Feature scaling

Usually machine learning algorithms are not trained with raw data (aka end-to-end) but with features that model the entities of interest.
With respect to speech samples these features might be for example average pitch value over the whole utterance or length of utterance.

Now if the pitch value is given in Hz and the length in seconds, the pitch value will be in the range of [80, 300] and the length, say, in the range of [1.5, 6].
Machine learning approaches now would give higher consideration on the avr. pitch because the values are higher and differ by a larger amount, which is in the most cases not a good idea because it's a totally different feature.

A solution to this problem is to scale all values so that the features have a mean of 0 and standard deviation of 1.
This can be easily done with the preprocessing API from sklearn:

from sklearn import preprocessing
scaler = StandardScaler()
scaled_features = preprocessing.scaler.fit_transform(features)

Be aware that the use of the standard scaler only makes sense if the data follows a normal distribution.

Recording and transcribing a speech sample on Google colab“

Set up the recording method using java script:

# all imports
from IPython.display import Javascript
from google.colab import output
from base64 import b64decode

RECORD = """
const sleep  = time => new Promise(resolve => setTimeout(resolve, time))
const b2text = blob => new Promise(resolve => {
  const reader = new FileReader()
  reader.onloadend = e => resolve(e.srcElement.result)
  reader.readAsDataURL(blob)
})
var record = time => new Promise(async resolve => {
  stream = await navigator.mediaDevices.getUserMedia({ audio: true })
  recorder = new MediaRecorder(stream)
  chunks = []
  recorder.ondataavailable = e => chunks.push(e.data)
  recorder.start()
  await sleep(time)
  recorder.onstop = async ()=>{
    blob = new Blob(chunks)
    text = await b2text(blob)
    resolve(text)
  }
  recorder.stop()
})
"""

def record(fn, sec):
  display(Javascript(RECORD))
  s = output.eval_js('record(%d)' % (sec*1000))
  b = b64decode(s.split(',')[1])
  with open(fn,'wb') as f:
    f.write(b)
  return fn

Record something:

 filename = 'felixtest.wav'
record(filename, 5)

Play it back:

import IPython
IPython.display.Audio(filename)

install Google speechbrain

%%capture
!pip install speechbrain
import speechbrain as sb

Load the ASR nodel train on libri speech:

from speechbrain.pretrained import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-rnnlm-librispeech", savedir="pretrained_model")

And get a transcript on your audio:

asr_model.transcribe_file(audio_file )

Record sound from microphone

This works if you got "PortAudio" on your system.

import audiofile as af
import sounddevice as sd

def record_audio(filename, seconds):
    fs = 16000
    print("recording {} ({}s) ...".format(filename, seconds))
    y = sd.rec(int(seconds * fs), samplerate=fs, channels=1)
    sd.wait()
    y = y.T
    af.write(filename, y, fs)
    print("  ... saved to {}".format(filename))

Use speechalyzer to walk through a large set of audio files

I wrote speechalyzer in Java to process a large set of audio files. Here's how you could use this on your audio set.

Install and configure

1) Get it and put it somewhere on your file system, don't forget to also install its GUI, the Labeltool
2) Make sure you got Java on your system.
3) Configure both programs by editing the resource files.

Run

The easiest case is if all of your files are in one directory. You would simply start the Speechalyzer like so (you need to be in the same directory):

java - jar Speechalyzer.jar -rd <path to folder with audio files> &

make sure you configured the right audio extension and sampling rate in the config file (wav format, 16kHz is default).
Then change to the Labeltool directory and start it simply like this:

java - jar Labeltool.jar &

again you might have to adapt the sample rate in the config file (or set it in the GUI). Note you need to be inside the Labeltool directory. Here is a screenshot of the Labeltool displaying some files which can be annotated, labeled or simply played in a chain:

How to compare formant tracks extracted with opensmile vs. Praat

First, some imports

import pandas as pd
import parselmouth 
from parselmouth import praat
import opensmile
import audiofile

Then, a test file:

testfile = '/home/felix/data/data/audio/testsatz.wav'
signal, sampling_rate = audiofile.read(testfile)
print('length in seconds: {}'.format(len(signal)/sampling_rate))

Get the opensmile formant tracks by copying them from the official GeMAPS config file

smile = opensmile.Smile(
    feature_set=opensmile.FeatureSet.GeMAPSv01b,
    feature_level=opensmile.FeatureLevel.LowLevelDescriptors,
)
result_df = smile.process_file(testfile)
centerformantfreqs = ['F1frequency_sma3nz', 'F2frequency_sma3nz', 'F3frequency_sma3nz']
formant_df = result_df[centerformantfreqs]

Get the Praat tracks (smile configuration computes every 10 msec with frame length 20 msec)

sound = parselmouth.Sound(testfile) 
formants = praat.call(sound, "To Formant (burg)", 0.01, 4, 5000, 0.02, 50)
f1_list = []
f2_list = []
f3_list = []
for i in range(2, formants.get_number_of_frames()+1):
    f1 = formants.get_value_at_time(1, formants.get_time_step()*i)
    f2 = formants.get_value_at_time(2, formants.get_time_step()*i)
    f3 = formants.get_value_at_time(3, formants.get_time_step()*i)
    f1_list.append(f1)
    f2_list.append(f2)
    f3_list.append(f3)

To be sure: compare the size of the output:

print('{}, {}'.format(result_df.shape[0], len(f1_list)))

combine and inspect the result:

formant_df['F1_praat'] = f1_list
formant_df['F2_praat'] = f2_list
formant_df['F3_praat'] = f3_list
formant_df.head()

How to extract formant tracks with Praat and python

This tutorial was adapted based on the examples from David R Feinberg

This tutorial assumes you started a Jupyter notebook . If you don't know what this is, here's a tutorial on how to set one up (first part)

First you should install the parselmouth package, which interfaces Praat with python:

!pip install -U praat-parselmouth

which you would then import:

import parselmouth 
from parselmouth import praat

You do need some audio input (wav header, 16 kHz sample rate)

testfile = '/home/felix/data/data/audio/testsatz.wav'

And would then read in the sound with parselmouth like this:

sound = parselmouth.Sound(testfile) 

Here's the code to extract the first three formant tracks, I guess it's more or less self-explanatory if you know Praat.

First, compute the occurrences of periodic instances in the signal:

f0min=75
f0max=300
pointProcess = praat.call(sound, "To PointProcess (periodic, cc)", f0min, f0max)

then, compute the formants:

formants = praat.call(sound, "To Formant (burg)", 0.0025, 5, 5000, 0.025, 50)

And finally assign formant values with times where they make sense (periodic instances)

numPoints = praat.call(pointProcess, "Get number of points")
f1_list = []
f2_list = []
f3_list = []
for point in range(0, numPoints):
    point += 1
    t = praat.call(pointProcess, "Get time from index", point)
    f1 = praat.call(formants, "Get value at time", 1, t, 'Hertz', 'Linear')
    f2 = praat.call(formants, "Get value at time", 2, t, 'Hertz', 'Linear')
    f3 = praat.call(formants, "Get value at time", 3, t, 'Hertz', 'Linear')
    f1_list.append(f1)
    f2_list.append(f2)
    f3_list.append(f3)

How to synthesize a text to speech with Google speech API

This tutorial assumes you started a Jupyter notebook . If you don't know what this is, here's a tutorial on how to set one up (first part)

There is a library for this that's based on the Google translation service that still seems to work: gtts.
You would start by installing the packages used in this tutorial:

!pip install -U gtts pygame python-vlc

The you can import the package:

from gtts import gTTS

, define a text and a configuration:

text = 'Das ist jetzt mal was ich so sage, ich finde das Klasse!'
tts = gTTS(text, lang='de')

and synthesize to a file on disk:

audio_file = './hello.mp3'
tts.save(audio_file)

which you could then play back with vlc

from pygame import mixer  
import vlc
p = vlc.MediaPlayer(audio_file)
p.play()

How to get my speech recognized with Google ASR and python

What you need to do this at first is to get yourselg a Google API key,

  • you need to register with Google speech APIs, i.e. get a Google cloud platform account
  • you need to share payment details, but (at the time of writing, i think) the first 60 minutes of processed speech per month are free.

I export my API key each time I want to use this like so:

export GOOGLE_APPLICATION_CREDENTIALS="/home/felix/data/research/Google/api_key.json"

This tutorial assumes you did that and you started a Jupyter notebook . If you don't know what this is, here's a tutorial on how to set one up (first part)

Bevor you can import the Google speech api make shure it's installed:

!pip  install google-cloud 

Then you would import the Google Cloud client library

from google.cloud import speech
import io

Instantiate a client

client = speech.SpeechClient()

And load yourself a recorded speech file, should be wav format 16kHz sample rate

speech_file = '/home/felix/tmp/google_speech_api_test.wav'
with io.open(speech_file, "rb") as audio_file:
    content = audio_file.read()

get yourself an audio object

audio = speech.RecognitionAudio(content = content)

Configure the ASR

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    language_code="de-DE",
)

Detects speech in the audio file

response = client.recognize(config=config, audio=audio)

and show what you got (with my trial only the first alternative was filled):

for result in response.results:
    for index, alternative in enumerate(result.alternatives):
        print("Transcript {}: {}".format(index, alternative.transcript))