YAML Metadata
Error:
"language[0]" must only contain lowercase characters
YAML Metadata
Error:
"language[0]" with value "pa-IN" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set:
- Loss: 1.0855
- Wer: 0.4755
Evaluation Commands
- To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1 --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test --log_outputs
- To evaluate on speech-recognition-community-v2/dev_data
Punjabi language isn't available in speech-recognition-community-v2/dev_data
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 1200
- num_epochs: 100.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
3.4607 | 9.26 | 500 | 2.7746 | 1.0416 |
0.3442 | 18.52 | 1000 | 0.9114 | 0.5911 |
0.2213 | 27.78 | 1500 | 0.9687 | 0.5751 |
0.1242 | 37.04 | 2000 | 1.0204 | 0.5461 |
0.0998 | 46.3 | 2500 | 1.0250 | 0.5233 |
0.0727 | 55.56 | 3000 | 1.1072 | 0.5382 |
0.0605 | 64.81 | 3500 | 1.0588 | 0.5073 |
0.0458 | 74.07 | 4000 | 1.0818 | 0.5069 |
0.0338 | 83.33 | 4500 | 1.0948 | 0.5108 |
0.0223 | 92.59 | 5000 | 1.0986 | 0.4775 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
- Downloads last month
- 19
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1
Evaluation results
- Test WER on Common Voice 8self-reported0.487
- Test CER on Common Voice 8self-reported0.169
- Test WER on Robust Speech Event - Dev Dataself-reportedNA
- Test CER on Robust Speech Event - Dev Dataself-reportedNA