adding a python api for offline decode (#110)

+#!/usr/bin/env python3
+#
+# Copyright (c)  2023 by manyeyes
+
+"""
+This file demonstrates how to use sherpa-onnx Python API to transcribe
+file(s) with a non-streaming model.
+
+paraformer Usage:
+    ./python-api-examples/offline-decode-files.py  \
+      --tokens=/path/to/tokens.txt \
+      --paraformer=/path/to/paraformer.onnx \
+      --num-threads=2 \
+      --decoding-method=greedy_search \
+      --debug=false \
+      --sample-rate=16000 \
+      --feature-dim=80 \
+      /path/to/0.wav \
+      /path/to/1.wav
+
+transducer Usage:
+    ./python-api-examples/offline-decode-files.py  \
+      --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 \
+      --debug=false \
+      --sample-rate=16000 \
+      --feature-dim=80 \
+      /path/to/0.wav \
+      /path/to/1.wav
+
+Please refer to
+https://k2-fsa.github.io/sherpa/onnx/index.html
+to install sherpa-onnx and to download the pre-trained models
+used in this file.
+"""
+import argparse
+import time
+import wave
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+import sherpa_onnx
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="Path to tokens.txt",
+    )
+
+    parser.add_argument(
+        "--encoder",
+        default="",
+        type=str,
+        help="Path to the encoder model",
+    )
+
+    parser.add_argument(
+        "--decoder",
+        default="",
+        type=str,
+        help="Path to the decoder model",
+    )
+
+    parser.add_argument(
+        "--joiner",
+        default="",
+        type=str,
+        help="Path to the joiner model",
+    )
+
+    parser.add_argument(
+        "--paraformer",
+        default="",
+        type=str,
+        help="Path to the paraformer model",
+    )
+
+    parser.add_argument(
+        "--num-threads",
+        type=int,
+        default=1,
+        help="Number of threads for neural network computation",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="Valid values are greedy_search and modified_beam_search",
+    )
+    parser.add_argument(
+        "--debug",
+        type=bool,
+        default=False,
+        help="True to show debug messages",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="Sample rate of the feature extractor. Must match the one expected by the model. Note: The input sound files can have a different sample rate from this argument.",
+    )
+
+    parser.add_argument(
+        "--feature-dim",
+        type=int,
+        default=80,
+        help="Feature dimension. Must match the one expected by the model",
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to decode. Each file must be of WAVE"
+        "format with a single channel, and each sample has 16-bit, "
+        "i.e., int16_t. "
+        "The sample rate of the file can be arbitrary and does not need to "
+        "be 16 kHz",
+    )
+
+    return parser.parse_args()
+
+
+def assert_file_exists(filename: str):
+    assert Path(filename).is_file(), (
+        f"{filename} does not exist!\n"
+        "Please refer to "
+        "https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
+    )
+
+
+def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
+    """
+    Args:
+      wave_filename:
+        Path to a wave file. It should be single channel and each sample should
+        be 16-bit. Its sample rate does not need to be 16kHz.
+    Returns:
+      Return a tuple containing:
+       - A 1-D array of dtype np.float32 containing the samples, which are
+       normalized to the range [-1, 1].
+       - sample rate of the wave file
+    """
+
+    with wave.open(wave_filename) as f:
+        assert f.getnchannels() == 1, f.getnchannels()
+        assert f.getsampwidth() == 2, f.getsampwidth()  # it is in bytes
+        num_samples = f.getnframes()
+        samples = f.readframes(num_samples)
+        samples_int16 = np.frombuffer(samples, dtype=np.int16)
+        samples_float32 = samples_int16.astype(np.float32)
+
+        samples_float32 = samples_float32 / 32768
+        return samples_float32, f.getframerate()
+
+def main():
+    args = get_args()
+    assert_file_exists(args.tokens)
+    assert args.num_threads > 0, args.num_threads
+    if len(args.encoder) > 0:
+        assert_file_exists(args.encoder)
+        assert_file_exists(args.decoder)
+        assert_file_exists(args.joiner)
+        assert len(args.paraformer) == 0, args.paraformer
+        recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
+            encoder=args.encoder,
+            decoder=args.decoder,
+            joiner=args.joiner,
+            tokens=args.tokens,
+            num_threads=args.num_threads,
+            sample_rate=args.sample_rate,
+            feature_dim=args.feature_dim,
+            decoding_method=args.decoding_method,
+            debug=args.debug
+        )
+    else:
+        assert_file_exists(args.paraformer)
+        recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
+            paraformer=args.paraformer,
+            tokens=args.tokens,
+            num_threads=args.num_threads,
+            sample_rate=args.sample_rate,
+            feature_dim=args.feature_dim,
+            decoding_method=args.decoding_method,
+            debug=args.debug
+        )
+
+
+    print("Started!")
+    start_time = time.time()
+
+    streams = []
+    total_duration = 0
+    for wave_filename in args.sound_files:
+        assert_file_exists(wave_filename)
+        samples, sample_rate = read_wave(wave_filename)
+        duration = len(samples) / sample_rate
+        total_duration += duration
+
+        s = recognizer.create_stream()
+        s.accept_waveform(sample_rate, samples)
+
+        tail_paddings = np.zeros(int(0.2 * sample_rate), dtype=np.float32)
+        s.accept_waveform(sample_rate, tail_paddings)
+
+        streams.append(s)
+
+
+    recognizer.decode_streams(streams)
+    results = [s.result.text for s in streams]
+    end_time = time.time()
+    print("Done!")
+
+    for wave_filename, result in zip(args.sound_files, results):
+        print(f"{wave_filename}\n{result}")
+        print("-" * 10)
+
+    elapsed_seconds = end_time - start_time
+    rtf = elapsed_seconds / duration
+    print(f"num_threads: {args.num_threads}")
+    print(f"decoding_method: {args.decoding_method}")
+    print(f"Wave duration: {duration:.3f} s")
+    print(f"Elapsed time: {elapsed_seconds:.3f} s")
+    print(f"Real time factor (RTF): {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}")
+
+
+if __name__ == "__main__":
+    main()

@@ -16,20 +16,7 @@
 
 namespace sherpa_onnx {
 
-struct OfflineRecognitionResult {
-  // Recognition results.
-  // For English, it consists of space separated words.
-  // For Chinese, it consists of Chinese words without spaces.
-  std::string text;
-
-  // Decoded results at the token level.
-  // For instance, for BPE-based models it consists of a list of BPE tokens.
-  std::vector<std::string> tokens;
-
-  /// timestamps.size() == tokens.size()
-  /// timestamps[i] records the time in seconds when tokens[i] is decoded.
-  std::vector<float> timestamps;
-};
+struct OfflineRecognitionResult;
 
 struct OfflineRecognizerConfig {
   OfflineFeatureExtractorConfig feat_config;

@@ -13,7 +13,21 @@
 #include "sherpa-onnx/csrc/parse-options.h"
 
 namespace sherpa_onnx {
-struct OfflineRecognitionResult;
+
+struct OfflineRecognitionResult {
+  // Recognition results.
+  // For English, it consists of space separated words.
+  // For Chinese, it consists of Chinese words without spaces.
+  std::string text;
+
+  // Decoded results at the token level.
+  // For instance, for BPE-based models it consists of a list of BPE tokens.
+  std::vector<std::string> tokens;
+
+  /// timestamps.size() == tokens.size()
+  /// timestamps[i] records the time in seconds when tokens[i] is decoded.
+  std::vector<float> timestamps;
+};
 
 struct OfflineFeatureExtractorConfig {
   // Sampling rate used by the feature extractor. If it is different from

@@ -4,6 +4,11 @@ pybind11_add_module(_sherpa_onnx
   display.cc
   endpoint.cc
   features.cc
+  offline-model-config.cc
+  offline-paraformer-model-config.cc
+  offline-recognizer.cc
+  offline-stream.cc
+  offline-transducer-model-config.cc
   online-recognizer.cc
   online-stream.cc
   online-transducer-model-config.cc

+// sherpa-onnx/python/csrc/offline-model-config.cc
+//
+// Copyright (c)  2023 by manyeyes
+
+#include "sherpa-onnx/python/csrc/offline-model-config.h"
+
+#include <string>
+#include <vector>
+
+#include "sherpa-onnx/python/csrc/offline-transducer-model-config.h"
+#include "sherpa-onnx/python/csrc/offline-paraformer-model-config.h"
+
+#include "sherpa-onnx/csrc/offline-model-config.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineModelConfig(py::module *m) {
+  PybindOfflineTransducerModelConfig(m);
+  PybindOfflineParaformerModelConfig(m);
+
+  using PyClass = OfflineModelConfig;
+  py::class_<PyClass>(*m, "OfflineModelConfig")
+      .def(py::init<OfflineTransducerModelConfig &,
+              OfflineParaformerModelConfig &,
+              const std::string &, int32_t, bool>(),
+           py::arg("transducer"), py::arg("paraformer"), py::arg("tokens"),
+           py::arg("num_threads"), py::arg("debug") = false)
+      .def_readwrite("transducer", &PyClass::transducer)
+      .def_readwrite("paraformer", &PyClass::paraformer)
+      .def_readwrite("tokens", &PyClass::tokens)
+      .def_readwrite("num_threads", &PyClass::num_threads)
+      .def_readwrite("debug", &PyClass::debug)
+      .def("__str__", &PyClass::ToString);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/offline-model-config.h
+//
+// Copyright (c)  2023 by manyeyes
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_MODEL_CONFIG_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_MODEL_CONFIG_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineModelConfig(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_MODEL_CONFIG_H_

+// sherpa-onnx/python/csrc/offline-paraformer-model-config.cc
+//
+// Copyright (c)  2023 by manyeyes
+
+#include "sherpa-onnx/python/csrc/offline-paraformer-model-config.h"
+
+
+#include <string>
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-paraformer-model-config.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineParaformerModelConfig(py::module *m) {
+  using PyClass = OfflineParaformerModelConfig;
+  py::class_<PyClass>(*m, "OfflineParaformerModelConfig")
+      .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/offline-paraformer-model-config.h
+//
+// Copyright (c)  2023 by manyeyes
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_PARAFORMER_MODEL_CONFIG_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_PARAFORMER_MODEL_CONFIG_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineParaformerModelConfig(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_PARAFORMER_MODEL_CONFIG_H_

+// sherpa-onnx/python/csrc/offline-recognizer.cc
+//
+// Copyright (c)  2023 by manyeyes
+
+#include "sherpa-onnx/python/csrc/offline-recognizer.h"
+
+#include <string>
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-recognizer.h"
+
+namespace sherpa_onnx {
+
+
+
+static void PybindOfflineRecognizerConfig(py::module *m) {
+  using PyClass = OfflineRecognizerConfig;
+  py::class_<PyClass>(*m, "OfflineRecognizerConfig")
+      .def(py::init<const OfflineFeatureExtractorConfig &,
+                    const OfflineModelConfig &, const std::string &>(),
+           py::arg("feat_config"), py::arg("model_config"),
+           py::arg("decoding_method"))
+      .def_readwrite("feat_config", &PyClass::feat_config)
+      .def_readwrite("model_config", &PyClass::model_config)
+      .def_readwrite("decoding_method", &PyClass::decoding_method)
+      .def("__str__", &PyClass::ToString);
+}
+
+void PybindOfflineRecognizer(py::module *m) {
+  PybindOfflineRecognizerConfig(m);
+
+  using PyClass = OfflineRecognizer;
+  py::class_<PyClass>(*m, "OfflineRecognizer")
+      .def(py::init<const OfflineRecognizerConfig &>(), py::arg("config"))
+      .def("create_stream", &PyClass::CreateStream)
+      .def("decode_stream", &PyClass::DecodeStream)
+      .def("decode_streams",
+           [](PyClass &self, std::vector<OfflineStream *> ss) {
+             self.DecodeStreams(ss.data(), ss.size());
+           });
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/offline-recognizer.h
+//
+// Copyright (c)  2023 by manyeyes
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_RECOGNIZER_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_RECOGNIZER_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineRecognizer(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_RECOGNIZER_H_

+// sherpa-onnx/python/csrc/offline-stream.cc
+//
+// Copyright (c)  2023 by manyeyes
+
+#include "sherpa-onnx/python/csrc/offline-stream.h"
+
+#include "sherpa-onnx/csrc/offline-stream.h"
+
+namespace sherpa_onnx {
+
+constexpr const char *kAcceptWaveformUsage = R"(
+Process audio samples.
+
+Args:
+  sample_rate:
+    Sample rate of the input samples. If it is different from the one
+    expected by the model, we will do resampling inside.
+  waveform:
+    A 1-D float32 tensor containing audio samples. It must be normalized
+    to the range [-1, 1].
+)";
+
+static void PybindOfflineRecognitionResult(py::module *m) {  // NOLINT
+  using PyClass = OfflineRecognitionResult;
+  py::class_<PyClass>(*m, "OfflineRecognitionResult")
+      .def_property_readonly("text",
+                             [](const PyClass &self) { return self.text; })
+      .def_property_readonly("tokens",
+                             [](const PyClass &self) { return self.tokens; })
+      .def_property_readonly(
+          "timestamps", [](const PyClass &self) { return self.timestamps; });
+}
+
+
+static void PybindOfflineFeatureExtractorConfig(py::module *m) {
+  using PyClass = OfflineFeatureExtractorConfig;
+  py::class_<PyClass>(*m, "OfflineFeatureExtractorConfig")
+      .def(py::init<int32_t, int32_t>(), py::arg("sampling_rate") = 16000,
+           py::arg("feature_dim") = 80)
+      .def_readwrite("sampling_rate", &PyClass::sampling_rate)
+      .def_readwrite("feature_dim", &PyClass::feature_dim)
+      .def("__str__", &PyClass::ToString);
+}
+
+
+void PybindOfflineStream(py::module *m) {
+  PybindOfflineFeatureExtractorConfig(m);
+  PybindOfflineRecognitionResult(m);
+
+  using PyClass = OfflineStream;
+  py::class_<PyClass>(*m, "OfflineStream")
+      .def(
+          "accept_waveform",
+          [](PyClass &self, float sample_rate, py::array_t<float> waveform) {
+            self.AcceptWaveform(sample_rate, waveform.data(), waveform.size());
+          },
+          py::arg("sample_rate"), py::arg("waveform"), kAcceptWaveformUsage)
+        .def_property_readonly("result", &PyClass::GetResult);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/offline-stream.h
+//
+// Copyright (c)  2023 by manyeyes
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_STREAM_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_STREAM_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineStream(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_STREAM_H_

+// sherpa-onnx/python/csrc/offline-transducer-model-config.cc
+//
+// Copyright (c)  2023 by manyeyes
+
+#include "sherpa-onnx/python/csrc/offline-transducer-model-config.h"
+
+
+#include <string>
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-transducer-model-config.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineTransducerModelConfig(py::module *m) {
+  using PyClass = OfflineTransducerModelConfig;
+  py::class_<PyClass>(*m, "OfflineTransducerModelConfig")
+      .def(py::init<const std::string &, const std::string &,
+               const std::string &>(),
+           py::arg("encoder_filename"), py::arg("decoder_filename"),
+           py::arg("joiner_filename"))
+      .def_readwrite("encoder_filename", &PyClass::encoder_filename)
+      .def_readwrite("decoder_filename", &PyClass::decoder_filename)
+      .def_readwrite("joiner_filename", &PyClass::joiner_filename)
+      .def("__str__", &PyClass::ToString);
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/python/csrc/offline-transducer-model-config.h
+//
+// Copyright (c)  2023 by manyeyes
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_TRANSDUCER_MODEL_CONFIG_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_TRANSDUCER_MODEL_CONFIG_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineTransducerModelConfig(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_TRANSDUCER_MODEL_CONFIG_H_

@@ -11,10 +11,17 @@
 #include "sherpa-onnx/python/csrc/online-stream.h"
 #include "sherpa-onnx/python/csrc/online-transducer-model-config.h"
 
+#include "sherpa-onnx/python/csrc/offline-model-config.h"
+#include "sherpa-onnx/python/csrc/offline-paraformer-model-config.h"
+#include "sherpa-onnx/python/csrc/offline-recognizer.h"
+#include "sherpa-onnx/python/csrc/offline-stream.h"
+#include "sherpa-onnx/python/csrc/offline-transducer-model-config.h"
+
 namespace sherpa_onnx {
 
 PYBIND11_MODULE(_sherpa_onnx, m) {
   m.doc() = "pybind11 binding of sherpa-onnx";
+
   PybindFeatures(&m);
   PybindOnlineTransducerModelConfig(&m);
   PybindOnlineStream(&m);
@@ -22,6 +29,10 @@ PYBIND11_MODULE(_sherpa_onnx, m) {
   PybindOnlineRecognizer(&m);
 
   PybindDisplay(&m);
+
+  PybindOfflineStream(&m);
+  PybindOfflineModelConfig(&m);
+  PybindOfflineRecognizer(&m);
 }
 
 }  // namespace sherpa_onnx

 from _sherpa_onnx import Display
 
 from .online_recognizer import OnlineRecognizer
+from .offline_recognizer import OfflineRecognizer

+# Copyright (c)  2023 by manyeyes
+from pathlib import Path
+from typing import List
+
+from _sherpa_onnx import (
+    OfflineFeatureExtractorConfig,
+    OfflineRecognizer as _Recognizer,
+    OfflineRecognizerConfig,
+    OfflineStream,
+    OfflineModelConfig,
+    OfflineTransducerModelConfig,
+    OfflineParaformerModelConfig,
+)
+
+
+def _assert_file_exists(f: str):
+    assert Path(f).is_file(), f"{f} does not exist"
+
+
+class OfflineRecognizer(object):
+    """A class for offline speech recognition."""
+
+    @classmethod
+    def from_transducer(
+            cls,
+            encoder: str,
+            decoder: str,
+            joiner: str,
+            tokens: str,
+            num_threads: int,
+            sample_rate: int = 16000,
+            feature_dim: int = 80,
+            decoding_method: str = "greedy_search",
+            debug: bool = False,
+    ):
+        """
+        Please refer to
+        `<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
+        to download pre-trained models for different languages, e.g., Chinese,
+        English, etc.
+
+        Args:
+          tokens:
+            Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
+            columns::
+
+                symbol integer_id
+
+          encoder:
+            Path to ``encoder.onnx``.
+          decoder:
+            Path to ``decoder.onnx``.
+          joiner:
+            Path to ``joiner.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.
+          decoding_method:
+            Valid values are greedy_search, modified_beam_search.
+          debug:
+            True to show debug messages.
+        """
+        self = cls.__new__(cls)
+        model_config = OfflineModelConfig(
+            transducer=OfflineTransducerModelConfig(
+                encoder_filename=encoder,
+                decoder_filename=decoder,
+                joiner_filename=joiner
+            ),
+            paraformer=OfflineParaformerModelConfig(
+                model=""
+            ),
+            tokens=tokens,
+            num_threads=num_threads,
+            debug=debug
+        )
+
+        feat_config = OfflineFeatureExtractorConfig(
+            sampling_rate=sample_rate,
+            feature_dim=feature_dim,
+        )
+
+        recognizer_config = OfflineRecognizerConfig(
+            feat_config=feat_config,
+            model_config=model_config,
+            decoding_method=decoding_method,
+        )
+        self.recognizer = _Recognizer(recognizer_config)
+        return self
+
+    @classmethod
+    def from_paraformer(
+            cls,
+            paraformer: str,
+            tokens: str,
+            num_threads: int,
+            sample_rate: int = 16000,
+            feature_dim: int = 80,
+            decoding_method: str = "greedy_search",
+            debug: bool = False,
+    ):
+        """
+        Please refer to
+        `<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
+        to download pre-trained models for different languages, e.g., Chinese,
+        English, etc.
+
+        Args:
+          tokens:
+            Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
+            columns::
+
+                symbol integer_id
+
+          paraformer:
+            Path to ``paraformer.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.
+          decoding_method:
+            Valid values are greedy_search, modified_beam_search.
+          debug:
+            True to show debug messages.
+        """
+        self = cls.__new__(cls)
+        model_config = OfflineModelConfig(
+            transducer=OfflineTransducerModelConfig(
+                encoder_filename="",
+                decoder_filename="",
+                joiner_filename=""
+            ),
+            paraformer=OfflineParaformerModelConfig(
+                model=paraformer
+            ),
+            tokens=tokens,
+            num_threads=num_threads,
+            debug=debug
+        )
+
+        feat_config = OfflineFeatureExtractorConfig(
+            sampling_rate=sample_rate,
+            feature_dim=feature_dim,
+        )
+
+        recognizer_config = OfflineRecognizerConfig(
+            feat_config=feat_config,
+            model_config=model_config,
+            decoding_method=decoding_method,
+        )
+        self.recognizer = _Recognizer(recognizer_config)
+        return self
+
+    def create_stream(self):
+        return self.recognizer.create_stream()
+
+    def decode_stream(self, s: OfflineStream):
+        self.recognizer.decode_stream(s)
+
+    def decode_streams(self, ss: List[OfflineStream]):
+        self.recognizer.decode_streams(ss)
+