Whisper Small Personal
This model is a fine-tuned version of openai/whisper-small
trained on personal speech recordings using Mimic Record Studio. It is designed for automatic speech recognition (ASR) tasks and achieves a low Word Error Rate (WER) on the custom dataset.
Model Details
- Model Type: Whisper Small
- Training Dataset: Personal recordings using Mimic Record Studio
- Framework: PyTorch
- Language: Primarily fine-tuned on [insert language(s)]
- Batch Size: 16 (with gradient accumulation)
- Learning Rate: 1e-5
- Mixed Precision: FP16
- Evaluation Strategy: Steps (every 1000 steps)
- WER on Validation Set:
- Step 2000: 0.079441
Hyperparameters
- Max Training Steps: 4000
- Warmup Steps: 500
- Gradient Checkpointing: Enabled
- Evaluation: Performed every 1000 steps
- Logging: Every 25 steps to TensorBoard
- Metric for Best Model: Word Error Rate (WER)
Usage
You can use this model for ASR tasks by loading it directly from the Hugging Face Model Hub:
from transformers import WhisperProcessor, WhisperForConditionalGeneration
processor = WhisperProcessor.from_pretrained("your-username/whisper-small-personal")
model = WhisperForConditionalGeneration.from_pretrained("your-username/whisper-small-personal")
# Example inference
inputs = processor("path_to_audio_file.wav", return_tensors="pt").input_features
generated_ids = model.generate(inputs)
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
Training Procedure
The model was trained using the following setup:
- Training Batch Size: 16
- Gradient Accumulation: 1
- Evaluation Batch Size: 8
- Max Generation Length: 225 tokens
- Learning Rate: 1e-5
- Mixed Precision: FP16
- Optimizer: AdamW
The best model was selected based on the Word Error Rate (WER), and the final model was pushed to the Hugging Face Model Hub.
Results
Step | Training Loss | Validation Loss | WER |
---|---|---|---|
500 | 0.089500 | 0.229938 | 0.101203 |
1000 | 0.005200 | 0.215078 | 0.087757 |
1500 | 0.000400 | 0.222333 | 0.080502 |
2000 | 0.000200 | 0.226987 | 0.079441 |
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