Instructions to use joshuarwanda/wav2vec2-large-xls-r-300m-swahili-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joshuarwanda/wav2vec2-large-xls-r-300m-swahili-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="joshuarwanda/wav2vec2-large-xls-r-300m-swahili-colab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("joshuarwanda/wav2vec2-large-xls-r-300m-swahili-colab") model = AutoModelForCTC.from_pretrained("joshuarwanda/wav2vec2-large-xls-r-300m-swahili-colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: facebook/wav2vec2-xls-r-300m | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - common_voice_13_0 | |
| model-index: | |
| - name: wav2vec2-large-xls-r-300m-swahili-colab | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # wav2vec2-large-xls-r-300m-swahili-colab | |
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset. | |
| It achieves the following results on the evaluation set: | |
| - eval_loss: inf | |
| - eval_wer: 0.5730 | |
| - eval_runtime: 1031.5347 | |
| - eval_samples_per_second: 10.926 | |
| - eval_steps_per_second: 1.366 | |
| - epoch: 0.37 | |
| - step: 400 | |
| ## 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: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 30 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - Transformers 4.35.0 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.14.6 | |
| - Tokenizers 0.14.1 | |