code refactoring and add CI (#11)
This commit is contained in:
Fangjun Kuang
@@ -1,101 +1,99 @@
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#include <vector>
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#include <iostream>
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#include <algorithm>
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#include <time.h>
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#include <math.h>
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#include <fstream>
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#include <iostream>
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#include <math.h>
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#include <time.h>
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#include <vector>
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#include "fbank_features.h"
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#include "rnnt_beam_search.h"
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#include "sherpa-onnx/csrc/fbank_features.h"
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#include "sherpa-onnx/csrc/rnnt_beam_search.h"
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#include "kaldi-native-fbank/csrc/online-feature.h"
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int main(int argc, char *argv[]) {
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char *encoder_path = argv[1];
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char *decoder_path = argv[2];
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char *joiner_path = argv[3];
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char *joiner_encoder_proj_path = argv[4];
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char *joiner_decoder_proj_path = argv[5];
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char *token_path = argv[6];
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std::string search_method = argv[7];
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char *filename = argv[8];
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int main(int argc, char* argv[]) {
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char* encoder_path = argv[1];
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char* decoder_path = argv[2];
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char* joiner_path = argv[3];
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char* joiner_encoder_proj_path = argv[4];
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char* joiner_decoder_proj_path = argv[5];
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char* token_path = argv[6];
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std::string search_method = argv[7];
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char* filename = argv[8];
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// General parameters
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int numberOfThreads = 16;
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// General parameters
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int numberOfThreads = 16;
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// Initialize fbanks
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knf::FbankOptions opts;
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opts.frame_opts.dither = 0;
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opts.frame_opts.samp_freq = 16000;
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opts.frame_opts.frame_shift_ms = 10.0f;
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opts.frame_opts.frame_length_ms = 25.0f;
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opts.mel_opts.num_bins = 80;
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opts.frame_opts.window_type = "povey";
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opts.frame_opts.snip_edges = false;
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knf::OnlineFbank fbank(opts);
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// Initialize fbanks
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knf::FbankOptions opts;
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opts.frame_opts.dither = 0;
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opts.frame_opts.samp_freq = 16000;
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opts.frame_opts.frame_shift_ms = 10.0f;
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opts.frame_opts.frame_length_ms = 25.0f;
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opts.mel_opts.num_bins = 80;
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opts.frame_opts.window_type = "povey";
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opts.frame_opts.snip_edges = false;
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knf::OnlineFbank fbank(opts);
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// set session opts
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// https://onnxruntime.ai/docs/performance/tune-performance.html
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session_options.SetIntraOpNumThreads(numberOfThreads);
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session_options.SetInterOpNumThreads(numberOfThreads);
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session_options.SetGraphOptimizationLevel(
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GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
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session_options.SetLogSeverityLevel(4);
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session_options.SetExecutionMode(ExecutionMode::ORT_SEQUENTIAL);
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// set session opts
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// https://onnxruntime.ai/docs/performance/tune-performance.html
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session_options.SetIntraOpNumThreads(numberOfThreads);
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session_options.SetInterOpNumThreads(numberOfThreads);
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session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
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session_options.SetLogSeverityLevel(4);
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session_options.SetExecutionMode(ExecutionMode::ORT_SEQUENTIAL);
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api.CreateTensorRTProviderOptions(&tensorrt_options);
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std::unique_ptr<OrtTensorRTProviderOptionsV2, decltype(api.ReleaseTensorRTProviderOptions)> rel_trt_options(tensorrt_options, api.ReleaseTensorRTProviderOptions);
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api.SessionOptionsAppendExecutionProvider_TensorRT_V2(static_cast<OrtSessionOptions*>(session_options), rel_trt_options.get());
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api.CreateTensorRTProviderOptions(&tensorrt_options);
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std::unique_ptr<OrtTensorRTProviderOptionsV2,
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decltype(api.ReleaseTensorRTProviderOptions)>
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rel_trt_options(tensorrt_options, api.ReleaseTensorRTProviderOptions);
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api.SessionOptionsAppendExecutionProvider_TensorRT_V2(
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static_cast<OrtSessionOptions *>(session_options), rel_trt_options.get());
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// Define model
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auto model = get_model(
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encoder_path,
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decoder_path,
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joiner_path,
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joiner_encoder_proj_path,
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joiner_decoder_proj_path,
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token_path
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);
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std::vector<std::string> filename_list {
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filename
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};
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// Define model
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auto model =
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get_model(encoder_path, decoder_path, joiner_path,
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joiner_encoder_proj_path, joiner_decoder_proj_path, token_path);
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for (auto filename : filename_list){
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std::cout << filename << std::endl;
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auto samples = readWav(filename, true);
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int numSamples = samples.NumCols();
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std::vector<std::string> filename_list{filename};
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auto features = ComputeFeatures(fbank, opts, samples);
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for (auto filename : filename_list) {
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std::cout << filename << std::endl;
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auto samples = readWav(filename, true);
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int numSamples = samples.NumCols();
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auto tic = std::chrono::high_resolution_clock::now();
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auto features = ComputeFeatures(fbank, opts, samples);
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// # === Encoder Out === #
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int num_frames = features.size() / opts.mel_opts.num_bins;
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auto encoder_out = model.encoder_forward(features,
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std::vector<int64_t> {num_frames},
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std::vector<int64_t> {1, num_frames, 80},
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std::vector<int64_t> {1},
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memory_info);
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auto tic = std::chrono::high_resolution_clock::now();
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// # === Search === #
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std::vector<std::vector<int32_t>> hyps;
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if (search_method == "greedy")
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hyps = GreedySearch(&model, &encoder_out);
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else{
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std::cout << "wrong search method!" << std::endl;
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exit(0);
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}
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auto results = hyps2result(model.tokens_map, hyps);
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// # === Encoder Out === #
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int num_frames = features.size() / opts.mel_opts.num_bins;
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auto encoder_out =
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model.encoder_forward(features, std::vector<int64_t>{num_frames},
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std::vector<int64_t>{1, num_frames, 80},
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std::vector<int64_t>{1}, memory_info);
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// # === Print Elapsed Time === #
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auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::high_resolution_clock::now() - tic);
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std::cout << "Elapsed: " << float(elapsed.count()) / 1000 << " seconds" << std::endl;
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std::cout << "rtf: " << float(elapsed.count()) / 1000 / (numSamples / 16000) << std::endl;
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print_hyps(hyps);
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std::cout << results[0] << std::endl;
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// # === Search === #
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std::vector<std::vector<int32_t>> hyps;
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if (search_method == "greedy")
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hyps = GreedySearch(&model, &encoder_out);
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else {
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std::cout << "wrong search method!" << std::endl;
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exit(0);
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}
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auto results = hyps2result(model.tokens_map, hyps);
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return 0;
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// # === Print Elapsed Time === #
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auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(
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std::chrono::high_resolution_clock::now() - tic);
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std::cout << "Elapsed: " << float(elapsed.count()) / 1000 << " seconds"
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<< std::endl;
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std::cout << "rtf: " << float(elapsed.count()) / 1000 / (numSamples / 16000)
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<< std::endl;
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print_hyps(hyps);
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std::cout << results[0] << std::endl;
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}
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return 0;
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}
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