Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
msp
Generated from Trainer
custom_code
Instructions to use MahmoodAnaam/MSP-AVSR-MD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MahmoodAnaam/MSP-AVSR-MD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MahmoodAnaam/MSP-AVSR-MD", trust_remote_code=True)# Load model directly from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("MahmoodAnaam/MSP-AVSR-MD", 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: 1.1921
- Wer: 0.1954
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: 0.0002
- 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: 500
- training_steps: 10000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.3649 | 0.05 | 500 | 1.1951 | 0.2032 |
| 2.3195 | 0.1 | 1000 | 1.3344 | 0.2257 |
| 2.2183 | 0.15 | 1500 | 1.4732 | 0.2762 |
| 2.3529 | 0.2 | 2000 | 1.3084 | 0.2243 |
| 2.1855 | 0.25 | 2500 | 1.4419 | 0.2512 |
| 2.1410 | 0.3 | 3000 | 1.2315 | 0.2163 |
| 1.9520 | 0.35 | 3500 | 1.2516 | 0.2175 |
| 2.1277 | 0.4 | 4000 | 1.2095 | 0.2108 |
| 2.1164 | 0.45 | 4500 | 1.2529 | 0.2251 |
| 2.3793 | 0.5 | 5000 | 1.2943 | 0.2208 |
| 2.0423 | 0.55 | 5500 | 1.1921 | 0.1954 |
| 2.1330 | 0.6 | 6000 | 1.2455 | 0.2142 |
| 2.0254 | 0.65 | 6500 | 1.1977 | 0.1988 |
| 2.0638 | 0.7 | 7000 | 1.3258 | 0.2297 |
| 2.0226 | 0.75 | 7500 | 1.3211 | 0.2253 |
| 2.2216 | 0.8 | 8000 | 1.4072 | 0.2438 |
| 1.9457 | 0.85 | 8500 | 1.2661 | 0.2148 |
| 2.0084 | 0.9 | 9000 | 1.3744 | 0.2348 |
| 2.0407 | 0.95 | 9500 | 1.3376 | 0.2270 |
| 1.9916 | 1.0 | 10000 | 1.3328 | 0.2258 |
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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