metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-lv60
tags:
- automatic-speech-recognition
- edinburghcstr/ami
- generated_from_trainer
datasets:
- ami
metrics:
- wer
model-index:
- name: wav2vec2-large-ami-fine-tuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: EDINBURGHCSTR/AMI - IHM
type: ami
config: ihm
split: None
args: 'Config: ihm, Training split: train, Eval split: validation'
metrics:
- name: Wer
type: wer
value: 0.9958454640117305
wav2vec2-large-ami-fine-tuned
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the EDINBURGHCSTR/AMI - IHM dataset. It achieves the following results on the evaluation set:
- Loss: 1.9555
- Wer: 0.9958
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: 0.00014
- train_batch_size: 16
- eval_batch_size: 16
- 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: 2.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.5455 | 0.1565 | 1000 | 1.3698 | 0.8373 |
| 1.3019 | 0.3131 | 2000 | 0.7275 | 0.4146 |
| 0.9922 | 0.4696 | 3000 | 0.6047 | 0.3663 |
| 0.5129 | 0.6262 | 4000 | 0.5773 | 0.3658 |
| 0.85 | 0.7827 | 5000 | 0.5387 | 0.3538 |
| 1.4588 | 0.9393 | 6000 | 0.5581 | 0.3326 |
| 0.2646 | 1.0958 | 7000 | 0.5216 | 0.3294 |
| 0.1923 | 1.2523 | 8000 | 0.4975 | 0.3159 |
| 0.2897 | 1.4089 | 9000 | 0.4757 | 0.3066 |
| 0.1536 | 1.5654 | 10000 | 0.4784 | 0.3066 |
| 0.3964 | 1.7220 | 11000 | 0.4899 | 0.3097 |
| 1.1026 | 1.8785 | 12000 | 0.9830 | 0.8711 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0a0+gitcd033a1
- Datasets 2.19.1
- Tokenizers 0.19.1