XLS-R-300M - Hausa

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: 0.6094
Wer: 0.5234

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: 13
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 1000
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9599	6.56	400	2.8650	1.0
2.7357	13.11	800	2.7377	0.9951
1.3012	19.67	1200	0.6686	0.7111
1.0454	26.23	1600	0.5686	0.6137
0.9069	32.79	2000	0.5576	0.5815
0.82	39.34	2400	0.5502	0.5591
0.7413	45.9	2800	0.5970	0.5586
0.6872	52.46	3200	0.5817	0.5428
0.634	59.02	3600	0.5636	0.5314
0.6022	65.57	4000	0.5780	0.5229
0.5705	72.13	4400	0.6036	0.5323
0.5408	78.69	4800	0.6119	0.5336
0.5225	85.25	5200	0.6105	0.5270
0.5265	91.8	5600	0.6034	0.5231
0.5154	98.36	6000	0.6094	0.5234

Framework versions

Transformers 4.16.1
Pytorch 1.10.0+cu111
Datasets 1.18.2
Tokenizers 0.11.0

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-ha-cv8 --dataset mozilla-foundation/common_voice_8_0 --config ha --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-ha-cv8"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ha", 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
# => "kakin hade ya ke da kyautar"

Eval results on Common Voice 8 "test" (WER):

Without LM	With LM (run `./eval.py`)
47.821	36.295