Wav2vec2 Large Xlsr 53 Punjabi

🚀 wav2vec2-large-xlsr-53-punjabi

This model is a fine - tuned version of Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 on the common_voice dataset. It offers a solution for automatic speech recognition in Punjabi, achieving high - quality results on the evaluation set.

✨ Features

• Language Support: Specialized for Punjabi (pa).
• License: Licensed under Apache - 2.0.
• Tags: Associated with automatic - speech - recognition, hf - asr - leaderboard, and robust - speech - event.
• Datasets: Trained on mozilla - foundation/common_voice_8_0.
• Metrics: Evaluated using WER (Word Error Rate) and CER (Character Error Rate).

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "kingabzpro/wav2vec2-large-xlsr-53-punjabi"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "pa-IN", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
    logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text

Advanced Usage

There is no advanced usage example provided in the original document.

📚 Documentation

Model Information

Evaluation Results

This model achieves the following results on the evaluation set:

• Loss: 1.2101
• Wer: 0.4939
• Cer: 0.2238

Evaluation Commands

1. To evaluate on mozilla - foundation/common_voice_8_0 with split test

python eval.py --model_id kingabzpro/wav2vec2-large-xlsr-53-punjabi --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test

Training Details

Hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 30
• mixed_precision_training: Native AMP

Training Results

Framework Versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0

📄 License

This model is licensed under Apache - 2.0.