|
--- |
|
language: |
|
- ar |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- hf-asr-leaderboard |
|
- robust-speech-event |
|
datasets: |
|
- mozilla-foundation/common_voice_8_0 |
|
metrics: |
|
- wer |
|
- cer |
|
model-index: |
|
- name: Sinai Voice Arabic Speech Recognition Model |
|
results: |
|
- task: |
|
type: automatic-speech-recognition |
|
name: Speech Recognition |
|
dataset: |
|
type: mozilla-foundation/common_voice_8_0 |
|
name: Common Voice ar |
|
args: ar |
|
metrics: |
|
- type: wer |
|
value: 0.181 |
|
name: Test WER |
|
- type: cer |
|
value: 0.049 |
|
name: Test CER |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Robust Speech Event - Dev Data |
|
type: speech-recognition-community-v2/dev_data |
|
args: ar |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 93.03 |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Robust Speech Event - Test Data |
|
type: speech-recognition-community-v2/eval_data |
|
args: ar |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 90.79 |
|
widget: |
|
- example_title: Example 1 |
|
src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19077324.mp3 |
|
- example_title: Example 2 |
|
src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19205138.mp3 |
|
- example_title: Example 3 |
|
src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19331711.mp3 |
|
--- |
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# 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](https://huggingface.co/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 |
|
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` |
|
|
|
```bash |
|
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 |
