Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
msp
Generated from Trainer
custom_code
Instructions to use MahmoodAnaam/MSP-AVSR-E2E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MahmoodAnaam/MSP-AVSR-E2E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MahmoodAnaam/MSP-AVSR-E2E", trust_remote_code=True)# Load model directly from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("MahmoodAnaam/MSP-AVSR-E2E", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
MSP-AVSR
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1193
- Wer: 0.1736
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000.0
- training_steps: 30000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.2821 | 0.0333 | 1000 | 1.5690 | 0.2983 |
| 2.1664 | 0.0667 | 2000 | 1.3053 | 0.2234 |
| 2.0936 | 0.1 | 3000 | 1.1193 | 0.1736 |
| 2.2092 | 0.1333 | 4000 | 1.2822 | 0.2420 |
| 2.3215 | 0.1667 | 5000 | 1.4375 | 0.2676 |
| 2.0802 | 0.2 | 6000 | 1.3366 | 0.2295 |
| 2.0530 | 0.2333 | 7000 | 1.2210 | 0.1985 |
| 2.1923 | 0.2667 | 8000 | 1.3655 | 0.2465 |
| 2.1158 | 0.3 | 9000 | 1.4065 | 0.2484 |
| 2.0377 | 0.3333 | 10000 | 1.4430 | 0.2271 |
| 2.0040 | 0.3667 | 11000 | 1.3231 | 0.2188 |
| 1.9140 | 0.4 | 12000 | 1.5497 | 0.2668 |
| 2.1310 | 0.4333 | 13000 | 1.1773 | 0.1909 |
| 2.0774 | 0.4667 | 14000 | 1.1900 | 0.1888 |
| 1.8577 | 0.5 | 15000 | 1.4826 | 0.2567 |
| 1.8542 | 0.5333 | 16000 | 1.3038 | 0.2196 |
| 1.9256 | 0.5667 | 17000 | 1.1100 | 0.1767 |
| 2.0081 | 0.6 | 18000 | 1.3671 | 0.2258 |
| 1.8983 | 0.6333 | 19000 | 1.2541 | 0.2035 |
| 1.8399 | 0.6667 | 20000 | 1.2768 | 0.2127 |
| 1.9230 | 0.0333 | 21000 | 1.3529 | 0.2279 |
| 1.7808 | 0.0667 | 22000 | 1.3944 | 0.2262 |
| 1.8058 | 0.1 | 23000 | 1.2840 | 0.2044 |
| 1.8971 | 0.1333 | 24000 | 1.2563 | 0.2056 |
| 2.0928 | 0.1667 | 25000 | 1.2498 | 0.2054 |
| 1.8353 | 0.2 | 26000 | 1.2187 | 0.1997 |
| 1.8451 | 0.2333 | 27000 | 1.2304 | 0.1999 |
| 2.0854 | 0.2667 | 28000 | 1.2552 | 0.2079 |
| 1.9424 | 0.3 | 29000 | 1.2496 | 0.2042 |
| 1.8996 | 0.3333 | 30000 | 1.2481 | 0.2046 |
Framework versions
- Transformers 5.10.2
- Pytorch 2.10.0+rocm7.2.4.git3d3aa833
- Datasets 4.0.0
- Tokenizers 0.22.2
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