daniel_whisper_finetune_small_v2
This model is a fine-tuned version of
openai/whisper-small on an unknown dataset.
It achieves the following results on the evaluation set:
Model description
This is a personal fine-tune of the Whisper small model, trained on approximately 1 hour of audio featuring Daniel Rosehill's voice. The training data includes domain-specific vocabulary focused on:
- Technology and software development terminology
- A few Hebrew words and phrases
This model was created as a proof of concept for fine-tuning Whisper models for personal use and improved transcription accuracy on domain-specific content.
Training Infrastructure
Fine-tuning was performed using Modal GPU inference infrastructure.
Converted Formats
In addition to the standard SafeTensors format, this repository includes converted model formats in the converted/ directory:
-
GGML format (
converted/ggml/): For use with
whisper.cpp
- Cross-platform inference (desktop, mobile, edge devices)
- Optimized for CPU and CUDA (NVIDIA GPU) acceleration
- Compatible with iOS, Android, Raspberry Pi, and other platforms
-
CTranslate2 format (
converted/ctranslate2/): For use with
faster-whisper
- Highly optimized inference engine (4x faster than OpenAI Whisper)
- Excellent CPU and GPU (CUDA) support
- Lower memory usage with 8-bit and 16-bit quantization
Intended uses & limitations
This model is optimized for:
- Transcribing Daniel Rosehill's voice
- Technical and software development content
- Mixed English with occasional Hebrew terms
Limitations:
- Performance may degrade on voices significantly different from the training data
- Limited to the vocabulary and accent patterns in the training set
- Best suited for personal use rather than general-purpose transcription
Training and evaluation data
Training dataset consisted of approximately 1 hour of recorded audio featuring:
- Technical discussions and software development content
- Mixed English with occasional Hebrew vocabulary
- Single speaker (Daniel Rosehill)
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 50
- training_steps: 250
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|
| 1.2524 | 1.3158 | 50 | 1.1232 |
| 0.6387 | 2.6316 | 100 | 0.6857 |
| 0.0747 | 3.9474 | 150 | 0.1990 |
| 0.0239 | 5.2632 | 200 | 0.1919 |
| 0.0085 | 6.5789 | 250 | 0.1994 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
