Automatic Speech Recognition
Transformers
Safetensors
wav2vec2-bert
Generated from Trainer
Eval Results (legacy)
Instructions to use mtsotras/w2v-bert-2.0-bengali-colab-100train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtsotras/w2v-bert-2.0-bengali-colab-100train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mtsotras/w2v-bert-2.0-bengali-colab-100train")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mtsotras/w2v-bert-2.0-bengali-colab-100train") model = AutoModelForCTC.from_pretrained("mtsotras/w2v-bert-2.0-bengali-colab-100train") - Notebooks
- Google Colab
- Kaggle
w2v-bert-2.0-bengali-colab-100train
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.9999
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
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Model tree for mtsotras/w2v-bert-2.0-bengali-colab-100train
Base model
facebook/w2v-bert-2.0Evaluation results
- Wer on common_voice_11_0test set self-reported1.000