XLS-R-300M - Latvian
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - LV dataset. It achieves the following results on the evaluation set:
- Loss: 0.1660
- Wer: 0.1705
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: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.489 | 2.56 | 400 | 3.3590 | 1.0 |
| 2.9903 | 5.13 | 800 | 2.9704 | 1.0001 |
| 1.6712 | 7.69 | 1200 | 0.6179 | 0.6566 |
| 1.2635 | 10.26 | 1600 | 0.3176 | 0.4531 |
| 1.0819 | 12.82 | 2000 | 0.2517 | 0.3508 |
| 1.0136 | 15.38 | 2400 | 0.2257 | 0.3124 |
| 0.9625 | 17.95 | 2800 | 0.1975 | 0.2311 |
| 0.901 | 20.51 | 3200 | 0.1986 | 0.2097 |
| 0.8842 | 23.08 | 3600 | 0.1904 | 0.2039 |
| 0.8542 | 25.64 | 4000 | 0.1847 | 0.1981 |
| 0.8244 | 28.21 | 4400 | 0.1805 | 0.1847 |
| 0.7689 | 30.77 | 4800 | 0.1736 | 0.1832 |
| 0.7825 | 33.33 | 5200 | 0.1698 | 0.1821 |
| 0.7817 | 35.9 | 5600 | 0.1758 | 0.1803 |
| 0.7488 | 38.46 | 6000 | 0.1663 | 0.1760 |
| 0.7171 | 41.03 | 6400 | 0.1636 | 0.1721 |
| 0.7222 | 43.59 | 6800 | 0.1663 | 0.1729 |
| 0.7156 | 46.15 | 7200 | 0.1633 | 0.1715 |
| 0.7121 | 48.72 | 7600 | 0.1666 | 0.1718 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_8_0with splittest
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config lv --split test
- To evaluate on
speech-recognition-community-v2/dev_data
python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config lv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
Inference With LM
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "lv", 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
# => "domāju ka viņam viss labi"
Eval results on Common Voice 8 "test" (WER):
| Without LM | With LM (run ./eval.py) |
|---|---|
| 16.997 | 9.633 |