whisper-medium-he[WIP]
This model is a fine-tuned version of imvladikon/whisper-medium-he on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2061
- Wer: 13.4020
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0983 | 0.1 | 1000 | 0.3072 | 16.4362 |
| 0.1219 | 0.2 | 2000 | 0.2923 | 15.6642 |
| 0.134 | 0.3 | 3000 | 0.2345 | 13.7450 |
| 0.2113 | 0.39 | 4000 | 0.2061 | 13.4020 |
Inference
HF
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="imvladikon/whisper-medium-he", device_map="auto") # requires `pip install accelerate`
print(recognize("sample.mp3"))
whisper.cpp
Prepared : https://huggingface.co/imvladikon/whisper-medium-he/blob/main/ggml-hebrew.bin
But if need to convert:
git clone https://github.com/openai/whisper
git clone https://github.com/ggerganov/whisper.cpp
git clone https://huggingface.co/imvladikon/whisper-medium-he
python3 ./whisper.cpp/models/convert-h5-to-ggml.py ./whisper-medium-he/ ./whisper .
Then possible to check (if produced model is ggml-model.bin):
cd whisper.cpp && ./main -m ../ggml-model.bin -f ../sample.wav
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0