Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - hi | |
| license: apache-2.0 | |
| tags: | |
| - automatic-speech-recognition | |
| - robust-speech-event | |
| datasets: | |
| - mozilla-foundation/common_voice_7_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: wav2vec2-large-xls-r-300m-hi-wx1 | |
| results: | |
| - task: | |
| type: automatic-speech-recognition | |
| name: Speech Recognition | |
| dataset: | |
| type: mozilla-foundation/common_voice_7_0 | |
| name: Common Voice 7 | |
| args: hi | |
| metrics: | |
| - type: wer | |
| value: 0.3719684845500431 | |
| name: Test WER | |
| - name: Test CER | |
| type: cer | |
| value: 0.11763235514672798 | |
| <!-- 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-large-xls-r-300m-hi-wx1 | |
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 -HI dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6552 | |
| - Wer: 0.3200 | |
| Evaluation Commands | |
| 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split | |
| python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs | |
| 2. To evaluate on speech-recognition-community-v2/dev_data | |
| NA | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.00024 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 1800 | |
| - num_epochs: 50 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 12.2663 | 1.36 | 200 | 5.9245 | 1.0 | | |
| | 4.1856 | 2.72 | 400 | 3.4968 | 1.0 | | |
| | 3.3908 | 4.08 | 600 | 2.9970 | 1.0 | | |
| | 1.5444 | 5.44 | 800 | 0.9071 | 0.6139 | | |
| | 0.7237 | 6.8 | 1000 | 0.6508 | 0.4862 | | |
| | 0.5323 | 8.16 | 1200 | 0.6217 | 0.4647 | | |
| | 0.4426 | 9.52 | 1400 | 0.5785 | 0.4288 | | |
| | 0.3933 | 10.88 | 1600 | 0.5935 | 0.4217 | | |
| | 0.3532 | 12.24 | 1800 | 0.6358 | 0.4465 | | |
| | 0.3319 | 13.6 | 2000 | 0.5789 | 0.4118 | | |
| | 0.2877 | 14.96 | 2200 | 0.6163 | 0.4056 | | |
| | 0.2663 | 16.33 | 2400 | 0.6176 | 0.3893 | | |
| | 0.2511 | 17.68 | 2600 | 0.6065 | 0.3999 | | |
| | 0.2275 | 19.05 | 2800 | 0.6183 | 0.3842 | | |
| | 0.2098 | 20.41 | 3000 | 0.6486 | 0.3864 | | |
| | 0.1943 | 21.77 | 3200 | 0.6365 | 0.3885 | | |
| | 0.1877 | 23.13 | 3400 | 0.6013 | 0.3677 | | |
| | 0.1679 | 24.49 | 3600 | 0.6451 | 0.3795 | | |
| | 0.1667 | 25.85 | 3800 | 0.6410 | 0.3635 | | |
| | 0.1514 | 27.21 | 4000 | 0.6000 | 0.3577 | | |
| | 0.1453 | 28.57 | 4200 | 0.6020 | 0.3518 | | |
| | 0.134 | 29.93 | 4400 | 0.6531 | 0.3517 | | |
| | 0.1354 | 31.29 | 4600 | 0.6874 | 0.3578 | | |
| | 0.1224 | 32.65 | 4800 | 0.6519 | 0.3492 | | |
| | 0.1199 | 34.01 | 5000 | 0.6553 | 0.3490 | | |
| | 0.1077 | 35.37 | 5200 | 0.6621 | 0.3429 | | |
| | 0.0997 | 36.73 | 5400 | 0.6641 | 0.3413 | | |
| | 0.0964 | 38.09 | 5600 | 0.6722 | 0.3385 | | |
| | 0.0931 | 39.45 | 5800 | 0.6365 | 0.3363 | | |
| | 0.0944 | 40.81 | 6000 | 0.6454 | 0.3326 | | |
| | 0.0862 | 42.18 | 6200 | 0.6497 | 0.3256 | | |
| | 0.0848 | 43.54 | 6400 | 0.6599 | 0.3226 | | |
| | 0.0793 | 44.89 | 6600 | 0.6625 | 0.3232 | | |
| | 0.076 | 46.26 | 6800 | 0.6463 | 0.3186 | | |
| | 0.0749 | 47.62 | 7000 | 0.6559 | 0.3225 | | |
| | 0.0663 | 48.98 | 7200 | 0.6552 | 0.3200 | | |
| ### Framework versions | |
| - Transformers 4.16.2 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 1.18.3 | |
| - Tokenizers 0.11.0 | |