Audio Signal Split At Word Level Boundary

I am working with audio file using webrtcvad and pydub. The split of any fragment is by silence of the sentence. Is there any way by which the split can be done at word level bound

Solution 1:

Simple audio segmentation problems can be handled by using a Hidden Markov Model, after preprocessing the audio into suitable features. Typical features for speech would be soundlevel, vocal activity / voicedness. To get word-level segmentation (as opposed to sentence), this needs to have rather high time resolution. Unfortunately the pyWebRTCVAD does not have adjustable time smoothening so it might not be suited for the task.

In your audio sample there is a radio host speaking rather quickly in German. Looking at the soundlevels wrt to the word boundaries you have marked it is clear that between some words the soundlevel doesnt really drop. That rules out a simple soundlevel segmentation model.

All in all, getting good results for general speech signals can be quite hard. But fortunately this is very well researched, and with off-the-shelf solutions being available. These use typically an acoustic model (how words and phonemes sound), as well as a language model (likely orders of words), learned over many hours of audio.

Word segmentation using Speech Recognition library

All these features are included in a Speech Recognition framework, and many allow to get word-level outputs with timing. Below is some working code for this using Vosk.

Alternatives to Vosk would be PocketSphinx. Or using an online speech recognition service from Google Cloud, Amazon Web Services, Azure Cloud etc.

import sys
import os
import subprocess
import json
import math

# tested with VOSK 0.3.15import vosk
import librosa
import numpy
import pandas



defextract_words(res):
   jres = json.loads(res)
   ifnot'result'in jres:
       return []
   words = jres['result']
   return words

deftranscribe_words(recognizer, bytes):
    results = []

    chunk_size = 4000for chunk_no inrange(math.ceil(len(bytes)/chunk_size)):
        start = chunk_no*chunk_size
        end = min(len(bytes), (chunk_no+1)*chunk_size)
        data = bytes[start:end]

        if recognizer.AcceptWaveform(data):
            words = extract_words(recognizer.Result())
            results += words
    results += extract_words(recognizer.FinalResult())

    return results

defmain():

    vosk.SetLogLevel(-1)

    audio_path = sys.argv[1]
    out_path = sys.argv[2]

    model_path = 'vosk-model-small-de-0.15'
    sample_rate = 16000

    audio, sr = librosa.load(audio_path, sr=16000)

    # convert to 16bit signed PCM, as expected by VOSK
    int16 = numpy.int16(audio * 32768).tobytes()

    # XXX: Model must be downloaded from https://alphacephei.com/vosk/models# https://alphacephei.com/vosk/models/vosk-model-small-de-0.15.zipifnot os.path.exists(model_path):
        raise ValueError(f"Could not find VOSK model at {model_path}")

    model = vosk.Model(model_path)
    recognizer = vosk.KaldiRecognizer(model, sample_rate)

    res = transcribe_words(recognizer, int16)
    df = pandas.DataFrame.from_records(res)
    df = df.sort_values('start')

    df.to_csv(out_path, index=False)
    print('Word segments saved to', out_path)

if __name__ == '__main__':
    main()

Run the program with the .WAV file and the path to an output file.

python vosk_words.py attached_problem/main.wav out.csv

The script outputs words and their times in the CSV. These timings can then be used to split the audio file. Here is example output:

conf,end,start,word
0.618949,1.11,0.84,also
1.0,1.32,1.116314,eine
1.0,1.59,1.32,woche
0.411941,1.77,1.59,des

Comparing the output (bottom) with the example file you provided (top), it looks pretty good.

It actually picked up a word that your annotations did not include, "und" at 42.25 seconds.

Solution 2:

Delimiting words is out of the audio domain and requires a kind of intelligence. Doing it manually is easy because we are intelligent and know exactly what we are looking for, but automatizing the process is hard because, as you already noticed, a silence is not (not only, not always) a word delimiter.

At audio level, we can only approach a solution and this require both analyzing the amplitude of the signal and adding some time mechanisms. As an example, Protools provides a nice tool named Strip Silence that cuts audio regions automatically based on the amplitude of the signal. It always keeps the material at its original position in the timeline and naturally each region knows its own duration. In addition to the threshold in dB, and to prevent creating too much regions, it provides several useful parameters in the time domain : a minimum length for the created regions, a delay before the cut (the delay is computed from the point the amplitude passes below the threshold), an inverted delay before reopening the gate (the delay is computed backward from the point the amplitude passes above the threshold).

This could be a good starting point for you. Implementing such a system probably won't be 100 % successful, but you could obtain a quite good ratio if the settings are well adjusted to the speaker. Even if it's not perfect, it will significantly reduce the need for manual work.