Realtime ASR

Let say you want to transcribe realtime recording / input using PyAudio, malaya-speech able to do that.

This tutorial is available as an IPython notebook at malaya-speech/example/realtime-asr.

This module is not language independent, so it not save to use on different languages. Pretrained models trained on hyperlocal languages.

This is an application of malaya-speech Pipeline, read more about malaya-speech Pipeline at malaya-speech/example/pipeline.

import malaya_speech
from malaya_speech import Pipeline
from malaya_speech.utils.astype import float_to_int

Cannot import beam_search_ops from Tensorflow Addons, ['malaya.jawi_rumi.deep_model', 'malaya.phoneme.deep_model', 'malaya.rumi_jawi.deep_model', 'malaya.stem.deep_model'] will not available to use, make sure Tensorflow Addons version >= 0.12.0
check compatible Tensorflow version with Tensorflow Addons at https://github.com/tensorflow/addons/releases
/Users/huseinzolkepli/.pyenv/versions/3.9.4/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
torchaudio.io.StreamReader exception: FFmpeg libraries are not found. Please install FFmpeg.
`torchaudio.io.StreamReader` is not available, `malaya_speech.streaming.torchaudio.stream` is not able to use.
`openai-whisper` is not available, native whisper processor is not available, will use huggingface processor instead.
`torchaudio.io.StreamReader` is not available, `malaya_speech.streaming.torchaudio` is not able to use.

Load VAD model

We are going to use WebRTC VAD model, read more about VAD at https://malaya-speech.readthedocs.io/en/latest/load-vad.html

vad_model = malaya_speech.vad.webrtc()

p_vad = Pipeline()
pipeline = (
    p_vad.map(lambda x: float_to_int(x, divide_max_abs=False))
    .map(vad_model)
)
p_vad.visualize()

Starting malaya-speech 1.4.0, streaming always returned a float32 array between -1 and +1 values.

Streaming interface

def stream(
    vad_model=None,
    asr_model=None,
    classification_model=None,
    sample_rate: int = 16000,
    segment_length: int = 2560,
    num_padding_frames: int = 20,
    ratio: float = 0.75,
    min_length: float = 0.1,
    max_length: float = 10.0,
    realtime_print: bool = True,
    **kwargs,
):
    """
    Stream an audio using pyaudio library.

    Parameters
    ----------
    vad_model: object, optional (default=None)
        vad model / pipeline.
    asr_model: object, optional (default=None)
        ASR model / pipeline, will transcribe each subsamples realtime.
    classification_model: object, optional (default=None)
        classification pipeline, will classify each subsamples realtime.
    device: None, optional (default=None)
        `device` parameter for pyaudio, check available devices from `sounddevice.query_devices()`.
    sample_rate: int, optional (default = 16000)
        output sample rate.
    segment_length: int, optional (default=2560)
        usually derived from asr_model.segment_length * asr_model.hop_length,
        size of audio chunks, actual size in term of second is `segment_length` / `sample_rate`.
    ratio: float, optional (default = 0.75)
        if 75% of the queue is positive, assumed it is a voice activity.
    min_length: float, optional (default=0.1)
        minimum length (second) to accept a subsample.
    max_length: float, optional (default=10.0)
        maximum length (second) to accept a subsample.
    realtime_print: bool, optional (default=True)
        Will print results for ASR.
    **kwargs: vector argument
        vector argument pass to malaya_speech.streaming.pyaudio.Audio interface.

    Returns
    -------
    result : List[dict]
    """

Check available devices

import sounddevice

sounddevice.query_devices()

> 0 MacBook Air Microphone, Core Audio (1 in, 0 out)
< 1 MacBook Air Speakers, Core Audio (0 in, 2 out)

By default it will use 0 index.

Load ASR model

malaya_speech.stt.transducer.available_pt_transformer()

	Size (MB)	malay-malaya	malay-fleur102	Language	singlish
mesolitica/conformer-tiny	38.5	{'WER': 0.17341180814, 'CER': 0.05957485024}	{'WER': 0.19524478979, 'CER': 0.0830808938}	[malay]	NaN
mesolitica/conformer-base	121	{'WER': 0.122076123261, 'CER': 0.03879606324}	{'WER': 0.1326737206665, 'CER': 0.05032914857}	[malay]	NaN
mesolitica/conformer-medium	243	{'WER': 0.1054817492564, 'CER': 0.0313518992842}	{'WER': 0.1172708897486, 'CER': 0.0431050488}	[malay]	NaN
mesolitica/emformer-base	162	{'WER': 0.175762423786, 'CER': 0.06233919000537}	{'WER': 0.18303839134, 'CER': 0.0773853362}	[malay]	NaN
mesolitica/conformer-base-singlish	121	NaN	NaN	[singlish]	{'WER': 0.06517537334361, 'CER': 0.03265430876}
mesolitica/conformer-medium-mixed	243	{'WER': 0.111166517935, 'CER': 0.03410958328}	{'WER': 0.108354748, 'CER': 0.037785722}	[malay, singlish]	{'WER': 0.091969755225, 'CER': 0.044627194623}
mesolitica/conformer-medium-mixed-augmented	243	{'WER': 0.1015719878, 'CER': 0.0326360923}	{'WER': 0.1103884742, 'CER': 0.0385676182}	[malay, singlish]	{'WER': 0.086342166, 'CER': 0.0413572066}
mesolitica/conformer-large-mixed-augmented	413	{'WER': 0.0919852874, 'CER': 0.026612152}	{'WER': 0.103593636, 'CER': 0.036611048}	[malay, singlish]	{'WER': 0.08727157, 'CER': 0.04318735972}

model = malaya_speech.stt.transducer.pt_transformer(model = 'mesolitica/conformer-medium')

_ = model.eval()

ASR Pipeline

Feel free to add speech enhancement or any function, but in this example, I just keep it simple.

p_asr = Pipeline()
pipeline_asr = (
    p_asr.map(lambda x: model.beam_decoder([x])[0], name = 'speech-to-text')
)
p_asr.visualize()

You need to make sure the last output should named as ``speech-to-text`` or else the streaming interface will throw an error.

Start Recording

Again, once you start to run the code below, it will straight away recording your voice.

If you run in jupyter notebook, press button stop up there to stop recording, if in terminal, press CTRL + c.

samples = malaya_speech.streaming.pyaudio.stream(vad_model = p_vad, asr_model = p_asr,
                                                segment_length = 320)

helo nama saya hussein saya busuk tak suka mandi tiap macam suka makan ayam dan isteri saya sangat busuk  dia tak suka ayam itu masalahnya terima kasih

samples

[{'wav_data': array([ 0.0007282 ,  0.00219776,  0.00208024, ...,  0.00155422,
         -0.00042624, -0.00223646], dtype=float32),
  'start': 0.0,
  'asr_model': 'helo',
  'end': 3.68},
 {'wav_data': array([-0.00611142, -0.00589947, -0.00219211, ...,  0.01116544,
          0.00946136,  0.00831654], dtype=float32),
  'start': 5.2,
  'asr_model': 'nama saya hussein',
  'end': 7.24},
 {'wav_data': array([ 0.00372992,  0.00254511,  0.0054974 , ..., -0.01673304,
         -0.01337129, -0.00979899], dtype=float32),
  'start': 8.96,
  'asr_model': 'saya busuk',
  'end': 9.780000000000001},
 {'wav_data': array([0.00679095, 0.00291635, 0.00242032, ..., 0.00051756, 0.00035704,
         0.00017065], dtype=float32),
  'start': 11.26,
  'asr_model': 'tak suka mandi',
  'end': 12.32},
 {'wav_data': array([-0.00579788, -0.00428703, -0.0036685 , ..., -0.00599275,
         -0.00840599, -0.01050649], dtype=float32),
  'start': 13.78,
  'asr_model': 'tiap macam',
  'end': 15.28},
 {'wav_data': array([ 0.00571198,  0.00568363,  0.00634803, ..., -0.00067981,
         -0.00236918, -0.00169709], dtype=float32),
  'start': 18.42,
  'asr_model': 'suka makan ayam',
  'end': 20.200000000000003},
 {'wav_data': array([-0.00094647, -0.00221979, -0.00072025, ..., -0.01173976,
         -0.00775938, -0.00747842], dtype=float32),
  'start': 21.88,
  'asr_model': 'dan isteri saya sangat busuk',
  'end': 24.5},
 {'wav_data': array([-0.00507851, -0.00617037, -0.00514752, ...,  0.00142657,
          0.0016751 ,  0.00102312], dtype=float32),
  'start': 26.58,
  'asr_model': '',
  'end': 27.74},
 {'wav_data': array([ 0.00188579,  0.00156929,  0.00019072, ..., -0.0013421 ,
         -0.00143564, -0.00096948], dtype=float32),
  'start': 28.08,
  'asr_model': 'dia tak suka ayam',
  'end': 29.619999999999997},
 {'wav_data': array([ 0.01441266,  0.01060689,  0.00785537, ..., -0.00457305,
         -0.00490392, -0.00662707], dtype=float32),
  'start': 29.84,
  'asr_model': 'itu masalahnya',
  'end': 31.36},
 {'wav_data': array([-0.00284222, -0.00255975, -0.00070129, ..., -0.00597112,
         -0.00702858, -0.00861282], dtype=float32),
  'start': 33.08,
  'asr_model': 'terima kasih',
  'end': 34.28}]

Actually it is pretty nice. As you can see, it able to transcribe realtime, you can try it by yourself.

len(samples)

11

samples[-1]

{'wav_data': array([-0.00284222, -0.00255975, -0.00070129, ..., -0.00597112,
        -0.00702858, -0.00861282], dtype=float32),
 'start': 33.08,
 'asr_model': 'terima kasih',
 'end': 34.28}

import IPython.display as ipd
import numpy as np

ipd.Audio(samples[-1]['wav_data'], rate = 16000)

ipd.Audio(np.concatenate([s['wav_data'] for s in samples[:10]]), rate = 16000)