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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- openai/whisper-large-v3 |
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metrics: |
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- accuracy |
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--- |
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# Whisper Model for Incorrect English Phrases |
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## Overview |
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This fine-tuned version of OpenAI’s Whisper model is specifically trained to handle incorrect English phrases. |
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It is designed to transcribe and process non-standard or erroneous English input, including mispronunciations, |
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grammatical mistakes, slang, and non-native speaker errors. This model helps improve transcription accuracy |
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in scenarios where speakers use incorrect or informal English, making it useful in language learning, |
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transcription of casual conversations, or analyzing spoken communication from non-native English speakers. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 100000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:-------:| |
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| 0.9094 | 0.1270 | 500 | 0.6347 | 24.3686 | |
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| 0.5517 | 0.2541 | 1000 | 0.4835 | 18.0769 | |
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| 0.5364 | 0.3811 | 1500 | 0.4330 | 15.1149 | |
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| 0.5503 | 0.5081 | 2000 | 0.4113 | 13.6524 | |
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| 0.6521 | 0.6352 | 2500 | 0.3987 | 13.5897 | |
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| 0.6044 | 0.7622 | 3000 | 0.3912 | 13.0538 | |
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| 0.5487 | 0.8892 | 3500 | 0.3835 | 12.6119 | |
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| 0.5297 | 1.0163 | 4000 | 0.3791 | 12.4408 | |
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| 0.46 | 1.1433 | 4500 | 0.3751 | 12.3525 | |
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| 0.4947 | 1.2703 | 5000 | 0.3721 | 12.1415 | |
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| 0.524 | 1.3974 | 5500 | 0.3682 | 13.0139 | |
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| 0.4743 | 1.5244 | 6000 | 0.3649 | 13.3388 | |
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| 0.5338 | 1.6514 | 6500 | 0.3621 | 12.9397 | |
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| 0.5162 | 1.7785 | 7000 | 0.3597 | 13.3246 | |
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| 0.5004 | 1.9055 | 7500 | 0.3590 | 12.3268 | |
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## Usage Guide |
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This project was executed on an Ubuntu 22.04.3 system running Linux kernel 6.8.0-40-generic. |
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Whisper large-v3 is supported in Hugging Face Transformers. To run the model, first install the Transformers library. |
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For this example, we'll also install Hugging Face Datasets to load toy audio dataset from |
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the Hugging Face Hub, and Hugging Face Accelerate to reduce the model loading time: |
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```bash |
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pip install --upgrade pip |
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pip install --upgrade transformers datasets[audio] accelerate |
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``` |
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The model can be used with the pipeline class to transcribe audios of arbitrary length: |
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```python |
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import torch |
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline |
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from datasets import load_dataset |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
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def download_adapter_model(): |
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model_name = "whisper-v3-LoRA-en_students" |
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print(f"Downloading the adapter model '{model_name}' from the Hugging Face Hub.", flush=True) |
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# Define the path for the directory |
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local_directory = os.path.expanduser("~/.cache/huggingface/hub") |
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# Check if the directory exists |
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if not os.path.exists(local_directory): |
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# If it doesn't exist, create it |
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os.makedirs(local_directory) |
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print(f"Directory '{local_directory}' created.", flush=True) |
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else: |
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print(f"Directory '{local_directory}' already exists.", flush=True) |
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repo_id = f"Transducens/{model_name}" |
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repo_adapter_dir = f"{model_name}/checkpoint-5000/adapter_model" |
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repo_filename_config = f"{repo_adapter_dir}/adapter_config.json" |
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repo_filename_tensors = f"{repo_adapter_dir}/adapter_model.safetensors" |
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adapter_config = hf_hub_download(repo_id=repo_id, filename=repo_filename_config, local_dir=local_directory) |
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adapter_model_tensors = hf_hub_download(repo_id=repo_id, filename=repo_filename_tensors, local_dir=local_directory) |
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print(f"Dowloaded the adapter model '{model_name}' from the Hugging Face Hub.", flush=True) |
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return os.path.join(local_directory, repo_adapter_dir) |
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peft_model_id = adapter_path # Use the same model ID as before. |
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peft_config = PeftConfig.from_pretrained(peft_model_id) |
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model = WhisperForConditionalGeneration.from_pretrained( |
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peft_config.base_model_name_or_path, load_in_8bit=False) |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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model.generation_config.language = "<|en|>" |
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model.generation_config.task = "transcribe" |
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tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-large-v3", task="transcribe") |
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feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-large-v3") |
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pipe = pipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor, task="automatic-speech-recognition", device=device) |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.4 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
