xuning / sherpaonnx

转到一个项目
Toggle navigation Toggle navigation pinning
Fangjun Kuang
Committed by GitHub
dffb0fd4 1 parent 55722462

Refactor offline recognizer. (#94) 

* Refactor offline recognizer.

The purpose is to make it easier to support different types of models.
内嵌 并排对比
正在显示 9 个修改的文件 包含 283 行增加 和 134 行删除
... ... @@ -6,11 +6,12 @@ set(sources
features.cc
file-utils.cc
hypothesis.cc
offline-recognizer-impl.cc
offline-recognizer.cc
offline-stream.cc
offline-transducer-greedy-search-decoder.cc
offline-transducer-model-config.cc
offline-transducer-model.cc
offline-recognizer.cc
online-lstm-transducer-model.cc
online-recognizer.cc
online-stream.cc
... ...
... ... @@ -23,36 +23,55 @@
} while (0)
#endif
// Read an integer
#define SHERPA_ONNX_READ_META_DATA(dst, src_key) \
do { \
auto value = \
meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
if (!value) { \
fprintf(stderr, "%s does not exist in the metadata\n", src_key); \
SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key); \
exit(-1); \
} \
\
dst = atoi(value.get()); \
if (dst <= 0) { \
fprintf(stderr, "Invalid value %d for %s\n", dst, src_key); \
SHERPA_ONNX_LOGE("Invalid value %d for %s", dst, src_key); \
exit(-1); \
} \
} while (0)
#define SHERPA_ONNX_READ_META_DATA_VEC(dst, src_key) \
do { \
auto value = \
meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
if (!value) { \
fprintf(stderr, "%s does not exist in the metadata\n", src_key); \
exit(-1); \
} \
\
bool ret = SplitStringToIntegers(value.get(), ",", true, &dst); \
if (!ret) { \
fprintf(stderr, "Invalid value %s for %s\n", value.get(), src_key); \
exit(-1); \
} \
// read a vector of integers
#define SHERPA_ONNX_READ_META_DATA_VEC(dst, src_key) \
do { \
auto value = \
meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
if (!value) { \
SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key); \
exit(-1); \
} \
\
bool ret = SplitStringToIntegers(value.get(), ",", true, &dst); \
if (!ret) { \
SHERPA_ONNX_LOGE("Invalid value %s for %s", value.get(), src_key); \
exit(-1); \
} \
} while (0)
// Read a string
#define SHERPA_ONNX_READ_META_DATA_STR(dst, src_key) \
do { \
auto value = \
meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
if (!value) { \
SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key); \
exit(-1); \
} \
\
dst = value.get(); \
if (dst.empty()) { \
SHERPA_ONNX_LOGE("Invalid value for %s\n", src_key); \
exit(-1); \
} \
} while (0)
#endif // SHERPA_ONNX_CSRC_MACROS_H_
... ...
// sherpa-onnx/csrc/offline-recognizer-impl.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
#include <string>
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/offline-recognizer-transducer-impl.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#include "sherpa-onnx/csrc/text-utils.h"
namespace sherpa_onnx {
std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
const OfflineRecognizerConfig &config) {
Ort::Env env;
Ort::SessionOptions sess_opts;
auto buf = ReadFile(config.model_config.encoder_filename);
auto encoder_sess =
std::make_unique<Ort::Session>(env, buf.data(), buf.size(), sess_opts);
Ort::ModelMetadata meta_data = encoder_sess->GetModelMetadata();
Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
std::string model_type;
SHERPA_ONNX_READ_META_DATA_STR(model_type, "model_type");
if (model_type == "conformer") {
return std::make_unique<OfflineRecognizerTransducerImpl>(config);
}
SHERPA_ONNX_LOGE("Unsupported model_type: %s\n", model_type.c_str());
exit(-1);
}
} // namespace sherpa_onnx
... ...
// sherpa-onnx/csrc/offline-recognizer-impl.h
//
// Copyright (c) 2023 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_IMPL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_IMPL_H_
#include <memory>
#include "sherpa-onnx/csrc/offline-recognizer.h"
#include "sherpa-onnx/csrc/offline-stream.h"
namespace sherpa_onnx {
class OfflineRecognizerImpl {
public:
static std::unique_ptr<OfflineRecognizerImpl> Create(
const OfflineRecognizerConfig &config);
virtual ~OfflineRecognizerImpl() = default;
virtual std::unique_ptr<OfflineStream> CreateStream() const = 0;
virtual void DecodeStreams(OfflineStream **ss, int32_t n) const = 0;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_IMPL_H_
... ...
// sherpa-onnx/csrc/offline-recognizer-transducer-impl.h
//
// Copyright (c) 2022 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
#include "sherpa-onnx/csrc/offline-recognizer.h"
#include "sherpa-onnx/csrc/offline-transducer-decoder.h"
#include "sherpa-onnx/csrc/offline-transducer-greedy-search-decoder.h"
#include "sherpa-onnx/csrc/offline-transducer-model.h"
#include "sherpa-onnx/csrc/pad-sequence.h"
#include "sherpa-onnx/csrc/symbol-table.h"
namespace sherpa_onnx {
static OfflineRecognitionResult Convert(
const OfflineTransducerDecoderResult &src, const SymbolTable &sym_table,
int32_t frame_shift_ms, int32_t subsampling_factor) {
OfflineRecognitionResult r;
r.tokens.reserve(src.tokens.size());
r.timestamps.reserve(src.timestamps.size());
std::string text;
for (auto i : src.tokens) {
auto sym = sym_table[i];
text.append(sym);
r.tokens.push_back(std::move(sym));
}
r.text = std::move(text);
float frame_shift_s = frame_shift_ms / 1000. * subsampling_factor;
for (auto t : src.timestamps) {
float time = frame_shift_s * t;
r.timestamps.push_back(time);
}
return r;
}
class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
public:
explicit OfflineRecognizerTransducerImpl(
const OfflineRecognizerConfig &config)
: config_(config),
symbol_table_(config_.model_config.tokens),
model_(std::make_unique<OfflineTransducerModel>(config_.model_config)) {
if (config_.decoding_method == "greedy_search") {
decoder_ =
std::make_unique<OfflineTransducerGreedySearchDecoder>(model_.get());
} else if (config_.decoding_method == "modified_beam_search") {
SHERPA_ONNX_LOGE("TODO: modified_beam_search is to be implemented");
exit(-1);
} else {
SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
config_.decoding_method.c_str());
exit(-1);
}
}
std::unique_ptr<OfflineStream> CreateStream() const override {
return std::make_unique<OfflineStream>(config_.feat_config);
}
void DecodeStreams(OfflineStream **ss, int32_t n) const override {
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
int32_t feat_dim = ss[0]->FeatureDim();
std::vector<Ort::Value> features;
features.reserve(n);
std::vector<std::vector<float>> features_vec(n);
std::vector<int64_t> features_length_vec(n);
for (int32_t i = 0; i != n; ++i) {
auto f = ss[i]->GetFrames();
int32_t num_frames = f.size() / feat_dim;
features_length_vec[i] = num_frames;
features_vec[i] = std::move(f);
std::array<int64_t, 2> shape = {num_frames, feat_dim};
Ort::Value x = Ort::Value::CreateTensor(
memory_info, features_vec[i].data(), features_vec[i].size(),
shape.data(), shape.size());
features.push_back(std::move(x));
}
std::vector<const Ort::Value *> features_pointer(n);
for (int32_t i = 0; i != n; ++i) {
features_pointer[i] = &features[i];
}
std::array<int64_t, 1> features_length_shape = {n};
Ort::Value x_length = Ort::Value::CreateTensor(
memory_info, features_length_vec.data(), n,
features_length_shape.data(), features_length_shape.size());
Ort::Value x = PadSequence(model_->Allocator(), features_pointer,
-23.025850929940457f);
auto t = model_->RunEncoder(std::move(x), std::move(x_length));
auto results = decoder_->Decode(std::move(t.first), std::move(t.second));
int32_t frame_shift_ms = 10;
for (int32_t i = 0; i != n; ++i) {
auto r = Convert(results[i], symbol_table_, frame_shift_ms,
model_->SubsamplingFactor());
ss[i]->SetResult(r);
}
}
private:
OfflineRecognizerConfig config_;
SymbolTable symbol_table_;
std::unique_ptr<OfflineTransducerModel> model_;
std::unique_ptr<OfflineTransducerDecoder> decoder_;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_
... ...
... ... @@ -5,42 +5,11 @@
#include "sherpa-onnx/csrc/offline-recognizer.h"
#include <memory>
#include <utility>
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/offline-transducer-decoder.h"
#include "sherpa-onnx/csrc/offline-transducer-greedy-search-decoder.h"
#include "sherpa-onnx/csrc/offline-transducer-model.h"
#include "sherpa-onnx/csrc/pad-sequence.h"
#include "sherpa-onnx/csrc/symbol-table.h"
#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
namespace sherpa_onnx {
static OfflineRecognitionResult Convert(
const OfflineTransducerDecoderResult &src, const SymbolTable &sym_table,
int32_t frame_shift_ms, int32_t subsampling_factor) {
OfflineRecognitionResult r;
r.tokens.reserve(src.tokens.size());
r.timestamps.reserve(src.timestamps.size());
std::string text;
for (auto i : src.tokens) {
auto sym = sym_table[i];
text.append(sym);
r.tokens.push_back(std::move(sym));
}
r.text = std::move(text);
float frame_shift_s = frame_shift_ms / 1000. * subsampling_factor;
for (auto t : src.timestamps) {
float time = frame_shift_s * t;
r.timestamps.push_back(time);
}
return r;
}
void OfflineRecognizerConfig::Register(ParseOptions *po) {
feat_config.Register(po);
model_config.Register(po);
... ... @@ -65,90 +34,8 @@ std::string OfflineRecognizerConfig::ToString() const {
return os.str();
}
class OfflineRecognizer::Impl {
public:
explicit Impl(const OfflineRecognizerConfig &config)
: config_(config),
symbol_table_(config_.model_config.tokens),
model_(std::make_unique<OfflineTransducerModel>(config_.model_config)) {
if (config_.decoding_method == "greedy_search") {
decoder_ =
std::make_unique<OfflineTransducerGreedySearchDecoder>(model_.get());
} else if (config_.decoding_method == "modified_beam_search") {
SHERPA_ONNX_LOGE("TODO: modified_beam_search is to be implemented");
exit(-1);
} else {
SHERPA_ONNX_LOGE("Unsupported decoding method: %s",
config_.decoding_method.c_str());
exit(-1);
}
}
std::unique_ptr<OfflineStream> CreateStream() const {
return std::make_unique<OfflineStream>(config_.feat_config);
}
void DecodeStreams(OfflineStream **ss, int32_t n) const {
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
int32_t feat_dim = ss[0]->FeatureDim();
std::vector<Ort::Value> features;
features.reserve(n);
std::vector<std::vector<float>> features_vec(n);
std::vector<int64_t> features_length_vec(n);
for (int32_t i = 0; i != n; ++i) {
auto f = ss[i]->GetFrames();
int32_t num_frames = f.size() / feat_dim;
features_length_vec[i] = num_frames;
features_vec[i] = std::move(f);
std::array<int64_t, 2> shape = {num_frames, feat_dim};
Ort::Value x = Ort::Value::CreateTensor(
memory_info, features_vec[i].data(), features_vec[i].size(),
shape.data(), shape.size());
features.push_back(std::move(x));
}
std::vector<const Ort::Value *> features_pointer(n);
for (int32_t i = 0; i != n; ++i) {
features_pointer[i] = &features[i];
}
std::array<int64_t, 1> features_length_shape = {n};
Ort::Value x_length = Ort::Value::CreateTensor(
memory_info, features_length_vec.data(), n,
features_length_shape.data(), features_length_shape.size());
Ort::Value x = PadSequence(model_->Allocator(), features_pointer,
-23.025850929940457f);
auto t = model_->RunEncoder(std::move(x), std::move(x_length));
auto results = decoder_->Decode(std::move(t.first), std::move(t.second));
int32_t frame_shift_ms = 10;
for (int32_t i = 0; i != n; ++i) {
auto r = Convert(results[i], symbol_table_, frame_shift_ms,
model_->SubsamplingFactor());
ss[i]->SetResult(r);
}
}
private:
OfflineRecognizerConfig config_;
SymbolTable symbol_table_;
std::unique_ptr<OfflineTransducerModel> model_;
std::unique_ptr<OfflineTransducerDecoder> decoder_;
};
OfflineRecognizer::OfflineRecognizer(const OfflineRecognizerConfig &config)
: impl_(std::make_unique<Impl>(config)) {}
: impl_(OfflineRecognizerImpl::Create(config)) {}
OfflineRecognizer::~OfflineRecognizer() = default;
... ...
... ... @@ -52,6 +52,8 @@ struct OfflineRecognizerConfig {
std::string ToString() const;
};
class OfflineRecognizerImpl;
class OfflineRecognizer {
public:
~OfflineRecognizer();
... ... @@ -78,8 +80,7 @@ class OfflineRecognizer {
void DecodeStreams(OfflineStream **ss, int32_t n) const;
private:
class Impl;
std::unique_ptr<Impl> impl_;
std::unique_ptr<OfflineRecognizerImpl> impl_;
};
} // namespace sherpa_onnx
... ...
... ... @@ -5,6 +5,8 @@
#include "sherpa-onnx/csrc/text-utils.h"
#include <assert.h>
#include <string>
#include <vector>
... ... @@ -27,4 +29,31 @@ void SplitStringToVector(const std::string &full, const char *delim,
}
}
template <class F>
bool SplitStringToFloats(const std::string &full, const char *delim,
bool omit_empty_strings, // typically false
std::vector<F> *out) {
assert(out != nullptr);
if (*(full.c_str()) == '\0') {
out->clear();
return true;
}
std::vector<std::string> split;
SplitStringToVector(full, delim, omit_empty_strings, &split);
out->resize(split.size());
for (size_t i = 0; i < split.size(); ++i) {
// assume atof never fails
(*out)[i] = atof(split[i].c_str());
}
return true;
}
// Instantiate the template above for float and double.
template bool SplitStringToFloats(const std::string &full, const char *delim,
bool omit_empty_strings,
std::vector<float> *out);
template bool SplitStringToFloats(const std::string &full, const char *delim,
bool omit_empty_strings,
std::vector<double> *out);
} // namespace sherpa_onnx
... ...
... ... @@ -80,6 +80,12 @@ bool SplitStringToIntegers(const std::string &full, const char *delim,
return true;
}
// This is defined for F = float and double.
template <class F>
bool SplitStringToFloats(const std::string &full, const char *delim,
bool omit_empty_strings, // typically false
std::vector<F> *out);
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_TEXT_UTILS_H_
... ...