Instructions to use haohaa/wav2vec2-large-mms-1b-shan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haohaa/wav2vec2-large-mms-1b-shan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="haohaa/wav2vec2-large-mms-1b-shan")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("haohaa/wav2vec2-large-mms-1b-shan") model = AutoModelForCTC.from_pretrained("haohaa/wav2vec2-large-mms-1b-shan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-large-mms-1b-shan
This model is a fine-tuned version of facebook/mms-1b-all on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3066
- Wer: 1.0
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4342 | 10.0 | 100 | 0.3066 | 1.0 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for haohaa/wav2vec2-large-mms-1b-shan
Base model
facebook/mms-1b-all