--- library_name: transformers language: - fa license: apache-2.0 base_model: openai/whisper-large-v2 tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 model-index: - name: Whisper large-v2 Fa - Common Voice results: [] --- # Whisper large-v2 Fa - Common Voice This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 17.0 dataset. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Test results - Best test WER (Word Error Rate): 0.322 - Best test CER (Character Error Rate): 0.106 ### Usage ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "aictsharif/whisper-large-v2-fa" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch_dtype, device=device, ) result = pipe('sample.mp3') print(result["text"]) ``` ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1