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from transformers import Wav 2 Vec 2 ForCTC, Wav 2 Vec 2 Tokenizer import torch import librosa import soundfile as sf model = Wav 2
from transformers import WavVecForCTC, WavVecTokenizer
import torch
import librosa
import soundfile as sf
model WavVecForCTC.frompretrainedtheainerdWavVeclargexlsrhindi'
tokenizer WavVecTokenizer.frompretrainedtheainerdWavVeclargexlsrhindi'
import pandas as pd
df pdreadcsvtraincsv
dfhead
df dfdropcolumns age"gender","upvotes","downvotes","accents","variant","locale","segment"
printdfcolumns
## df dfdropcolumns clientid
list
sentences
samples
for i in rangesamples:
list.appenddfpathi
sentences.appenddfsentencei
printlist
printsentences
## printlensentences
import os
path "commonvoiceclips
filepath
for i in rangesamples:
filepath.appendospath.joinpathlisti
## printfilepathi
# Function to transcribe an audio file
def transcribeaudiofilepath:
# Load the audio file
audio, sr librosa.loadfilepath, sr ## resampling
# Convert the audio to a format that can be input to the model
inputvalues tokenizeraudio returntensorsptinputvalues
# Run the audio through the model to generate the transcription
#with torch.nograd:
logits modelinputvalueslogits
predictedids torch.argmaxlogits dim
transcription tokenizer.decodepredictedids
return transcription
transcription
for i in rangesamples:
transcription.appendtranscribeaudiofilepathi
printfTranscription for filepathi : transcriptioni
help me tp finetune this model and add code in my written code for fine tuning and explain in detail and alsoexplain in detail any other alternative approach
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