# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("eugenetanjc/trained_french")
model = AutoModelForCTC.from_pretrained("eugenetanjc/trained_french", device_map="auto")Quick Links
trained_french
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.8493
- Wer: 1.0
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: 0.003
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 60
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6.2268 | 5.53 | 50 | 4.9813 | 1.0 |
| 5.724 | 11.11 | 100 | 4.8808 | 1.0 |
| 5.629 | 16.63 | 150 | 4.9001 | 1.0 |
| 5.3351 | 22.21 | 200 | 4.8457 | 1.0 |
| 5.2043 | 27.74 | 250 | 4.8386 | 1.0 |
| 5.1709 | 33.32 | 300 | 4.8647 | 1.0 |
| 5.065 | 38.84 | 350 | 4.8574 | 1.0 |
| 5.0685 | 44.42 | 400 | 4.8449 | 1.0 |
| 5.0584 | 49.95 | 450 | 4.8412 | 1.0 |
| 4.9626 | 55.53 | 500 | 4.8493 | 1.0 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="eugenetanjc/trained_french")