MaixCAM MaixPy Real-time voice recognition

Introduction

MaixCAM has ported the Maix-Speech offline speech library, enabling continuous Chinese numeral recognition, keyword recognition, and large vocabulary speech recognition capabilities. It supports audio recognition in PCM and WAV formats, and can accept input recognition via the onboard microphone.

Maix-Speech

Maix-Speech is an offline speech library specifically designed for embedded environments. It features deep optimization of speech recognition algorithms, achieving a significant lead in memory usage while maintaining excellent WER. For more details on the principles, please refer to the open-source project.

Continuous Large Vocabulary Speech Recognition

from maix import app, nn

speech = nn.Speech("/root/models/am_3332_192_int8.mud")
speech.init(nn.SpeechDevice.DEVICE_MIC, "hw:0,0")

def callback(data: tuple[str, str], len: int):
    print(data)

lmS_path = "/root/models/lmS/"

speech.lvcsr(lmS_path + "lg_6m.sfst", lmS_path + "lg_6m.sym", \
             lmS_path + "phones.bin", lmS_path + "words_utf.bin", \
             callback)

while not app.need_exit():
    frames = speech.run(1)
    if frames < 1:
        print("run out\n")
        speech.deinit()
        break

Usage

1. Import the app and nn modules

from maix import app, nn

2. Load the acoustic model

speech = nn.Speech("/root/models/am_3332_192_int8.mud")

• You can also load the am_7332 acoustic model; larger models provide higher accuracy but consume more resources.

3. Choose the corresponding audio device

speech.init(nn.SpeechDevice.DEVICE_MIC, "hw:0,0")

• This uses the onboard microphone and supports both WAV and PCM audio as input devices.

speech.init(nn.SpeechDevice.DEVICE_WAV, "path/audio.wav")   # Using WAV audio input

speech.init(nn.SpeechDevice.DEVICE_PCM, "path/audio.pcm")   # Using PCM audio input

• Note that WAV must be 16KHz sample rate with S16_LE storage format. You can use the arecord tool for conversion.

arecord -d 5 -r 16000 -c 1 -f S16_LE audio.wav

• When recognizing PCM/WAV , if you want to reset the data source, such as for the next WAV file recognition, you can use the speech.devive method, which will automatically clear the cache:

speech.devive(nn.SpeechDevice.DEVICE_WAV, "path/next.wav")

4. Set up the decoder

def callback(data: tuple[str, str], len: int):
    print(data)

lmS_path = "/root/models/lmS/"

speech.lvcsr(lmS_path + "lg_6m.sfst", lmS_path + "lg_6m.sym", \
             lmS_path + "phones.bin", lmS_path + "words_utf.bin", \
             callback)

• Users can register several decoders (or none), which decode the results from the acoustic model and execute the corresponding user callback. Here, a lvcsr decoder is registered to output continuous speech recognition results (for fewer than 1024 Chinese characters). For other decoder usages, please refer to the sections on continuous Chinese numeral recognition and keyword recognition.

• When setting up the lvcsr decoder, you need to specify the paths for the sfst file, the sym file (output symbol table), the path for phones.bin (phonetic table), and the path for words.bin (dictionary). Lastly, a callback function must be set to handle the decoded data.

• After registering the decoder, use the speech.deinit() method to clear the initialization.

5. Recognition

while not app.need_exit():
    frames = speech.run(1)
    if frames < 1:
        print("run out\n")
        speech.deinit()
        break

• Use the speech.run method to run speech recognition. The parameter specifies the number of frames to run each time, returning the actual number of frames processed. Users can choose to run 1 frame each time and then perform other processing, or run continuously in a single thread, stopping it with an external thread.

Recognition Results

If the above program runs successfully, speaking into the onboard microphone will yield real-time speech recognition results, such as:

### SIL to clear decoder!
('今天天气 怎么样 ', 'jin1 tian1 tian1 qi4 zen3 me yang4 ')