Support paraformer. (#95)

@@ -25,36 +25,59 @@ log "Download pretrained model and test-data from $repo_url"
 GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
 pushd $repo
 git lfs pull --include "*.onnx"
-cd test_wavs
 popd
 
-waves=(
-$repo/test_wavs/0.wav
-$repo/test_wavs/1.wav
-$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
-done
+time $EXE \
+  --tokens=$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 \
+  $repo/test_wavs/0.wav \
+  $repo/test_wavs/1.wav \
+  $repo/test_wavs/2.wav
 
 
 if command -v sox &> /dev/null; then
   echo "test 8kHz"
   sox $repo/test_wavs/0.wav -r 8000 8k.wav
+
   time $EXE \
-    $repo/tokens.txt \
-    $repo/encoder-epoch-99-avg-1.onnx \
-    $repo/decoder-epoch-99-avg-1.onnx \
-    $repo/joiner-epoch-99-avg-1.onnx \
-    8k.wav \
-    2
+    --tokens=$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 \
+    $repo/test_wavs/0.wav \
+    $repo/test_wavs/1.wav \
+    $repo/test_wavs/2.wav \
+    8k.wav
 fi
 
 rm -rf $repo
+
+log "------------------------------------------------------------"
+log "Run Paraformer (Chinese)"
+log "------------------------------------------------------------"
+
+repo_url=https://huggingface.co/csukuangfj/sherpa-onnx-paraformer-zh-2023-03-28
+log "Start testing ${repo_url}"
+repo=$(basename $repo_url)
+log "Download pretrained model and test-data from $repo_url"
+
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+pushd $repo
+git lfs pull --include "*.onnx"
+popd
+
+time $EXE \
+  --tokens=$repo/tokens.txt \
+  --paraformer=$repo/model.onnx \
+  --num-threads=2 \
+  --decoding-method=greedy_search \
+  $repo/test_wavs/0.wav \
+  $repo/test_wavs/1.wav \
+  $repo/test_wavs/2.wav \
+  $repo/test_wavs/8k.wav
+
+rm -rf $repo

@@ -71,7 +71,15 @@ jobs:
 
           ls -lh ./bin/Release/sherpa-onnx.exe
 
-      - name: Test sherpa-onnx for Windows x64
+      - name: Test offline transducer for Windows x64
+        shell: bash
+        run: |
+          export PATH=$PWD/build/bin/Release:$PATH
+          export EXE=sherpa-onnx-offline.exe
+
+          .github/scripts/test-offline-transducer.sh
+
+      - name: Test online transducer for Windows x64
         shell: bash
         run: |
           export PATH=$PWD/build/bin/Release:$PATH

@@ -71,7 +71,15 @@ jobs:
 
           ls -lh ./bin/Release/sherpa-onnx.exe
 
-      - name: Test sherpa-onnx for Windows x86
+      - name: Test offline transducer for Windows x86
+        shell: bash
+        run: |
+          export PATH=$PWD/build/bin/Release:$PATH
+          export EXE=sherpa-onnx-offline.exe
+
+          .github/scripts/test-offline-transducer.sh
+
+      - name: Test online transducer for Windows x86
         shell: bash
         run: |
           export PATH=$PWD/build/bin/Release:$PATH

@@ -41,3 +41,7 @@ android/SherpaOnnx/app/src/main/assets/
 *.ncnn.*
 run-sherpa-onnx-offline.sh
 sherpa-onnx-conformer-en-2023-03-18
+paraformer-onnxruntime-python-example
+run-sherpa-onnx-offline-paraformer.sh
+run-sherpa-onnx-offline-transducer.sh
+sherpa-onnx-paraformer-zh-2023-03-28

@@ -6,6 +6,10 @@ set(sources
   features.cc
   file-utils.cc
   hypothesis.cc
+  offline-model-config.cc
+  offline-paraformer-greedy-search-decoder.cc
+  offline-paraformer-model-config.cc
+  offline-paraformer-model.cc
   offline-recognizer-impl.cc
   offline-recognizer.cc
   offline-stream.cc

@@ -57,6 +57,23 @@
     }                                                                    \
   } while (0)
 
+// read a vector of floats
+#define SHERPA_ONNX_READ_META_DATA_VEC_FLOAT(dst, src_key)               \
+  do {                                                                   \
+    auto value =                                                         \
+        meta_data.LookupCustomMetadataMapAllocated(src_key, allocator);  \
+    if (!value) {                                                        \
+      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);    \
+      exit(-1);                                                          \
+    }                                                                    \
+                                                                         \
+    bool ret = SplitStringToFloats(value.get(), ",", true, &dst);        \
+    if (!ret) {                                                          \
+      SHERPA_ONNX_LOGE("Invalid value %s for %s", value.get(), src_key); \
+      exit(-1);                                                          \
+    }                                                                    \
+  } while (0)
+
 // Read a string
 #define SHERPA_ONNX_READ_META_DATA_STR(dst, src_key)                    \
   do {                                                                  \

+// sherpa-onnx/csrc/offline-model-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#include "sherpa-onnx/csrc/offline-model-config.h"
+
+#include <string>
+
+#include "sherpa-onnx/csrc/file-utils.h"
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+void OfflineModelConfig::Register(ParseOptions *po) {
+  transducer.Register(po);
+  paraformer.Register(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("debug", &debug,
+               "true to print model information while loading it.");
+}
+
+bool OfflineModelConfig::Validate() const {
+  if (num_threads < 1) {
+    SHERPA_ONNX_LOGE("num_threads should be > 0. Given %d", num_threads);
+    return false;
+  }
+
+  if (!FileExists(tokens)) {
+    SHERPA_ONNX_LOGE("%s does not exist", tokens.c_str());
+    return false;
+  }
+
+  if (!paraformer.model.empty()) {
+    return paraformer.Validate();
+  }
+
+  return transducer.Validate();
+}
+
+std::string OfflineModelConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "OfflineModelConfig(";
+  os << "transducer=" << transducer.ToString() << ", ";
+  os << "paraformer=" << paraformer.ToString() << ", ";
+  os << "tokens=\"" << tokens << "\", ";
+  os << "num_threads=" << num_threads << ", ";
+  os << "debug=" << (debug ? "True" : "False") << ")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/offline-model-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_MODEL_CONFIG_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_MODEL_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/offline-paraformer-model-config.h"
+#include "sherpa-onnx/csrc/offline-transducer-model-config.h"
+
+namespace sherpa_onnx {
+
+struct OfflineModelConfig {
+  OfflineTransducerModelConfig transducer;
+  OfflineParaformerModelConfig paraformer;
+
+  std::string tokens;
+  int32_t num_threads = 2;
+  bool debug = false;
+
+  OfflineModelConfig() = default;
+  OfflineModelConfig(const OfflineTransducerModelConfig &transducer,
+                     const OfflineParaformerModelConfig &paraformer,
+                     const std::string &tokens, int32_t num_threads, bool debug)
+      : transducer(transducer),
+        paraformer(paraformer),
+        tokens(tokens),
+        num_threads(num_threads),
+        debug(debug) {}
+
+  void Register(ParseOptions *po);
+  bool Validate() const;
+
+  std::string ToString() const;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_MODEL_CONFIG_H_

+// sherpa-onnx/csrc/offline-paraformer-decoder.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_DECODER_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_DECODER_H_
+
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+
+namespace sherpa_onnx {
+
+struct OfflineParaformerDecoderResult {
+  /// The decoded token IDs
+  std::vector<int64_t> tokens;
+};
+
+class OfflineParaformerDecoder {
+ public:
+  virtual ~OfflineParaformerDecoder() = default;
+
+  /** Run beam search given the output from the paraformer model.
+   *
+   * @param log_probs A 3-D tensor of shape (N, T, vocab_size)
+   * @param token_num A 2-D tensor of shape (N, T). Its dtype is int64_t.
+   *                  log_probs[i].argmax(axis=-1) equals to token_num[i]
+   *
+   * @return Return a vector of size `N` containing the decoded results.
+   */
+  virtual std::vector<OfflineParaformerDecoderResult> Decode(
+      Ort::Value log_probs, Ort::Value token_num) = 0;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_DECODER_H_

+// sherpa-onnx/csrc/offline-paraformer-greedy-search-decoder.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-paraformer-greedy-search-decoder.h"
+
+#include <vector>
+
+namespace sherpa_onnx {
+
+std::vector<OfflineParaformerDecoderResult>
+OfflineParaformerGreedySearchDecoder::Decode(Ort::Value /*log_probs*/,
+                                             Ort::Value token_num) {
+  std::vector<int64_t> shape = token_num.GetTensorTypeAndShapeInfo().GetShape();
+  int32_t batch_size = shape[0];
+  int32_t num_tokens = shape[1];
+
+  std::vector<OfflineParaformerDecoderResult> results(batch_size);
+
+  const int64_t *p = token_num.GetTensorData<int64_t>();
+  for (int32_t i = 0; i != batch_size; ++i) {
+    for (int32_t k = 0; k != num_tokens; ++k) {
+      if (p[k] == eos_id_) break;
+
+      results[i].tokens.push_back(p[k]);
+    }
+
+    p += num_tokens;
+  }
+
+  return results;
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/offline-paraformer-greedy-search-decoder.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_GREEDY_SEARCH_DECODER_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_GREEDY_SEARCH_DECODER_H_
+
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-paraformer-decoder.h"
+
+namespace sherpa_onnx {
+
+class OfflineParaformerGreedySearchDecoder : public OfflineParaformerDecoder {
+ public:
+  explicit OfflineParaformerGreedySearchDecoder(int32_t eos_id)
+      : eos_id_(eos_id) {}
+
+  std::vector<OfflineParaformerDecoderResult> Decode(
+      Ort::Value /*log_probs*/, Ort::Value token_num) override;
+
+ private:
+  int32_t eos_id_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_GREEDY_SEARCH_DECODER_H_

+// sherpa-onnx/csrc/offline-paraformer-model-config.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-paraformer-model-config.h"
+
+#include "sherpa-onnx/csrc/file-utils.h"
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+void OfflineParaformerModelConfig::Register(ParseOptions *po) {
+  po->Register("paraformer", &model, "Path to model.onnx of paraformer.");
+}
+
+bool OfflineParaformerModelConfig::Validate() const {
+  if (!FileExists(model)) {
+    SHERPA_ONNX_LOGE("%s does not exist", model.c_str());
+    return false;
+  }
+
+  return true;
+}
+
+std::string OfflineParaformerModelConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "OfflineParaformerModelConfig(";
+  os << "model=\"" << model << "\")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/offline-paraformer-model-config.h
+//
+// Copyright (c)  2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_MODEL_CONFIG_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_MODEL_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/parse-options.h"
+
+namespace sherpa_onnx {
+
+struct OfflineParaformerModelConfig {
+  std::string model;
+
+  OfflineParaformerModelConfig() = default;
+  explicit OfflineParaformerModelConfig(const std::string &model)
+      : model(model) {}
+
+  void Register(ParseOptions *po);
+  bool Validate() const;
+
+  std::string ToString() const;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_MODEL_CONFIG_H_

+// sherpa-onnx/csrc/offline-paraformer-model.cc
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-paraformer-model.h"
+
+#include <algorithm>
+#include <string>
+
+#include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
+#include "sherpa-onnx/csrc/text-utils.h"
+
+namespace sherpa_onnx {
+
+class OfflineParaformerModel::Impl {
+ public:
+  explicit Impl(const OfflineModelConfig &config)
+      : config_(config),
+        env_(ORT_LOGGING_LEVEL_ERROR),
+        sess_opts_{},
+        allocator_{} {
+    sess_opts_.SetIntraOpNumThreads(config_.num_threads);
+    sess_opts_.SetInterOpNumThreads(config_.num_threads);
+
+    Init();
+  }
+
+  std::pair<Ort::Value, Ort::Value> Forward(Ort::Value features,
+                                            Ort::Value features_length) {
+    std::array<Ort::Value, 2> inputs = {std::move(features),
+                                        std::move(features_length)};
+
+    auto out =
+        sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
+                   output_names_ptr_.data(), output_names_ptr_.size());
+
+    return {std::move(out[0]), std::move(out[1])};
+  }
+
+  int32_t VocabSize() const { return vocab_size_; }
+
+  int32_t LfrWindowSize() const { return lfr_window_size_; }
+
+  int32_t LfrWindowShift() const { return lfr_window_shift_; }
+
+  const std::vector<float> &NegativeMean() const { return neg_mean_; }
+
+  const std::vector<float> &InverseStdDev() const { return inv_stddev_; }
+
+  OrtAllocator *Allocator() const { return allocator_; }
+
+ private:
+  void Init() {
+    auto buf = ReadFile(config_.paraformer.model);
+
+    sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
+                                           sess_opts_);
+
+    GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
+
+    GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
+
+    // get meta data
+    Ort::ModelMetadata meta_data = sess_->GetModelMetadata();
+    if (config_.debug) {
+      std::ostringstream os;
+      PrintModelMetadata(os, meta_data);
+      SHERPA_ONNX_LOGE("%s\n", os.str().c_str());
+    }
+
+    Ort::AllocatorWithDefaultOptions allocator;  // used in the macro below
+    SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
+    SHERPA_ONNX_READ_META_DATA(lfr_window_size_, "lfr_window_size");
+    SHERPA_ONNX_READ_META_DATA(lfr_window_shift_, "lfr_window_shift");
+
+    SHERPA_ONNX_READ_META_DATA_VEC_FLOAT(neg_mean_, "neg_mean");
+    SHERPA_ONNX_READ_META_DATA_VEC_FLOAT(inv_stddev_, "inv_stddev");
+  }
+
+ private:
+  OfflineModelConfig config_;
+  Ort::Env env_;
+  Ort::SessionOptions sess_opts_;
+  Ort::AllocatorWithDefaultOptions allocator_;
+
+  std::unique_ptr<Ort::Session> sess_;
+
+  std::vector<std::string> input_names_;
+  std::vector<const char *> input_names_ptr_;
+
+  std::vector<std::string> output_names_;
+  std::vector<const char *> output_names_ptr_;
+
+  std::vector<float> neg_mean_;
+  std::vector<float> inv_stddev_;
+
+  int32_t vocab_size_ = 0;  // initialized in Init
+  int32_t lfr_window_size_ = 0;
+  int32_t lfr_window_shift_ = 0;
+};
+
+OfflineParaformerModel::OfflineParaformerModel(const OfflineModelConfig &config)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+OfflineParaformerModel::~OfflineParaformerModel() = default;
+
+std::pair<Ort::Value, Ort::Value> OfflineParaformerModel::Forward(
+    Ort::Value features, Ort::Value features_length) {
+  return impl_->Forward(std::move(features), std::move(features_length));
+}
+
+int32_t OfflineParaformerModel::VocabSize() const { return impl_->VocabSize(); }
+
+int32_t OfflineParaformerModel::LfrWindowSize() const {
+  return impl_->LfrWindowSize();
+}
+int32_t OfflineParaformerModel::LfrWindowShift() const {
+  return impl_->LfrWindowShift();
+}
+const std::vector<float> &OfflineParaformerModel::NegativeMean() const {
+  return impl_->NegativeMean();
+}
+const std::vector<float> &OfflineParaformerModel::InverseStdDev() const {
+  return impl_->InverseStdDev();
+}
+
+OrtAllocator *OfflineParaformerModel::Allocator() const {
+  return impl_->Allocator();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/offline-paraformer-model.h
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_MODEL_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_MODEL_H_
+
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/offline-model-config.h"
+
+namespace sherpa_onnx {
+
+class OfflineParaformerModel {
+ public:
+  explicit OfflineParaformerModel(const OfflineModelConfig &config);
+  ~OfflineParaformerModel();
+
+  /** Run the forward method of the model.
+   *
+   * @param features  A tensor of shape (N, T, C). It is changed in-place.
+   * @param features_length  A 1-D tensor of shape (N,) containing number of
+   *                         valid frames in `features` before padding.
+   *                         Its dtype is int32_t.
+   *
+   * @return Return a pair containing:
+   *  - log_probs: A 3-D tensor of shape (N, T', vocab_size)
+   *  - token_num: A 1-D tensor of shape (N, T') containing number
+   *               of valid tokens in each utterance. Its dtype is int64_t.
+   */
+  std::pair<Ort::Value, Ort::Value> Forward(Ort::Value features,
+                                            Ort::Value features_length);
+
+  /** Return the vocabulary size of the model
+   */
+  int32_t VocabSize() const;
+
+  /** It is lfr_m in config.yaml
+   */
+  int32_t LfrWindowSize() const;
+
+  /** It is lfr_n in config.yaml
+   */
+  int32_t LfrWindowShift() const;
+
+  /** Return negative mean for CMVN
+   */
+  const std::vector<float> &NegativeMean() const;
+
+  /** Return inverse stddev for CMVN
+   */
+  const std::vector<float> &InverseStdDev() const;
+
+  /** Return an allocator for allocating memory
+   */
+  OrtAllocator *Allocator() const;
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_PARAFORMER_MODEL_H_

@@ -8,6 +8,7 @@
 
 #include "onnxruntime_cxx_api.h"  // NOLINT
 #include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/offline-recognizer-paraformer-impl.h"
 #include "sherpa-onnx/csrc/offline-recognizer-transducer-impl.h"
 #include "sherpa-onnx/csrc/onnx-utils.h"
 #include "sherpa-onnx/csrc/text-utils.h"
@@ -16,10 +17,20 @@ namespace sherpa_onnx {
 
 std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
     const OfflineRecognizerConfig &config) {
-  Ort::Env env;
+  Ort::Env env(ORT_LOGGING_LEVEL_ERROR);
 
   Ort::SessionOptions sess_opts;
-  auto buf = ReadFile(config.model_config.encoder_filename);
+  std::string model_filename;
+  if (!config.model_config.transducer.encoder_filename.empty()) {
+    model_filename = config.model_config.transducer.encoder_filename;
+  } else if (!config.model_config.paraformer.model.empty()) {
+    model_filename = config.model_config.paraformer.model;
+  } else {
+    SHERPA_ONNX_LOGE("Please provide a model");
+    exit(-1);
+  }
+
+  auto buf = ReadFile(model_filename);
 
   auto encoder_sess =
       std::make_unique<Ort::Session>(env, buf.data(), buf.size(), sess_opts);
@@ -35,7 +46,16 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
     return std::make_unique<OfflineRecognizerTransducerImpl>(config);
   }
 
-  SHERPA_ONNX_LOGE("Unsupported model_type: %s\n", model_type.c_str());
+  if (model_type == "paraformer") {
+    return std::make_unique<OfflineRecognizerParaformerImpl>(config);
+  }
+
+  SHERPA_ONNX_LOGE(
+      "\nUnsupported model_type: %s\n"
+      "We support only the following model types at present: \n"
+      " - transducer models from icefall\n"
+      " - Paraformer models from FunASR\n",
+      model_type.c_str());
 
   exit(-1);
 }

+// sherpa-onnx/csrc/offline-recognizer-paraformer-impl.h
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_PARAFORMER_IMPL_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_PARAFORMER_IMPL_H_
+
+#include <algorithm>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-model-config.h"
+#include "sherpa-onnx/csrc/offline-paraformer-decoder.h"
+#include "sherpa-onnx/csrc/offline-paraformer-greedy-search-decoder.h"
+#include "sherpa-onnx/csrc/offline-paraformer-model.h"
+#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
+#include "sherpa-onnx/csrc/offline-recognizer.h"
+#include "sherpa-onnx/csrc/pad-sequence.h"
+#include "sherpa-onnx/csrc/symbol-table.h"
+
+namespace sherpa_onnx {
+
+static OfflineRecognitionResult Convert(
+    const OfflineParaformerDecoderResult &src, const SymbolTable &sym_table) {
+  OfflineRecognitionResult r;
+  r.tokens.reserve(src.tokens.size());
+
+  std::string text;
+  for (auto i : src.tokens) {
+    auto sym = sym_table[i];
+    text.append(sym);
+
+    r.tokens.push_back(std::move(sym));
+  }
+  r.text = std::move(text);
+
+  return r;
+}
+
+class OfflineRecognizerParaformerImpl : public OfflineRecognizerImpl {
+ public:
+  explicit OfflineRecognizerParaformerImpl(
+      const OfflineRecognizerConfig &config)
+      : config_(config),
+        symbol_table_(config_.model_config.tokens),
+        model_(std::make_unique<OfflineParaformerModel>(config.model_config)) {
+    if (config.decoding_method == "greedy_search") {
+      int32_t eos_id = symbol_table_["</s>"];
+      decoder_ = std::make_unique<OfflineParaformerGreedySearchDecoder>(eos_id);
+    } else {
+      SHERPA_ONNX_LOGE("Only greedy_search is supported at present. Given %s",
+                       config.decoding_method.c_str());
+      exit(-1);
+    }
+
+    // Paraformer models assume input samples are in the range
+    // [-32768, 32767], so we set normalize_samples to false
+    config_.feat_config.normalize_samples = false;
+  }
+
+  std::unique_ptr<OfflineStream> CreateStream() const override {
+    return std::make_unique<OfflineStream>(config_.feat_config);
+  }
+
+  void DecodeStreams(OfflineStream **ss, int32_t n) const override {
+    // 1. Apply LFR
+    // 2. Apply CMVN
+    //
+    // Please refer to
+    // https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45555.pdf
+    // for what LFR means
+    //
+    // "Lower Frame Rate Neural Network Acoustic Models"
+    auto memory_info =
+        Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
+
+    std::vector<Ort::Value> features;
+    features.reserve(n);
+
+    int32_t feat_dim =
+        config_.feat_config.feature_dim * model_->LfrWindowSize();
+
+    std::vector<std::vector<float>> features_vec(n);
+    std::vector<int32_t> features_length_vec(n);
+    for (int32_t i = 0; i != n; ++i) {
+      std::vector<float> f = ss[i]->GetFrames();
+
+      f = ApplyLFR(f);
+      ApplyCMVN(&f);
+
+      int32_t num_frames = f.size() / feat_dim;
+      features_vec[i] = std::move(f);
+
+      features_length_vec[i] = num_frames;
+
+      std::array<int64_t, 2> shape = {num_frames, feat_dim};
+
+      Ort::Value x = Ort::Value::CreateTensor(
+          memory_info, features_vec[i].data(), features_vec[i].size(),
+          shape.data(), shape.size());
+      features.push_back(std::move(x));
+    }
+
+    std::vector<const Ort::Value *> features_pointer(n);
+    for (int32_t i = 0; i != n; ++i) {
+      features_pointer[i] = &features[i];
+    }
+
+    std::array<int64_t, 1> features_length_shape = {n};
+    Ort::Value x_length = Ort::Value::CreateTensor(
+        memory_info, features_length_vec.data(), n,
+        features_length_shape.data(), features_length_shape.size());
+
+    // Caution(fangjun): We cannot pad it with log(eps),
+    // i.e., -23.025850929940457f
+    Ort::Value x = PadSequence(model_->Allocator(), features_pointer, 0);
+
+    auto t = model_->Forward(std::move(x), std::move(x_length));
+
+    auto results = decoder_->Decode(std::move(t.first), std::move(t.second));
+
+    for (int32_t i = 0; i != n; ++i) {
+      auto r = Convert(results[i], symbol_table_);
+      ss[i]->SetResult(r);
+    }
+  }
+
+ private:
+  std::vector<float> ApplyLFR(const std::vector<float> &in) const {
+    int32_t lfr_window_size = model_->LfrWindowSize();
+    int32_t lfr_window_shift = model_->LfrWindowShift();
+    int32_t in_feat_dim = config_.feat_config.feature_dim;
+
+    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;
+    int32_t out_feat_dim = in_feat_dim * lfr_window_size;
+
+    std::vector<float> out(out_num_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;
+  }
+
+  void ApplyCMVN(std::vector<float> *v) const {
+    const std::vector<float> &neg_mean = model_->NegativeMean();
+    const std::vector<float> &inv_stddev = model_->InverseStdDev();
+
+    int32_t dim = neg_mean.size();
+    int32_t num_frames = v->size() / dim;
+
+    float *p = v->data();
+
+    for (int32_t i = 0; i != num_frames; ++i) {
+      for (int32_t k = 0; k != dim; ++k) {
+        p[k] = (p[k] + neg_mean[k]) * inv_stddev[k];
+      }
+
+      p += dim;
+    }
+  }
+
+  OfflineRecognizerConfig config_;
+  SymbolTable symbol_table_;
+  std::unique_ptr<OfflineParaformerModel> model_;
+  std::unique_ptr<OfflineParaformerDecoder> decoder_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_PARAFORMER_IMPL_H_

 // sherpa-onnx/csrc/offline-recognizer-transducer-impl.h
 //
-// Copyright (c)  2022  Xiaomi Corporation
+// Copyright (c)  2022-2023  Xiaomi Corporation
 
 #ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_
 #define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_TRANSDUCER_IMPL_H_

@@ -6,6 +6,8 @@
 
 #include <memory>
 
+#include "sherpa-onnx/csrc/file-utils.h"
+#include "sherpa-onnx/csrc/macros.h"
 #include "sherpa-onnx/csrc/offline-recognizer-impl.h"
 
 namespace sherpa_onnx {

@@ -9,6 +9,7 @@
 #include <string>
 #include <vector>
 
+#include "sherpa-onnx/csrc/offline-model-config.h"
 #include "sherpa-onnx/csrc/offline-stream.h"
 #include "sherpa-onnx/csrc/offline-transducer-model-config.h"
 #include "sherpa-onnx/csrc/parse-options.h"
@@ -32,7 +33,7 @@ struct OfflineRecognitionResult {
 
 struct OfflineRecognizerConfig {
   OfflineFeatureExtractorConfig feat_config;
-  OfflineTransducerModelConfig model_config;
+  OfflineModelConfig model_config;
 
   std::string decoding_method = "greedy_search";
   // only greedy_search is implemented
@@ -40,7 +41,7 @@ struct OfflineRecognizerConfig {
 
   OfflineRecognizerConfig() = default;
   OfflineRecognizerConfig(const OfflineFeatureExtractorConfig &feat_config,
-                          const OfflineTransducerModelConfig &model_config,
+                          const OfflineModelConfig &model_config,
                           const std::string &decoding_method)
       : feat_config(feat_config),
         model_config(model_config),

@@ -38,7 +38,7 @@ std::string OfflineFeatureExtractorConfig::ToString() const {
 
 class OfflineStream::Impl {
  public:
-  explicit Impl(const OfflineFeatureExtractorConfig &config) {
+  explicit Impl(const OfflineFeatureExtractorConfig &config) : config_(config) {
     opts_.frame_opts.dither = 0;
     opts_.frame_opts.snip_edges = false;
     opts_.frame_opts.samp_freq = config.sampling_rate;
@@ -48,6 +48,19 @@ class OfflineStream::Impl {
   }
 
   void AcceptWaveform(int32_t sampling_rate, const float *waveform, int32_t n) {
+    if (config_.normalize_samples) {
+      AcceptWaveformImpl(sampling_rate, waveform, n);
+    } else {
+      std::vector<float> buf(n);
+      for (int32_t i = 0; i != n; ++i) {
+        buf[i] = waveform[i] * 32768;
+      }
+      AcceptWaveformImpl(sampling_rate, buf.data(), n);
+    }
+  }
+
+  void AcceptWaveformImpl(int32_t sampling_rate, const float *waveform,
+                          int32_t n) {
     if (sampling_rate != opts_.frame_opts.samp_freq) {
       SHERPA_ONNX_LOGE(
           "Creating a resampler:\n"
@@ -101,6 +114,7 @@ class OfflineStream::Impl {
   const OfflineRecognitionResult &GetResult() const { return r_; }
 
  private:
+  OfflineFeatureExtractorConfig config_;
   std::unique_ptr<knf::OnlineFbank> fbank_;
   knf::FbankOptions opts_;
   OfflineRecognitionResult r_;

@@ -23,6 +23,13 @@ struct OfflineFeatureExtractorConfig {
   // Feature dimension
   int32_t feature_dim = 80;
 
+  // Set internally by some models, e.g., paraformer
+  // This parameter is not exposed to users from the commandline
+  // If true, the feature extractor expects inputs to be normalized to
+  // the range [-1, 1].
+  // If false, we will multiply the inputs by 32768
+  bool normalize_samples = true;
+
   std::string ToString() const;
 
   void Register(ParseOptions *po);

@@ -14,20 +14,9 @@ void OfflineTransducerModelConfig::Register(ParseOptions *po) {
   po->Register("encoder", &encoder_filename, "Path to encoder.onnx");
   po->Register("decoder", &decoder_filename, "Path to decoder.onnx");
   po->Register("joiner", &joiner_filename, "Path to joiner.onnx");
-  po->Register("tokens", &tokens, "Path to tokens.txt");
-  po->Register("num_threads", &num_threads,
-               "Number of threads to run the neural network");
-
-  po->Register("debug", &debug,
-               "true to print model information while loading it.");
 }
 
 bool OfflineTransducerModelConfig::Validate() const {
-  if (!FileExists(tokens)) {
-    SHERPA_ONNX_LOGE("%s does not exist", tokens.c_str());
-    return false;
-  }
-
   if (!FileExists(encoder_filename)) {
     SHERPA_ONNX_LOGE("%s does not exist", encoder_filename.c_str());
     return false;
@@ -43,11 +32,6 @@ bool OfflineTransducerModelConfig::Validate() const {
     return false;
   }
 
-  if (num_threads < 1) {
-    SHERPA_ONNX_LOGE("num_threads should be > 0. Given %d", num_threads);
-    return false;
-  }
-
   return true;
 }
 
@@ -57,10 +41,7 @@ std::string OfflineTransducerModelConfig::ToString() const {
   os << "OfflineTransducerModelConfig(";
   os << "encoder_filename=\"" << encoder_filename << "\", ";
   os << "decoder_filename=\"" << decoder_filename << "\", ";
-  os << "joiner_filename=\"" << joiner_filename << "\", ";
-  os << "tokens=\"" << tokens << "\", ";
-  os << "num_threads=" << num_threads << ", ";
-  os << "debug=" << (debug ? "True" : "False") << ")";
+  os << "joiner_filename=\"" << joiner_filename << "\")";
 
   return os.str();
 }

@@ -14,22 +14,14 @@ struct OfflineTransducerModelConfig {
   std::string encoder_filename;
   std::string decoder_filename;
   std::string joiner_filename;
-  std::string tokens;
-  int32_t num_threads = 2;
-  bool debug = false;
 
   OfflineTransducerModelConfig() = default;
   OfflineTransducerModelConfig(const std::string &encoder_filename,
                                const std::string &decoder_filename,
-                               const std::string &joiner_filename,
-                               const std::string &tokens, int32_t num_threads,
-                               bool debug)
+                               const std::string &joiner_filename)
       : encoder_filename(encoder_filename),
         decoder_filename(decoder_filename),
-        joiner_filename(joiner_filename),
-        tokens(tokens),
-        num_threads(num_threads),
-        debug(debug) {}
+        joiner_filename(joiner_filename) {}
 
   void Register(ParseOptions *po);
   bool Validate() const;

@@ -16,7 +16,7 @@ namespace sherpa_onnx {
 
 class OfflineTransducerModel::Impl {
  public:
-  explicit Impl(const OfflineTransducerModelConfig &config)
+  explicit Impl(const OfflineModelConfig &config)
       : config_(config),
         env_(ORT_LOGGING_LEVEL_WARNING),
         sess_opts_{},
@@ -24,17 +24,17 @@ class OfflineTransducerModel::Impl {
     sess_opts_.SetIntraOpNumThreads(config.num_threads);
     sess_opts_.SetInterOpNumThreads(config.num_threads);
     {
-      auto buf = ReadFile(config.encoder_filename);
+      auto buf = ReadFile(config.transducer.encoder_filename);
       InitEncoder(buf.data(), buf.size());
     }
 
     {
-      auto buf = ReadFile(config.decoder_filename);
+      auto buf = ReadFile(config.transducer.decoder_filename);
       InitDecoder(buf.data(), buf.size());
     }
 
     {
-      auto buf = ReadFile(config.joiner_filename);
+      auto buf = ReadFile(config.transducer.joiner_filename);
       InitJoiner(buf.data(), buf.size());
     }
   }
@@ -164,7 +164,7 @@ class OfflineTransducerModel::Impl {
   }
 
  private:
-  OfflineTransducerModelConfig config_;
+  OfflineModelConfig config_;
   Ort::Env env_;
   Ort::SessionOptions sess_opts_;
   Ort::AllocatorWithDefaultOptions allocator_;
@@ -195,8 +195,7 @@ class OfflineTransducerModel::Impl {
   int32_t context_size_ = 0;  // initialized in InitDecoder
 };
 
-OfflineTransducerModel::OfflineTransducerModel(
-    const OfflineTransducerModelConfig &config)
+OfflineTransducerModel::OfflineTransducerModel(const OfflineModelConfig &config)
     : impl_(std::make_unique<Impl>(config)) {}
 
 OfflineTransducerModel::~OfflineTransducerModel() = default;

@@ -9,7 +9,7 @@
 #include <vector>
 
 #include "onnxruntime_cxx_api.h"  // NOLINT
-#include "sherpa-onnx/csrc/offline-transducer-model-config.h"
+#include "sherpa-onnx/csrc/offline-model-config.h"
 
 namespace sherpa_onnx {
 
@@ -17,7 +17,7 @@ struct OfflineTransducerDecoderResult;
 
 class OfflineTransducerModel {
  public:
-  explicit OfflineTransducerModel(const OfflineTransducerModelConfig &config);
+  explicit OfflineTransducerModel(const OfflineModelConfig &config);
   ~OfflineTransducerModel();
 
   /** Run the encoder.
@@ -25,6 +25,7 @@ class OfflineTransducerModel {
    * @param features  A tensor of shape (N, T, C). It is changed in-place.
    * @param features_length  A 1-D tensor of shape (N,) containing number of
    *                         valid frames in `features` before padding.
+   *                         Its dtype is int64_t.
    *
    * @return Return a pair containing:
    *  - encoder_out: A 3-D tensor of shape (N, T', encoder_dim)

@@ -5,6 +5,7 @@
 
 #include <algorithm>
 #include <fstream>
+#include <sstream>
 #include <string>
 #include <vector>
 
@@ -133,19 +134,24 @@ void Print1D(Ort::Value *v) {
   fprintf(stderr, "\n");
 }
 
+template <typename T /*= float*/>
 void Print2D(Ort::Value *v) {
   std::vector<int64_t> shape = v->GetTensorTypeAndShapeInfo().GetShape();
-  const float *d = v->GetTensorData<float>();
+  const T *d = v->GetTensorData<T>();
 
+  std::ostringstream os;
   for (int32_t r = 0; r != static_cast<int32_t>(shape[0]); ++r) {
     for (int32_t c = 0; c != static_cast<int32_t>(shape[1]); ++c, ++d) {
-      fprintf(stderr, "%.3f ", *d);
+      os << *d << " ";
     }
-    fprintf(stderr, "\n");
+    os << "\n";
   }
-  fprintf(stderr, "\n");
+  fprintf(stderr, "%s\n", os.str().c_str());
 }
 
+template void Print2D<int64_t>(Ort::Value *v);
+template void Print2D<float>(Ort::Value *v);
+
 void Print3D(Ort::Value *v) {
   std::vector<int64_t> shape = v->GetTensorTypeAndShapeInfo().GetShape();
   const float *d = v->GetTensorData<float>();

@@ -24,18 +24,6 @@
 
 namespace sherpa_onnx {
 
-#ifdef _MSC_VER
-// See
-// https://stackoverflow.com/questions/2573834/c-convert-string-or-char-to-wstring-or-wchar-t
-static std::wstring ToWide(const std::string &s) {
-  std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
-  return converter.from_bytes(s);
-}
-#define SHERPA_MAYBE_WIDE(s) ToWide(s)
-#else
-#define SHERPA_MAYBE_WIDE(s) s
-#endif
-
 /**
  * Get the input names of a model.
  *
@@ -79,6 +67,7 @@ Ort::Value Clone(OrtAllocator *allocator, const Ort::Value *v);
 void Print1D(Ort::Value *v);
 
 // Print a 2-D tensor to stderr
+template <typename T = float>
 void Print2D(Ort::Value *v);
 
 // Print a 3-D tensor to stderr

@@ -9,24 +9,35 @@
 #include <vector>
 
 #include "sherpa-onnx/csrc/offline-recognizer.h"
-#include "sherpa-onnx/csrc/offline-stream.h"
-#include "sherpa-onnx/csrc/offline-transducer-decoder.h"
-#include "sherpa-onnx/csrc/offline-transducer-greedy-search-decoder.h"
-#include "sherpa-onnx/csrc/offline-transducer-model.h"
-#include "sherpa-onnx/csrc/pad-sequence.h"
-#include "sherpa-onnx/csrc/symbol-table.h"
+#include "sherpa-onnx/csrc/parse-options.h"
 #include "sherpa-onnx/csrc/wave-reader.h"
 
 int main(int32_t argc, char *argv[]) {
-  if (argc < 6 || argc > 8) {
-    const char *usage = R"usage(
+  const char *kUsageMessage = R"usage(
 Usage:
+
+(1) Transducer from icefall
+
+  ./bin/sherpa-onnx-offline \
+    --tokens=/path/to/tokens.txt \
+    --encoder=/path/to/encoder.onnx \
+    --decoder=/path/to/decoder.onnx \
+    --joiner=/path/to/joiner.onnx \
+    --num-threads=2 \
+    --decoding-method=greedy_search \
+    /path/to/foo.wav [bar.wav foobar.wav ...]
+
+
+(2) Paraformer from FunASR
+
   ./bin/sherpa-onnx-offline \
-    /path/to/tokens.txt \
-    /path/to/encoder.onnx \
-    /path/to/decoder.onnx \
-    /path/to/joiner.onnx \
-    /path/to/foo.wav [num_threads [decoding_method]]
+    --tokens=/path/to/tokens.txt \
+    --paraformer=/path/to/model.onnx \
+    --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.
@@ -37,29 +48,15 @@ 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::OfflineRecognizerConfig config;
+  config.Register(&po);
 
-  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.model_config.num_threads = 2;
-  if (argc == 7 && atoi(argv[6]) > 0) {
-    config.model_config.num_threads = atoi(argv[6]);
-  }
-
-  if (argc == 8) {
-    config.decoding_method = argv[7];
+  po.Read(argc, argv);
+  if (po.NumArgs() < 1) {
+    po.PrintUsage();
+    exit(EXIT_FAILURE);
   }
 
   fprintf(stderr, "%s\n", config.ToString().c_str());
@@ -69,8 +66,17 @@ for a list of pre-trained models to download.
     return -1;
   }
 
-  int32_t sampling_rate = -1;
+  sherpa_onnx::OfflineRecognizer recognizer(config);
+
+  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);
+    int32_t sampling_rate = -1;
     bool is_ok = false;
     std::vector<float> samples =
         sherpa_onnx::ReadWave(wav_filename, &sampling_rate, &is_ok);
@@ -78,26 +84,25 @@ for a list of pre-trained models to download.
       fprintf(stderr, "Failed to read %s\n", wav_filename.c_str());
       return -1;
     }
-  fprintf(stderr, "sampling rate of input file: %d\n", sampling_rate);
-
-  float duration = samples.size() / static_cast<float>(sampling_rate);
+    duration += samples.size() / static_cast<float>(sampling_rate);
 
-  sherpa_onnx::OfflineRecognizer recognizer(config);
     auto s = recognizer.CreateStream();
-
-  auto begin = std::chrono::steady_clock::now();
-  fprintf(stderr, "Started\n");
-
     s->AcceptWaveform(sampling_rate, samples.data(), samples.size());
 
-  recognizer.DecodeStream(s.get());
-
-  fprintf(stderr, "Done!\n");
+    ss.push_back(std::move(s));
+    ss_pointers.push_back(ss.back().get());
+  }
 
-  fprintf(stderr, "Recognition result for %s:\n%s\n", wav_filename.c_str(),
-          s->GetResult().text.c_str());
+  recognizer.DecodeStreams(ss_pointers.data(), ss_pointers.size());
 
   auto end = std::chrono::steady_clock::now();
+
+  fprintf(stderr, "Done!\n\n");
+  for (int32_t i = 1; i <= po.NumArgs(); ++i) {
+    fprintf(stderr, "%s\n%s\n----\n", po.GetArg(i).c_str(),
+            ss[i - 1]->GetResult().text.c_str());
+  }
+
   float elapsed_seconds =
       std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
           .count() /