Support GigaAM CTC models for Russian ASR (#1464)

See also https://github.com/salute-developers/GigaAM

@@ -16,6 +16,21 @@ echo "PATH: $PATH"
 which $EXE
 
 log "------------------------------------------------------------"
+log "Run NeMo GigaAM Russian models"
+log "------------------------------------------------------------"
+curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24.tar.bz2
+tar xvf sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24.tar.bz2
+rm sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24.tar.bz2
+
+$EXE \
+  --nemo-ctc-model=./sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24/model.int8.onnx \
+  --tokens=./sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24/tokens.txt \
+  --debug=1 \
+  ./sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24/test_wavs/example.wav
+
+rm -rf sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24
+
+log "------------------------------------------------------------"
 log "Run SenseVoice models"
 log "------------------------------------------------------------"
 curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17.tar.bz2

+name: export-nemo-giga-am-to-onnx
+
+on:
+  workflow_dispatch:
+
+concurrency:
+  group: export-nemo-giga-am-to-onnx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  export-nemo-am-giga-to-onnx:
+    if: github.repository_owner == 'k2-fsa' || github.repository_owner == 'csukuangfj'
+    name: export nemo GigaAM models to ONNX
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos-latest]
+        python-version: ["3.10"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Run CTC
+        shell: bash
+        run: |
+          pushd scripts/nemo/GigaAM
+          ./run-ctc.sh
+          popd
+
+          d=sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24
+          mkdir $d
+          mkdir $d/test_wavs
+          rm scripts/nemo/GigaAM/model.onnx
+          mv -v scripts/nemo/GigaAM/*.int8.onnx $d/
+          mv -v scripts/nemo/GigaAM/*.md $d/
+          mv -v scripts/nemo/GigaAM/*.pdf $d/
+          mv -v scripts/nemo/GigaAM/tokens.txt $d/
+          mv -v scripts/nemo/GigaAM/*.wav $d/test_wavs/
+          mv -v scripts/nemo/GigaAM/run-ctc.sh $d/
+          mv -v scripts/nemo/GigaAM/*-ctc.py $d/
+
+          ls -lh scripts/nemo/GigaAM/
+
+          ls -lh $d
+
+          tar cjvf ${d}.tar.bz2 $d
+
+      - name: Release
+        uses: svenstaro/upload-release-action@v2
+        with:
+          file_glob: true
+          file: ./*.tar.bz2
+          overwrite: true
+          repo_name: k2-fsa/sherpa-onnx
+          repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
+          tag: asr-models
+
+      - name: Publish to huggingface (CTC)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v3
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            d=sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24
+            export GIT_LFS_SKIP_SMUDGE=1
+            export GIT_CLONE_PROTECTION_ACTIVE=false
+            git clone https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/$d huggingface
+            mv -v $d/* ./huggingface
+            cd huggingface
+            git lfs track "*.onnx"
+            git lfs track "*.wav"
+            git status
+            git add .
+            git status
+            git commit -m "add models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/$d main

@@ -149,6 +149,16 @@ jobs:
           name: release-${{ matrix.build_type }}-with-shared-lib-${{ matrix.shared_lib }}-with-tts-${{ matrix.with_tts }}
           path: install/*
 
+      - name: Test offline CTC
+        shell: bash
+        run: |
+          du -h -d1 .
+          export PATH=$PWD/build/bin:$PATH
+          export EXE=sherpa-onnx-offline
+
+          .github/scripts/test-offline-ctc.sh
+          du -h -d1 .
+
       - name: Test C++ API
         shell: bash
         run: |
@@ -180,16 +190,6 @@ jobs:
           .github/scripts/test-offline-transducer.sh
           du -h -d1 .
 
-      - name: Test offline CTC
-        shell: bash
-        run: |
-          du -h -d1 .
-          export PATH=$PWD/build/bin:$PATH
-          export EXE=sherpa-onnx-offline
-
-          .github/scripts/test-offline-ctc.sh
-          du -h -d1 .
-
       - name: Test online punctuation
         shell: bash
         run: |

@@ -336,6 +336,24 @@ def get_models():
             popd
             """,
         ),
+        Model(
+            model_name="sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24",
+            idx=19,
+            lang="ru",
+            short_name="nemo_ctc_giga_am",
+            cmd="""
+            pushd $model_name
+
+            rm -rfv test_wavs
+
+            rm -fv *.sh
+            rm -fv *.py
+
+            ls -lh
+
+            popd
+            """,
+        ),
     ]
     return models
 

+# Introduction
+
+This folder contains scripts for converting models from
+https://github.com/salute-developers/GigaAM
+to sherpa-onnx.
+
+The ASR models are for Russian speech recognition in this folder.
+
+Please see the license of the models at
+https://github.com/salute-developers/GigaAM/blob/main/GigaAM%20License_NC.pdf

+#!/usr/bin/env python3
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+from typing import Dict
+
+import onnx
+import torch
+import torchaudio
+from nemo.collections.asr.models import EncDecCTCModel
+from nemo.collections.asr.modules.audio_preprocessing import (
+    AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor,
+)
+from nemo.collections.asr.parts.preprocessing.features import (
+    FilterbankFeaturesTA as NeMoFilterbankFeaturesTA,
+)
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+
+class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA):
+    def __init__(self, mel_scale: str = "htk", wkwargs=None, **kwargs):
+        if "window_size" in kwargs:
+            del kwargs["window_size"]
+        if "window_stride" in kwargs:
+            del kwargs["window_stride"]
+
+        super().__init__(**kwargs)
+
+        self._mel_spec_extractor: torchaudio.transforms.MelSpectrogram = (
+            torchaudio.transforms.MelSpectrogram(
+                sample_rate=self._sample_rate,
+                win_length=self.win_length,
+                hop_length=self.hop_length,
+                n_mels=kwargs["nfilt"],
+                window_fn=self.torch_windows[kwargs["window"]],
+                mel_scale=mel_scale,
+                norm=kwargs["mel_norm"],
+                n_fft=kwargs["n_fft"],
+                f_max=kwargs.get("highfreq", None),
+                f_min=kwargs.get("lowfreq", 0),
+                wkwargs=wkwargs,
+            )
+        )
+
+
+class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor):
+    def __init__(self, mel_scale: str = "htk", **kwargs):
+        super().__init__(**kwargs)
+        kwargs["nfilt"] = kwargs["features"]
+        del kwargs["features"]
+        self.featurizer = (
+            FilterbankFeaturesTA(  # Deprecated arguments; kept for config compatibility
+                mel_scale=mel_scale,
+                **kwargs,
+            )
+        )
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+
+    onnx.save(model, filename)
+
+
+def main():
+    model = EncDecCTCModel.from_config_file("./ctc_model_config.yaml")
+    ckpt = torch.load("./ctc_model_weights.ckpt", map_location="cpu")
+    model.load_state_dict(ckpt, strict=False)
+    model.eval()
+
+    with open("tokens.txt", "w", encoding="utf-8") as f:
+        for i, t in enumerate(model.cfg.labels):
+            f.write(f"{t} {i}\n")
+        f.write(f"<blk> {i+1}\n")
+
+    filename = "model.onnx"
+    model.export(filename)
+
+    meta_data = {
+        "vocab_size": len(model.cfg.labels) + 1,
+        "normalize_type": "",
+        "subsampling_factor": 4,
+        "model_type": "EncDecCTCModel",
+        "version": "1",
+        "model_author": "https://github.com/salute-developers/GigaAM",
+        "license": "https://github.com/salute-developers/GigaAM/blob/main/GigaAM%20License_NC.pdf",
+        "language": "Russian",
+        "is_giga_am": 1,
+    }
+    add_meta_data(filename, meta_data)
+
+    filename_int8 = "model.int8.onnx"
+    quantize_dynamic(
+        model_input=filename,
+        model_output=filename_int8,
+        weight_type=QuantType.QUInt8,
+    )
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env bash
+# Copyright    2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+set -ex
+
+function install_nemo() {
+  curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
+  python3 get-pip.py
+
+  pip install torch==2.4.0 torchaudio==2.4.0 -f https://download.pytorch.org/whl/torch_stable.html
+
+  pip install -qq wget text-unidecode matplotlib>=3.3.2 onnx onnxruntime pybind11 Cython einops kaldi-native-fbank soundfile librosa
+  pip install -qq ipython
+
+  # sudo apt-get install -q -y sox libsndfile1 ffmpeg python3-pip ipython
+
+  BRANCH='main'
+  python3 -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[asr]
+
+  pip install numpy==1.26.4
+}
+
+function download_files() {
+  curl -SL -O https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_weights.ckpt
+  curl -SL -O https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/ctc_model_config.yaml
+  curl -SL -O https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/example.wav
+  curl -SL -O https://n-ws-q0bez.s3pd12.sbercloud.ru/b-ws-q0bez-jpv/GigaAM/long_example.wav
+  curl -SL -O https://huggingface.co/csukuangfj/tmp-files/resolve/main/GigaAM%20License_NC.pdf
+}
+
+install_nemo
+download_files
+
+python3 ./export-onnx-ctc.py
+ls -lh
+python3 ./test-onnx-ctc.py

+#!/usr/bin/env python3
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+# https://github.com/salute-developers/GigaAM
+
+import kaldi_native_fbank as knf
+import librosa
+import numpy as np
+import onnxruntime as ort
+import soundfile as sf
+import torch
+
+
+def create_fbank():
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.remove_dc_offset = False
+    opts.frame_opts.preemph_coeff = 0
+    opts.frame_opts.window_type = "hann"
+
+    # Even though GigaAM uses 400 for fft, here we use 512
+    # since kaldi-native-fbank only support fft for power of 2.
+    opts.frame_opts.round_to_power_of_two = True
+
+    opts.mel_opts.low_freq = 0
+    opts.mel_opts.high_freq = 8000
+    opts.mel_opts.num_bins = 64
+
+    fbank = knf.OnlineFbank(opts)
+    return fbank
+
+
+def compute_features(audio, fbank) -> np.ndarray:
+    """
+    Args:
+      audio: (num_samples,), np.float32
+      fbank: the fbank extractor
+    Returns:
+      features: (num_frames, feat_dim), np.float32
+    """
+    assert len(audio.shape) == 1, audio.shape
+    fbank.accept_waveform(16000, audio)
+    ans = []
+    processed = 0
+    while processed < fbank.num_frames_ready:
+        ans.append(np.array(fbank.get_frame(processed)))
+        processed += 1
+    ans = np.stack(ans)
+    return ans
+
+
+def display(sess):
+    print("==========Input==========")
+    for i in sess.get_inputs():
+        print(i)
+    print("==========Output==========")
+    for i in sess.get_outputs():
+        print(i)
+
+
+"""
+==========Input==========
+NodeArg(name='audio_signal', type='tensor(float)', shape=['audio_signal_dynamic_axes_1', 64, 'audio_signal_dynamic_axes_2'])
+NodeArg(name='length', type='tensor(int64)', shape=['length_dynamic_axes_1'])
+==========Output==========
+NodeArg(name='logprobs', type='tensor(float)', shape=['logprobs_dynamic_axes_1', 'logprobs_dynamic_axes_2', 34])
+"""
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+        display(self.model)
+
+    def __call__(self, x: np.ndarray):
+        # x: (T, C)
+        x = torch.from_numpy(x)
+        x = x.t().unsqueeze(0)
+        # x: [1, C, T]
+        x_lens = torch.tensor([x.shape[-1]], dtype=torch.int64)
+
+        log_probs = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x.numpy(),
+                self.model.get_inputs()[1].name: x_lens.numpy(),
+            },
+        )[0]
+        # [batch_size, T, dim]
+        return log_probs
+
+
+def main():
+    filename = "./model.int8.onnx"
+    tokens = "./tokens.txt"
+    wav = "./example.wav"
+
+    model = OnnxModel(filename)
+
+    id2token = dict()
+    with open(tokens, encoding="utf-8") as f:
+        for line in f:
+            fields = line.split()
+            if len(fields) == 1:
+                id2token[int(fields[0])] = " "
+            else:
+                t, idx = fields
+                id2token[int(idx)] = t
+
+    fbank = create_fbank()
+    audio, sample_rate = sf.read(wav, dtype="float32", always_2d=True)
+    audio = audio[:, 0]  # only use the first channel
+    if sample_rate != 16000:
+        audio = librosa.resample(
+            audio,
+            orig_sr=sample_rate,
+            target_sr=16000,
+        )
+        sample_rate = 16000
+
+    features = compute_features(audio, fbank)
+    print("features.shape", features.shape)
+
+    blank = len(id2token) - 1
+    prev = -1
+    ans = []
+    log_probs = model(features)
+    print("log_probs", log_probs.shape)
+    log_probs = torch.from_numpy(log_probs)[0]
+    ids = torch.argmax(log_probs, dim=1).tolist()
+    for i in ids:
+        if i != blank and i != prev:
+            ans.append(i)
+        prev = i
+
+    tokens = [id2token[i] for i in ans]
+
+    text = "".join(tokens)
+    print(wav)
+    print(text)
+
+
+if __name__ == "__main__":
+    main()

@@ -193,6 +193,7 @@ class FeatureExtractor::Impl {
     opts_.frame_opts.frame_shift_ms = config_.frame_shift_ms;
     opts_.frame_opts.frame_length_ms = config_.frame_length_ms;
     opts_.frame_opts.remove_dc_offset = config_.remove_dc_offset;
+    opts_.frame_opts.preemph_coeff = config_.preemph_coeff;
     opts_.frame_opts.window_type = config_.window_type;
 
     opts_.mel_opts.num_bins = config_.feature_dim;
@@ -211,6 +212,7 @@ class FeatureExtractor::Impl {
     mfcc_opts_.frame_opts.frame_shift_ms = config_.frame_shift_ms;
     mfcc_opts_.frame_opts.frame_length_ms = config_.frame_length_ms;
     mfcc_opts_.frame_opts.remove_dc_offset = config_.remove_dc_offset;
+    mfcc_opts_.frame_opts.preemph_coeff = config_.preemph_coeff;
     mfcc_opts_.frame_opts.window_type = config_.window_type;
 
     mfcc_opts_.mel_opts.num_bins = config_.feature_dim;

@@ -57,6 +57,7 @@ struct FeatureExtractorConfig {
   float frame_length_ms = 25.0f;  // in milliseconds.
   bool is_librosa = false;
   bool remove_dc_offset = true;       // Subtract mean of wave before FFT.
+  float preemph_coeff = 0.97f;        // Preemphasis coefficient.
   std::string window_type = "povey";  // e.g. Hamming window
 
   // For models from NeMo

@@ -10,8 +10,8 @@
 
 #include "cppjieba/Jieba.hpp"
 #include "sherpa-onnx/csrc/file-utils.h"
-#include "sherpa-onnx/csrc/lexicon.h"
 #include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/symbol-table.h"
 #include "sherpa-onnx/csrc/text-utils.h"
 
 namespace sherpa_onnx {

@@ -21,6 +21,7 @@
 
 #include "sherpa-onnx/csrc/macros.h"
 #include "sherpa-onnx/csrc/onnx-utils.h"
+#include "sherpa-onnx/csrc/symbol-table.h"
 #include "sherpa-onnx/csrc/text-utils.h"
 
 namespace sherpa_onnx {
@@ -74,45 +75,6 @@ static std::vector<std::string> ProcessHeteronyms(
   return ans;
 }
 
-// Note: We don't use SymbolTable here since tokens may contain a blank
-// in the first column
-std::unordered_map<std::string, int32_t> ReadTokens(std::istream &is) {
-  std::unordered_map<std::string, int32_t> token2id;
-
-  std::string line;
-
-  std::string sym;
-  int32_t id = -1;
-  while (std::getline(is, line)) {
-    std::istringstream iss(line);
-    iss >> sym;
-    if (iss.eof()) {
-      id = atoi(sym.c_str());
-      sym = " ";
-    } else {
-      iss >> id;
-    }
-
-    // eat the trailing \r\n on windows
-    iss >> std::ws;
-    if (!iss.eof()) {
-      SHERPA_ONNX_LOGE("Error: %s", line.c_str());
-      exit(-1);
-    }
-
-#if 0
-    if (token2id.count(sym)) {
-      SHERPA_ONNX_LOGE("Duplicated token %s. Line %s. Existing ID: %d",
-                       sym.c_str(), line.c_str(), token2id.at(sym));
-      exit(-1);
-    }
-#endif
-    token2id.insert({std::move(sym), id});
-  }
-
-  return token2id;
-}
-
 std::vector<int32_t> ConvertTokensToIds(
     const std::unordered_map<std::string, int32_t> &token2id,
     const std::vector<std::string> &tokens) {

@@ -67,12 +67,6 @@ class Lexicon : public OfflineTtsFrontend {
   bool debug_ = false;
 };
 
-std::unordered_map<std::string, int32_t> ReadTokens(std::istream &is);
-
-std::vector<int32_t> ConvertTokensToIds(
-    const std::unordered_map<std::string, int32_t> &token2id,
-    const std::vector<std::string> &tokens);
-
 }  // namespace sherpa_onnx
 
 #endif  // SHERPA_ONNX_CSRC_LEXICON_H_

@@ -41,13 +41,13 @@
     auto value =                                                        \
         meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
     if (!value) {                                                       \
-      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);   \
+      SHERPA_ONNX_LOGE("'%s' does not exist in the metadata", src_key); \
       exit(-1);                                                         \
     }                                                                   \
                                                                         \
     dst = atoi(value.get());                                            \
     if (dst < 0) {                                                      \
-      SHERPA_ONNX_LOGE("Invalid value %d for %s", dst, src_key);        \
+      SHERPA_ONNX_LOGE("Invalid value %d for '%s'", dst, src_key);      \
       exit(-1);                                                         \
     }                                                                   \
   } while (0)
@@ -61,7 +61,7 @@
     } else {                                                                 \
       dst = atoi(value.get());                                               \
       if (dst < 0) {                                                         \
-        SHERPA_ONNX_LOGE("Invalid value %d for %s", dst, src_key);           \
+        SHERPA_ONNX_LOGE("Invalid value %d for '%s'", dst, src_key);         \
         exit(-1);                                                            \
       }                                                                      \
     }                                                                        \
@@ -73,13 +73,13 @@
     auto value =                                                             \
         meta_data.LookupCustomMetadataMapAllocated(src_key, allocator);      \
     if (!value) {                                                            \
-      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);    \
+      SHERPA_ONNX_LOGE("'%s' does not exist in the metadata", src_key);      \
       exit(-1);                                                              \
     }                                                                        \
                                                                              \
     bool ret = SplitStringToIntegers(value.get(), ",", true, &dst);          \
     if (!ret) {                                                              \
-      SHERPA_ONNX_LOGE("Invalid value %s for %s", value.get(), src_key); \
+      SHERPA_ONNX_LOGE("Invalid value '%s' for '%s'", value.get(), src_key); \
       exit(-1);                                                              \
     }                                                                        \
   } while (0)
@@ -96,7 +96,7 @@
                                                                              \
     bool ret = SplitStringToFloats(value.get(), ",", true, &dst);            \
     if (!ret) {                                                              \
-      SHERPA_ONNX_LOGE("Invalid value %s for %s", value.get(), src_key); \
+      SHERPA_ONNX_LOGE("Invalid value '%s' for '%s'", value.get(), src_key); \
       exit(-1);                                                              \
     }                                                                        \
   } while (0)
@@ -107,14 +107,14 @@
     auto value =                                                        \
         meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
     if (!value) {                                                       \
-      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);         \
+      SHERPA_ONNX_LOGE("'%s' does not exist in the metadata", src_key); \
       exit(-1);                                                         \
     }                                                                   \
     SplitStringToVector(value.get(), ",", false, &dst);                 \
                                                                         \
     if (dst.empty()) {                                                  \
-      SHERPA_ONNX_LOGE("Invalid value %s for %s. Empty vector!", value.get(), \
-                       src_key);                                              \
+      SHERPA_ONNX_LOGE("Invalid value '%s' for '%s'. Empty vector!",    \
+                       value.get(), src_key);                           \
       exit(-1);                                                         \
     }                                                                   \
   } while (0)
@@ -125,14 +125,14 @@
     auto value =                                                        \
         meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
     if (!value) {                                                       \
-      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);         \
+      SHERPA_ONNX_LOGE("'%s' does not exist in the metadata", src_key); \
       exit(-1);                                                         \
     }                                                                   \
     SplitStringToVector(value.get(), sep, false, &dst);                 \
                                                                         \
     if (dst.empty()) {                                                  \
-      SHERPA_ONNX_LOGE("Invalid value %s for %s. Empty vector!", value.get(), \
-                       src_key);                                              \
+      SHERPA_ONNX_LOGE("Invalid value '%s' for '%s'. Empty vector!",    \
+                       value.get(), src_key);                           \
       exit(-1);                                                         \
     }                                                                   \
   } while (0)
@@ -143,17 +143,29 @@
     auto value =                                                        \
         meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
     if (!value) {                                                       \
-      SHERPA_ONNX_LOGE("%s does not exist in the metadata", src_key);   \
+      SHERPA_ONNX_LOGE("'%s' does not exist in the metadata", src_key); \
       exit(-1);                                                         \
     }                                                                   \
                                                                         \
     dst = value.get();                                                  \
     if (dst.empty()) {                                                  \
-      SHERPA_ONNX_LOGE("Invalid value for %s\n", src_key);              \
+      SHERPA_ONNX_LOGE("Invalid value for '%s'\n", src_key);            \
       exit(-1);                                                         \
     }                                                                   \
   } while (0)
 
+#define SHERPA_ONNX_READ_META_DATA_STR_ALLOW_EMPTY(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);                                                         \
+    }                                                                   \
+                                                                        \
+    dst = value.get();                                                  \
+  } while (0)
+
 #define SHERPA_ONNX_READ_META_DATA_STR_WITH_DEFAULT(dst, src_key,       \
                                                     default_value)      \
   do {                                                                  \
@@ -164,7 +176,7 @@
     } else {                                                            \
       dst = value.get();                                                \
       if (dst.empty()) {                                                \
-        SHERPA_ONNX_LOGE("Invalid value for %s\n", src_key);            \
+        SHERPA_ONNX_LOGE("Invalid value for '%s'\n", src_key);          \
         exit(-1);                                                       \
       }                                                                 \
     }                                                                   \

@@ -10,8 +10,8 @@
 
 #include "cppjieba/Jieba.hpp"
 #include "sherpa-onnx/csrc/file-utils.h"
-#include "sherpa-onnx/csrc/lexicon.h"
 #include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/symbol-table.h"
 #include "sherpa-onnx/csrc/text-utils.h"
 
 namespace sherpa_onnx {

@@ -21,6 +21,7 @@ namespace {
 
 enum class ModelType : std::uint8_t {
   kEncDecCTCModelBPE,
+  kEncDecCTCModel,
   kEncDecHybridRNNTCTCBPEModel,
   kTdnn,
   kZipformerCtc,
@@ -75,6 +76,8 @@ static ModelType GetModelType(char *model_data, size_t model_data_length,
 
   if (model_type.get() == std::string("EncDecCTCModelBPE")) {
     return ModelType::kEncDecCTCModelBPE;
+  } else if (model_type.get() == std::string("EncDecCTCModel")) {
+    return ModelType::kEncDecCTCModel;
   } else if (model_type.get() == std::string("EncDecHybridRNNTCTCBPEModel")) {
     return ModelType::kEncDecHybridRNNTCTCBPEModel;
   } else if (model_type.get() == std::string("tdnn")) {
@@ -121,22 +124,18 @@ std::unique_ptr<OfflineCtcModel> OfflineCtcModel::Create(
   switch (model_type) {
     case ModelType::kEncDecCTCModelBPE:
       return std::make_unique<OfflineNemoEncDecCtcModel>(config);
-      break;
+    case ModelType::kEncDecCTCModel:
+      return std::make_unique<OfflineNemoEncDecCtcModel>(config);
     case ModelType::kEncDecHybridRNNTCTCBPEModel:
       return std::make_unique<OfflineNemoEncDecHybridRNNTCTCBPEModel>(config);
-      break;
     case ModelType::kTdnn:
       return std::make_unique<OfflineTdnnCtcModel>(config);
-      break;
     case ModelType::kZipformerCtc:
       return std::make_unique<OfflineZipformerCtcModel>(config);
-      break;
     case ModelType::kWenetCtc:
       return std::make_unique<OfflineWenetCtcModel>(config);
-      break;
     case ModelType::kTeleSpeechCtc:
       return std::make_unique<OfflineTeleSpeechCtcModel>(config);
-      break;
     case ModelType::kUnknown:
       SHERPA_ONNX_LOGE("Unknown model type in offline CTC!");
       return nullptr;
@@ -177,23 +176,19 @@ std::unique_ptr<OfflineCtcModel> OfflineCtcModel::Create(
   switch (model_type) {
     case ModelType::kEncDecCTCModelBPE:
       return std::make_unique<OfflineNemoEncDecCtcModel>(mgr, config);
-      break;
+    case ModelType::kEncDecCTCModel:
+      return std::make_unique<OfflineNemoEncDecCtcModel>(mgr, config);
     case ModelType::kEncDecHybridRNNTCTCBPEModel:
       return std::make_unique<OfflineNemoEncDecHybridRNNTCTCBPEModel>(mgr,
                                                                       config);
-      break;
     case ModelType::kTdnn:
       return std::make_unique<OfflineTdnnCtcModel>(mgr, config);
-      break;
     case ModelType::kZipformerCtc:
       return std::make_unique<OfflineZipformerCtcModel>(mgr, config);
-      break;
     case ModelType::kWenetCtc:
       return std::make_unique<OfflineWenetCtcModel>(mgr, config);
-      break;
     case ModelType::kTeleSpeechCtc:
       return std::make_unique<OfflineTeleSpeechCtcModel>(mgr, config);
-      break;
     case ModelType::kUnknown:
       SHERPA_ONNX_LOGE("Unknown model type in offline CTC!");
       return nullptr;

@@ -66,6 +66,10 @@ class OfflineCtcModel {
 
   // Return true if the model supports batch size > 1
   virtual bool SupportBatchProcessing() const { return true; }
+
+  // return true for models from https://github.com/salute-developers/GigaAM
+  // return false otherwise
+  virtual bool IsGigaAM() const { return false; }
 };
 
 }  // namespace sherpa_onnx

@@ -72,6 +72,8 @@ class OfflineNemoEncDecCtcModel::Impl {
 
   std::string FeatureNormalizationMethod() const { return normalize_type_; }
 
+  bool IsGigaAM() const { return is_giga_am_; }
+
  private:
   void Init(void *model_data, size_t model_data_length) {
     sess_ = std::make_unique<Ort::Session>(env_, model_data, model_data_length,
@@ -92,7 +94,9 @@ class OfflineNemoEncDecCtcModel::Impl {
     Ort::AllocatorWithDefaultOptions allocator;  // used in the macro below
     SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
     SHERPA_ONNX_READ_META_DATA(subsampling_factor_, "subsampling_factor");
-    SHERPA_ONNX_READ_META_DATA_STR(normalize_type_, "normalize_type");
+    SHERPA_ONNX_READ_META_DATA_STR_ALLOW_EMPTY(normalize_type_,
+                                               "normalize_type");
+    SHERPA_ONNX_READ_META_DATA_WITH_DEFAULT(is_giga_am_, "is_giga_am", 0);
   }
 
  private:
@@ -112,6 +116,10 @@ class OfflineNemoEncDecCtcModel::Impl {
   int32_t vocab_size_ = 0;
   int32_t subsampling_factor_ = 0;
   std::string normalize_type_;
+
+  // it is 1 for models from
+  // https://github.com/salute-developers/GigaAM
+  int32_t is_giga_am_ = 0;
 };
 
 OfflineNemoEncDecCtcModel::OfflineNemoEncDecCtcModel(
@@ -146,4 +154,6 @@ std::string OfflineNemoEncDecCtcModel::FeatureNormalizationMethod() const {
   return impl_->FeatureNormalizationMethod();
 }
 
+bool OfflineNemoEncDecCtcModel::IsGigaAM() const { return impl_->IsGigaAM(); }
+
 }  // namespace sherpa_onnx

@@ -76,6 +76,8 @@ class OfflineNemoEncDecCtcModel : public OfflineCtcModel {
   // for details
   std::string FeatureNormalizationMethod() const override;
 
+  bool IsGigaAM() const override;
+
  private:
   class Impl;
   std::unique_ptr<Impl> impl_;

@@ -104,12 +104,21 @@ class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
     }
 
     if (!config_.model_config.nemo_ctc.model.empty()) {
+      if (model_->IsGigaAM()) {
+        config_.feat_config.low_freq = 0;
+        config_.feat_config.high_freq = 8000;
+        config_.feat_config.remove_dc_offset = false;
+        config_.feat_config.preemph_coeff = 0;
+        config_.feat_config.window_type = "hann";
+        config_.feat_config.feature_dim = 64;
+      } else {
         config_.feat_config.low_freq = 0;
         config_.feat_config.high_freq = 0;
         config_.feat_config.is_librosa = true;
         config_.feat_config.remove_dc_offset = false;
         config_.feat_config.window_type = "hann";
       }
+    }
 
     if (!config_.model_config.wenet_ctc.model.empty()) {
       // WeNet CTC models assume input samples are in the range

@@ -172,7 +172,7 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
     return std::make_unique<OfflineRecognizerTransducerNeMoImpl>(config);
   }
 
-  if (model_type == "EncDecCTCModelBPE" ||
+  if (model_type == "EncDecCTCModelBPE" || model_type == "EncDecCTCModel" ||
       model_type == "EncDecHybridRNNTCTCBPEModel" || model_type == "tdnn" ||
       model_type == "zipformer2_ctc" || model_type == "wenet_ctc" ||
       model_type == "telespeech_ctc") {
@@ -189,6 +189,7 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
       " - Non-streaming transducer models from icefall\n"
       " - Non-streaming Paraformer models from FunASR\n"
       " - EncDecCTCModelBPE models from NeMo\n"
+      " - EncDecCTCModel models from NeMo\n"
       " - EncDecHybridRNNTCTCBPEModel models from NeMo\n"
       " - Whisper models\n"
       " - Tdnn models\n"
@@ -343,7 +344,7 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
     return std::make_unique<OfflineRecognizerTransducerNeMoImpl>(mgr, config);
   }
 
-  if (model_type == "EncDecCTCModelBPE" ||
+  if (model_type == "EncDecCTCModelBPE" || model_type == "EncDecCTCModel" ||
       model_type == "EncDecHybridRNNTCTCBPEModel" || model_type == "tdnn" ||
       model_type == "zipformer2_ctc" || model_type == "wenet_ctc" ||
       model_type == "telespeech_ctc") {
@@ -360,6 +361,7 @@ std::unique_ptr<OfflineRecognizerImpl> OfflineRecognizerImpl::Create(
       " - Non-streaming transducer models from icefall\n"
       " - Non-streaming Paraformer models from FunASR\n"
       " - EncDecCTCModelBPE models from NeMo\n"
+      " - EncDecCTCModel models from NeMo\n"
       " - EncDecHybridRNNTCTCBPEModel models from NeMo\n"
       " - Whisper models\n"
       " - Tdnn models\n"

@@ -7,6 +7,8 @@
 #include <cassert>
 #include <fstream>
 #include <sstream>
+#include <string>
+#include <utility>
 
 #if __ANDROID_API__ >= 9
 #include <strstream>
@@ -16,10 +18,54 @@
 #endif
 
 #include "sherpa-onnx/csrc/base64-decode.h"
+#include "sherpa-onnx/csrc/lexicon.h"
 #include "sherpa-onnx/csrc/onnx-utils.h"
 
 namespace sherpa_onnx {
 
+std::unordered_map<std::string, int32_t> ReadTokens(
+    std::istream &is,
+    std::unordered_map<int32_t, std::string> *id2token /*= nullptr*/) {
+  std::unordered_map<std::string, int32_t> token2id;
+
+  std::string line;
+
+  std::string sym;
+  int32_t id = -1;
+  while (std::getline(is, line)) {
+    std::istringstream iss(line);
+    iss >> sym;
+    if (iss.eof()) {
+      id = atoi(sym.c_str());
+      sym = " ";
+    } else {
+      iss >> id;
+    }
+
+    // eat the trailing \r\n on windows
+    iss >> std::ws;
+    if (!iss.eof()) {
+      SHERPA_ONNX_LOGE("Error: %s", line.c_str());
+      exit(-1);
+    }
+
+#if 0
+    if (token2id.count(sym)) {
+      SHERPA_ONNX_LOGE("Duplicated token %s. Line %s. Existing ID: %d",
+                       sym.c_str(), line.c_str(), token2id.at(sym));
+      exit(-1);
+    }
+#endif
+    if (id2token) {
+      id2token->insert({id, sym});
+    }
+
+    token2id.insert({std::move(sym), id});
+  }
+
+  return token2id;
+}
+
 SymbolTable::SymbolTable(const std::string &filename, bool is_file) {
   if (is_file) {
     std::ifstream is(filename);
@@ -39,25 +85,7 @@ SymbolTable::SymbolTable(AAssetManager *mgr, const std::string &filename) {
 }
 #endif
 
-void SymbolTable::Init(std::istream &is) {
-  std::string sym;
-  int32_t id = 0;
-  while (is >> sym >> id) {
-#if 0
-    // we disable the test here since for some multi-lingual BPE models
-    // from NeMo, the same symbol can appear multiple times with different IDs.
-    if (sym != " ") {
-      assert(sym2id_.count(sym) == 0);
-    }
-#endif
-
-    assert(id2sym_.count(id) == 0);
-
-    sym2id_.insert({sym, id});
-    id2sym_.insert({id, sym});
-  }
-  assert(is.eof());
-}
+void SymbolTable::Init(std::istream &is) { sym2id_ = ReadTokens(is, &id2sym_); }
 
 std::string SymbolTable::ToString() const {
   std::ostringstream os;

@@ -5,8 +5,10 @@
 #ifndef SHERPA_ONNX_CSRC_SYMBOL_TABLE_H_
 #define SHERPA_ONNX_CSRC_SYMBOL_TABLE_H_
 
+#include <istream>
 #include <string>
 #include <unordered_map>
+#include <vector>
 
 #if __ANDROID_API__ >= 9
 #include "android/asset_manager.h"
@@ -15,6 +17,16 @@
 
 namespace sherpa_onnx {
 
+// The same token can be mapped to different integer IDs, so
+// we need an id2token argument here.
+std::unordered_map<std::string, int32_t> ReadTokens(
+    std::istream &is,
+    std::unordered_map<int32_t, std::string> *id2token = nullptr);
+
+std::vector<int32_t> ConvertTokensToIds(
+    const std::unordered_map<std::string, int32_t> &token2id,
+    const std::vector<std::string> &tokens);
+
 /// It manages mapping between symbols and integer IDs.
 class SymbolTable {
  public:

@@ -394,6 +394,16 @@ fun getOfflineModelConfig(type: Int): OfflineModelConfig? {
                 modelType = "transducer",
             )
         }
+
+        19 -> {
+            val modelDir = "sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24"
+            return OfflineModelConfig(
+                nemo = OfflineNemoEncDecCtcModelConfig(
+                    model = "$modelDir/model.int8.onnx",
+                ),
+                tokens = "$modelDir/tokens.txt",
+            )
+        }
     }
     return null
 }