Automatic Speech Recognition
Transformers
Safetensors
Latvian
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use arturslogins/whisper-medium-lv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arturslogins/whisper-medium-lv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arturslogins/whisper-medium-lv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arturslogins/whisper-medium-lv") model = AutoModelForSpeechSeq2Seq.from_pretrained("arturslogins/whisper-medium-lv") - Notebooks
- Google Colab
- Kaggle
Whisper medium LV - Arturs Logins
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 16.1 dataset.
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1
Training results
Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.2.2+cpu
- Datasets 2.18.0
- Tokenizers 0.19.1
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Model tree for arturslogins/whisper-medium-lv
Base model
openai/whisper-medium