ASR_MODEL

Nepali Automatic Speech Recognition (ASR)

Overview

Fine-tuning and inference for Nepali language speech recognition using Wav2Vec2 and Whisper models.

Model Details

Purpose

• Convert Nepali speech audio to text
• Fine-tune Wav2Vec2 on Nepali datasets
• Evaluate ASR performance using WER metric

Contents

Usage

Load Model

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC

model_name = "Saugat212/ASR_MODEL"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)

Inference

import torchaudio
import torch

# Load audio
waveform, sample_rate = torchaudio.load("audio.wav")

# Process
input_values = processor(waveform.squeeze(), return_tensors="pt", sampling_rate=sample_rate).input_values

# Infer
with torch.no_grad():
    logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)

# Decode
transcription = processor.batch_decode(predicted_ids)[0]
print(transcription)

Models Available

• Wav2Vec2: Saugat212/ASR_MODEL - Fine-tuned Nepali ASR
• Whisper: OpenAI Whisper for alternative transcription

Dataset

• Located in Dataset/
• Contains final_transcriptions.csv with audio paths and transcriptions
• Cleaned data in cleaned_data.csv

Requirements

• transformers
• torchaudio
• datasets
• evaluate
• jiwer

Fine-tuning

See wav2vec2_finetuning.ipynb for complete fine-tuning pipeline.