Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
ga-IE
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use jcmc/wav2vec-cv7-1b-ir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcmc/wav2vec-cv7-1b-ir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jcmc/wav2vec-cv7-1b-ir")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jcmc/wav2vec-cv7-1b-ir") model = AutoModelForCTC.from_pretrained("jcmc/wav2vec-cv7-1b-ir") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Error:"language[0]" must only contain lowercase characters
YAML Metadata Error:"language[0]" with value "ga-IE" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GA-IE dataset. It achieves the following results on the evaluation set:
- Loss: 0.9562
- Wer: 0.4801
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.3731 | 15.62 | 500 | 1.5517 | 0.9499 |
| 1.3312 | 31.25 | 1000 | 0.8717 | 0.6189 |
| 0.9135 | 46.86 | 1500 | 0.8299 | 0.5310 |
| 0.6719 | 62.49 | 2000 | 0.8842 | 0.5044 |
| 0.5583 | 78.12 | 2500 | 0.9093 | 0.4801 |
| 0.4728 | 93.74 | 3000 | 0.9488 | 0.4813 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
- Downloads last month
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Evaluation results
- Test WER on Common Voice 7self-reported39.100
- Test CER on Common Voice 7self-reported16.400