Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
German
wav2vec2
mozilla-foundation/common_voice_10_0
Generated from Trainer
Instructions to use aware-ai/wav2vec2-xls-r-1b-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aware-ai/wav2vec2-xls-r-1b-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aware-ai/wav2vec2-xls-r-1b-german")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aware-ai/wav2vec2-xls-r-1b-german") model = AutoModelForCTC.from_pretrained("aware-ai/wav2vec2-xls-r-1b-german") - Notebooks
- Google Colab
- Kaggle
wav2vec2-xls-r-1b-german
This model is a fine-tuned version of wav2vec2-xls-r-1b-german on the MOZILLA-FOUNDATION/COMMON_VOICE_10_0 - DE dataset. It achieves the following results on the evaluation set:
- Loss: 0.3513
- Wer: 0.2931
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: 3e-07
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2009 | 1.0 | 4816 | 0.3486 | 0.2940 |
| 0.1637 | 2.0 | 9632 | 0.3459 | 0.2915 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
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