Downloads · 30 days
7
5% of all-time downloads
wav2vec2-base-960h-fsc
gokuls/wav2vec2-base-960h-fsc
wav2vec2-base-960h-fsc is a machine learning model from gokuls. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
128
Public
Parameters
94.6M
8.3 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors378 MB · 100%
At a glance
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Oct 4, 2024
- Updated
- Oct 4, 2024
- SHA
- c4078806
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
wav2vec2-base-960h-fsc
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0218
- Accuracy: 0.9947
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: 0.0005
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9959 | 120 | 0.3651 | 0.9380 |
| No log | 2.0 | 241 | 0.2352 | 0.9404 |
| No log | 2.9959 | 361 | 0.4245 | 0.8684 |
| No log | 4.0 | 482 | 0.0721 | 0.9837 |
| No log | 4.9959 | 602 | 0.0961 | 0.9768 |
| No log | 6.0 | 723 | 0.0632 | 0.9860 |
| No log | 6.9959 | 843 | 0.0498 | 0.9905 |
| No log | 8.0 | 964 | 0.0715 | 0.9834 |
| 0.4012 | 8.9959 | 1084 | 0.0907 | 0.9829 |
| 0.4012 | 10.0 | 1205 | 0.0644 | 0.9860 |
| 0.4012 | 10.9959 | 1325 | 0.0322 | 0.9921 |
| 0.4012 | 12.0 | 1446 | 0.0524 | 0.9881 |
| 0.4012 | 12.9959 | 1566 | 0.0450 | 0.9910 |
| 0.4012 | 14.0 | 1687 | 0.0227 | 0.9942 |
| 0.4012 | 14.9959 | 1807 | 0.0437 | 0.9908 |
| 0.4012 | 16.0 | 1928 | 0.0381 | 0.9924 |
| 0.1096 | 16.9959 | 2048 | 0.0218 | 0.9947 |
| 0.1096 | 18.0 | 2169 | 0.0300 | 0.9934 |
| 0.1096 | 18.9959 | 2289 | 0.0356 | 0.9931 |
| 0.1096 | 20.0 | 2410 | 0.0380 | 0.9937 |
| 0.1096 | 20.9959 | 2530 | 0.0417 | 0.9934 |
| 0.1096 | 22.0 | 2651 | 0.0268 | 0.9947 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Oct 4, 2024
- Updated
- Oct 4, 2024