
OnlineStream,
@@ -56,7 +59,6 @@ class OnlineRecognizer(object):
hotwords_score: float = 1.5,
blank_penalty: float = 0.0,
hotwords_file: str = "",
provider: str = "cpu",
model_type: str = "",
modeling_unit: str = "cjkchar",
bpe_vocab: str = "",
@@ -66,6 +68,19 @@ class OnlineRecognizer(object):
debug: bool = False,
rule_fsts: str = "",
rule_fars: str = "",
provider: str = "cpu",
device: int = 0,
cudnn_conv_algo_search: int = 1,
trt_max_workspace_size: int = 2147483647,
trt_max_partition_iterations: int = 10,
trt_min_subgraph_size: int = 5,
trt_fp16_enable: bool = True,
trt_detailed_build_log: bool = False,
trt_engine_cache_enable: bool = True,
trt_timing_cache_enable: bool = True,
trt_engine_cache_path: str ="",
trt_timing_cache_path: str ="",
trt_dump_subgraphs: bool = False,
):
"""
Please refer to
@@ -135,8 +150,6 @@ class OnlineRecognizer(object):
Temperature scaling for output symbol confidence estiamation.
It affects only confidence values, the decoding uses the original
logits without temperature.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
model_type:
Online transducer model type. Valid values are: conformer, lstm,
zipformer, zipformer2. All other values lead to loading the model twice.
@@ -156,6 +169,32 @@ class OnlineRecognizer(object):
rule_fars:
If not empty, it specifies fst archives for inverse text normalization.
If there are multiple archives, they are separated by a comma.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
device:
onnxruntime cuda device index.
cudnn_conv_algo_search:
onxrt CuDNN convolution search algorithm selection. CUDA EP
trt_max_workspace_size:
Set TensorRT EP GPU memory usage limit. TensorRT EP
trt_max_partition_iterations:
Limit partitioning iterations for model conversion. TensorRT EP
trt_min_subgraph_size:
Set minimum size for subgraphs in partitioning. TensorRT EP
trt_fp16_enable: bool = True,
Enable FP16 precision for faster performance. TensorRT EP
trt_detailed_build_log: bool = False,
Enable detailed logging of build steps. TensorRT EP
trt_engine_cache_enable: bool = True,
Enable caching of TensorRT engines. TensorRT EP
trt_timing_cache_enable: bool = True,
"Enable use of timing cache to speed up builds." TensorRT EP
trt_engine_cache_path: str ="",
"Set path to store cached TensorRT engines." TensorRT EP
trt_timing_cache_path: str ="",
"Set path for storing timing cache." TensorRT EP
trt_dump_subgraphs: bool = False,
"Dump optimized subgraphs for debugging." TensorRT EP
"""
self = cls.__new__(cls)
_assert_file_exists(tokens)
@@ -171,11 +210,35 @@ class OnlineRecognizer(object):
joiner=joiner,
)
cuda_config = CudaConfig(
cudnn_conv_algo_search=cudnn_conv_algo_search,
)
trt_config = TensorrtConfig(
trt_max_workspace_size=trt_max_workspace_size,
trt_max_partition_iterations=trt_max_partition_iterations,
trt_min_subgraph_size=trt_min_subgraph_size,
trt_fp16_enable=trt_fp16_enable,
trt_detailed_build_log=trt_detailed_build_log,
trt_engine_cache_enable=trt_engine_cache_enable,
trt_timing_cache_enable=trt_timing_cache_enable,
trt_engine_cache_path=trt_engine_cache_path,
trt_timing_cache_path=trt_timing_cache_path,
trt_dump_subgraphs=trt_dump_subgraphs,
)
provider_config = ProviderConfig(
trt_config=trt_config,
cuda_config=cuda_config,
provider=provider,
device=device,
)
model_config = OnlineModelConfig(
transducer=transducer_config,
tokens=tokens,
num_threads=num_threads,
provider=provider,
provider_config=provider_config,
model_type=model_type,
modeling_unit=modeling_unit,
bpe_vocab=bpe_vocab,
@@ -251,6 +314,7 @@ class OnlineRecognizer(object):
debug: bool = False,
rule_fsts: str = "",
rule_fars: str = "",
device: int = 0,
):
"""
Please refer to
@@ -301,6 +365,8 @@ class OnlineRecognizer(object):
rule_fars:
If not empty, it specifies fst archives for inverse text normalization.
If there are multiple archives, they are separated by a comma.
device:
onnxruntime cuda device index.
"""
self = cls.__new__(cls)
_assert_file_exists(tokens)
@@ -314,11 +380,16 @@ class OnlineRecognizer(object):
decoder=decoder,
)
provider_config = ProviderConfig(
provider=provider,
device=device,
)
model_config = OnlineModelConfig(
paraformer=paraformer_config,
tokens=tokens,
num_threads=num_threads,
provider=provider,
provider_config=provider_config,
model_type="paraformer",
debug=debug,
)
@@ -367,6 +438,7 @@ class OnlineRecognizer(object):
debug: bool = False,
rule_fsts: str = "",
rule_fars: str = "",
device: int = 0,
):
"""
Please refer to
@@ -421,6 +493,8 @@ class OnlineRecognizer(object):
rule_fars:
If not empty, it specifies fst archives for inverse text normalization.
If there are multiple archives, they are separated by a comma.
device:
onnxruntime cuda device index.
"""
self = cls.__new__(cls)
_assert_file_exists(tokens)
@@ -430,11 +504,16 @@ class OnlineRecognizer(object):
zipformer2_ctc_config = OnlineZipformer2CtcModelConfig(model=model)
provider_config = ProviderConfig(
provider=provider,
device=device,
)
model_config = OnlineModelConfig(
zipformer2_ctc=zipformer2_ctc_config,
tokens=tokens,
num_threads=num_threads,
provider=provider,
provider_config=provider_config,
debug=debug,
)
@@ -486,6 +565,7 @@ class OnlineRecognizer(object):
debug: bool = False,
rule_fsts: str = "",
rule_fars: str = "",
device: int = 0,
):
"""
Please refer to
@@ -535,6 +615,8 @@ class OnlineRecognizer(object):
rule_fars:
If not empty, it specifies fst archives for inverse text normalization.
If there are multiple archives, they are separated by a comma.
device:
onnxruntime cuda device index.
"""
self = cls.__new__(cls)
_assert_file_exists(tokens)
@@ -546,11 +628,16 @@ class OnlineRecognizer(object):
model=model,
)
provider_config = ProviderConfig(
provider=provider,
device=device,
)
model_config = OnlineModelConfig(
nemo_ctc=nemo_ctc_config,
tokens=tokens,
num_threads=num_threads,
provider=provider,
provider_config=provider_config,
debug=debug,
)
@@ -598,6 +685,7 @@ class OnlineRecognizer(object):
debug: bool = False,
rule_fsts: str = "",
rule_fars: str = "",
device: int = 0,
):
"""
Please refer to
@@ -650,6 +738,8 @@ class OnlineRecognizer(object):
rule_fars:
If not empty, it specifies fst archives for inverse text normalization.
If there are multiple archives, they are separated by a comma.
device:
onnxruntime cuda device index.
"""
self = cls.__new__(cls)
_assert_file_exists(tokens)
@@ -663,11 +753,16 @@ class OnlineRecognizer(object):
num_left_chunks=num_left_chunks,
)
provider_config = ProviderConfig(
provider=provider,
device=device,
)
model_config = OnlineModelConfig(
wenet_ctc=wenet_ctc_config,
tokens=tokens,
num_threads=num_threads,
provider=provider,
provider_config=provider_config,
debug=debug,
)