Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use internalhell/wav2vec2-russian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internalhell/wav2vec2-russian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="internalhell/wav2vec2-russian")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("internalhell/wav2vec2-russian") model = AutoModelForSpeechSeq2Seq.from_pretrained("internalhell/wav2vec2-russian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: jonatasgrosman/wav2vec2-large-xlsr-53-russian | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - common_voice_17_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: wav2vec2-russian | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: common_voice_17_0 | |
| type: common_voice_17_0 | |
| config: ru | |
| split: None | |
| args: ru | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 32.86787139169209 | |
| <!-- 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. --> | |
| # wav2vec2-russian | |
| This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-russian](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-russian) on the common_voice_17_0 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2723 | |
| - Wer: 32.8679 | |
| ## 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: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.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: 2 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:------:|:----:|:---------------:|:-------:| | |
| | 0.2061 | 1.2780 | 400 | 0.2723 | 32.8679 | | |
| ### Framework versions | |
| - Transformers 4.48.3 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.0 | |