Audio-transcriptor-russian-

This script will split audio file on silence, transcript it with google recognition and save it in LJSpeech-1.1 dataset manner.

This script will create the folder structure in the same manner as LJSpeech-1.1 dataset.
This script will splitt the audio files on silenses and send the audio chunks to google recognition service
Google will return the recognized text.
Text normalization will change integers and text written with latin letters into russian text.
Punctuation will set commas in text.
This text will be writen to metadata.csv in the same manner as in LJSpeech-1.1 dataset.
Audio chunks will be normalized on loudness.
Audio chunks will also be saved in the same manner as in LJSpeech-1.1 dataset.
See script for further description.

requirements:

pydub

SpeechRecognition

torch --> for normalizer

tqdm --> for normalizer

pytorch_pretrained_bert==0.6.2 --> for punctuation (bert)

pymorphy2==0.8 --> for punctuation (bert)

to work with mp3-files you will need to install ffmpeg and put it to PATH. https://github.com/FFmpeg/FFmpeg Windows installation instruction here http://blog.gregzaal.com/how-to-install-ffmpeg-on-windows/

Text normalization from https://github.com/snakers4/russian_stt_text_normalization is implemented here.

Punctuation (comma placement) from https://github.com/vlomme/Bert-Russian-punctuation dont forget to download pretrained model (see "how to use").

how to use

This script must be in the same folder with audio files that should be transcripted
The names of the audio files must be as follows: 01.mp3, 02.mp3, ..., 99.mp3 (or) 01.wav, 02.wav, ..., 99.wav
download pretrained bert model https://drive.google.com/file/d/190dLqhRjqgNJLKBqz0OxQ3TzxSm5Qbfx/view and place it in folder bert
start the audio_transcribe.py in IDLE

how to optimize for your audio

if you want to transcribe .wav-files instead of mp3 change

source_format = 'mp3' --> source_format = 'wav'

if you want to remove chunks with strange symbol rate (text symbols per second audio), set

symbols_gate = True

and set

symbol_rate_min and symbol_rate_max

you can use it to separate one speaker speaking with special speaking speed, and/or poor transcriptions. You will also get additional information about rate and audio length in metadata.csv.

if you want to remove chunks without speech, set

additional_clean = True

use it if you have audio with bad quality and chunks without speach.

assign an Speaker_id

Speaker_id = 'R001_'

change silence duration (in ms) for cut

min_silence_len = 500

change silence value (in dBFS)

silence_thresh = -36

change silense duration (in ms) to keep in audio

keep_silence = 100

change audio framerate (in Hz) in result audio

frame_rate = 16000

change length (in ms) of output audio file

target_length = 1000

if you want to set commas in yout text:

punctuation = True

if you want to use other language then russian:

change language in line

rec = r.recognize_google(audio_listened, language="ru-RU").lower()

as discribed here https://cloud.google.com/speech-to-text/docs/languages

replace line

rec = norm.norm_text(rec)

and set

punctuation = False

zirumandbigbro / audio-transcriptor-russian- Goto Github PK