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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 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 = "facebook/wav2vec2-large-960h-lv60-self"
elif language == "Russian":
model = "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)
#read the file
speech, sample_rate = librosa.load(audio_file, 16000)
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/Это приложение отображает текст Вашего голосового сообщения",
theme="grass").launch() |