Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Marathi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use simran14/mr-iteration1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simran14/mr-iteration1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="simran14/mr-iteration1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("simran14/mr-iteration1") model = AutoModelForSpeechSeq2Seq.from_pretrained("simran14/mr-iteration1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from simran14/mr-iteration1: direct link, hf CLI and curl.
- Browser
- Download file 2.01 kB
-
https://huggingface.co/simran14/mr-iteration1/resolve/main/README.md
- Command line
-
hf download hf://simran14/mr-iteration1/README.md
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curl -L -o README.md https://huggingface.co/simran14/mr-iteration1/resolve/main/README.md
2.01 kB
| library_name: transformers | |
| language: | |
| - mr | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - mozilla-foundation/common_voice_17_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper small marathi iteration 01 | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Common Voice 17.0 | |
| type: mozilla-foundation/common_voice_17_0 | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 22.641190803599432 | |
| <!-- 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 marathi iteration 01 | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4289 | |
| - Wer: 22.6412 | |
| ## 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-07 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - 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: 100 | |
| - num_epochs: 2 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:------:|:----:|:---------------:|:-------:| | |
| | 0.4705 | 0.6098 | 1000 | 0.5771 | 25.9077 | | |
| | 0.3648 | 1.2195 | 2000 | 0.4920 | 23.5024 | | |
| | 0.2982 | 1.8293 | 3000 | 0.4289 | 22.6412 | | |
| ### Framework versions | |
| - Transformers 4.46.0.dev0 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.1 | |