python text to speech
The best library because you dont have to save the
text file or open the file to start the speech
pip install pyttsx3
import pyttsx3
engine = pyttsx3.init()
engine.say("Hello world")
engine.runAndWait()python text to speech
The best library because you dont have to save the
text file or open the file to start the speech
pip install pyttsx3
import pyttsx3
engine = pyttsx3.init()
engine.say("Hello world")
engine.runAndWait()python text to speech
#run in Cmd or in terminal
#pip install pyttsx3
import pyttsx3
pyttsx3.speak("hi user")python speech to text
import speech_recognition as sr
def main():
r = sr.Recognizer()
with sr.Microphone() as source:
r.adjust_for_ambient_noise(source)
audio = r.listen(source)
try:
print(r.recognize_google(audio))
except Exception as e:
print("Error : " + str(e))
with open("recorded.wav", "wb") as f:
f.write(audio.get_wav_data())
if __name__ == "__main__":
main()python code voice to text
# 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_textCopyright © 2021 Codeinu
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