Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
speech-encoder-decoder
Generated from Trainer
Instructions to use nacielo/wav2BertMusicfreezeTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nacielo/wav2BertMusicfreezeTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nacielo/wav2BertMusicfreezeTest")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("nacielo/wav2BertMusicfreezeTest") model = AutoModelForSpeechSeq2Seq.from_pretrained("nacielo/wav2BertMusicfreezeTest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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wav2BertMusicfreezeTest
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.6761
- Rouge1: 16.6206
- Rouge2: 0.9994
- Rougel: 16.1142
- Rougelsum: 16.1149
- Gen Len: 95.3
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: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 7.7819 | 1.0 | 1361 | 7.0617 | 15.5901 | 0.9705 | 15.3625 | 15.389 | 97.98 |
| 6.8553 | 2.0 | 2722 | 6.6761 | 16.6206 | 0.9994 | 16.1142 | 16.1149 | 95.3 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
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