Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use syedmuhammad/Wav2Vec2-Urdu-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syedmuhammad/Wav2Vec2-Urdu-300M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="syedmuhammad/Wav2Vec2-Urdu-300M")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("syedmuhammad/Wav2Vec2-Urdu-300M") model = AutoModelForCTC.from_pretrained("syedmuhammad/Wav2Vec2-Urdu-300M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Wav2Vec2-Urdu-300M
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.3825
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 14
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.9658 | 2.58 | 1200 | inf | 0.5573 |
| 0.5669 | 5.17 | 2400 | inf | 0.4473 |
| 0.4117 | 7.75 | 3600 | inf | 0.4121 |
| 0.3197 | 10.33 | 4800 | inf | 0.4039 |
| 0.2667 | 12.92 | 6000 | inf | 0.3825 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for syedmuhammad/Wav2Vec2-Urdu-300M
Base model
facebook/wav2vec2-large-xlsr-53Evaluation results
- Wer on common_voice_13_0test set self-reported0.383