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---
language: ar
datasets:
- common_voice
- arabic_speech_corpus
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Arabic by Jonatas Grosman
  results:
  - task: 
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice ar
      type: common_voice
      args: ar
    metrics:
       - name: Test WER
         type: wer
         value: 39.59
       - name: Test CER
         type: cer
         value: 18.18
---

# Wav2Vec2-Large-XLSR-53-Arabic

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice) and [Arabic Speech Corpus](https://huggingface.co/datasets/arabic_speech_corpus).
When using this model, make sure that your speech input is sampled at 16kHz.

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "ar"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
SAMPLES = 10

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)
```

| Reference  | Prediction |
| ------------- | ------------- |
| ุฃู„ุฏูŠูƒ ู‚ู„ู… ุŸ | ุฃู„ุฏูŠูƒ ู‚ู„ู… |
| ู„ูŠุณุช ู‡ู†ุงูƒ ู…ุณุงูุฉ ุนู„ู‰ ู‡ุฐู‡ ุงู„ุฃุฑุถ ุฃุจุนุฏ ู…ู† ูŠูˆู… ุฃู…ุณ. | ู„ูŠุณุช ู†ุงู„ูƒ ู…ุณุงูุฉ ุนู„ู‰ ู‡ุฐู‡ ุงู„ุฃุฑุถ ุฃุจุนุฏ ู…ู† ูŠูˆู… ุงู„ุฃู…ุณ  ู… |
| ุฅู†ูƒ ุชูƒุจุฑ ุงู„ู…ุดูƒู„ุฉ. | ุฅู†ูƒ ุชูƒุจุฑ ุงู„ู…ุดูƒู„ุฉ |
| ูŠุฑุบุจ ุฃู† ูŠู„ุชู‚ูŠ ุจูƒ. | ูŠุฑุบุจ ุฃู† ูŠู„ุชู‚ูŠ ุจูƒ |
| ุฅู†ู‡ู… ู„ุง ูŠุนุฑููˆู† ู„ู…ุงุฐุง ุญุชู‰. | ุฅู†ู‡ู… ู„ุง ูŠุนุฑููˆู† ู„ู…ุงุฐุง ุญุชู‰ |
| ุณูŠุณุนุฏู†ูŠ ู…ุณุงุนุฏุชูƒ ุฃูŠ ูˆู‚ุช ุชุญุจ. | ุณูŠุณุฆุฏู†ูŠู…ุณุงุนุฏุชูƒ ุฃูŠ ูˆู‚ุฏ ุชุญุจ |
| ุฃูŽุญูŽุจู‘ู ู†ุธุฑูŠู‘ุฉ ุนู„ู…ูŠุฉ ุฅู„ูŠู‘ ู‡ูŠ ุฃู† ุญู„ู‚ุงุช ุฒุญู„ ู…ูƒูˆู†ุฉ ุจุงู„ูƒุงู…ู„ ู…ู† ุงู„ุฃู…ุชุนุฉ ุงู„ู…ูู‚ูˆุฏุฉ. | ุฃุญุจ ู†ุธุฑูŠุฉ ุนู„ู…ูŠุฉ ุฅู„ูŠ  ู‡ูŠ ุฃู† ุญู„ ู‚ุชุฒุญ ุงู„ู…ูƒูˆูŠู†ุง ุจุงู„ูƒุงู…ู„ ู…ู† ุงู„ุฃู…ุช ุนู† ุงู„ู…ูู‚ูˆุฏุฉ |
| ุณุฃุดุชุฑูŠ ู„ู‡ ู‚ู„ู…ุงู‹. | ุณุฃุดุชุฑูŠ ู„ู‡ ู‚ู„ู…ุง |
| ุฃูŠู† ุงู„ู…ุดูƒู„ุฉ ุŸ | ุฃูŠู† ุงู„ู…ุดูƒู„ |
| ูˆูŽู„ูู„ู‘ูŽู‡ู ูŠูŽุณู’ุฌูุฏู ู…ูŽุง ูููŠ ุงู„ุณู‘ูŽู…ูŽุงูˆูŽุงุชู ูˆูŽู…ูŽุง ูููŠ ุงู„ู’ุฃูŽุฑู’ุถู ู…ูู†ู’ ุฏูŽุงุจู‘ูŽุฉู ูˆูŽุงู„ู’ู…ูŽู„ูŽุงุฆููƒูŽุฉู ูˆูŽู‡ูู…ู’ ู„ูŽุง ูŠูŽุณู’ุชูŽูƒู’ุจูุฑููˆู†ูŽ | ูˆู„ู„ู‡ ูŠุณุฌุฏ ู…ุง ููŠ ุงู„ุณู…ุงูˆุงุช ูˆู…ุง ููŠ ุงู„ุฃุฑุถ ู…ู† ุฏุงุจุฉ ูˆุงู„ู…ู„ุงุฆูƒุฉ ูˆู‡ู… ู„ุง ูŠุณุชูƒุจุฑูˆู† |

## Evaluation

The model can be evaluated as follows on the Arabic test data of Common Voice.

```python
import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "ar"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
DEVICE = "cuda"

CHARS_TO_IGNORE = [",", "?", "ยฟ", ".", "!", "ยก", ";", "๏ผ›", ":", '""', "%", '"', "๏ฟฝ", "สฟ", "ยท", "แƒป", "~", "ีž",
                  "ุŸ", "ุŒ", "เฅค", "เฅฅ", "ยซ", "ยป", "โ€ž", "โ€œ", "โ€", "ใ€Œ", "ใ€", "โ€˜", "โ€™", "ใ€Š", "ใ€‹", "(", ")", "[", "]",
                  "{", "}", "=", "`", "_", "+", "<", ">", "โ€ฆ", "โ€“", "ยฐ", "ยด", "สพ", "โ€น", "โ€บ", "ยฉ", "ยฎ", "โ€”", "โ†’", "ใ€‚",
                  "ใ€", "๏น‚", "๏น", "โ€ง", "๏ฝž", "๏น", "๏ผŒ", "๏ฝ›", "๏ฝ", "๏ผˆ", "๏ผ‰", "๏ผป", "๏ผฝ", "ใ€", "ใ€‘", "โ€ฅ", "ใ€ฝ",
                  "ใ€Ž", "ใ€", "ใ€", "ใ€Ÿ", "โŸจ", "โŸฉ", "ใ€œ", "๏ผš", "๏ผ", "๏ผŸ", "โ™ช", "ุ›", "/", "\\", "ยบ", "โˆ’", "^", "'", "สป", "ห†"]

test_dataset = load_dataset("common_voice", LANG_ID, split="test")

wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py

chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]

print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
```

**Test Result**:

In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-14). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.

| Model | WER | CER |
| ------------- | ------------- | ------------- |
| jonatasgrosman/wav2vec2-large-xlsr-53-arabic | **39.59%** | **18.18%** |
| bakrianoo/sinai-voice-ar-stt | 45.30% | 21.84% |
| othrif/wav2vec2-large-xlsr-arabic | 45.93% | 20.51% |
| kmfoda/wav2vec2-large-xlsr-arabic | 54.14% | 26.07% |
| mohammed/wav2vec2-large-xlsr-arabic | 56.11% | 26.79% |
| anas/wav2vec2-large-xlsr-arabic | 62.02% | 27.09% |
| elgeish/wav2vec2-large-xlsr-53-arabic | 100.00% | 100.56% |