Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Basa (Cameroon)
wav2vec2
common_voice
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use emre/wav2vec2-xls-r-300m-bas-CV8-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emre/wav2vec2-xls-r-300m-bas-CV8-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="emre/wav2vec2-xls-r-300m-bas-CV8-v2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("emre/wav2vec2-xls-r-300m-bas-CV8-v2") model = AutoModelForCTC.from_pretrained("emre/wav2vec2-xls-r-300m-bas-CV8-v2") - Notebooks
- Google Colab
- Kaggle
wav2vec2-xls-r-300m-bas-CV8-v2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.6121
- Wer: 0.5697
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: 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: 300
- num_epochs: 90
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.5211 | 16.13 | 500 | 1.2661 | 0.9153 |
| 0.7026 | 32.25 | 1000 | 0.6245 | 0.6516 |
| 0.3752 | 48.38 | 1500 | 0.6039 | 0.6148 |
| 0.2752 | 64.51 | 2000 | 0.6080 | 0.5808 |
| 0.2155 | 80.63 | 2500 | 0.6121 | 0.5697 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
- Downloads last month
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Evaluation results
- Test WER on Common Voice 8self-reported56.970