offline-sense-voice-model-rknn.cc
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// sherpa-onnx/csrc/rknn/offline-sense-voice-model-rknn.cc
//
// Copyright (c) 2025 Xiaomi Corporation
#include "sherpa-onnx/csrc/rknn/offline-sense-voice-model-rknn.h"
#include <algorithm>
#include <array>
#include <utility>
#include <vector>
#if __ANDROID_API__ >= 9
#include "android/asset_manager.h"
#include "android/asset_manager_jni.h"
#endif
#if __OHOS__
#include "rawfile/raw_file_manager.h"
#endif
#include "sherpa-onnx/csrc/file-utils.h"
#include "sherpa-onnx/csrc/rknn/macros.h"
#include "sherpa-onnx/csrc/rknn/utils.h"
namespace sherpa_onnx {
class OfflineSenseVoiceModelRknn::Impl {
public:
~Impl() {
auto ret = rknn_destroy(ctx_);
if (ret != RKNN_SUCC) {
SHERPA_ONNX_LOGE("Failed to destroy the context");
}
}
explicit Impl(const OfflineModelConfig &config) : config_(config) {
{
auto buf = ReadFile(config_.sense_voice.model);
Init(buf.data(), buf.size());
}
}
template <typename Manager>
Impl(Manager *mgr, const OfflineModelConfig &config) : config_(config) {
{
auto buf = ReadFile(mgr, config_.sense_voice.model);
Init(buf.data(), buf.size());
}
}
const OfflineSenseVoiceModelMetaData &GetModelMetadata() const {
return meta_data_;
}
std::vector<float> Run(std::vector<float> features, int32_t language,
int32_t text_norm) {
features = ApplyLFR(std::move(features));
std::vector<rknn_input> inputs(input_attrs_.size());
std::array<int32_t, 4> prompt{language, 1, 2, text_norm};
inputs[0].index = input_attrs_[0].index;
inputs[0].type = RKNN_TENSOR_FLOAT32;
inputs[0].fmt = input_attrs_[0].fmt;
inputs[0].buf = reinterpret_cast<void *>(features.data());
inputs[0].size = features.size() * sizeof(float);
inputs[1].index = input_attrs_[1].index;
inputs[1].type = RKNN_TENSOR_INT32;
inputs[1].fmt = input_attrs_[1].fmt;
inputs[1].buf = reinterpret_cast<void *>(prompt.data());
inputs[1].size = prompt.size() * sizeof(int32_t);
std::vector<float> out(output_attrs_[0].n_elems);
std::vector<rknn_output> outputs(output_attrs_.size());
outputs[0].index = output_attrs_[0].index;
outputs[0].is_prealloc = 1;
outputs[0].want_float = 1;
outputs[0].size = out.size() * sizeof(float);
outputs[0].buf = reinterpret_cast<void *>(out.data());
rknn_context ctx = 0;
auto ret = rknn_dup_context(&ctx_, &ctx);
SHERPA_ONNX_RKNN_CHECK(ret, "Failed to duplicate the ctx");
SetCoreMask(ctx, config_.num_threads);
ret = rknn_inputs_set(ctx, inputs.size(), inputs.data());
SHERPA_ONNX_RKNN_CHECK(ret, "Failed to set inputs");
ret = rknn_run(ctx, nullptr);
SHERPA_ONNX_RKNN_CHECK(ret, "Failed to run the model");
ret = rknn_outputs_get(ctx, outputs.size(), outputs.data(), nullptr);
SHERPA_ONNX_RKNN_CHECK(ret, "Failed to get model output");
rknn_destroy(ctx);
return out;
}
private:
void Init(void *model_data, size_t model_data_length) {
InitContext(model_data, model_data_length, config_.debug, &ctx_);
InitInputOutputAttrs(ctx_, config_.debug, &input_attrs_, &output_attrs_);
rknn_custom_string custom_string = GetCustomString(ctx_, config_.debug);
auto meta = Parse(custom_string, config_.debug);
#define SHERPA_ONNX_RKNN_READ_META_DATA_INT(dst, src_key) \
do { \
if (!meta.count(#src_key)) { \
SHERPA_ONNX_LOGE("'%s' does not exist in the custom_string", #src_key); \
SHERPA_ONNX_EXIT(-1); \
} \
\
dst = atoi(meta.at(#src_key).c_str()); \
} while (0)
SHERPA_ONNX_RKNN_READ_META_DATA_INT(meta_data_.with_itn_id, with_itn);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(meta_data_.without_itn_id, without_itn);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(meta_data_.window_size,
lfr_window_size);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(meta_data_.window_shift,
lfr_window_shift);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(meta_data_.vocab_size, vocab_size);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(meta_data_.normalize_samples,
normalize_samples);
int32_t lang_auto = 0;
int32_t lang_zh = 0;
int32_t lang_en = 0;
int32_t lang_ja = 0;
int32_t lang_ko = 0;
int32_t lang_yue = 0;
SHERPA_ONNX_RKNN_READ_META_DATA_INT(lang_auto, lang_auto);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(lang_zh, lang_zh);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(lang_en, lang_en);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(lang_ja, lang_ja);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(lang_ko, lang_ko);
SHERPA_ONNX_RKNN_READ_META_DATA_INT(lang_yue, lang_yue);
meta_data_.lang2id = {
{"auto", lang_auto}, {"zh", lang_zh}, {"en", lang_en},
{"ja", lang_ja}, {"ko", lang_ko}, {"yue", lang_yue},
};
// for rknn models, neg_mean and inv_stddev are stored inside the model
#undef SHERPA_ONNX_RKNN_READ_META_DATA_INT
num_input_frames_ = input_attrs_[0].dims[1];
}
std::vector<float> ApplyLFR(std::vector<float> in) const {
int32_t lfr_window_size = meta_data_.window_size;
int32_t lfr_window_shift = meta_data_.window_shift;
int32_t in_feat_dim = 80;
int32_t in_num_frames = in.size() / in_feat_dim;
int32_t out_num_frames =
(in_num_frames - lfr_window_size) / lfr_window_shift + 1;
if (out_num_frames > num_input_frames_) {
SHERPA_ONNX_LOGE(
"Number of input frames %d is too large. Truncate it to %d frames.",
out_num_frames, num_input_frames_);
SHERPA_ONNX_LOGE(
"Recognition result may be truncated/incomplete. Please select a "
"model accepting longer audios.");
out_num_frames = num_input_frames_;
}
int32_t out_feat_dim = in_feat_dim * lfr_window_size;
std::vector<float> out(num_input_frames_ * out_feat_dim);
const float *p_in = in.data();
float *p_out = out.data();
for (int32_t i = 0; i != out_num_frames; ++i) {
std::copy(p_in, p_in + out_feat_dim, p_out);
p_out += out_feat_dim;
p_in += lfr_window_shift * in_feat_dim;
}
return out;
}
private:
OfflineModelConfig config_;
rknn_context ctx_ = 0;
std::vector<rknn_tensor_attr> input_attrs_;
std::vector<rknn_tensor_attr> output_attrs_;
OfflineSenseVoiceModelMetaData meta_data_;
int32_t num_input_frames_ = -1;
};
OfflineSenseVoiceModelRknn::~OfflineSenseVoiceModelRknn() = default;
OfflineSenseVoiceModelRknn::OfflineSenseVoiceModelRknn(
const OfflineModelConfig &config)
: impl_(std::make_unique<Impl>(config)) {}
template <typename Manager>
OfflineSenseVoiceModelRknn::OfflineSenseVoiceModelRknn(
Manager *mgr, const OfflineModelConfig &config)
: impl_(std::make_unique<Impl>(mgr, config)) {}
std::vector<float> OfflineSenseVoiceModelRknn::Run(std::vector<float> features,
int32_t language,
int32_t text_norm) const {
return impl_->Run(std::move(features), language, text_norm);
}
const OfflineSenseVoiceModelMetaData &
OfflineSenseVoiceModelRknn::GetModelMetadata() const {
return impl_->GetModelMetadata();
}
#if __ANDROID_API__ >= 9
template OfflineSenseVoiceModelRknn::OfflineSenseVoiceModelRknn(
AAssetManager *mgr, const OfflineModelConfig &config);
#endif
#if __OHOS__
template OfflineSenseVoiceModelRknn::OfflineSenseVoiceModelRknn(
NativeResourceManager *mgr, const OfflineModelConfig &config);
#endif
} // namespace sherpa_onnx