whisper-medium-he

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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

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