Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use amanuelbyte/mms-som-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amanuelbyte/mms-som-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="amanuelbyte/mms-som-finetuned")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("amanuelbyte/mms-som-finetuned") model = AutoModelForCTC.from_pretrained("amanuelbyte/mms-som-finetuned") - Notebooks
- Google Colab
- Kaggle
mms-som-finetuned
This model is a fine-tuned version of facebook/mms-1b-all on the generator dataset. It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.4267
- Cer: 0.1179
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 50
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.5498 | 0.7105 | 100 | inf | 0.4894 | 0.1388 |
| 2.8703 | 1.0 | 141 | inf | 0.4267 | 0.1179 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.7.1+cu118
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for amanuelbyte/mms-som-finetuned
Base model
facebook/mms-1b-allEvaluation results
- Wer on generatorself-reported0.427