metadata
language: ary
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: 0.167967
Wav2Vec2-Large-XLSR-53-Moroccan-Darija
wav2vec2-large-xlsr-53
- Fine-tuned on 32 hours (32 people) of labeled Darija Audios.
- Each hour of audio is pronounced by a different person.
- Transcriptions are performed by a single individual.
- Fine-tuning is ongoing 24/7 to enhance accuracy.
- We are consistently adding more data to the model every day.
- Audio database is organized (by sex, age, region, ..)
| Training Loss | Validation | Loss Wer |
|---|---|---|
| 0.015400 | 0.248209 | 0.167967 |
Usage
The model can be used directly as follows:
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