Added provider option to sherpa-onnx and decode-file-c-api (#162)

@@ -36,22 +36,22 @@ $repo/test_wavs/8k.wav
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.onnx \
-  $repo/decoder-epoch-99-avg-1.onnx \
-  $repo/joiner-epoch-99-avg-1.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.int8.onnx \
-  $repo/decoder-epoch-99-avg-1.int8.onnx \
-  $repo/joiner-epoch-99-avg-1.int8.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.int8.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.int8.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.int8.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 rm -rf $repo
@@ -79,22 +79,22 @@ $repo/test_wavs/8k.wav
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-11-avg-1.onnx \
-  $repo/decoder-epoch-11-avg-1.onnx \
-  $repo/joiner-epoch-11-avg-1.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-11-avg-1.onnx \
+  --decoder=$repo/decoder-epoch-11-avg-1.onnx \
+  --joiner=$repo/joiner-epoch-11-avg-1.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-11-avg-1.int8.onnx \
-  $repo/decoder-epoch-11-avg-1.int8.onnx \
-  $repo/joiner-epoch-11-avg-1.int8.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-11-avg-1.int8.onnx \
+  --decoder=$repo/decoder-epoch-11-avg-1.int8.onnx \
+  --joiner=$repo/joiner-epoch-11-avg-1.int8.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 rm -rf $repo
@@ -122,24 +122,24 @@ $repo/test_wavs/8k.wav
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.onnx \
-  $repo/decoder-epoch-99-avg-1.onnx \
-  $repo/joiner-epoch-99-avg-1.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 # test int8
 #
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.int8.onnx \
-  $repo/decoder-epoch-99-avg-1.int8.onnx \
-  $repo/joiner-epoch-99-avg-1.int8.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.int8.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.int8.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.int8.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 rm -rf $repo
@@ -169,22 +169,22 @@ $repo/test_wavs/8k.wav
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.onnx \
-  $repo/decoder-epoch-99-avg-1.onnx \
-  $repo/joiner-epoch-99-avg-1.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.int8.onnx \
-  $repo/decoder-epoch-99-avg-1.int8.onnx \
-  $repo/joiner-epoch-99-avg-1.int8.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.int8.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.int8.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.int8.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 # Decode a URL
@@ -233,22 +233,22 @@ $repo/test_wavs/2.wav
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.onnx \
-  $repo/decoder-epoch-99-avg-1.onnx \
-  $repo/joiner-epoch-99-avg-1.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 for wave in ${waves[@]}; do
   time $EXE \
-  $repo/tokens.txt \
-  $repo/encoder-epoch-99-avg-1.int8.onnx \
-  $repo/decoder-epoch-99-avg-1.int8.onnx \
-  $repo/joiner-epoch-99-avg-1.int8.onnx \
-  $wave \
-  2
+  --tokens=$repo/tokens.txt \
+  --encoder=$repo/encoder-epoch-99-avg-1.int8.onnx \
+  --decoder=$repo/decoder-epoch-99-avg-1.int8.onnx \
+  --joiner=$repo/joiner-epoch-99-avg-1.int8.onnx \
+  --num-threads=2 \
+  $wave
 done
 
 rm -rf $repo

+include(cargs)
+
 include_directories(${CMAKE_SOURCE_DIR})
 add_executable(decode-file-c-api decode-file-c-api.c)
-target_link_libraries(decode-file-c-api sherpa-onnx-c-api)
+target_link_libraries(decode-file-c-api sherpa-onnx-c-api cargs)

@@ -5,50 +5,85 @@
 // This file shows how to use sherpa-onnx C API
 // to decode a file.
 
+#include "cargs.h"
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
 
 #include "sherpa-onnx/c-api/c-api.h"
 
+static struct cag_option options[] = {
+  {
+    .identifier = 't',
+    .access_letters = NULL,
+    .access_name = "tokens",
+    .value_name = "tokens",
+    .description = "Tokens file"
+  }, {
+    .identifier = 'e',
+    .access_letters = NULL,
+    .access_name = "encoder",
+    .value_name = "encoder",
+    .description = "Encoder ONNX file"
+  }, {
+    .identifier = 'd',
+    .access_letters = NULL,
+    .access_name = "decoder",
+    .value_name = "decoder",
+    .description = "Decoder ONNX file"
+  }, {
+    .identifier = 'j',
+    .access_letters = NULL,
+    .access_name = "joiner",
+    .value_name = "joiner",
+    .description = "Joiner ONNX file"
+  }, {
+    .identifier = 'n',
+    .access_letters = NULL,
+    .access_name = "num-threads",
+    .value_name = "num-threads",
+    .description = "Number of threads"
+  }, {
+    .identifier = 'p',
+    .access_letters = NULL,
+    .access_name = "provider",
+    .value_name = "provider",
+    .description = "Provider: cpu (default), cuda, coreml"
+  }, {
+    .identifier = 'm',
+    .access_letters = NULL,
+    .access_name = "decoding-method",
+    .value_name = "decoding-method",
+    .description = 
+      "Decoding method: greedy_search (default), modified_beam_search"
+  }
+};
+
 const char *kUsage =
     "\n"
     "Usage:\n "
     "  ./bin/decode-file-c-api \\\n"
-    "    /path/to/tokens.txt \\\n"
-    "    /path/to/encoder.onnx \\\n"
-    "    /path/to/decoder.onnx \\\n"
-    "    /path/to/joiner.onnx \\\n"
-    "    /path/to/foo.wav [num_threads [decoding_method]]\n"
+    "    --tokens=/path/to/tokens.txt \\\n"
+    "    --encoder=/path/to/encoder.onnx \\\n"
+    "    --decoder=/path/to/decoder.onnx \\\n"
+    "    --joiner=/path/to/joiner.onnx \\\n"
+    "    /path/to/foo.wav\n"
     "\n\n"
     "Default num_threads is 1.\n"
     "Valid decoding_method: greedy_search (default), modified_beam_search\n\n"
+    "Valid provider: cpu (default), cuda, coreml\n\n"
     "Please refer to \n"
     "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html\n"
     "for a list of pre-trained models to download.\n";
 
 int32_t main(int32_t argc, char *argv[]) {
-  if (argc < 6 || argc > 8) {
-    fprintf(stderr, "%s\n", kUsage);
-    return -1;
-  }
   SherpaOnnxOnlineRecognizerConfig config;
-  config.model_config.tokens = argv[1];
-  config.model_config.encoder = argv[2];
-  config.model_config.decoder = argv[3];
-  config.model_config.joiner = argv[4];
-
-  int32_t num_threads = 1;
-  if (argc == 7 && atoi(argv[6]) > 0) {
-    num_threads = atoi(argv[6]);
-  }
-  config.model_config.num_threads = num_threads;
+
   config.model_config.debug = 0;
+  config.model_config.num_threads = 1;
+  config.model_config.provider = "cpu";
 
   config.decoding_method = "greedy_search";
-  if (argc == 8) {
-    config.decoding_method = argv[7];
-  }
 
   config.max_active_paths = 4;
 
@@ -60,13 +95,36 @@ int32_t main(int32_t argc, char *argv[]) {
   config.rule2_min_trailing_silence = 1.2;
   config.rule3_min_utterance_length = 300;
 
+  cag_option_context context;
+  char identifier;
+  const char *value;
+
+  cag_option_prepare(&context, options, CAG_ARRAY_SIZE(options), argc, argv);
+
+  while (cag_option_fetch(&context)) {
+    identifier = cag_option_get(&context);
+    value = cag_option_get_value(&context);
+    switch (identifier) {
+      case 't': config.model_config.tokens = value; break;
+      case 'e': config.model_config.encoder = value; break;
+      case 'd': config.model_config.decoder = value; break;
+      case 'j': config.model_config.joiner = value; break;
+      case 'n': config.model_config.num_threads = atoi(value); break;
+      case 'p': config.model_config.provider = value; break;
+      case 'm': config.decoding_method = value; break;
+      default: 
+        // do nothing as config already have valid default values
+        break;
+    }
+  }
+  
   SherpaOnnxOnlineRecognizer *recognizer = CreateOnlineRecognizer(&config);
   SherpaOnnxOnlineStream *stream = CreateOnlineStream(recognizer);
 
   SherpaOnnxDisplay *display = CreateDisplay(50);
   int32_t segment_id = 0;
 
-  const char *wav_filename = argv[5];
+  const char *wav_filename = argv[context.index];
   FILE *fp = fopen(wav_filename, "rb");
   if (!fp) {
     fprintf(stderr, "Failed to open %s\n", wav_filename);

+function(download_cargs)
+  include(FetchContent)
+
+  set(cargs_URL "https://github.com/likle/cargs/archive/refs/tags/v1.0.3.tar.gz")
+  set(cargs_HASH "SHA256=ddba25bd35e9c6c75bc706c126001b8ce8e084d40ef37050e6aa6963e836eb8b")
+
+  # If you don't have access to the Internet,
+  # please pre-download asio
+  set(possible_file_locations
+    $ENV{HOME}/Downloads/cargs-v1-0-3.tar.gz
+    ${PROJECT_SOURCE_DIR}/cargs-v1-0-3.tar.gz
+    ${PROJECT_BINARY_DIR}/cargs-v1-0-3.tar.gz
+    /tmp/cargs-v1-0-3.tar.gz
+    /star-fj/fangjun/download/github/cargs-v1-0-3.tar.gz
+  )
+
+  foreach(f IN LISTS possible_file_locations)
+    if(EXISTS ${f})
+      set(cargs_URL  "${f}")
+      file(TO_CMAKE_PATH "${cargs_URL}" cargs_URL)
+      break()
+    endif()
+  endforeach()
+
+  FetchContent_Declare(cargs URL ${cargs_URL} URL_HASH ${cargs_HASH})
+
+  FetchContent_GetProperties(cargs)
+  if(NOT cargs_POPULATED)
+    message(STATUS "Downloading cargs ${cargs_URL}")
+    FetchContent_Populate(cargs)
+  endif()
+  message(STATUS "cargs is downloaded to ${cargs_SOURCE_DIR}")
+  add_subdirectory(${cargs_SOURCE_DIR} ${cargs_BINARY_DIR} EXCLUDE_FROM_ALL)
+endfunction()
+
+download_cargs()

@@ -41,6 +41,7 @@ SherpaOnnxOnlineRecognizer *CreateOnlineRecognizer(
   recognizer_config.model_config.joiner_filename = config->model_config.joiner;
   recognizer_config.model_config.tokens = config->model_config.tokens;
   recognizer_config.model_config.num_threads = config->model_config.num_threads;
+  recognizer_config.model_config.provider = config->model_config.provider;
   recognizer_config.model_config.debug = config->model_config.debug;
 
   recognizer_config.decoding_method = config->decoding_method;

@@ -52,6 +52,7 @@ SHERPA_ONNX_API typedef struct SherpaOnnxOnlineTransducerModelConfig {
   const char *joiner;
   const char *tokens;
   int32_t num_threads;
+  const char *provider;
   int32_t debug;  // true to print debug information of the model
 } SherpaOnnxOnlineTransducerModelConfig;
 

@@ -17,6 +17,8 @@ void OnlineTransducerModelConfig::Register(ParseOptions *po) {
   po->Register("tokens", &tokens, "Path to tokens.txt");
   po->Register("num_threads", &num_threads,
                "Number of threads to run the neural network");
+  po->Register("provider", &provider,
+               "Specify a provider to use: cpu, cuda, coreml");
 
   po->Register("debug", &debug,
                "true to print model information while loading it.");
@@ -60,6 +62,7 @@ std::string OnlineTransducerModelConfig::ToString() const {
   os << "joiner_filename=\"" << joiner_filename << "\", ";
   os << "tokens=\"" << tokens << "\", ";
   os << "num_threads=" << num_threads << ", ";
+  os << "provider=\"" << provider << "\", ";
   os << "debug=" << (debug ? "True" : "False") << ")";
 
   return os.str();

@@ -69,17 +69,17 @@ for a list of pre-trained models to download.
   fprintf(stderr, "Creating recognizer ...\n");
   sherpa_onnx::OfflineRecognizer recognizer(config);
 
-  auto begin = std::chrono::steady_clock::now();
+  const auto begin = std::chrono::steady_clock::now();
   fprintf(stderr, "Started\n");
 
   std::vector<std::unique_ptr<sherpa_onnx::OfflineStream>> ss;
   std::vector<sherpa_onnx::OfflineStream *> ss_pointers;
   float duration = 0;
   for (int32_t i = 1; i <= po.NumArgs(); ++i) {
-    std::string wav_filename = po.GetArg(i);
+    const std::string wav_filename = po.GetArg(i);
     int32_t sampling_rate = -1;
     bool is_ok = false;
-    std::vector<float> samples =
+    const std::vector<float> samples =
         sherpa_onnx::ReadWave(wav_filename, &sampling_rate, &is_ok);
     if (!is_ok) {
       fprintf(stderr, "Failed to read %s\n", wav_filename.c_str());
@@ -96,7 +96,7 @@ for a list of pre-trained models to download.
 
   recognizer.DecodeStreams(ss_pointers.data(), ss_pointers.size());
 
-  auto end = std::chrono::steady_clock::now();
+  const auto end = std::chrono::steady_clock::now();
 
   fprintf(stderr, "Done!\n\n");
   for (int32_t i = 1; i <= po.NumArgs(); ++i) {

@@ -11,22 +11,28 @@
 #include "sherpa-onnx/csrc/online-recognizer.h"
 #include "sherpa-onnx/csrc/online-stream.h"
 #include "sherpa-onnx/csrc/symbol-table.h"
+#include "sherpa-onnx/csrc/parse-options.h"
 #include "sherpa-onnx/csrc/wave-reader.h"
 
-// TODO(fangjun): Use ParseOptions as we are getting more args
 int main(int32_t argc, char *argv[]) {
-  if (argc < 6 || argc > 9) {
-    const char *usage = R"usage(
+  const char *kUsageMessage = R"usage(
 Usage:
+
   ./bin/sherpa-onnx \
-    /path/to/tokens.txt \
-    /path/to/encoder.onnx \
-    /path/to/decoder.onnx \
-    /path/to/joiner.onnx \
-    /path/to/foo.wav [num_threads [decoding_method [/path/to/rnn_lm.onnx]]]
+    --tokens=/path/to/tokens.txt \
+    --encoder=/path/to/encoder.onnx \
+    --decoder=/path/to/decoder.onnx \
+    --joiner=/path/to/joiner.onnx \
+    --provider=cpu \
+    --num-threads=2 \
+    --decoding-method=greedy_search \
+    /path/to/foo.wav [bar.wav foobar.wav ...]
+
+Note: It supports decoding multiple files in batches
 
 Default value for num_threads is 2.
 Valid values for decoding_method: greedy_search (default), modified_beam_search.
+Valid values for provider: cpu (default), cuda, coreml.
 foo.wav should be of single channel, 16-bit PCM encoded wave file; its
 sampling rate can be arbitrary and does not need to be 16kHz.
 
@@ -34,33 +40,17 @@ Please refer to
 https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
 for a list of pre-trained models to download.
 )usage";
-    fprintf(stderr, "%s\n", usage);
-
-    return 0;
-  }
 
+  sherpa_onnx::ParseOptions po(kUsageMessage);
   sherpa_onnx::OnlineRecognizerConfig config;
 
-  config.model_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.Register(&po);
 
-  config.model_config.num_threads = 2;
-  if (argc == 7 && atoi(argv[6]) > 0) {
-    config.model_config.num_threads = atoi(argv[6]);
+  po.Read(argc, argv);
+  if (po.NumArgs() < 1) {
+    po.PrintUsage();
+    exit(EXIT_FAILURE);
   }
-  if (argc == 8) {
-    config.decoding_method = argv[7];
-  }
-  if (argc == 9) {
-    config.lm_config.model = argv[8];
-  }
-  config.max_active_paths = 4;
 
   fprintf(stderr, "%s\n", config.ToString().c_str());
 
@@ -71,10 +61,13 @@ for a list of pre-trained models to download.
 
   sherpa_onnx::OnlineRecognizer recognizer(config);
 
+  float duration = 0;
+  for (int32_t i = 1; i <= po.NumArgs(); ++i) {
+    const std::string wav_filename = po.GetArg(i);
     int32_t sampling_rate = -1;
 
     bool is_ok = false;
-  std::vector<float> samples =
+    const std::vector<float> samples =
         sherpa_onnx::ReadWave(wav_filename, &sampling_rate, &is_ok);
 
     if (!is_ok) {
@@ -83,20 +76,21 @@ for a list of pre-trained models to download.
     }
     fprintf(stderr, "sampling rate of input file: %d\n", sampling_rate);
 
-  float duration = samples.size() / static_cast<float>(sampling_rate);
+    const float duration = samples.size() / static_cast<float>(sampling_rate);
 
     fprintf(stderr, "wav filename: %s\n", wav_filename.c_str());
     fprintf(stderr, "wav duration (s): %.3f\n", duration);
 
-  auto begin = std::chrono::steady_clock::now();
     fprintf(stderr, "Started\n");
+    const auto begin = std::chrono::steady_clock::now();
 
     auto s = recognizer.CreateStream();
     s->AcceptWaveform(sampling_rate, samples.data(), samples.size());
 
     std::vector<float> tail_paddings(static_cast<int>(0.3 * sampling_rate));
     // Note: We can call AcceptWaveform() multiple times.
-  s->AcceptWaveform(sampling_rate, tail_paddings.data(), tail_paddings.size());
+    s->AcceptWaveform(
+      sampling_rate, tail_paddings.data(), tail_paddings.size());
 
     // Call InputFinished() to indicate that no audio samples are available
     s->InputFinished();
@@ -105,19 +99,17 @@ for a list of pre-trained models to download.
       recognizer.DecodeStream(s.get());
     }
 
-  std::string text = recognizer.GetResult(s.get()).AsJsonString();
-
-  fprintf(stderr, "Done!\n");
+    const std::string text = recognizer.GetResult(s.get()).AsJsonString();
 
-  fprintf(stderr, "Recognition result for %s:\n%s\n", wav_filename.c_str(),
-          text.c_str());
-
-  auto end = std::chrono::steady_clock::now();
-  float elapsed_seconds =
+    const auto end = std::chrono::steady_clock::now();
+    const float elapsed_seconds =
         std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
-          .count() /
-      1000.;
+            .count() / 1000.;
 
+    fprintf(stderr, "Done!\n");
+    fprintf(stderr,
+            "Recognition result for %s:\n%s\n",
+            wav_filename.c_str(), text.c_str());
     fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
     fprintf(stderr, "decoding method: %s\n", config.decoding_method.c_str());
     if (config.decoding_method == "modified_beam_search") {
@@ -125,9 +117,10 @@ for a list of pre-trained models to download.
     }
 
     fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
-  float rtf = elapsed_seconds / duration;
+    const float rtf = elapsed_seconds / duration;
     fprintf(stderr, "Real time factor (RTF): %.3f / %.3f = %.3f\n",
             elapsed_seconds, duration, rtf);
+  }
 
   return 0;
 }