# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("snehatyagi/wav2vec2_timit")
model = AutoModelForCTC.from_pretrained("snehatyagi/wav2vec2_timit", device_map="auto")Quick Links
wav2vec2_timit
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0791
- 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: 0.01
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.1506 | 2.4 | 300 | 3.1294 | 1.0 |
| 3.0957 | 4.8 | 600 | 3.0791 | 1.0 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.6
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="snehatyagi/wav2vec2_timit")