Automatic Speech Recognition
Transformers
Safetensors
wav2vec2-bert
Generated from Trainer
Eval Results (legacy)
Instructions to use aman-batazia/wav2vec-commonvoice-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aman-batazia/wav2vec-commonvoice-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aman-batazia/wav2vec-commonvoice-1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aman-batazia/wav2vec-commonvoice-1") model = AutoModelForCTC.from_pretrained("aman-batazia/wav2vec-commonvoice-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
wav2vec-commonvoice-1
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.2990
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6465 | 1.0 | 918 | inf | 0.1938 |
| 0.5151 | 2.0 | 1836 | inf | 0.2995 |
| 0.5589 | 3.0 | 2754 | inf | 0.3003 |
| 0.5585 | 4.0 | 3672 | inf | 0.2999 |
| 0.5593 | 5.0 | 4590 | inf | 0.2990 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.4.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for aman-batazia/wav2vec-commonvoice-1
Base model
facebook/w2v-bert-2.0Evaluation results
- Wer on common_voice_17_0test set self-reported0.299