from transformers import Wav$2$Vec$2$ForCTC, Wav$2$Vec$2$Tokenizer

import torch

import librosa

import soundfile as sf

model $=$ Wav$2$Vec$2$ForCTC.from$\_$pretrained$(\text{'}$theainerd$/$Wav$2$Vec$2-$large$-$xlsr$-$hindi'$)$

tokenizer $=$ Wav$2$Vec$2$Tokenizer.from$\_$pretrained$(\text{'}$theainerd$/$Wav$2$Vec$2-$large$-$xlsr$-$hindi'$)$

import pandas as pd

df $=$ pd$.$read$\_$csv$("$train$.$csv$")$

df$.$head$(1)$

df $=$ df$.$drop$($columns $=["$age$",$"gender","up$\_$votes","down$\_$votes","accents","variant","locale","segment"$])$

print$($df$.$columns$)$

## df $=$ df$.$drop$($columns $=["$client$\_$id$"])$

list $=[]$

sentences $=[]$

samples $=30$

for i in range$(0,$samples$)$:

list.append$($df$["$path$"][$i$])$

sentences.append$($df$["$sentence$"][$i$])$

print$($list$)$

print$($sentences$)$

## print$($len$($sentences$))$

import os

path $=$ "common$\_$voice$\backslash$clips$"$

file$\_$path $=[]$

for i in range$(0,$samples$)$:

file$\_$path.append$($os$.$path.join$($path$,$list$[$i$]))$

## print$($file$\_$path$[$i$])$

# Function to transcribe an audio file

def transcribe$\_$audio$($file$\_$path$)$:

# Load the audio file

audio, sr $=$ librosa.load$($file$\_$path, sr$=16000)$ ## resampling

# Convert the audio to a format that can be input to the model

input$\_$values $=$ tokenizer$($audio$,$ return$\_$tensors$="$pt$").$input$\_$values

# Run the audio through the model to generate the transcription

#with torch.no$\_$grad$()$:

logits $=$ model$($input$\_$values$).$logits

predicted$\_$ids $=$ torch.argmax$($logits$,$ dim$=-1)$

transcription $=$ tokenizer.decode$($predicted$\_$ids$[0])$

return transcription

transcription $=[]$

for i in range$(0,$samples$)$:

transcription.append$($transcribe$\_$audio$($file$\_$path$[$i$]))$

print$($f$"$Transcription for $\{$file$\_$path$[$i$]\}$ : $\{$transcription$[$i$]\}")$

help me to fine$-$tune this model and add code in my written code for fine tuning that should work correctly without error and explain in detail and also explain in detail any other alternative approach please