This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AR dataset. It achieves the following results on the evaluation set:

Loss: 0.2141
Wer: 0.1808

It achieves the following results on the evaluation set:

eval_loss = 0.2141
eval_samples = 10388
eval_wer = 0.181
eval_cer = 0.049

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id bakrianoo/sinai-voice-ar-stt --dataset mozilla-foundation/common_voice_8_0 --config ar --split test

Inference Without LM

python

from transformers import (Wav2Vec2Processor, Wav2Vec2ForCTC)
import torchaudio
import torch

def speech_file_to_array_fn(voice_path, resampling_to=16000):
    speech_array, sampling_rate = torchaudio.load(voice_path)
    resampler = torchaudio.transforms.Resample(sampling_rate, resampling_to)
    
    return resampler(speech_array)[0].numpy(), sampling_rate

# load the model
cp = "bakrianoo/sinai-voice-ar-stt"
processor = Wav2Vec2Processor.from_pretrained(cp)
model = Wav2Vec2ForCTC.from_pretrained(cp)

# recognize the text in a sample sound file
sound_path = './my_voice.mp3'

sample, sr = speech_file_to_array_fn(sound_path)
inputs = processor([sample], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values,).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 32
eval_batch_size: 10
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 256
total_eval_batch_size: 80
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.354	0.64	1000	0.4109	0.4493
0.5886	1.28	2000	0.2798	0.3099
0.4977	1.92	3000	0.2387	0.2673
0.4253	2.56	4000	0.2266	0.2523
0.3942	3.2	5000	0.2171	0.2437
0.3619	3.84	6000	0.2076	0.2253
0.3245	4.48	7000	0.2088	0.2186
0.308	5.12	8000	0.2086	0.2206
0.2881	5.76	9000	0.2089	0.2105
0.2557	6.4	10000	0.2015	0.2004
0.248	7.04	11000	0.2044	0.1953
0.2251	7.68	12000	0.2058	0.1932
0.2052	8.32	13000	0.2117	0.1878
0.1976	8.96	14000	0.2104	0.1825
0.1845	9.6	15000	0.2156	0.1821

Framework versions

Transformers 4.16.2
Pytorch 1.10.2+cu113
Datasets 1.18.3
Tokenizers 0.11.0