|
--- |
|
language: ary |
|
base_model: facebook/wav2vec2-large-xlsr-53 |
|
metrics: |
|
- wer |
|
tags: |
|
- audio |
|
- automatic-speech-recognition |
|
- speech |
|
- xlsr-fine-tuning-week |
|
license: apache-2.0 |
|
model-index: |
|
- name: XLSR Wav2Vec2 Moroccan Arabic dialect by Boumehdi |
|
results: |
|
- task: |
|
name: Speech Recognition |
|
type: automatic-speech-recognition |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 0.084904 |
|
--- |
|
# Wav2Vec2-Large-XLSR-53-Moroccan-Darija |
|
|
|
**wav2vec2-large-xlsr-53 new model** |
|
|
|
- Fine-tuned on 57 hours of labeled Darija Audios extracted from MDVC (https://ijeecs.iaescore.com/index.php/IJEECS/article/view/35709) which contains more than 1000 hours of Moroccan Darija "ary". |
|
- Fine-tuning is ongoing 24/7 to enhance accuracy. |
|
- We are consistently adding data to the model every day (We prefer not to add all MDVC Corpus at once as we are trying to standardize more and more the way we write the Moroccan Darija). |
|
|
|
<table><thead><tr><th><strong>Training Loss</strong></th> <th><strong>Validation</strong></th> <th><strong>Loss Wer</strong></th></tr></thead> <tbody><tr> |
|
<td>0.121300</td> |
|
<td>0.103430</td> |
|
<td>0.084904</td> |
|
</tr> </tbody></table> |
|
|
|
## Usage |
|
|
|
The model can be used directly as follows: |
|
|
|
```python |
|
import librosa |
|
import torch |
|
from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC, Wav2Vec2Processor, TrainingArguments, Wav2Vec2FeatureExtractor, Trainer |
|
|
|
tokenizer = Wav2Vec2CTCTokenizer("./vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|") |
|
processor = Wav2Vec2Processor.from_pretrained('boumehdi/wav2vec2-large-xlsr-moroccan-darija', tokenizer=tokenizer) |
|
model=Wav2Vec2ForCTC.from_pretrained('boumehdi/wav2vec2-large-xlsr-moroccan-darija') |
|
|
|
|
|
# load the audio data (use your own wav file here!) |
|
input_audio, sr = librosa.load('file.wav', sr=16000) |
|
|
|
# tokenize |
|
input_values = processor(input_audio, return_tensors="pt", padding=True).input_values |
|
|
|
# retrieve logits |
|
logits = model(input_values).logits |
|
|
|
tokens = torch.argmax(logits, axis=-1) |
|
|
|
# decode using n-gram |
|
transcription = tokenizer.batch_decode(tokens) |
|
|
|
# print the output |
|
print(transcription) |
|
``` |
|
|
|
Output: قالت ليا هاد السيد هادا ما كاينش بحالو |
|
|
|
email: souregh@gmail.com |
|
|
|
BOUMEHDI Ahmed |
|
|
