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_text
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