Add C++ support for streaming NeMo CTC models. (#857)

@@ -14,6 +14,28 @@ echo "PATH: $PATH"
 which $EXE
 
 log "------------------------------------------------------------"
+log "Run streaming NeMo CTC                                      "
+log "------------------------------------------------------------"
+
+url=https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms.tar.bz2
+name=$(basename $url)
+repo=$(basename -s .tar.bz2 $name)
+
+curl -SL -O $url
+tar xvf $name
+rm $name
+ls -lh $repo
+
+$EXE \
+  --nemo-ctc-model=$repo/model.onnx \
+  --tokens=$repo/tokens.txt \
+  $repo/test_wavs/0.wav \
+  $repo/test_wavs/1.wav \
+  $repo/test_wavs/8k.wav
+
+rm -rf $repo
+
+log "------------------------------------------------------------"
 log "Run streaming Zipformer2 CTC HLG decoding                   "
 log "------------------------------------------------------------"
 curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-zipformer-ctc-small-2024-03-18.tar.bz2

@@ -8,6 +8,19 @@ log() {
   echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
 }
 
+log "test online NeMo CTC"
+
+url=https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms.tar.bz2
+name=$(basename $url)
+repo=$(basename -s .tar.bz2 $name)
+
+curl -SL -O $url
+tar xvf $name
+rm $name
+ls -lh $repo
+python3 ./python-api-examples/online-nemo-ctc-decode-files.py
+rm -rf $repo
+
 log "test offline punctuation"
 
 curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/punctuation-models/sherpa-onnx-punct-ct-transformer-zh-en-vocab272727-2024-04-12.tar.bz2

@@ -128,6 +128,14 @@ jobs:
           name: release-${{ matrix.build_type }}-with-shared-lib-${{ matrix.shared_lib }}-with-tts-${{ matrix.with_tts }}
           path: install/*
 
+      - name: Test online CTC
+        shell: bash
+        run: |
+          export PATH=$PWD/build/bin:$PATH
+          export EXE=sherpa-onnx
+
+          .github/scripts/test-online-ctc.sh
+
       - name: Test offline transducer
         shell: bash
         run: |
@@ -163,14 +171,6 @@ jobs:
 
           .github/scripts/test-offline-ctc.sh
 
-      - name: Test online CTC
-        shell: bash
-        run: |
-          export PATH=$PWD/build/bin:$PATH
-          export EXE=sherpa-onnx
-
-          .github/scripts/test-online-ctc.sh
-
       - name: Test offline punctuation
         shell: bash
         run: |

+#!/usr/bin/env python3
+
+"""
+This file shows how to use a streaming CTC model from NeMo
+to decode files.
+
+Please download model files from
+https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
+
+
+The example model is converted from
+https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_streaming_80ms
+"""
+
+from pathlib import Path
+
+import numpy as np
+import sherpa_onnx
+import soundfile as sf
+
+
+def create_recognizer():
+    model = "./sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms/model.onnx"
+    tokens = "./sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms/tokens.txt"
+
+    test_wav = "./sherpa-onnx-nemo-streaming-fast-conformer-ctc-en-80ms/test_wavs/0.wav"
+
+    if not Path(model).is_file() or not Path(test_wav).is_file():
+        raise ValueError(
+            """Please download model files from
+            https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
+            """
+        )
+    return (
+        sherpa_onnx.OnlineRecognizer.from_nemo_ctc(
+            model=model,
+            tokens=tokens,
+            debug=True,
+        ),
+        test_wav,
+    )
+
+
+def main():
+    recognizer, wave_filename = create_recognizer()
+
+    audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
+    audio = audio[:, 0]  # only use the first channel
+
+    # audio is a 1-D float32 numpy array normalized to the range [-1, 1]
+    # sample_rate does not need to be 16000 Hz
+
+    stream = recognizer.create_stream()
+    stream.accept_waveform(sample_rate, audio)
+
+    tail_paddings = np.zeros(int(0.66 * sample_rate), dtype=np.float32)
+    stream.accept_waveform(sample_rate, tail_paddings)
+    stream.input_finished()
+
+    while recognizer.is_ready(stream):
+        recognizer.decode_stream(stream)
+    print(wave_filename)
+    print(recognizer.get_result_all(stream))
+
+
+if __name__ == "__main__":
+    main()

@@ -100,7 +100,7 @@ class OnnxModel:
             dtype=torch.float32,
         ).numpy()
 
-        self.cache_last_channel_len = torch.ones([1], dtype=torch.int64).numpy()
+        self.cache_last_channel_len = torch.zeros([1], dtype=torch.int64).numpy()
 
     def __call__(self, x: np.ndarray):
         # x: (T, C)

@@ -142,7 +142,7 @@ class OnnxModel:
             dtype=torch.float32,
         ).numpy()
 
-        self.cache_last_channel_len = torch.ones([1], dtype=torch.int64).numpy()
+        self.cache_last_channel_len = torch.zeros([1], dtype=torch.int64).numpy()
 
     def run_encoder(self, x: np.ndarray):
         # x: (T, C)

@@ -61,6 +61,8 @@ set(sources
   online-lm.cc
   online-lstm-transducer-model.cc
   online-model-config.cc
+  online-nemo-ctc-model-config.cc
+  online-nemo-ctc-model.cc
   online-paraformer-model-config.cc
   online-paraformer-model.cc
   online-recognizer-impl.cc

@@ -4,11 +4,12 @@
 #ifndef SHERPA_ONNX_CSRC_OFFLINE_PUNCTUATION_CT_TRANSFORMER_IMPL_H_
 #define SHERPA_ONNX_CSRC_OFFLINE_PUNCTUATION_CT_TRANSFORMER_IMPL_H_
 
+#include <math.h>
+
 #include <memory>
 #include <string>
 #include <utility>
 #include <vector>
-#include <math.h>
 
 #if __ANDROID_API__ >= 9
 #include "android/asset_manager.h"
@@ -61,7 +62,9 @@ class OfflinePunctuationCtTransformerImpl : public OfflinePunctuationImpl {
 
     int32_t segment_size = 20;
     int32_t max_len = 200;
-    int32_t num_segments = ceil(((float)token_ids.size() + segment_size - 1) / segment_size);
+    int32_t num_segments =
+        ceil((static_cast<float>(token_ids.size()) + segment_size - 1) /
+             segment_size);
 
     std::vector<int32_t> punctuations;
     int32_t last = -1;

@@ -10,6 +10,7 @@
 #include <string>
 
 #include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/online-nemo-ctc-model.h"
 #include "sherpa-onnx/csrc/online-wenet-ctc-model.h"
 #include "sherpa-onnx/csrc/online-zipformer2-ctc-model.h"
 #include "sherpa-onnx/csrc/onnx-utils.h"
@@ -22,6 +23,8 @@ std::unique_ptr<OnlineCtcModel> OnlineCtcModel::Create(
     return std::make_unique<OnlineWenetCtcModel>(config);
   } else if (!config.zipformer2_ctc.model.empty()) {
     return std::make_unique<OnlineZipformer2CtcModel>(config);
+  } else if (!config.nemo_ctc.model.empty()) {
+    return std::make_unique<OnlineNeMoCtcModel>(config);
   } else {
     SHERPA_ONNX_LOGE("Please specify a CTC model");
     exit(-1);
@@ -36,6 +39,8 @@ std::unique_ptr<OnlineCtcModel> OnlineCtcModel::Create(
     return std::make_unique<OnlineWenetCtcModel>(mgr, config);
   } else if (!config.zipformer2_ctc.model.empty()) {
     return std::make_unique<OnlineZipformer2CtcModel>(mgr, config);
+  } else if (!config.nemo_ctc.model.empty()) {
+    return std::make_unique<OnlineNeMoCtcModel>(mgr, config);
   } else {
     SHERPA_ONNX_LOGE("Please specify a CTC model");
     exit(-1);

@@ -15,6 +15,7 @@ void OnlineModelConfig::Register(ParseOptions *po) {
   paraformer.Register(po);
   wenet_ctc.Register(po);
   zipformer2_ctc.Register(po);
+  nemo_ctc.Register(po);
 
   po->Register("tokens", &tokens, "Path to tokens.txt");
 
@@ -31,11 +32,11 @@ void OnlineModelConfig::Register(ParseOptions *po) {
   po->Register("provider", &provider,
                "Specify a provider to use: cpu, cuda, coreml");
 
-  po->Register(
-      "model-type", &model_type,
-      "Specify it to reduce model initialization time. "
-      "Valid values are: conformer, lstm, zipformer, zipformer2, wenet_ctc"
-      "All other values lead to loading the model twice.");
+  po->Register("model-type", &model_type,
+               "Specify it to reduce model initialization time. "
+               "Valid values are: conformer, lstm, zipformer, zipformer2, "
+               "wenet_ctc, nemo_ctc. "
+               "All other values lead to loading the model twice.");
 }
 
 bool OnlineModelConfig::Validate() const {
@@ -61,6 +62,10 @@ bool OnlineModelConfig::Validate() const {
     return zipformer2_ctc.Validate();
   }
 
+  if (!nemo_ctc.model.empty()) {
+    return nemo_ctc.Validate();
+  }
+
   return transducer.Validate();
 }
 
@@ -72,6 +77,7 @@ std::string OnlineModelConfig::ToString() const {
   os << "paraformer=" << paraformer.ToString() << ", ";
   os << "wenet_ctc=" << wenet_ctc.ToString() << ", ";
   os << "zipformer2_ctc=" << zipformer2_ctc.ToString() << ", ";
+  os << "nemo_ctc=" << nemo_ctc.ToString() << ", ";
   os << "tokens=\"" << tokens << "\", ";
   os << "num_threads=" << num_threads << ", ";
   os << "warm_up=" << warm_up << ", ";

@@ -6,6 +6,7 @@
 
 #include <string>
 
+#include "sherpa-onnx/csrc/online-nemo-ctc-model-config.h"
 #include "sherpa-onnx/csrc/online-paraformer-model-config.h"
 #include "sherpa-onnx/csrc/online-transducer-model-config.h"
 #include "sherpa-onnx/csrc/online-wenet-ctc-model-config.h"
@@ -18,6 +19,7 @@ struct OnlineModelConfig {
   OnlineParaformerModelConfig paraformer;
   OnlineWenetCtcModelConfig wenet_ctc;
   OnlineZipformer2CtcModelConfig zipformer2_ctc;
+  OnlineNeMoCtcModelConfig nemo_ctc;
   std::string tokens;
   int32_t num_threads = 1;
   int32_t warm_up = 0;
@@ -30,6 +32,7 @@ struct OnlineModelConfig {
   //  - zipformer, zipformer transducer from icefall
   //  - zipformer2, zipformer2 transducer or CTC from icefall
   //  - wenet_ctc, wenet CTC model
+  //  - nemo_ctc, NeMo CTC model
   //
   // All other values are invalid and lead to loading the model twice.
   std::string model_type;
@@ -39,6 +42,7 @@ struct OnlineModelConfig {
                     const OnlineParaformerModelConfig &paraformer,
                     const OnlineWenetCtcModelConfig &wenet_ctc,
                     const OnlineZipformer2CtcModelConfig &zipformer2_ctc,
+                    const OnlineNeMoCtcModelConfig &nemo_ctc,
                     const std::string &tokens, int32_t num_threads,
                     int32_t warm_up, bool debug, const std::string &provider,
                     const std::string &model_type)
@@ -46,6 +50,7 @@ struct OnlineModelConfig {
         paraformer(paraformer),
         wenet_ctc(wenet_ctc),
         zipformer2_ctc(zipformer2_ctc),
+        nemo_ctc(nemo_ctc),
         tokens(tokens),
         num_threads(num_threads),
         warm_up(warm_up),

+// sherpa-onnx/csrc/online-nemo-ctc-model-config.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/online-nemo-ctc-model-config.h"
+
+#include "sherpa-onnx/csrc/file-utils.h"
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+void OnlineNeMoCtcModelConfig::Register(ParseOptions *po) {
+  po->Register("nemo-ctc-model", &model,
+               "Path to CTC model.onnx from NeMo. Please see "
+               "https://github.com/k2-fsa/sherpa-onnx/pull/843");
+}
+
+bool OnlineNeMoCtcModelConfig::Validate() const {
+  if (!FileExists(model)) {
+    SHERPA_ONNX_LOGE("NeMo CTC model '%s' does not exist", model.c_str());
+    return false;
+  }
+
+  return true;
+}
+
+std::string OnlineNeMoCtcModelConfig::ToString() const {
+  std::ostringstream os;
+
+  os << "OnlineNeMoCtcModelConfig(";
+  os << "model=\"" << model << "\")";
+
+  return os.str();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/online-nemo-ctc-model-config.h
+//
+// Copyright (c)  2024  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_ONLINE_NEMO_CTC_MODEL_CONFIG_H_
+#define SHERPA_ONNX_CSRC_ONLINE_NEMO_CTC_MODEL_CONFIG_H_
+
+#include <string>
+
+#include "sherpa-onnx/csrc/parse-options.h"
+
+namespace sherpa_onnx {
+
+struct OnlineNeMoCtcModelConfig {
+  std::string model;
+
+  OnlineNeMoCtcModelConfig() = default;
+
+  explicit OnlineNeMoCtcModelConfig(const std::string &model) : model(model) {}
+
+  void Register(ParseOptions *po);
+  bool Validate() const;
+
+  std::string ToString() const;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_ONLINE_NEMO_CTC_MODEL_CONFIG_H_

+// sherpa-onnx/csrc/online-nemo-ctc-model.cc
+//
+// Copyright (c)  2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/online-nemo-ctc-model.h"
+
+#include <algorithm>
+#include <cmath>
+#include <string>
+
+#if __ANDROID_API__ >= 9
+#include "android/asset_manager.h"
+#include "android/asset_manager_jni.h"
+#endif
+
+#include "sherpa-onnx/csrc/cat.h"
+#include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/onnx-utils.h"
+#include "sherpa-onnx/csrc/session.h"
+#include "sherpa-onnx/csrc/text-utils.h"
+#include "sherpa-onnx/csrc/transpose.h"
+#include "sherpa-onnx/csrc/unbind.h"
+
+namespace sherpa_onnx {
+
+class OnlineNeMoCtcModel::Impl {
+ public:
+  explicit Impl(const OnlineModelConfig &config)
+      : config_(config),
+        env_(ORT_LOGGING_LEVEL_ERROR),
+        sess_opts_(GetSessionOptions(config)),
+        allocator_{} {
+    {
+      auto buf = ReadFile(config.nemo_ctc.model);
+      Init(buf.data(), buf.size());
+    }
+  }
+
+#if __ANDROID_API__ >= 9
+  Impl(AAssetManager *mgr, const OnlineModelConfig &config)
+      : config_(config),
+        env_(ORT_LOGGING_LEVEL_WARNING),
+        sess_opts_(GetSessionOptions(config)),
+        allocator_{} {
+    {
+      auto buf = ReadFile(mgr, config.nemo_ctc.model);
+      Init(buf.data(), buf.size());
+    }
+  }
+#endif
+
+  std::vector<Ort::Value> Forward(Ort::Value x,
+                                  std::vector<Ort::Value> states) {
+    Ort::Value &cache_last_channel = states[0];
+    Ort::Value &cache_last_time = states[1];
+    Ort::Value &cache_last_channel_len = states[2];
+
+    int32_t batch_size = x.GetTensorTypeAndShapeInfo().GetShape()[0];
+
+    std::array<int64_t, 1> length_shape{batch_size};
+
+    Ort::Value length = Ort::Value::CreateTensor<int64_t>(
+        allocator_, length_shape.data(), length_shape.size());
+
+    int64_t *p_length = length.GetTensorMutableData<int64_t>();
+
+    std::fill(p_length, p_length + batch_size, ChunkLength());
+
+    // (B, T, C) -> (B, C, T)
+    x = Transpose12(allocator_, &x);
+
+    std::array<Ort::Value, 5> inputs = {
+        std::move(x), View(&length), std::move(cache_last_channel),
+        std::move(cache_last_time), std::move(cache_last_channel_len)};
+
+    auto out =
+        sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
+                   output_names_ptr_.data(), output_names_ptr_.size());
+    // out[0]: logit
+    // out[1] logit_length
+    // out[2:] states_next
+    //
+    // we need to remove out[1]
+
+    std::vector<Ort::Value> ans;
+    ans.reserve(out.size() - 1);
+
+    for (int32_t i = 0; i != out.size(); ++i) {
+      if (i == 1) {
+        continue;
+      }
+
+      ans.push_back(std::move(out[i]));
+    }
+
+    return ans;
+  }
+
+  int32_t VocabSize() const { return vocab_size_; }
+
+  int32_t ChunkLength() const { return window_size_; }
+
+  int32_t ChunkShift() const { return chunk_shift_; }
+
+  OrtAllocator *Allocator() const { return allocator_; }
+
+  // Return a vector containing 3 tensors
+  // - cache_last_channel
+  // - cache_last_time_
+  // - cache_last_channel_len
+  std::vector<Ort::Value> GetInitStates() {
+    std::vector<Ort::Value> ans;
+    ans.reserve(3);
+    ans.push_back(View(&cache_last_channel_));
+    ans.push_back(View(&cache_last_time_));
+    ans.push_back(View(&cache_last_channel_len_));
+
+    return ans;
+  }
+
+  std::vector<Ort::Value> StackStates(
+      std::vector<std::vector<Ort::Value>> states) const {
+    int32_t batch_size = static_cast<int32_t>(states.size());
+    if (batch_size == 1) {
+      return std::move(states[0]);
+    }
+
+    std::vector<Ort::Value> ans;
+
+    // stack cache_last_channel
+    std::vector<const Ort::Value *> buf(batch_size);
+
+    // there are 3 states to be stacked
+    for (int32_t i = 0; i != 3; ++i) {
+      buf.clear();
+      buf.reserve(batch_size);
+
+      for (int32_t b = 0; b != batch_size; ++b) {
+        assert(states[b].size() == 3);
+        buf.push_back(&states[b][i]);
+      }
+
+      Ort::Value c{nullptr};
+      if (i == 2) {
+        c = Cat<int64_t>(allocator_, buf, 0);
+      } else {
+        c = Cat(allocator_, buf, 0);
+      }
+
+      ans.push_back(std::move(c));
+    }
+
+    return ans;
+  }
+
+  std::vector<std::vector<Ort::Value>> UnStackStates(
+      std::vector<Ort::Value> states) const {
+    assert(states.size() == 3);
+
+    std::vector<std::vector<Ort::Value>> ans;
+
+    auto shape = states[0].GetTensorTypeAndShapeInfo().GetShape();
+    int32_t batch_size = shape[0];
+    ans.resize(batch_size);
+
+    if (batch_size == 1) {
+      ans[0] = std::move(states);
+      return ans;
+    }
+
+    for (int32_t i = 0; i != 3; ++i) {
+      std::vector<Ort::Value> v;
+      if (i == 2) {
+        v = Unbind<int64_t>(allocator_, &states[i], 0);
+      } else {
+        v = Unbind(allocator_, &states[i], 0);
+      }
+
+      assert(v.size() == batch_size);
+
+      for (int32_t b = 0; b != batch_size; ++b) {
+        ans[b].push_back(std::move(v[b]));
+      }
+    }
+
+    return ans;
+  }
+
+ private:
+  void Init(void *model_data, size_t model_data_length) {
+    sess_ = std::make_unique<Ort::Session>(env_, model_data, model_data_length,
+                                           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(window_size_, "window_size");
+    SHERPA_ONNX_READ_META_DATA(chunk_shift_, "chunk_shift");
+    SHERPA_ONNX_READ_META_DATA(subsampling_factor_, "subsampling_factor");
+    SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
+    SHERPA_ONNX_READ_META_DATA(cache_last_channel_dim1_,
+                               "cache_last_channel_dim1");
+    SHERPA_ONNX_READ_META_DATA(cache_last_channel_dim2_,
+                               "cache_last_channel_dim2");
+    SHERPA_ONNX_READ_META_DATA(cache_last_channel_dim3_,
+                               "cache_last_channel_dim3");
+    SHERPA_ONNX_READ_META_DATA(cache_last_time_dim1_, "cache_last_time_dim1");
+    SHERPA_ONNX_READ_META_DATA(cache_last_time_dim2_, "cache_last_time_dim2");
+    SHERPA_ONNX_READ_META_DATA(cache_last_time_dim3_, "cache_last_time_dim3");
+
+    // need to increase by 1 since the blank token is not included in computing
+    // vocab_size in NeMo.
+    vocab_size_ += 1;
+
+    InitStates();
+  }
+
+  void InitStates() {
+    std::array<int64_t, 4> cache_last_channel_shape{1, cache_last_channel_dim1_,
+                                                    cache_last_channel_dim2_,
+                                                    cache_last_channel_dim3_};
+
+    cache_last_channel_ = Ort::Value::CreateTensor<float>(
+        allocator_, cache_last_channel_shape.data(),
+        cache_last_channel_shape.size());
+
+    Fill<float>(&cache_last_channel_, 0);
+
+    std::array<int64_t, 4> cache_last_time_shape{
+        1, cache_last_time_dim1_, cache_last_time_dim2_, cache_last_time_dim3_};
+
+    cache_last_time_ = Ort::Value::CreateTensor<float>(
+        allocator_, cache_last_time_shape.data(), cache_last_time_shape.size());
+
+    Fill<float>(&cache_last_time_, 0);
+
+    int64_t shape = 1;
+    cache_last_channel_len_ =
+        Ort::Value::CreateTensor<int64_t>(allocator_, &shape, 1);
+
+    cache_last_channel_len_.GetTensorMutableData<int64_t>()[0] = 0;
+  }
+
+ private:
+  OnlineModelConfig 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_;
+
+  int32_t window_size_;
+  int32_t chunk_shift_;
+  int32_t subsampling_factor_;
+  int32_t vocab_size_;
+  int32_t cache_last_channel_dim1_;
+  int32_t cache_last_channel_dim2_;
+  int32_t cache_last_channel_dim3_;
+  int32_t cache_last_time_dim1_;
+  int32_t cache_last_time_dim2_;
+  int32_t cache_last_time_dim3_;
+
+  Ort::Value cache_last_channel_{nullptr};
+  Ort::Value cache_last_time_{nullptr};
+  Ort::Value cache_last_channel_len_{nullptr};
+};
+
+OnlineNeMoCtcModel::OnlineNeMoCtcModel(const OnlineModelConfig &config)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+#if __ANDROID_API__ >= 9
+OnlineNeMoCtcModel::OnlineNeMoCtcModel(AAssetManager *mgr,
+                                       const OnlineModelConfig &config)
+    : impl_(std::make_unique<Impl>(mgr, config)) {}
+#endif
+
+OnlineNeMoCtcModel::~OnlineNeMoCtcModel() = default;
+
+std::vector<Ort::Value> OnlineNeMoCtcModel::Forward(
+    Ort::Value x, std::vector<Ort::Value> states) const {
+  return impl_->Forward(std::move(x), std::move(states));
+}
+
+int32_t OnlineNeMoCtcModel::VocabSize() const { return impl_->VocabSize(); }
+
+int32_t OnlineNeMoCtcModel::ChunkLength() const { return impl_->ChunkLength(); }
+
+int32_t OnlineNeMoCtcModel::ChunkShift() const { return impl_->ChunkShift(); }
+
+OrtAllocator *OnlineNeMoCtcModel::Allocator() const {
+  return impl_->Allocator();
+}
+
+std::vector<Ort::Value> OnlineNeMoCtcModel::GetInitStates() const {
+  return impl_->GetInitStates();
+}
+
+std::vector<Ort::Value> OnlineNeMoCtcModel::StackStates(
+    std::vector<std::vector<Ort::Value>> states) const {
+  return impl_->StackStates(std::move(states));
+}
+
+std::vector<std::vector<Ort::Value>> OnlineNeMoCtcModel::UnStackStates(
+    std::vector<Ort::Value> states) const {
+  return impl_->UnStackStates(std::move(states));
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/online-nemo-ctc-model.h
+//
+// Copyright (c)  2024  Xiaomi Corporation
+#ifndef SHERPA_ONNX_CSRC_ONLINE_NEMO_CTC_MODEL_H_
+#define SHERPA_ONNX_CSRC_ONLINE_NEMO_CTC_MODEL_H_
+
+#include <memory>
+#include <utility>
+#include <vector>
+
+#if __ANDROID_API__ >= 9
+#include "android/asset_manager.h"
+#include "android/asset_manager_jni.h"
+#endif
+
+#include "onnxruntime_cxx_api.h"  // NOLINT
+#include "sherpa-onnx/csrc/online-ctc-model.h"
+#include "sherpa-onnx/csrc/online-model-config.h"
+
+namespace sherpa_onnx {
+
+class OnlineNeMoCtcModel : public OnlineCtcModel {
+ public:
+  explicit OnlineNeMoCtcModel(const OnlineModelConfig &config);
+
+#if __ANDROID_API__ >= 9
+  OnlineNeMoCtcModel(AAssetManager *mgr, const OnlineModelConfig &config);
+#endif
+
+  ~OnlineNeMoCtcModel() override;
+
+  // A list of 3 tensors:
+  //  - cache_last_channel
+  //  - cache_last_time
+  //  - cache_last_channel_len
+  std::vector<Ort::Value> GetInitStates() const override;
+
+  std::vector<Ort::Value> StackStates(
+      std::vector<std::vector<Ort::Value>> states) const override;
+
+  std::vector<std::vector<Ort::Value>> UnStackStates(
+      std::vector<Ort::Value> states) const override;
+
+  /**
+   *
+   * @param x A 3-D tensor of shape (N, T, C). N has to be 1.
+   * @param states  It is from GetInitStates() or returned from this method.
+   *
+   * @return Return a list of tensors
+   *    - ans[0] contains log_probs, of shape (N, T, C)
+   *    - ans[1:] contains next_states
+   */
+  std::vector<Ort::Value> Forward(
+      Ort::Value x, std::vector<Ort::Value> states) const override;
+
+  /** Return the vocabulary size of the model
+   */
+  int32_t VocabSize() const override;
+
+  /** Return an allocator for allocating memory
+   */
+  OrtAllocator *Allocator() const override;
+
+  // The model accepts this number of frames before subsampling as input
+  int32_t ChunkLength() const override;
+
+  // Similar to frame_shift in feature extractor, after processing
+  // ChunkLength() frames, we advance by ChunkShift() frames
+  // before we process the next chunk.
+  int32_t ChunkShift() const override;
+
+  bool SupportBatchProcessing() const override { return true; }
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_ONLINE_NEMO_CTC_MODEL_H_

@@ -21,7 +21,8 @@ std::unique_ptr<OnlineRecognizerImpl> OnlineRecognizerImpl::Create(
   }
 
   if (!config.model_config.wenet_ctc.model.empty() ||
-      !config.model_config.zipformer2_ctc.model.empty()) {
+      !config.model_config.zipformer2_ctc.model.empty() ||
+      !config.model_config.nemo_ctc.model.empty()) {
     return std::make_unique<OnlineRecognizerCtcImpl>(config);
   }
 
@@ -41,7 +42,8 @@ std::unique_ptr<OnlineRecognizerImpl> OnlineRecognizerImpl::Create(
   }
 
   if (!config.model_config.wenet_ctc.model.empty() ||
-      !config.model_config.zipformer2_ctc.model.empty()) {
+      !config.model_config.zipformer2_ctc.model.empty() ||
+      !config.model_config.nemo_ctc.model.empty()) {
     return std::make_unique<OnlineRecognizerCtcImpl>(mgr, config);
   }
 

@@ -23,6 +23,7 @@ set(srcs
   online-ctc-fst-decoder-config.cc
   online-lm-config.cc
   online-model-config.cc
+  online-nemo-ctc-model-config.cc
   online-paraformer-model-config.cc
   online-recognizer.cc
   online-stream.cc

@@ -9,6 +9,7 @@
 
 #include "sherpa-onnx/csrc/online-model-config.h"
 #include "sherpa-onnx/csrc/online-transducer-model-config.h"
+#include "sherpa-onnx/python/csrc/online-nemo-ctc-model-config.h"
 #include "sherpa-onnx/python/csrc/online-paraformer-model-config.h"
 #include "sherpa-onnx/python/csrc/online-transducer-model-config.h"
 #include "sherpa-onnx/python/csrc/online-wenet-ctc-model-config.h"
@@ -21,26 +22,30 @@ void PybindOnlineModelConfig(py::module *m) {
   PybindOnlineParaformerModelConfig(m);
   PybindOnlineWenetCtcModelConfig(m);
   PybindOnlineZipformer2CtcModelConfig(m);
+  PybindOnlineNeMoCtcModelConfig(m);
 
   using PyClass = OnlineModelConfig;
   py::class_<PyClass>(*m, "OnlineModelConfig")
       .def(py::init<const OnlineTransducerModelConfig &,
                     const OnlineParaformerModelConfig &,
                     const OnlineWenetCtcModelConfig &,
-                    const OnlineZipformer2CtcModelConfig &, const std::string &,
+                    const OnlineZipformer2CtcModelConfig &,
+                    const OnlineNeMoCtcModelConfig &, const std::string &,
                     int32_t, int32_t, bool, const std::string &,
                     const std::string &>(),
            py::arg("transducer") = OnlineTransducerModelConfig(),
            py::arg("paraformer") = OnlineParaformerModelConfig(),
            py::arg("wenet_ctc") = OnlineWenetCtcModelConfig(),
            py::arg("zipformer2_ctc") = OnlineZipformer2CtcModelConfig(),
-           py::arg("tokens"), py::arg("num_threads"), py::arg("warm_up") = 0,
+           py::arg("nemo_ctc") = OnlineNeMoCtcModelConfig(), py::arg("tokens"),
+           py::arg("num_threads"), py::arg("warm_up") = 0,
            py::arg("debug") = false, py::arg("provider") = "cpu",
            py::arg("model_type") = "")
       .def_readwrite("transducer", &PyClass::transducer)
       .def_readwrite("paraformer", &PyClass::paraformer)
       .def_readwrite("wenet_ctc", &PyClass::wenet_ctc)
       .def_readwrite("zipformer2_ctc", &PyClass::zipformer2_ctc)
+      .def_readwrite("nemo_ctc", &PyClass::nemo_ctc)
       .def_readwrite("tokens", &PyClass::tokens)
       .def_readwrite("num_threads", &PyClass::num_threads)
       .def_readwrite("debug", &PyClass::debug)

+// sherpa-onnx/python/csrc/online-nemo-ctc-model-config.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/online-nemo-ctc-model-config.h"
+
+#include <string>
+#include <vector>
+
+#include "sherpa-onnx/csrc/online-nemo-ctc-model-config.h"
+
+namespace sherpa_onnx {
+
+void PybindOnlineNeMoCtcModelConfig(py::module *m) {
+  using PyClass = OnlineNeMoCtcModelConfig;
+  py::class_<PyClass>(*m, "OnlineNeMoCtcModelConfig")
+      .def(py::init<const std::string &>(), py::arg("model"))
+      .def_readwrite("model", &PyClass::model)
+      .def("__str__", &PyClass::ToString);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/online-nemo-ctc-model-config.h
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_ONLINE_NEMO_CTC_MODEL_CONFIG_H_
+#define SHERPA_ONNX_PYTHON_CSRC_ONLINE_NEMO_CTC_MODEL_CONFIG_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOnlineNeMoCtcModelConfig(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_ONLINE_NEMO_CTC_MODEL_CONFIG_H_

@@ -42,6 +42,8 @@ static void PybindOnlineRecognizerResult(py::module *m) {
           "segment", [](PyClass &self) -> int32_t { return self.segment; })
       .def_property_readonly(
           "is_final", [](PyClass &self) -> bool { return self.is_final; })
+      .def("__str__", &PyClass::AsJsonString,
+           py::call_guard<py::gil_scoped_release>())
       .def("as_json_string", &PyClass::AsJsonString,
            py::call_guard<py::gil_scoped_release>());
 }
@@ -50,29 +52,17 @@ static void PybindOnlineRecognizerConfig(py::module *m) {
   using PyClass = OnlineRecognizerConfig;
   py::class_<PyClass>(*m, "OnlineRecognizerConfig")
       .def(
-          py::init<const FeatureExtractorConfig &,
-                   const OnlineModelConfig &,
-                   const OnlineLMConfig &,
-                   const EndpointConfig &,
-                   const OnlineCtcFstDecoderConfig &,
-                   bool,
-                   const std::string &,
-                   int32_t,
-                   const std::string &,
-                   float,
-                   float,
-                   float>(),
-          py::arg("feat_config"),
-          py::arg("model_config"),
+          py::init<const FeatureExtractorConfig &, const OnlineModelConfig &,
+                   const OnlineLMConfig &, const EndpointConfig &,
+                   const OnlineCtcFstDecoderConfig &, bool, const std::string &,
+                   int32_t, const std::string &, float, float, float>(),
+          py::arg("feat_config"), py::arg("model_config"),
           py::arg("lm_config") = OnlineLMConfig(),
           py::arg("endpoint_config") = EndpointConfig(),
           py::arg("ctc_fst_decoder_config") = OnlineCtcFstDecoderConfig(),
-          py::arg("enable_endpoint"),
-          py::arg("decoding_method"),
-          py::arg("max_active_paths") = 4,
-          py::arg("hotwords_file") = "",
-          py::arg("hotwords_score") = 0,
-          py::arg("blank_penalty") = 0.0,
+          py::arg("enable_endpoint"), py::arg("decoding_method"),
+          py::arg("max_active_paths") = 4, py::arg("hotwords_file") = "",
+          py::arg("hotwords_score") = 0, py::arg("blank_penalty") = 0.0,
           py::arg("temperature_scale") = 2.0)
       .def_readwrite("feat_config", &PyClass::feat_config)
       .def_readwrite("model_config", &PyClass::model_config)

@@ -12,9 +12,11 @@ from _sherpa_onnx import (
 from _sherpa_onnx import OnlineRecognizer as _Recognizer
 from _sherpa_onnx import (
     OnlineRecognizerConfig,
+    OnlineRecognizerResult,
     OnlineStream,
     OnlineTransducerModelConfig,
     OnlineWenetCtcModelConfig,
+    OnlineNeMoCtcModelConfig,
     OnlineZipformer2CtcModelConfig,
     OnlineCtcFstDecoderConfig,
 )
@@ -59,6 +61,7 @@ class OnlineRecognizer(object):
         lm: str = "",
         lm_scale: float = 0.1,
         temperature_scale: float = 2.0,
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -154,6 +157,7 @@ class OnlineRecognizer(object):
             num_threads=num_threads,
             provider=provider,
             model_type=model_type,
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -220,6 +224,7 @@ class OnlineRecognizer(object):
         rule3_min_utterance_length: float = 20.0,
         decoding_method: str = "greedy_search",
         provider: str = "cpu",
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -283,6 +288,7 @@ class OnlineRecognizer(object):
             num_threads=num_threads,
             provider=provider,
             model_type="paraformer",
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -324,6 +330,7 @@ class OnlineRecognizer(object):
         ctc_graph: str = "",
         ctc_max_active: int = 3000,
         provider: str = "cpu",
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -386,6 +393,7 @@ class OnlineRecognizer(object):
             tokens=tokens,
             num_threads=num_threads,
             provider=provider,
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -418,6 +426,106 @@ class OnlineRecognizer(object):
         return self
 
     @classmethod
+    def from_nemo_ctc(
+        cls,
+        tokens: str,
+        model: str,
+        num_threads: int = 2,
+        sample_rate: float = 16000,
+        feature_dim: int = 80,
+        enable_endpoint_detection: bool = False,
+        rule1_min_trailing_silence: float = 2.4,
+        rule2_min_trailing_silence: float = 1.2,
+        rule3_min_utterance_length: float = 20.0,
+        decoding_method: str = "greedy_search",
+        provider: str = "cpu",
+        debug: bool = False,
+    ):
+        """
+        Please refer to
+        `<https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models>`_
+        to download pre-trained models.
+
+        Args:
+          tokens:
+            Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
+            columns::
+
+                symbol integer_id
+
+          model:
+            Path to ``model.onnx``.
+          num_threads:
+            Number of threads for neural network computation.
+          sample_rate:
+            Sample rate of the training data used to train the model.
+          feature_dim:
+            Dimension of the feature used to train the model.
+          enable_endpoint_detection:
+            True to enable endpoint detection. False to disable endpoint
+            detection.
+          rule1_min_trailing_silence:
+            Used only when enable_endpoint_detection is True. If the duration
+            of trailing silence in seconds is larger than this value, we assume
+            an endpoint is detected.
+          rule2_min_trailing_silence:
+            Used only when enable_endpoint_detection is True. If we have decoded
+            something that is nonsilence and if the duration of trailing silence
+            in seconds is larger than this value, we assume an endpoint is
+            detected.
+          rule3_min_utterance_length:
+            Used only when enable_endpoint_detection is True. If the utterance
+            length in seconds is larger than this value, we assume an endpoint
+            is detected.
+          decoding_method:
+            The only valid value is greedy_search.
+          provider:
+            onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
+          debug:
+            True to show meta data in the model.
+        """
+        self = cls.__new__(cls)
+        _assert_file_exists(tokens)
+        _assert_file_exists(model)
+
+        assert num_threads > 0, num_threads
+
+        nemo_ctc_config = OnlineNeMoCtcModelConfig(
+            model=model,
+        )
+
+        model_config = OnlineModelConfig(
+            nemo_ctc=nemo_ctc_config,
+            tokens=tokens,
+            num_threads=num_threads,
+            provider=provider,
+            debug=debug,
+        )
+
+        feat_config = FeatureExtractorConfig(
+            sampling_rate=sample_rate,
+            feature_dim=feature_dim,
+        )
+
+        endpoint_config = EndpointConfig(
+            rule1_min_trailing_silence=rule1_min_trailing_silence,
+            rule2_min_trailing_silence=rule2_min_trailing_silence,
+            rule3_min_utterance_length=rule3_min_utterance_length,
+        )
+
+        recognizer_config = OnlineRecognizerConfig(
+            feat_config=feat_config,
+            model_config=model_config,
+            endpoint_config=endpoint_config,
+            enable_endpoint=enable_endpoint_detection,
+            decoding_method=decoding_method,
+        )
+
+        self.recognizer = _Recognizer(recognizer_config)
+        self.config = recognizer_config
+        return self
+
+    @classmethod
     def from_wenet_ctc(
         cls,
         tokens: str,
@@ -433,6 +541,7 @@ class OnlineRecognizer(object):
         rule3_min_utterance_length: float = 20.0,
         decoding_method: str = "greedy_search",
         provider: str = "cpu",
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -497,6 +606,7 @@ class OnlineRecognizer(object):
             tokens=tokens,
             num_threads=num_threads,
             provider=provider,
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -537,6 +647,9 @@ class OnlineRecognizer(object):
     def is_ready(self, s: OnlineStream) -> bool:
         return self.recognizer.is_ready(s)
 
+    def get_result_all(self, s: OnlineStream) -> OnlineRecognizerResult:
+        return self.recognizer.get_result(s)
+
     def get_result(self, s: OnlineStream) -> str:
         return self.recognizer.get_result(s).text.strip()