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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: 0.172139

Wav2Vec2-Large-XLSR-53-Moroccan-Darija

wav2vec2-large-xlsr-53

  • Fine-tuned on 31 hours (31 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
0.018800 0.262561 0.172139

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