daniel_whisper_finetune_small_v2

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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:

• Loss: 0.1994

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

Framework versions

• Transformers 4.57.1
• Pytorch 2.9.1+cu128
• Datasets 4.4.1
• Tokenizers 0.22.1