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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v2-english-2k-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-english-2k-steps
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the English CommonVoice dataset (v11).
## Model description
This model is finetuned for 2000 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
### 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-english-2k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-english-2k-steps").to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="en", task="transcribe")
# load the dataset
commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "en", 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)
```
### Evaluation:
Evaluates this model on `mozilla-foundation/common_voice_11_0` test split.
```python
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-english-2k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-english-2k-steps")
# dataset
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "en", 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 normalize(batch):
batch["gold_text"] = whisper_norm(batch['sentence'])
return batch
def map_wer(batch):
model.to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language = "en", 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"] = whisper_norm(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 |