A few weeks ago, we created a Text to Speech project in Python. Today we are going to create an opposite Speech to Text script.
Transform your spoken words into written text with this powerful Speech to Text Python script! Using state-of-the-art machine learning algorithms, this script converts spoken language into written text in real-time, making it perfect for dictation, transcription, and voice-controlled applications. The script is easy to set up, customize and integrate into your projects, and it supports multiple languages. Whether you’re looking to automate speech recognition tasks or build cutting-edge voice-controlled systems, this script is the perfect solution for you.
To get started, let’s install the required module:
pip3 install SpeechRecognition pydubMake sure you have an audio file in the current directory.
# Speech to Text Convertor with Python
import speech_recognition as sr
filename = "speech.wav"
# initialize the recognizer
r = sr.Recognizer()
# 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)
You can use this script for small or medium size audio files, but for larger files we are going to use the following script:
# importing libraries
import speech_recognition as sr
import os
from pydub import AudioSegment
from pydub.silence import split_on_silence
# create a speech recognition object
r = sr.Recognizer()
# 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
