Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Romanian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use iulik-pisik/vreme_model_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iulik-pisik/vreme_model_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="iulik-pisik/vreme_model_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("iulik-pisik/vreme_model_small") model = AutoModelForSpeechSeq2Seq.from_pretrained("iulik-pisik/vreme_model_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from iulik-pisik/vreme_model_small: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/iulik-pisik/vreme_model_small/resolve/main/README.md
- Command line
-
hf download hf://iulik-pisik/vreme_model_small/README.md
-
curl -L -o README.md https://huggingface.co/iulik-pisik/vreme_model_small/resolve/main/README.md
2.03 kB
metadata
language:
- ro
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- iulik-pisik/audio_vreme
metrics:
- wer
model-index:
- name: Whisper Small Romanian - Vreme
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Vreme ProTv
type: iulik-pisik/audio_vreme
config: default
split: None
args: 'config: ro, split: test'
metrics:
- name: Wer
type: wer
value: 7.994245787094123
Whisper Small Romanian - Vreme
This model is a fine-tuned version of openai/whisper-small on the Vreme ProTv dataset. It achieves the following results on the evaluation set:
- Loss: 0.1873
- Wer: 7.9942
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: 16
- eval_batch_size: 8
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0141 | 6.02 | 1000 | 0.1417 | 9.2067 |
| 0.0007 | 12.05 | 2000 | 0.1657 | 8.4464 |
| 0.0002 | 18.07 | 3000 | 0.1833 | 8.0353 |
| 0.0001 | 24.1 | 4000 | 0.1873 | 7.9942 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2