EngineX-Iluvatar/enginex_bi_series-sherpa-onnx
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add online-recognizer (#29)

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This commit is contained in:
Fangjun Kuang
2023-02-19 12:45:38 +08:00
committed by GitHub
parent d4b0c0590a
commit ebc3b47fb8
11 changed files with 267 additions and 61 deletions
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79
sherpa-onnx/csrc/sherpa-onnx.cc
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@@ -8,6 +8,7 @@
#include <string>
#include <vector>
#include "sherpa-onnx/csrc/online-recognizer.h"
#include "sherpa-onnx/csrc/online-stream.h"
#include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
#include "sherpa-onnx/csrc/online-transducer-model-config.h"
@@ -35,35 +36,26 @@ for a list of pre-trained models to download.
return 0;
}
std::string tokens = argv[1];
sherpa_onnx::OnlineTransducerModelConfig config;
config.debug = false;
config.encoder_filename = argv[2];
config.decoder_filename = argv[3];
config.joiner_filename = argv[4];
sherpa_onnx::OnlineRecognizerConfig config;
config.tokens = argv[1];
config.model_config.debug = false;
config.model_config.encoder_filename = argv[2];
config.model_config.decoder_filename = argv[3];
config.model_config.joiner_filename = argv[4];
std::string wav_filename = argv[5];
config.num_threads = 2;
config.model_config.num_threads = 2;
if (argc == 7) {
config.num_threads = atoi(argv[6]);
config.model_config.num_threads = atoi(argv[6]);
}
fprintf(stderr, "%s\n", config.ToString().c_str());
auto model = sherpa_onnx::OnlineTransducerModel::Create(config);
sherpa_onnx::OnlineRecognizer recognizer(config);
sherpa_onnx::SymbolTable sym(tokens);
Ort::AllocatorWithDefaultOptions allocator;
int32_t chunk_size = model->ChunkSize();
int32_t chunk_shift = model->ChunkShift();
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
std::vector<Ort::Value> states = model->GetEncoderInitStates();
float expected_sampling_rate = 16000;
float expected_sampling_rate = config.feat_config.sampling_rate;
bool is_ok = false;
std::vector<float> samples =
@@ -82,44 +74,21 @@ for a list of pre-trained models to download.
auto begin = std::chrono::steady_clock::now();
fprintf(stderr, "Started\n");
sherpa_onnx::OnlineStream stream;
stream.AcceptWaveform(expected_sampling_rate, samples.data(), samples.size());
auto s = recognizer.CreateStream();
s->AcceptWaveform(expected_sampling_rate, samples.data(), samples.size());
std::vector<float> tail_paddings(
static_cast<int>(0.2 * expected_sampling_rate));
stream.AcceptWaveform(expected_sampling_rate, tail_paddings.data(),
tail_paddings.size());
stream.InputFinished();
s->AcceptWaveform(expected_sampling_rate, tail_paddings.data(),
tail_paddings.size());
s->InputFinished();
int32_t num_frames = stream.NumFramesReady();
int32_t feature_dim = stream.FeatureDim();
std::array<int64_t, 3> x_shape{1, chunk_size, feature_dim};
sherpa_onnx::OnlineTransducerGreedySearchDecoder decoder(model.get());
std::vector<sherpa_onnx::OnlineTransducerDecoderResult> result = {
decoder.GetEmptyResult()};
while (stream.NumFramesReady() - stream.GetNumProcessedFrames() >
chunk_size) {
std::vector<float> features =
stream.GetFrames(stream.GetNumProcessedFrames(), chunk_size);
stream.GetNumProcessedFrames() += chunk_shift;
Ort::Value x =
Ort::Value::CreateTensor(memory_info, features.data(), features.size(),
x_shape.data(), x_shape.size());
auto pair = model->RunEncoder(std::move(x), states);
states = std::move(pair.second);
decoder.Decode(std::move(pair.first), &result);
}
decoder.StripLeadingBlanks(&result[0]);
const auto &hyp = result[0].tokens;
std::string text;
for (auto t : hyp) {
text += sym[t];
while (recognizer.IsReady(s.get())) {
recognizer.DecodeStream(s.get());
}
std::string text = recognizer.GetResult(s.get()).text;
fprintf(stderr, "Done!\n");
fprintf(stderr, "Recognition result for %s:\n%s\n", wav_filename.c_str(),
@@ -131,7 +100,7 @@ for a list of pre-trained models to download.
.count() /
1000.;
fprintf(stderr, "num threads: %d\n", config.num_threads);
fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
float rtf = elapsed_seconds / duration;