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wav2vec2-large-xls-r-300m-ur-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 1.1443
- Wer: 0.5677
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: 1000
- num_epochs: 200
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.6269 | 15.98 | 400 | 3.3246 | 1.0 |
| 3.0546 | 31.98 | 800 | 2.8148 | 0.9963 |
| 1.4589 | 47.98 | 1200 | 1.0237 | 0.6584 |
| 1.0911 | 63.98 | 1600 | 0.9524 | 0.5966 |
| 0.8879 | 79.98 | 2000 | 0.9827 | 0.5822 |
| 0.7467 | 95.98 | 2400 | 0.9923 | 0.5840 |
| 0.6427 | 111.98 | 2800 | 0.9988 | 0.5714 |
| 0.5685 | 127.98 | 3200 | 1.0872 | 0.5807 |
| 0.5068 | 143.98 | 3600 | 1.1194 | 0.5822 |
| 0.463 | 159.98 | 4000 | 1.1138 | 0.5692 |
| 0.4212 | 175.98 | 4400 | 1.1232 | 0.5714 |
| 0.4056 | 191.98 | 4800 | 1.1443 | 0.5677 |
Framework versions
- Transformers 4.16.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.11.0
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_8_0with splittest
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-ur-cv8 --dataset mozilla-foundation/common_voice_8_0 --config ur --split test
Inference With LM
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-large-xls-r-300m-ur-cv8"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ur", 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
# => "اب نے ٹ پیس ان لیتے ہیں"
Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run ./eval.py) |
|---|---|
| 52.146 | 42.376 |
