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#Importing all the necessary packages | |
import nltk | |
import librosa | |
import torch | |
import gradio as gr | |
from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC | |
nltk.download("punkt") | |
def correct_casing(input_sentence): | |
""" This function is for correcting the casing of the generated transcribed text | |
""" | |
sentences = nltk.sent_tokenize(input_sentence) | |
return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences])) | |
def asr_transcript(audio_file, language): | |
"""Generating transcripts for the audio input | |
""" | |
#Selecting the language and loading the model and the tokenizer | |
if language == "English": | |
model_name = "facebook/wav2vec2-large-960h-lv60-self" | |
elif language == "Russian": | |
model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-russian" | |
tokenizer = Wav2Vec2Tokenizer.from_pretrained(model) | |
model = Wav2Vec2ForCTC.from_pretrained(model) | |
#read the file and resample to 16KHz | |
stream = librosa.stream(audio_file.name, block_length=20, frame_length=16000, hop_length=16000) | |
for speech in stream: | |
if len(speech.shape) > 1: | |
speech = speech[:, 0] + speech[:, 1] | |
input_values = tokenizer(speech, return_tensors="pt").input_values | |
logits = model(input_values).logits | |
predicted_ids = torch.argmax(logits, dim=-1) | |
transcription = tokenizer.batch_decode(predicted_ids)[0] | |
transcript += transcription.lower() + " " | |
return transcript | |
gr.Interface(asr_transcript, | |
inputs = [gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Please record your message/Пожалуйста, введите Ваше сообщение"), | |
gr.inputs.Radio(label="Pick a language/Выберите язык", choices=["English", "Russian"]) | |
outputs = gr.outputs.Textbox(label="Output Text/Результат"), | |
title="Automatic speech recognition with voice recorder in Russian and English", | |
description = "This application displays transcribed text for given audio input", | |
theme="grass").launch() |