Automatic Speech Recognition
Transformers
TensorBoard
wav2vec2
edinburghcstr/ami
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("tz579/example_asr_wav2vec2")
model = AutoModelForCTC.from_pretrained("tz579/example_asr_wav2vec2")Quick Links
facebook/wav2vec2-large-lv60
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: 1.2723
- Wer: 0.9542
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: 0.0003
- 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: 500
- num_epochs: 2.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0919 | 0.1565 | 1000 | 1.0169 | 0.7064 |
| 1.4768 | 0.3131 | 2000 | 0.7156 | 0.4356 |
| 0.9728 | 0.4696 | 3000 | 0.6462 | 0.4030 |
| 0.5418 | 0.6262 | 4000 | 0.6171 | 0.3707 |
| 0.8492 | 0.7827 | 5000 | 0.5758 | 0.3695 |
| 1.4826 | 0.9393 | 6000 | 0.5801 | 0.3545 |
| 0.3274 | 1.0958 | 7000 | 0.5244 | 0.3375 |
| 0.2089 | 1.2523 | 8000 | 0.5047 | 0.3239 |
| 0.2916 | 1.4089 | 9000 | 0.4901 | 0.3171 |
| 0.1617 | 1.5654 | 10000 | 0.5070 | 0.3151 |
| 0.3815 | 1.7220 | 11000 | 0.4948 | 0.3180 |
| 1.0171 | 1.8785 | 12000 | 0.9465 | 0.8379 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0a0+gitcd033a1
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for tz579/example_asr_wav2vec2
Base model
facebook/wav2vec2-large-lv60Dataset used to train tz579/example_asr_wav2vec2
Evaluation results
- Wer on EDINBURGHCSTR/AMI - IHMself-reported0.954
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="tz579/example_asr_wav2vec2")