How to Convert Speech to Text in Python

Abdou Rockikz · 7 min read · Updated jul 2020 · Machine Learning

Speech recognition is the ability of a computer software to identify words and phrases in spoken language and convert them to human readable text. In this tutorial, you will learn how you can convert speech to text in Python using SpeechRecognition library.

As a result, we do not need to build any machine learning model from scratch, this library provides us with convenient wrappers for various well known public speech recognition APIs (such as Google Cloud Speech API, IBM Speech To Text, etc.).

Learn also: How to Translate Text in Python.

Alright, let's get started, installing the library using pip:

pip3 install SpeechRecognition pydub

Okey, open up a new Python file and import it:

import speech_recognition as sr

The nice thing about this library is it supports several recognition engines:

We gonna use Google Speech Recognition here, as it's straightforward and doesn't require any API key.

Reading from a File

Make sure you have an audio file in the current directory that contains english speech (if you want to follow along with me, get the audio file here):

filename = "16-122828-0002.wav"

This file was grabbed from LibriSpeech dataset, but you can use any audio WAV file you want, just change the name of the file, let's initialize our speech recognizer:

# initialize the recognizer
r = sr.Recognizer()

The below code is responsible for loading the audio file, and converting the speech into text using Google Speech Recognition:

# open the file
with sr.AudioFile(filename) as source:
    # listen for the data (load audio to memory)
    audio_data = r.record(source)
    # recognize (convert from speech to text)
    text = r.recognize_google(audio_data)
    print(text)

This will take few seconds to finish, as it uploads the file to Google and grabs the output, here is my result:

I believe you're just talking nonsense

The above code works well for small or medium size audio files. In the next section, we gonna write code for large files.

Reading Large Audio Files

If you want to perform speech recognition of a long audio file, then the below function handles that quite well:

import os 
from pydub import AudioSegment
from pydub.silence import split_on_silence

# a function that splits the audio file into chunks
# and applies speech recognition
def get_large_audio_transcription(path):
    """
    Splitting the large audio file into chunks
    and apply speech recognition on each of these chunks
    """
    # open the audio file using pydub
    sound = AudioSegment.from_wav(path)  
    # split audio sound where silence is 700 miliseconds or more and get chunks
    chunks = split_on_silence(sound,
        # experiment with this value for your target audio file
        min_silence_len = 500,
        # adjust this per requirement
        silence_thresh = sound.dBFS-14,
        # keep the silence for 1 second, adjustable as well
        keep_silence=500,
    )
    folder_name = "audio-chunks"
    # create a directory to store the audio chunks
    if not os.path.isdir(folder_name):
        os.mkdir(folder_name)
    whole_text = ""
    # process each chunk 
    for i, audio_chunk in enumerate(chunks, start=1):
        # export audio chunk and save it in
        # the `folder_name` directory.
        chunk_filename = os.path.join(folder_name, f"chunk{i}.wav")
        audio_chunk.export(chunk_filename, format="wav")
        # recognize the chunk
        with sr.AudioFile(chunk_filename) as source:
            audio_listened = r.record(source)
            # try converting it to text
            try:
                text = r.recognize_google(audio_listened)
            except sr.UnknownValueError as e:
                print("Error:", str(e))
            else:
                text = f"{text.capitalize()}. "
                print(chunk_filename, ":", text)
                whole_text += text
    # return the text for all chunks detected
    return whole_text

Note: You need to install Pydub using pip for the above code to work.

The above function uses split_on_silence() function from pydub.silence module to split audio data into chunks on silence. min_silence_len parameter is the minimum length of a silence to be used for a split.

silence_thresh is the threshold in which anything quieter than this will be considered silence, I have set it to the average dBFS minus 14, keep_silence argument is the amount of silence to leave at the beginning and the end of each chunk detected in milliseconds.

These parameters won't be perfect for all sound files, try to experiment with these parameters with your large audio needs.

After that, we iterate over all chunks and convert each speech audio into text and adding them up all together, here is an example run:

path = "7601-291468-0006.wav"
print("\nFull text:", get_large_audio_transcription(path))

Note: You can get 7601-291468-0006.wav file here.

Output:

audio-chunks\chunk1.wav : His abode which you had fixed in a bowery or country seat. 
audio-chunks\chunk2.wav : At a short distance from the city. 
audio-chunks\chunk3.wav : Just at what is now called dutch street. 
audio-chunks\chunk4.wav : Sooner bounded with proofs of his ingenuity. 
audio-chunks\chunk5.wav : Patent smokejacks. 
audio-chunks\chunk6.wav : It required a horse to work some. 
audio-chunks\chunk7.wav : Dutch oven roasted meat without fire. 
audio-chunks\chunk8.wav : Carts that went before the horses. 
audio-chunks\chunk9.wav : Weather cox that turned against the wind and other wrongheaded contrivances. 
audio-chunks\chunk10.wav : So just understand can found it all beholders. 

Full text: His abode which you had fixed in a bowery or country seat. At a short distance from the city. Just at what is now called dutch street. Sooner bounded with proofs of his ingenuity. Patent smokejacks. It required a horse to work some. Dutch oven roasted meat without fire. Carts that went before the horses. Weather cox that turned against the wind and other wrongheaded contrivances. So just understand can found it all beholders.

Reading from the Microphone

This requires PyAudio to be installed in your machine, here is the installation process depending on your operating system:

Windows

You can just pip install it:

pip3 install pyaudio

Linux

You need to first install the dependencies:

sudo apt-get install python-pyaudio python3-pyaudio
pip3 install pyaudio

MacOS

You need to first install portaudio, then you can just pip install it:

brew install portaudio
pip3 install pyaudio

Now let's use our microphone to convert our speech:

with sr.Microphone() as source:
    # read the audio data from the default microphone
    audio_data = r.record(source, duration=5)
    print("Recognizing...")
    # convert speech to text
    text = r.recognize_google(audio_data)
    print(text)

This will hear from your microphone for 5 seconds and then tries to convert that speech into text !

It is pretty similar to the previous code, but we are using Microphone() object here to read the audio from the default microphone, and then we used duration parameter in record() function to stop reading after 5 seconds and then uploads the audio data to Google to get the output text.

You can also use offset parameter in record() function to start recording after offset seconds.

Also, you can recognize different languages by passing language parameter to recognize_google() function. For instance, if you want to recognize spanish speech, you would use:

text = r.recognize_google(audio_data, language="es-ES")

Check out supported languages in this stackoverflow answer.

Conclusion

As you can see, it is pretty easy and simple to use this library for converting speech to text. This library is widely used out there in the wild, make sure you master it, check their official documentation.

If you want to convert text to speech in Python as well, check this tutorial.

Happy Coding ♥

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