#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()