File size: 2,087 Bytes
abe0763 f7997cf abe0763 f7997cf 0cf7378 47acf89 abe0763 d011b64 abe0763 47acf89 abe0763 f7997cf 47acf89 abe0763 47acf89 abe0763 47acf89 abe0763 47acf89 abe0763 47acf89 abe0763 47acf89 abe0763 f7997cf abe0763 0cf7378 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2Vec-Bert-2.0-ngram-Urdu
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Urdu split of the [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) 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 |