Automatic Speech Recognition
Transformers
Safetensors
Chukot
wav2vec2
chukchi
Generated from Trainer
Instructions to use tadgeis/mms-1b-ckt-pooled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tadgeis/mms-1b-ckt-pooled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="tadgeis/mms-1b-ckt-pooled")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("tadgeis/mms-1b-ckt-pooled") model = AutoModelForCTC.from_pretrained("tadgeis/mms-1b-ckt-pooled") - Notebooks
- Google Colab
- Kaggle
mms-1b-ckt-pooled
This model is a fine-tuned version of facebook/mms-1b-all.
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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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: 25
- num_epochs: 12
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 4.2676 | 0.2941 | 50 | 1.3063 | 0.9667 | 0.3346 |
| 0.7992 | 0.5882 | 100 | 1.0971 | 0.9159 | 0.2819 |
| 0.7067 | 0.8824 | 150 | 0.9256 | 0.8730 | 0.2374 |
| 0.6357 | 1.1765 | 200 | 0.9270 | 0.9032 | 0.2396 |
| 0.6663 | 1.4706 | 250 | 0.8971 | 0.8794 | 0.2409 |
| 0.6652 | 1.7647 | 300 | 0.8835 | 0.8730 | 0.2374 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for tadgeis/mms-1b-ckt-pooled
Base model
facebook/mms-1b-all