Sinai Voice Arabic Speech Recognition Model
نموذج صوت سيناء للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
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 splittest
python eval.py --model_id bakrianoo/sinai-voice-ar-stt --dataset mozilla-foundation/common_voice_8_0 --config ar --split test
Inference Without LM
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
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Dataset used to train marid/sst2
Evaluation results
- Test WER on Common Voice arself-reported0.181
- Test CER on Common Voice arself-reported0.049
- Test WER on Robust Speech Event - Dev Dataself-reported93.030
- Test WER on Robust Speech Event - Test Dataself-reported90.790