import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import gradio as gr
import scipy.signal as sps
import sox

def convert(inputfile, outfile):
    sox_tfm = sox.Transformer()
    sox_tfm.set_output_format(
        file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16
    )
    #print(this is not done)
    sox_tfm.build(inputfile, outfile)

def read_file(wav):
    sample_rate, signal = wav                                                                                                                        
    signal = signal.mean(-1)                                                                                                                              
    number_of_samples = round(len(signal) * float(16000) / sample_rate)                                                                                   
    resampled_signal = sps.resample(signal, number_of_samples)
    return resampled_signal


def parse_transcription(wav_file):
    '''
    filename = wav_file.name.split('.')[0]
    convert(wav_file.name, filename + "16k.wav")
    speech, _ = sf.read(filename + "16k.wav")
    
    '''
    speech = read_file(wav_file)
    input_values = processor(speech, sampling_rate=16_000, return_tensors="pt").input_values

    logits = model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)

    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
    return transcription
    

processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
    
    

processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
    
#input_ = gr.inputs.Audio(source="microphone", type="file") 
input_ = gr.inputs.Audio(source="microphone", type="numpy") 

gr.Interface(parse_transcription, inputs = input_,  outputs="text", 
             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);