add offline websocket server/client (#98)

 [flake8]
 show-source=true
 statistics=true
-max-line-length = 80
+max-line-length = 120
 
 exclude =
   .git,

@@ -30,9 +30,12 @@ ls -lh
 
 ls -lh $repo
 
-python3 ./python-api-examples/decode-file.py \
+python3 ./python-api-examples/online-decode-files.py \
   --tokens=$repo/tokens.txt \
   --encoder=$repo/encoder-epoch-99-avg-1.onnx \
   --decoder=$repo/decoder-epoch-99-avg-1.onnx \
   --joiner=$repo/joiner-epoch-99-avg-1.onnx \
-  --wave-filename=$repo/test_wavs/4.wav
+  $repo/test_wavs/0.wav \
+  $repo/test_wavs/1.wav \
+  $repo/test_wavs/2.wav \
+  $repo/test_wavs/3.wav

@@ -45,3 +45,4 @@ paraformer-onnxruntime-python-example
 run-sherpa-onnx-offline-paraformer.sh
 run-sherpa-onnx-offline-transducer.sh
 sherpa-onnx-paraformer-zh-2023-03-28
+run-offline-websocket-server-paraformer.sh

+#!/usr/bin/env python3
+#
+# Copyright (c)  2023  Xiaomi Corporation
+
+"""
+A websocket client for sherpa-onnx-offline-websocket-server
+
+This file shows how to transcribe multiple
+files in parallel. We create a separate connection for transcribing each file.
+
+Usage:
+    ./offline-websocket-client-decode-files-parallel.py \
+      --server-addr localhost \
+      --server-port 6006 \
+      /path/to/foo.wav \
+      /path/to/bar.wav \
+      /path/to/16kHz.wav \
+      /path/to/8kHz.wav
+
+(Note: You have to first start the server before starting the client)
+
+You can find the server at
+https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/csrc/offline-websocket-server.cc
+
+Note: The server is implemented in C++.
+"""
+
+import argparse
+import asyncio
+import logging
+import wave
+from typing import Tuple
+
+try:
+    import websockets
+except ImportError:
+    print("please run:")
+    print("")
+    print("  pip install websockets")
+    print("")
+    print("before you run this script")
+    print("")
+
+import numpy as np
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--server-addr",
+        type=str,
+        default="localhost",
+        help="Address of the server",
+    )
+
+    parser.add_argument(
+        "--server-port",
+        type=int,
+        default=6006,
+        help="Port of the server",
+    )
+
+    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 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()
+
+
+async def run(
+    server_addr: str,
+    server_port: int,
+    wave_filename: str,
+):
+    async with websockets.connect(
+        f"ws://{server_addr}:{server_port}"
+    ) as websocket:  # noqa
+        logging.info(f"Sending {wave_filename}")
+        samples, sample_rate = read_wave(wave_filename)
+        assert isinstance(sample_rate, int)
+        assert samples.dtype == np.float32, samples.dtype
+        assert samples.ndim == 1, samples.dim
+        buf = sample_rate.to_bytes(4, byteorder="little")  # 4 bytes
+        buf += (samples.size * 4).to_bytes(4, byteorder="little")
+        buf += samples.tobytes()
+
+        await websocket.send(buf)
+
+        decoding_results = await websocket.recv()
+        logging.info(f"{wave_filename}\n{decoding_results}")
+
+        # to signal that the client has sent all the data
+        await websocket.send("Done")
+
+
+async def main():
+    args = get_args()
+    logging.info(vars(args))
+
+    server_addr = args.server_addr
+    server_port = args.server_port
+    sound_files = args.sound_files
+
+    all_tasks = []
+    for wave_filename in sound_files:
+        task = asyncio.create_task(
+            run(
+                server_addr=server_addr,
+                server_port=server_port,
+                wave_filename=wave_filename,
+            )
+        )
+        all_tasks.append(task)
+
+    await asyncio.gather(*all_tasks)
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"  # noqa
+    )
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    asyncio.run(main())

+#!/usr/bin/env python3
+#
+# Copyright (c)  2023  Xiaomi Corporation
+
+"""
+A websocket client for sherpa-onnx-offline-websocket-server
+
+This file shows how to use a single connection to transcribe multiple
+files sequentially.
+
+Usage:
+    ./offline-websocket-client-decode-files-sequential.py \
+      --server-addr localhost \
+      --server-port 6006 \
+      /path/to/foo.wav \
+      /path/to/bar.wav \
+      /path/to/16kHz.wav \
+      /path/to/8kHz.wav
+
+(Note: You have to first start the server before starting the client)
+
+You can find the server at
+https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/csrc/offline-websocket-server.cc
+
+Note: The server is implemented in C++.
+"""
+
+import argparse
+import asyncio
+import logging
+import wave
+from typing import List, Tuple
+
+try:
+    import websockets
+except ImportError:
+    print("please run:")
+    print("")
+    print("  pip install websockets")
+    print("")
+    print("before you run this script")
+    print("")
+
+import numpy as np
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--server-addr",
+        type=str,
+        default="localhost",
+        help="Address of the server",
+    )
+
+    parser.add_argument(
+        "--server-port",
+        type=int,
+        default=6006,
+        help="Port of the server",
+    )
+
+    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 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()
+
+
+async def run(
+    server_addr: str,
+    server_port: int,
+    sound_files: List[str],
+):
+    async with websockets.connect(
+        f"ws://{server_addr}:{server_port}"
+    ) as websocket:  # noqa
+        for wave_filename in sound_files:
+            logging.info(f"Sending {wave_filename}")
+            samples, sample_rate = read_wave(wave_filename)
+            assert isinstance(sample_rate, int)
+            assert samples.dtype == np.float32, samples.dtype
+            assert samples.ndim == 1, samples.dim
+            buf = sample_rate.to_bytes(4, byteorder="little")  # 4 bytes
+            buf += (samples.size * 4).to_bytes(4, byteorder="little")
+            buf += samples.tobytes()
+
+            await websocket.send(buf)
+
+            decoding_results = await websocket.recv()
+            print(decoding_results)
+
+        # to signal that the client has sent all the data
+        await websocket.send("Done")
+
+
+async def main():
+    args = get_args()
+    logging.info(vars(args))
+
+    server_addr = args.server_addr
+    server_port = args.server_port
+    sound_files = args.sound_files
+
+    await run(
+        server_addr=server_addr,
+        server_port=server_port,
+        sound_files=sound_files,
+    )
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"  # noqa
+    )
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    asyncio.run(main())

 #!/usr/bin/env python3
 
 """
-This file demonstrates how to use sherpa-onnx Python API to recognize
-a single file.
+This file demonstrates how to use sherpa-onnx Python API to transcribe
+file(s) with a streaming model.
+
+Usage:
+    ./online-decode-files.py \
+      /path/to/foo.wav \
+      /path/to/bar.wav \
+      /path/to/16kHz.wav \
+      /path/to/8kHz.wav
 
 Please refer to
 https://k2-fsa.github.io/sherpa/onnx/index.html
@@ -13,17 +20,12 @@ import argparse
 import time
 import wave
 from pathlib import Path
+from typing import Tuple
 
 import numpy as np
 import sherpa_onnx
 
 
-def assert_file_exists(filename: str):
-    assert Path(
-        filename
-    ).is_file(), f"{filename} does not exist!\nPlease refer to https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
-
-
 def get_args():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -68,26 +70,58 @@ def get_args():
     )
 
     parser.add_argument(
-        "--wave-filename",
+        "sound_files",
         type=str,
-        help="""Path to the wave filename.
-        Should have a single channel with 16-bit samples.
-        It does not need to be 16kHz. It can have any sampling rate.
-        """,
+        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.encoder)
     assert_file_exists(args.decoder)
     assert_file_exists(args.joiner)
     assert_file_exists(args.tokens)
-    if not Path(args.wave_filename).is_file():
-        print(f"{args.wave_filename} does not exist!")
-        return
 
     recognizer = sherpa_onnx.OnlineRecognizer(
         tokens=args.tokens,
@@ -99,42 +133,44 @@ def main():
         feature_dim=80,
         decoding_method=args.decoding_method,
     )
-    with wave.open(args.wave_filename) as f:
-        # If the wave file has a different sampling rate from the one
-        # expected by the model (16 kHz in our case), we will do
-        # resampling inside sherpa-onnx
-        wave_file_sample_rate = f.getframerate()
-
-        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
 
-    duration = len(samples_float32) / wave_file_sample_rate
-
-    start_time = time.time()
     print("Started!")
+    start_time = time.time()
 
-    stream = recognizer.create_stream()
-
-    stream.accept_waveform(wave_file_sample_rate, samples_float32)
-
-    tail_paddings = np.zeros(int(0.2 * wave_file_sample_rate), dtype=np.float32)
-    stream.accept_waveform(wave_file_sample_rate, tail_paddings)
-
-    stream.input_finished()
-
-    while recognizer.is_ready(stream):
-        recognizer.decode_stream(stream)
+    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)
+
+        s.input_finished()
+
+        streams.append(s)
+
+    while True:
+        ready_list = []
+        for s in streams:
+            if recognizer.is_ready(s):
+                ready_list.append(s)
+        if len(ready_list) == 0:
+            break
+        recognizer.decode_streams(ready_list)
+    results = [recognizer.get_result(s) for s in streams]
+    end_time = time.time()
+    print("Done!")
 
-    print(recognizer.get_result(stream))
+    for wave_filename, result in zip(args.sound_files, results):
+        print(f"{wave_filename}\n{result}")
+        print("-" * 10)
 
-    print("Done!")
-    end_time = time.time()
     elapsed_seconds = end_time - start_time
     rtf = elapsed_seconds / duration
     print(f"num_threads: {args.num_threads}")

@@ -27,7 +27,6 @@ https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/csrc/online-websoc
 import argparse
 import asyncio
 import logging
-import time
 import wave
 
 try:

@@ -24,13 +24,12 @@ https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/csrc/online-websoc
 import argparse
 import asyncio
 import sys
-import time
 
 import numpy as np
 
 try:
     import sounddevice as sd
-except ImportError as e:
+except ImportError:
     print("Please install sounddevice first. You can use")
     print()
     print("  pip install sounddevice")
@@ -134,7 +133,7 @@ async def run(
             await websocket.send(indata.tobytes())
 
         decoding_results = await receive_task
-        print("\nFinal result is:\n{decoding_results}")
+        print(f"\nFinal result is:\n{decoding_results}")
 
 
 async def main():

@@ -13,7 +13,7 @@ from pathlib import Path
 
 try:
     import sounddevice as sd
-except ImportError as e:
+except ImportError:
     print("Please install sounddevice first. You can use")
     print()
     print("  pip install sounddevice")
@@ -25,9 +25,11 @@ import sherpa_onnx
 
 
 def assert_file_exists(filename: str):
-    assert Path(
-        filename
-    ).is_file(), f"{filename} does not exist!\nPlease refer to https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
+    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 get_args():

@@ -12,7 +12,7 @@ from pathlib import Path
 
 try:
     import sounddevice as sd
-except ImportError as e:
+except ImportError:
     print("Please install sounddevice first. You can use")
     print()
     print("  pip install sounddevice")
@@ -24,9 +24,11 @@ import sherpa_onnx
 
 
 def assert_file_exists(filename: str):
-    assert Path(
-        filename
-    ).is_file(), f"{filename} does not exist!\nPlease refer to https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html to download it"
+    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 get_args():

@@ -128,7 +128,6 @@ if(SHERPA_ONNX_ENABLE_WEBSOCKET)
   )
   target_link_libraries(sherpa-onnx-online-websocket-server sherpa-onnx-core)
 
-
   add_executable(sherpa-onnx-online-websocket-client
     online-websocket-client.cc
   )
@@ -142,6 +141,17 @@ if(SHERPA_ONNX_ENABLE_WEBSOCKET)
     target_compile_options(sherpa-onnx-online-websocket-client PRIVATE -Wno-deprecated-declarations)
   endif()
 
+  # For offline websocket
+  add_executable(sherpa-onnx-offline-websocket-server
+    offline-websocket-server-impl.cc
+    offline-websocket-server.cc
+  )
+  target_link_libraries(sherpa-onnx-offline-websocket-server sherpa-onnx-core)
+
+  if(NOT WIN32)
+    target_link_libraries(sherpa-onnx-offline-websocket-server -pthread)
+    target_compile_options(sherpa-onnx-offline-websocket-server PRIVATE -Wno-deprecated-declarations)
+  endif()
 endif()
 
 

+// sherpa-onnx/csrc/offline-websocket-server-impl.cc
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/offline-websocket-server-impl.h"
+
+#include <algorithm>
+
+#include "sherpa-onnx/csrc/macros.h"
+
+namespace sherpa_onnx {
+
+void OfflineWebsocketDecoderConfig::Register(ParseOptions *po) {
+  recognizer_config.Register(po);
+
+  po->Register("max-batch-size", &max_batch_size,
+               "Max batch size for decoding.");
+
+  po->Register(
+      "max-utterance-length", &max_utterance_length,
+      "Max utterance length in seconds. If we receive an utterance "
+      "longer than this value, we will reject the connection. "
+      "If you have enough memory, you can select a large value for it.");
+}
+
+void OfflineWebsocketDecoderConfig::Validate() const {
+  if (!recognizer_config.Validate()) {
+    SHERPA_ONNX_LOGE("Error in recongizer config");
+    exit(-1);
+  }
+
+  if (max_batch_size <= 0) {
+    SHERPA_ONNX_LOGE("Expect --max-batch-size > 0. Given: %d", max_batch_size);
+    exit(-1);
+  }
+
+  if (max_utterance_length <= 0) {
+    SHERPA_ONNX_LOGE("Expect --max-utterance-length > 0. Given: %f",
+                     max_utterance_length);
+    exit(-1);
+  }
+}
+
+OfflineWebsocketDecoder::OfflineWebsocketDecoder(OfflineWebsocketServer *server)
+    : config_(server->GetConfig().decoder_config),
+      server_(server),
+      recognizer_(config_.recognizer_config) {}
+
+void OfflineWebsocketDecoder::Push(connection_hdl hdl, ConnectionDataPtr d) {
+  std::lock_guard<std::mutex> lock(mutex_);
+  streams_.push_back({hdl, d});
+}
+
+void OfflineWebsocketDecoder::Decode() {
+  std::unique_lock<std::mutex> lock(mutex_);
+  if (streams_.empty()) {
+    return;
+  }
+
+  int32_t size =
+      std::min(static_cast<int32_t>(streams_.size()), config_.max_batch_size);
+  SHERPA_ONNX_LOGE("size: %d", size);
+
+  // We first lock the mutex for streams_, take items from it, and then
+  // unlock the mutex; in doing so we don't need to lock the mutex to
+  // access hdl and connection_data later.
+  std::vector<connection_hdl> handles(size);
+
+  // Store connection_data here to prevent the data from being freed
+  // while we are still using it.
+  std::vector<ConnectionDataPtr> connection_data(size);
+
+  std::vector<const float *> samples(size);
+  std::vector<int32_t> samples_length(size);
+  std::vector<std::unique_ptr<OfflineStream>> ss(size);
+  std::vector<OfflineStream *> p_ss(size);
+
+  for (int32_t i = 0; i != size; ++i) {
+    auto &p = streams_.front();
+    handles[i] = p.first;
+    connection_data[i] = p.second;
+    streams_.pop_front();
+
+    auto sample_rate = connection_data[i]->sample_rate;
+    auto samples =
+        reinterpret_cast<const float *>(&connection_data[i]->data[0]);
+    auto num_samples = connection_data[i]->expected_byte_size / sizeof(float);
+    auto s = recognizer_.CreateStream();
+    s->AcceptWaveform(sample_rate, samples, num_samples);
+
+    ss[i] = std::move(s);
+    p_ss[i] = ss[i].get();
+  }
+
+  lock.unlock();
+
+  // Note: DecodeStreams is thread-safe
+  recognizer_.DecodeStreams(p_ss.data(), size);
+
+  for (int32_t i = 0; i != size; ++i) {
+    connection_hdl hdl = handles[i];
+    asio::post(server_->GetConnectionContext(),
+               [this, hdl, text = ss[i]->GetResult().text]() {
+                 websocketpp::lib::error_code ec;
+                 server_->GetServer().send(
+                     hdl, text, websocketpp::frame::opcode::text, ec);
+                 if (ec) {
+                   server_->GetServer().get_alog().write(
+                       websocketpp::log::alevel::app, ec.message());
+                 }
+               });
+  }
+}
+
+void OfflineWebsocketServerConfig::Register(ParseOptions *po) {
+  decoder_config.Register(po);
+  po->Register("log-file", &log_file,
+               "Path to the log file. Logs are "
+               "appended to this file");
+}
+
+void OfflineWebsocketServerConfig::Validate() const {
+  decoder_config.Validate();
+}
+
+OfflineWebsocketServer::OfflineWebsocketServer(
+    asio::io_context &io_conn,  // NOLINT
+    asio::io_context &io_work,  // NOLINT
+    const OfflineWebsocketServerConfig &config)
+    : io_conn_(io_conn),
+      io_work_(io_work),
+      config_(config),
+      log_(config.log_file, std::ios::app),
+      tee_(std::cout, log_),
+      decoder_(this) {
+  SetupLog();
+
+  server_.init_asio(&io_conn_);
+
+  server_.set_open_handler([this](connection_hdl hdl) { OnOpen(hdl); });
+
+  server_.set_close_handler([this](connection_hdl hdl) { OnClose(hdl); });
+
+  server_.set_message_handler(
+      [this](connection_hdl hdl, server::message_ptr msg) {
+        OnMessage(hdl, msg);
+      });
+}
+
+void OfflineWebsocketServer::SetupLog() {
+  server_.clear_access_channels(websocketpp::log::alevel::all);
+  server_.set_access_channels(websocketpp::log::alevel::connect);
+  server_.set_access_channels(websocketpp::log::alevel::disconnect);
+
+  // So that it also prints to std::cout and std::cerr
+  server_.get_alog().set_ostream(&tee_);
+  server_.get_elog().set_ostream(&tee_);
+}
+
+void OfflineWebsocketServer::OnOpen(connection_hdl hdl) {
+  std::lock_guard<std::mutex> lock(mutex_);
+  connections_.emplace(hdl, std::make_shared<ConnectionData>());
+
+  SHERPA_ONNX_LOGE("Number of active connections: %d",
+                   static_cast<int32_t>(connections_.size()));
+}
+
+void OfflineWebsocketServer::OnClose(connection_hdl hdl) {
+  std::lock_guard<std::mutex> lock(mutex_);
+  connections_.erase(hdl);
+
+  SHERPA_ONNX_LOGE("Number of active connections: %d",
+                   static_cast<int32_t>(connections_.size()));
+}
+
+void OfflineWebsocketServer::OnMessage(connection_hdl hdl,
+                                       server::message_ptr msg) {
+  std::unique_lock<std::mutex> lock(mutex_);
+  auto connection_data = connections_.find(hdl)->second;
+  lock.unlock();
+  const std::string &payload = msg->get_payload();
+
+  switch (msg->get_opcode()) {
+    case websocketpp::frame::opcode::text:
+      if (payload == "Done") {
+        // The client will not send any more data. We can close the
+        // connection now.
+        Close(hdl, websocketpp::close::status::normal, "Done");
+      } else {
+        Close(hdl, websocketpp::close::status::normal,
+              std::string("Invalid payload: ") + payload);
+      }
+      break;
+
+    case websocketpp::frame::opcode::binary: {
+      auto p = reinterpret_cast<const int8_t *>(payload.data());
+
+      if (connection_data->expected_byte_size == 0) {
+        if (payload.size() < 8) {
+          Close(hdl, websocketpp::close::status::normal,
+                "Payload is too short");
+          break;
+        }
+
+        connection_data->sample_rate = *reinterpret_cast<const int32_t *>(p);
+
+        connection_data->expected_byte_size =
+            *reinterpret_cast<const int32_t *>(p + 4);
+
+        int32_t max_byte_size_ = decoder_.GetConfig().max_utterance_length *
+                                 connection_data->sample_rate * sizeof(float);
+        if (connection_data->expected_byte_size > max_byte_size_) {
+          float num_samples =
+              connection_data->expected_byte_size / sizeof(float);
+
+          float duration = num_samples / connection_data->sample_rate;
+
+          std::ostringstream os;
+          os << "Max utterance length is configured to "
+             << decoder_.GetConfig().max_utterance_length
+             << " seconds, received length is " << duration << " seconds. "
+             << "Payload is too large!";
+          Close(hdl, websocketpp::close::status::message_too_big, os.str());
+          break;
+        }
+
+        connection_data->data.resize(connection_data->expected_byte_size);
+        std::copy(payload.begin() + 8, payload.end(),
+                  connection_data->data.data());
+        connection_data->cur = payload.size() - 8;
+      } else {
+        std::copy(payload.begin(), payload.end(),
+                  connection_data->data.data() + connection_data->cur);
+        connection_data->cur += payload.size();
+      }
+
+      if (connection_data->expected_byte_size == connection_data->cur) {
+        auto d = std::make_shared<ConnectionData>(std::move(*connection_data));
+        // Clear it so that we can handle the next audio file from the client.
+        // The client can send multiple audio files for recognition without
+        // the need to create another connection.
+        connection_data->sample_rate = 0;
+        connection_data->expected_byte_size = 0;
+        connection_data->cur = 0;
+
+        decoder_.Push(hdl, d);
+
+        connection_data->Clear();
+
+        asio::post(io_work_, [this]() { decoder_.Decode(); });
+      }
+      break;
+    }
+
+    default:
+      // Unexpected message, ignore it
+      break;
+  }
+}
+
+void OfflineWebsocketServer::Close(connection_hdl hdl,
+                                   websocketpp::close::status::value code,
+                                   const std::string &reason) {
+  auto con = server_.get_con_from_hdl(hdl);
+
+  std::ostringstream os;
+  os << "Closing " << con->get_remote_endpoint() << " with reason: " << reason
+     << "\n";
+
+  websocketpp::lib::error_code ec;
+  server_.close(hdl, code, reason, ec);
+  if (ec) {
+    os << "Failed to close" << con->get_remote_endpoint() << ". "
+       << ec.message() << "\n";
+  }
+  server_.get_alog().write(websocketpp::log::alevel::app, os.str());
+}
+
+void OfflineWebsocketServer::Run(uint16_t port) {
+  server_.set_reuse_addr(true);
+  server_.listen(asio::ip::tcp::v4(), port);
+  server_.start_accept();
+}
+
+}  // namespace sherpa_onnx

+// sherpa-onnx/csrc/offline-websocket-server-impl.h
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_OFFLINE_WEBSOCKET_SERVER_IMPL_H_
+#define SHERPA_ONNX_CSRC_OFFLINE_WEBSOCKET_SERVER_IMPL_H_
+
+#include <deque>
+#include <fstream>
+#include <map>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-recognizer.h"
+#include "sherpa-onnx/csrc/parse-options.h"
+#include "sherpa-onnx/csrc/tee-stream.h"
+#include "websocketpp/config/asio_no_tls.hpp"  // TODO(fangjun): support TLS
+#include "websocketpp/server.hpp"
+
+using server = websocketpp::server<websocketpp::config::asio>;
+using connection_hdl = websocketpp::connection_hdl;
+
+namespace sherpa_onnx {
+
+/** Communication protocol
+ *
+ * The client sends a byte stream to the server. The first 4 bytes in little
+ * endian indicates the sample rate of the audio data that the client will send.
+ * The next 4 bytes in little endian indicates the total samples in bytes the
+ * client will send. The remaining bytes represent audio samples. Each audio
+ * sample is a float occupying 4 bytes and is normalized into the range
+ * [-1, 1].
+ *
+ * The byte stream can be broken into arbitrary number of messages.
+ * We require that the first message has to be at least 8 bytes so that
+ * we can get `sample_rate` and `expected_byte_size` from the first message.
+ */
+struct ConnectionData {
+  // Sample rate of the audio samples the client
+  int32_t sample_rate;
+
+  // Number of expected bytes sent from the client
+  int32_t expected_byte_size = 0;
+
+  // Number of bytes received so far
+  int32_t cur = 0;
+
+  // It saves the received samples from the client.
+  // We will **reinterpret_cast** it to float.
+  // We expect that data.size() == expected_byte_size
+  std::vector<int8_t> data;
+
+  void Clear() {
+    sample_rate = 0;
+    expected_byte_size = 0;
+    cur = 0;
+    data.clear();
+  }
+};
+
+using ConnectionDataPtr = std::shared_ptr<ConnectionData>;
+
+struct OfflineWebsocketDecoderConfig {
+  OfflineRecognizerConfig recognizer_config;
+
+  int32_t max_batch_size = 5;
+
+  float max_utterance_length = 300;  // seconds
+
+  void Register(ParseOptions *po);
+  void Validate() const;
+};
+
+class OfflineWebsocketServer;
+
+class OfflineWebsocketDecoder {
+ public:
+  /**
+   * @param config Configuration for the decoder.
+   * @param server **Borrowed** from outside.
+   */
+  explicit OfflineWebsocketDecoder(OfflineWebsocketServer *server);
+
+  /** Insert received data to the queue for decoding.
+   *
+   * @param hdl A handle to the connection. We can use it to send the result
+   *            back to the client once it finishes decoding.
+   * @param d  The received data
+   */
+  void Push(connection_hdl hdl, ConnectionDataPtr d);
+
+  /** It is called by one of the work thread.
+   */
+  void Decode();
+
+  const OfflineWebsocketDecoderConfig &GetConfig() const { return config_; }
+
+ private:
+  OfflineWebsocketDecoderConfig config_;
+
+  /** When we have received all the data from the client, we put it into
+   * this queue; the worker threads will get items from this queue for
+   * decoding.
+   *
+   * Number of items to take from this queue is determined by
+   * `--max-batch-size`. If there are not enough items in the queue, we won't
+   * wait and take whatever we have for decoding.
+   */
+  std::mutex mutex_;
+  std::deque<std::pair<connection_hdl, ConnectionDataPtr>> streams_;
+
+  OfflineWebsocketServer *server_;  // Not owned
+  OfflineRecognizer recognizer_;
+};
+
+struct OfflineWebsocketServerConfig {
+  OfflineWebsocketDecoderConfig decoder_config;
+  std::string log_file = "./log.txt";
+
+  void Register(ParseOptions *po);
+  void Validate() const;
+};
+
+class OfflineWebsocketServer {
+ public:
+  OfflineWebsocketServer(asio::io_context &io_conn,  // NOLINT
+                         asio::io_context &io_work,  // NOLINT
+                         const OfflineWebsocketServerConfig &config);
+
+  asio::io_context &GetConnectionContext() { return io_conn_; }
+  server &GetServer() { return server_; }
+
+  void Run(uint16_t port);
+
+  const OfflineWebsocketServerConfig &GetConfig() const { return config_; }
+
+ private:
+  void SetupLog();
+
+  // When a websocket client is connected, it will invoke this method
+  // (Not for HTTP)
+  void OnOpen(connection_hdl hdl);
+
+  // When a websocket client is disconnected, it will invoke this method
+  void OnClose(connection_hdl hdl);
+
+  // When a message is received from a websocket client, this method will
+  // be invoked.
+  //
+  // The protocol between the client and the server is as follows:
+  //
+  // (1) The client connects to the server
+  // (2) The client starts to send binary byte stream to the server.
+  //     The byte stream can be broken into multiple messages or it can
+  //     be put into a single message.
+  //     The first message has to contain at least 8 bytes. The first
+  //     4 bytes in little endian contains a int32_t indicating the
+  //     sampling rate. The next 4 bytes in little endian contains a int32_t
+  //     indicating total number of bytes of samples the client will send.
+  //     We assume each sample is a float containing 4 bytes and has been
+  //     normalized to the range [-1, 1].
+  // (4) When the server receives all the samples from the client, it will
+  //     start to decode them. Once decoded, the server sends a text message
+  //     to the client containing the decoded results
+  // (5) After receiving the decoded results from the server, if the client has
+  //     another audio file to send, it repeats (2), (3), (4)
+  // (6) If the client has no more audio files to decode, the client sends a
+  //     text message containing "Done" to the server and closes the connection
+  // (7) The server receives a text message "Done" and closes the connection
+  //
+  // Note:
+  //  (a) All models in icefall use features extracted from audio samples
+  //      normalized to the range [-1, 1]. Please send normalized audio samples
+  //      if you use models from icefall.
+  //  (b) Only sound files with a single channel is supported
+  //  (c) Only audio samples are sent. For instance, if we want to decode
+  //      a WAVE file, the RIFF header of the WAVE is not sent.
+  void OnMessage(connection_hdl hdl, server::message_ptr msg);
+
+  // Close a websocket connection with given code and reason
+  void Close(connection_hdl hdl, websocketpp::close::status::value code,
+             const std::string &reason);
+
+ private:
+  asio::io_context &io_conn_;
+  asio::io_context &io_work_;
+  server server_;
+
+  std::map<connection_hdl, ConnectionDataPtr, std::owner_less<connection_hdl>>
+      connections_;
+  std::mutex mutex_;
+
+  OfflineWebsocketServerConfig config_;
+
+  std::ofstream log_;
+  TeeStream tee_;
+
+  OfflineWebsocketDecoder decoder_;
+};
+
+}  // namespace sherpa_onnx
+
+#endif  // SHERPA_ONNX_CSRC_OFFLINE_WEBSOCKET_SERVER_IMPL_H_

+// sherpa-onnx/csrc/offline-websocket-server.cc
+//
+// Copyright (c)  2022-2023  Xiaomi Corporation
+
+#include "asio.hpp"
+#include "sherpa-onnx/csrc/macros.h"
+#include "sherpa-onnx/csrc/offline-websocket-server-impl.h"
+#include "sherpa-onnx/csrc/parse-options.h"
+
+static constexpr const char *kUsageMessage = R"(
+Automatic speech recognition with sherpa-onnx using websocket.
+
+Usage:
+
+./bin/sherpa-onnx-offline-websocket-server --help
+
+(1) For transducer models
+
+./bin/sherpa-onnx-offline-websocket-server \
+  --port=6006 \
+  --num-work-threads=5 \
+  --tokens=/path/to/tokens.txt \
+  --encoder=/path/to/encoder.onnx \
+  --decoder=/path/to/decoder.onnx \
+  --joiner=/path/to/joiner.onnx \
+  --log-file=./log.txt \
+  --max-batch-size=5
+
+(2) For Paraformer
+
+./bin/sherpa-onnx-offline-websocket-server \
+  --port=6006 \
+  --num-work-threads=5 \
+  --tokens=/path/to/tokens.txt \
+  --paraformer=/path/to/model.onnx \
+  --log-file=./log.txt \
+  --max-batch-size=5
+
+Please refer to
+https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
+for a list of pre-trained models to download.
+)";
+
+int32_t main(int32_t argc, char *argv[]) {
+  sherpa_onnx::ParseOptions po(kUsageMessage);
+
+  sherpa_onnx::OfflineWebsocketServerConfig config;
+
+  // the server will listen on this port
+  int32_t port = 6006;
+
+  // size of the thread pool for handling network connections
+  int32_t num_io_threads = 1;
+
+  // size of the thread pool for neural network computation and decoding
+  int32_t num_work_threads = 3;
+
+  po.Register("num-io-threads", &num_io_threads,
+              "Thread pool size for network connections.");
+
+  po.Register("num-work-threads", &num_work_threads,
+              "Thread pool size for for neural network "
+              "computation and decoding.");
+
+  po.Register("port", &port, "The port on which the server will listen.");
+
+  config.Register(&po);
+
+  if (argc == 1) {
+    po.PrintUsage();
+    exit(EXIT_FAILURE);
+  }
+
+  po.Read(argc, argv);
+
+  if (po.NumArgs() != 0) {
+    SHERPA_ONNX_LOGE("Unrecognized positional arguments!");
+    po.PrintUsage();
+    exit(EXIT_FAILURE);
+  }
+
+  config.Validate();
+
+  asio::io_context io_conn;  // for network connections
+  asio::io_context io_work;  // for neural network and decoding
+
+  sherpa_onnx::OfflineWebsocketServer server(io_conn, io_work, config);
+  server.Run(port);
+
+  SHERPA_ONNX_LOGE("Started!");
+  SHERPA_ONNX_LOGE("Listening on: %d", port);
+  SHERPA_ONNX_LOGE("Number of work threads: %d", num_work_threads);
+
+  // give some work to do for the io_work pool
+  auto work_guard = asio::make_work_guard(io_work);
+
+  std::vector<std::thread> io_threads;
+
+  // decrement since the main thread is also used for network communications
+  for (int32_t i = 0; i < num_io_threads - 1; ++i) {
+    io_threads.emplace_back([&io_conn]() { io_conn.run(); });
+  }
+
+  std::vector<std::thread> work_threads;
+  for (int32_t i = 0; i < num_work_threads; ++i) {
+    work_threads.emplace_back([&io_work]() { io_work.run(); });
+  }
+
+  io_conn.run();
+
+  for (auto &t : io_threads) {
+    t.join();
+  }
+
+  for (auto &t : work_threads) {
+    t.join();
+  }
+
+  return 0;
+}

@@ -76,6 +76,7 @@ int32_t main(int32_t argc, char *argv[]) {
   sherpa_onnx::OnlineWebsocketServer server(io_conn, io_work, config);
   server.Run(port);
 
+  SHERPA_ONNX_LOGE("Started!");
   SHERPA_ONNX_LOGE("Listening on: %d", port);
   SHERPA_ONNX_LOGE("Number of work threads: %d", num_work_threads);