README.md

# Nepali Automatic Speech Recognition (ASR)

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

## Model Details

| Property | Value |
|----------|-------|
| **Model ID** | `Saugat212/ASR_MODEL` |
| **Base Model** | facebook/wav2vec2-base |
| **Architecture** | wav2vec2 |
| **Parameters** | 0.3B |
| **Language** | Nepali |

## Purpose

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

## Contents

| File | Description |
|------|-------------|
| `whisper_transcription.ipynb` | Whisper model for Nepali speech-to-text transcription |
| `wav2vec2_finetuning.ipynb` | Wav2Vec2 fine-tuning recipe for Nepali ASR |
| `wav2vec2_finetune.py` | Python script for Wav2Vec2 fine-tuning |
| `finetune.py` | ASR fine-tuning script |
| `Dataset/` | Training datasets (CSV files with audio paths and transcriptions) |
| `Phase 1/Finetuning/` | Phase 1 training data, checkpoints, and inference notebooks |

## Usage

### Load Model
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC

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

### Inference
```python
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.