前几天虽然让树莓派学会了“听”（《树莓派智能语音助手之ASR – SpeechRecognition+PocketSphinx》），但是，PocketSphinx的识别效果真心不咋的。可我的树莓派因为系统等原因，一直装不了sherpa-onnx。正当我只能无奈地接受ASR低识别率的时候，忽然发现了有人推荐了一款新的语音识别工具——sherpa-ncnn。不多废话，直接开装。

官网教程：Python API — sherpa 1.3 documentation

CSDN上的介绍文章：https://blog.csdn.net/lstef/article/details/139680825

1.安装

sherpa-ncnn的安装很简单：

pip install sherpa-ncnn

当然，你也可以选择手动编译：

git clone https://github.com/k2-fsa/sherpa-ncnn

cd sherpa-ncnn

mkdir build

cd build

cmake \

  -D SHERPA_NCNN_ENABLE_PYTHON=ON \

  -D SHERPA_NCNN_ENABLE_PORTAUDIO=OFF \

  -D BUILD_SHARED_LIBS=ON \

  -DCMAKE_C_FLAGS="-march=armv7-a -mfloat-abi=hard -mfpu=neon" \

  -DCMAKE_CXX_FLAGS="-march=armv7-a -mfloat-abi=hard -mfpu=neon" \

  ..

make -j6

这两种方法我都亲自验证过（手动编译是python3.9下，直接pip install是python3.7,）。

2.下载模型

安装成功后就要下载预先训练好的模型：

Pre-trained models — sherpa 1.3 documentation

如图，我下载的是Small models，这里面也有4个模型可供选择。

模型的下载步骤：

cd /path/to/sherpa-ncnn

wget https://github.com/k2-fsa/sherpa-ncnn/releases/download/models/sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16.tar.bz2

tar xvf sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16.tar.bz2

3.测试

下载并解压成功后，可以在当前目录直接测试效果：

cd /path/to/sherpa-ncnn

for method in greedy_search modified_beam_search; do
  ./build/bin/sherpa-ncnn \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/tokens.txt \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/encoder_jit_trace-pnnx.ncnn.param \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/encoder_jit_trace-pnnx.ncnn.bin \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/decoder_jit_trace-pnnx.ncnn.param \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/decoder_jit_trace-pnnx.ncnn.bin \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/joiner_jit_trace-pnnx.ncnn.param \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/joiner_jit_trace-pnnx.ncnn.bin \
    ./sherpa-ncnn-streaming-zipformer-small-bilingual-zh-en-2023-02-16/test_wavs/1.wav \
    2 \
    $method
done

直接把上面的代码复制到linux终端执行就可以看到测试效果了。

4.python调用

接下来，就是自己编写代码了。sherpa-ncnn的录音用的是sounddevice这个模块，需要自行安装：pip install sounddevice。

直接复制官网教程的示例：

import sys

try:
    import sounddevice as sd
except ImportError as e:
    print("Please install sounddevice first. You can use")
    print()
    print("  pip install sounddevice")
    print()
    print("to install it")
    sys.exit(-1)

import sherpa_ncnn

def create_recognizer():
    # Please replace the model files if needed.
    # See https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html
    # for download links.
    recognizer = sherpa_ncnn.Recognizer(
        tokens="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/tokens.txt",
        encoder_param="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/encoder_jit_trace-pnnx.ncnn.param",
        encoder_bin="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/encoder_jit_trace-pnnx.ncnn.bin",
        decoder_param="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/decoder_jit_trace-pnnx.ncnn.param",
        decoder_bin="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/decoder_jit_trace-pnnx.ncnn.bin",
        joiner_param="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/joiner_jit_trace-pnnx.ncnn.param",
        joiner_bin="./sherpa-ncnn-conv-emformer-transducer-2022-12-06/joiner_jit_trace-pnnx.ncnn.bin",
        num_threads=4,
    )
    return recognizer


def main():
    print("Started! Please speak")
    recognizer = create_recognizer()
    sample_rate = recognizer.sample_rate
    samples_per_read = int(0.1 * sample_rate)  # 0.1 second = 100 ms
    last_result = ""
    with sd.InputStream(
        channels=1, dtype="float32", samplerate=sample_rate
    ) as s:
        while True:
            samples, _ = s.read(samples_per_read)  # a blocking read
            samples = samples.reshape(-1)
            recognizer.accept_waveform(sample_rate, samples)
            result = recognizer.text
            if last_result != result:
                last_result = result
                print(result)


if __name__ == "__main__":
    devices = sd.query_devices()
    print(devices)
    default_input_device_idx = sd.default.device[0]
    print(f'Use default device: {devices[default_input_device_idx]["name"]}')

    try:
        main()

好了，就这么方便。下图是我融合了snowboy唤醒+ASR的演示效果。