EngineX-Iluvatar/enginex-mr_series-sherpa-onnx
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Add Android APK for Silero VAD (#335)

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This commit is contained in:
Fangjun Kuang
2023-09-23 20:39:13 +08:00
committed by GitHub
parent 65ec4dc741
commit 6e60a77d89
68 changed files with 1562 additions and 45 deletions
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48
sherpa-onnx/csrc/sherpa-onnx-offline-parallel.cc
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@@ -32,7 +32,7 @@ std::vector<std::vector<std::string>> SplitToBatches(
process_num += batch_size;
}
if (itr != input.cend()) {
outputs.emplace_back(itr, input.cend());
outputs.emplace_back(itr, input.cend());
}
return outputs;
}
@@ -41,8 +41,8 @@ std::vector<std::string> LoadScpFile(const std::string &wav_scp_path) {
std::vector<std::string> wav_paths;
std::ifstream in(wav_scp_path);
if (!in.is_open()) {
fprintf(stderr, "Failed to open file: %s.\n", wav_scp_path.c_str());
return wav_paths;
fprintf(stderr, "Failed to open file: %s.\n", wav_scp_path.c_str());
return wav_paths;
}
std::string line, column1, column2;
while (std::getline(in, line)) {
@@ -55,8 +55,8 @@ std::vector<std::string> LoadScpFile(const std::string &wav_scp_path) {
}
void AsrInference(const std::vector<std::vector<std::string>> &chunk_wav_paths,
sherpa_onnx::OfflineRecognizer* recognizer,
float* total_length, float* total_time) {
sherpa_onnx::OfflineRecognizer *recognizer,
float *total_length, float *total_time) {
std::vector<std::unique_ptr<sherpa_onnx::OfflineStream>> ss;
std::vector<sherpa_onnx::OfflineStream *> ss_pointers;
float duration = 0.0f;
@@ -70,7 +70,7 @@ void AsrInference(const std::vector<std::vector<std::string>> &chunk_wav_paths,
sherpa_onnx::ReadWave(wav_filename, &sampling_rate, &is_ok);
if (!is_ok) {
fprintf(stderr, "Failed to read %s\n", wav_filename.c_str());
continue;
continue;
}
duration += samples.size() / static_cast<float>(sampling_rate);
auto s = recognizer->CreateStream();
@@ -97,7 +97,7 @@ void AsrInference(const std::vector<std::vector<std::string>> &chunk_wav_paths,
sherpa_onnx::ReadWave(wav_filename, &sampling_rate, &is_ok);
if (!is_ok) {
fprintf(stderr, "Failed to read %s\n", wav_filename.c_str());
continue;
continue;
}
duration += samples.size() / static_cast<float>(sampling_rate);
auto s = recognizer->CreateStream();
@@ -109,9 +109,9 @@ void AsrInference(const std::vector<std::vector<std::string>> &chunk_wav_paths,
recognizer->DecodeStreams(ss_pointers.data(), ss_pointers.size());
const auto end = std::chrono::steady_clock::now();
float elapsed_seconds =
std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
.count() /
1000.;
std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
.count() /
1000.;
elapsed_seconds_batch += elapsed_seconds;
int i = 0;
for (const auto &wav_filename : wav_paths) {
@@ -122,7 +122,7 @@ void AsrInference(const std::vector<std::vector<std::string>> &chunk_wav_paths,
ss_pointers.clear();
ss.clear();
}
fprintf(stderr, "thread %lu.\n", std::this_thread::get_id());
{
std::lock_guard<std::mutex> guard(mtx);
*total_length += duration;
@@ -132,7 +132,6 @@ void AsrInference(const std::vector<std::vector<std::string>> &chunk_wav_paths,
}
}
int main(int32_t argc, char *argv[]) {
const char *kUsageMessage = R"usage(
Speech recognition using non-streaming models with sherpa-onnx.
@@ -223,17 +222,17 @@ https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
for a list of pre-trained models to download.
)usage";
std::string wav_scp = ""; // file path, kaldi style wav list.
int32_t nj = 1; // thread number
int32_t batch_size = 1; // number of wav files processed at once.
int32_t nj = 1; // thread number
int32_t batch_size = 1; // number of wav files processed at once.
sherpa_onnx::ParseOptions po(kUsageMessage);
sherpa_onnx::OfflineRecognizerConfig config;
config.Register(&po);
po.Register("wav-scp", &wav_scp,
"a file including wav-id and wav-path, kaldi style wav list."
"default="". when it is not empty, wav files which positional "
"default="
". when it is not empty, wav files which positional "
"parameters provide are invalid.");
po.Register("nj", &nj,
"multi-thread num for decoding, default=1");
po.Register("nj", &nj, "multi-thread num for decoding, default=1");
po.Register("batch-size", &batch_size,
"number of wav files processed at once during the decoding"
"process. default=1");
@@ -262,7 +261,8 @@ for a list of pre-trained models to download.
1000.;
fprintf(stderr,
"Started nj: %d, batch_size: %d, wav_path: %s. recognizer init time: "
"%.6f\n", nj, batch_size, wav_scp.c_str(), elapsed_seconds);
"%.6f\n",
nj, batch_size, wav_scp.c_str(), elapsed_seconds);
std::this_thread::sleep_for(std::chrono::seconds(10)); // sleep 10s
std::vector<std::string> wav_paths;
if (!wav_scp.empty()) {
@@ -282,12 +282,12 @@ for a list of pre-trained models to download.
float total_length = 0.0f;
float total_time = 0.0f;
for (int i = 0; i < nj; i++) {
threads.emplace_back(std::thread(AsrInference, batch_wav_paths,
&recognizer, &total_length, &total_time));
threads.emplace_back(std::thread(AsrInference, batch_wav_paths, &recognizer,
&total_length, &total_time));
}
for (auto& thread : threads) {
thread.join();
for (auto &thread : threads) {
thread.join();
}
fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
@@ -297,8 +297,8 @@ for a list of pre-trained models to download.
}
fprintf(stderr, "Elapsed seconds: %.3f s\n", total_time);
float rtf = total_time / total_length;
fprintf(stderr, "Real time factor (RTF): %.6f / %.6f = %.4f\n",
total_time, total_length, rtf);
fprintf(stderr, "Real time factor (RTF): %.6f / %.6f = %.4f\n", total_time,
total_length, rtf);
fprintf(stderr, "SPEEDUP: %.4f\n", 1.0 / rtf);
return 0;