Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Bengali
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use Redve/BengaliModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Redve/BengaliModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Redve/BengaliModel")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Redve/BengaliModel") model = AutoModelForSpeechSeq2Seq.from_pretrained("Redve/BengaliModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Redve/BengaliModel: direct link, hf CLI and curl.
- Browser
- Download file 2.44 kB
-
https://huggingface.co/Redve/BengaliModel/resolve/main/README.md
- Command line
-
hf download hf://Redve/BengaliModel/README.md
-
curl -L -o README.md https://huggingface.co/Redve/BengaliModel/resolve/main/README.md
2.44 kB
| language: | |
| - bn | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - hf-asr-leaderboard | |
| - generated_from_trainer | |
| datasets: | |
| - mozilla-foundation/common_voice_11_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper Small bn - Group 4 | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Common Voice 11.0 | |
| type: mozilla-foundation/common_voice_11_0 | |
| config: default | |
| split: test | |
| args: 'config: bn, split: test' | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 42.212627219456316 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Whisper Small bn - Group 4 | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2751 | |
| - Wer: 42.2126 | |
| ## 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: 1e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 200 | |
| - training_steps: 5000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.163 | 1.06 | 500 | 0.2064 | 55.6065 | | |
| | 0.0862 | 2.12 | 1000 | 0.1675 | 47.1869 | | |
| | 0.0475 | 3.18 | 1500 | 0.1696 | 44.8561 | | |
| | 0.0239 | 4.24 | 2000 | 0.1848 | 43.2436 | | |
| | 0.0119 | 5.3 | 2500 | 0.2081 | 43.5608 | | |
| | 0.0058 | 6.36 | 3000 | 0.2262 | 43.0718 | | |
| | 0.0024 | 7.42 | 3500 | 0.2427 | 42.4726 | | |
| | 0.0009 | 8.47 | 4000 | 0.2611 | 42.6356 | | |
| | 0.0005 | 9.53 | 4500 | 0.2709 | 42.3492 | | |
| | 0.0004 | 10.59 | 5000 | 0.2751 | 42.2126 | | |
| ### Framework versions | |
| - Transformers 4.36.0.dev0 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |