metadata
license: mit
base_model: UmarRamzan/w2v2-bert-urdu
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-bert-urdu
results: []
language:
- ur
datasets:
- mozilla-foundation/common_voice_17_0
Wav2Vec-Bert-2.0-Urdu
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the Urdu split of the Common Voice 17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3681
- Wer: 0.2929
Usage Instructions
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-urdu")
model = Wav2Vec2BertModel.from_pretrained("UmarRamzan/w2v2-bert-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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4362 | 0.1695 | 50 | 0.4144 | 0.3213 |
| 0.3776 | 0.3390 | 100 | 0.4029 | 0.3137 |
| 0.3918 | 0.5085 | 150 | 0.4095 | 0.3060 |
| 0.3968 | 0.6780 | 200 | 0.3961 | 0.3060 |
| 0.3685 | 0.8475 | 250 | 0.3681 | 0.2929 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1