Instructions to use dmatekenya/wav2vec2-large-xls-r-300m-chichewa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmatekenya/wav2vec2-large-xls-r-300m-chichewa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmatekenya/wav2vec2-large-xls-r-300m-chichewa")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("dmatekenya/wav2vec2-large-xls-r-300m-chichewa") model = AutoModelForCTC.from_pretrained("dmatekenya/wav2vec2-large-xls-r-300m-chichewa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec2-large-xls-r-300m-chichewa
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.9669
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.2028 | 3.51 | 400 | inf | 0.9999 |
| 2.5353 | 7.02 | 800 | inf | 0.9743 |
| 1.8464 | 10.53 | 1200 | inf | 0.9777 |
| 1.6672 | 14.04 | 1600 | inf | 0.9669 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for dmatekenya/wav2vec2-large-xls-r-300m-chichewa
Base model
facebook/wav2vec2-xls-r-300m