import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
import gradio as gr
import sox
import subprocess


def read_file_and_process(wav_file):
    filename = wav_file.split('.')[0]
    filename_16k = filename + "16k.wav"
    resampler(wav_file, filename_16k)
    speech, _ = sf.read(filename_16k)
    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
    
    return inputs


def resampler(input_file_path, output_file_path):
    command = (
        f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
        f"{output_file_path}"
    )
    subprocess.call(command, shell=True)



def parse_transcription(logits,processor):
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
    return transcription


def parse(wav_file, language):

    if language == 'Hindi':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
    elif language == 'Odia':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-odia-orm-100")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-odia-orm-100")
    elif language == 'Assamese':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-assamese-asm-8")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-assamese-asm-8")
    elif language == 'Sanskrit':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-sanskrit-sam-60")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-sanskrit-sam-60")
    elif language == 'Punjabi':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10")
    elif language == 'Urdu':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-urdu-urm-60")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-urdu-urm-60")
    elif language == 'Rajasthani':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45")
    elif language == 'Marathi':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-marathi-mrm-100")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-marathi-mrm-100")
    elif language == 'Malayalam':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-malayalam-mlm-8")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-malayalam-mlm-8")
    elif language == 'Maithili':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-maithili-maim-50")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-maithili-maim-50")
    elif language == 'Dogri':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-dogri-doi-55")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-dogri-doi-55")
    elif language == 'Bhojpuri':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bhojpuri-bhom-60")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bhojpuri-bhom-60")
    elif language == 'Tamil':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
    elif language == 'Telugu':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-telugu-tem-100")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-telugu-tem-100")
    elif language == 'Nepali':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-nepali-nem-130")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-nepali-nem-130")
    elif language == 'Kannada':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-kannada-knm-560")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-kannada-knm-560")
    elif language == 'Gujarati':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100")
    elif language == 'Bengali':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200")
    elif language == 'English':
        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700")
        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700")
    

    input_values = read_file_and_process(wav_file)
    with torch.no_grad():
        logits = model(**input_values).logits

    return parse_transcription(logits, processor)


options = ['Hindi','Odia','Assamese','Sanskrit','Punjabi','Urdu','Rajasthani','Marathi','Malayalam','Maithili','Dogri','Bhojpuri','Tamil','Telugu','Nepali','Kannada','Gujarati','Bengali','English']


language = gr.Dropdown(options,label="Select language")  
input_ = gr.Audio(source="upload", type="filepath")  
txtbox = gr.Textbox(
            label="Output from model will appear here:",
            lines=5
        )


gr.Interface(parse, inputs = [input_,language ],  outputs=txtbox,
             streaming=True, interactive=True,
             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);