README.md

---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v2-arabic-5k-steps
  results: []
datasets:
- mozilla-foundation/common_voice_11_0
language:
- ar
---

<!-- 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. -->

# whisper-large-v2-arabic-5k-steps

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Arabic CommonVoice dataset (v11).
It achieves the following results on the evaluation set:
- Loss: 0.3434
- Wer: 0.4239

## Model description

This model is finetuned for 5000 steps for research purposes which means that the transcriptions might not be that satisfactory for users.

## Training and evaluation data

- Training Data: CommonVoice (v11) train split
- Validation Data: CommonVoice (v11) Validation split
- Test Data: CommonVoice (v11) Test split

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 50
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1638        | 1.78  | 1000 | 0.2295          | 0.4410 |
| 0.0587        | 3.57  | 2000 | 0.2337          | 0.4272 |
| 0.0125        | 5.35  | 3000 | 0.2745          | 0.4208 |
| 0.004         | 7.13  | 4000 | 0.3124          | 0.4252 |
| 0.0016        | 8.91  | 5000 | 0.3434          | 0.4239 |

### Transcription:

```python
from datasets import load_dataset, Audio
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# load the model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps").to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="ar", task="transcribe")

# load the dataset
commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ar", split="validation", streaming=True)
commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000))
sample = next(iter(commonvoice_eval))["audio"]

# features and generate token ids
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids)

# decode
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]

print("Transcription:", transcription)
Transcription: عمي هو أخو أبي.
```

### Evaluation:

Evaluates this model on `mozilla-foundation/common_voice_11_0` test split.

```python
import pyarabic.araby as araby
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
from datasets import load_dataset, Audio
import evaluate
import torch
import re
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# metric
wer_metric = evaluate.load("wer")

# model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps")

# dataset
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "ar", split="test", ) #cache_dir=args.cache_dir
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))

#for debuggings: it gets two examples
#dataset = dataset.shard(num_shards=10000, index=0)
#print(dataset)

def clean_text(text):
  """Normalizes TRANSCRIPT"""
  text = re.sub(r'[\,\?\.\!\-\;\:\"\“\%\٪\‘\”\�\«\»\،\.\:\؟\؛\*\>\<]', '', text) + " " # special characters
  text = re.sub(r'http\S+', '', text) + " " # links
  text = re.sub(r'[\[\]\(\)\-\/\{\}]', '', text) + " " # brackets
  text = re.sub(r'\s+', ' ', text) + " " # extra white space
  text = araby.strip_diacritics(text) # remove diacrirics
  return text.strip()
    
def normalize(batch):
  """Normalizes GOLD"""
  #batch["gold_text"] = whisper_norm(batch['sentence'])
  batch["gold_text"] = clean_text(batch['sentence'])
  return batch

def map_wer(batch):
  model.to(device)
  forced_decoder_ids = processor.get_decoder_prompt_ids(language = "ar", task = "transcribe")
  inputs = processor(batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt").input_features
  with torch.no_grad():
    generated_ids = model.generate(inputs=inputs.to(device), forced_decoder_ids=forced_decoder_ids)
    transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
  batch["predicted_text"] = clean_text(transcription)
  return batch

# process GOLD text
processed_dataset = dataset.map(normalize)
# get predictions
predicted = processed_dataset.map(map_wer)

# word error rate
wer = wer_metric.compute(references=predicted['gold_text'], predictions=predicted['predicted_text'])
wer = round(100 * wer, 2)
print("WER:", wer)
```

### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2