Instructions to use kesbeast23/mms-curriculum-wer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kesbeast23/mms-curriculum-wer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kesbeast23/mms-curriculum-wer")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kesbeast23/mms-curriculum-wer") model = AutoModelForCTC.from_pretrained("kesbeast23/mms-curriculum-wer") - Notebooks
- Google Colab
- Kaggle
mms-curriculum-wer
This model is a fine-tuned version of facebook/mms-1b-all on the None 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Use adamw_torch_fused with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 650
Training results
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.2
- Tokenizers 0.22.1
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Model tree for kesbeast23/mms-curriculum-wer
Base model
facebook/mms-1b-all