This model is a fine-tuned version of facebook/w2v-bert-2.0 on the Urdu split of the Common Voice 17 dataset. The fine-tuned model is enhanced with the addition of an ngram language model that has also been trained on the same dataset. It achieves the following results on the evaluation set:

Loss: 0.3681
Wer: 0.2407

Usage Instructions

python

from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
import torch
from datasets import load_dataset

dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
dataset = dataset.sort("id")
sampling_rate = dataset.features["audio"].sampling_rate

processor = AutoProcessor.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu")
model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-ngram-urdu")

# audio file is decoded on the fly
inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
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: 100
num_epochs: 1
mixed_precision_training: Native AMP

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1