Create README.md

README.md

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+---
+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