tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-en-in-lm
results: []
wav2vec2-large-en-in-lm
This model is a fine-tuned version of crossdelenna/wav2vec2-large-en-in-lm
It achieves the following results on the evaluation set:
- Loss: 0.0478
- Wer: 0.0951
Model description
Wav2vec2 Automatic speech recognition for Indian English accent using the language model.
Intended uses & limitations
This model is intended for my personal use only. Intentionally, the data set has absolutely no speech variance. It is fine-tuned only on my own data and I am using it for live speech dictation with Pyaudio non-blocking streaming microphone data (https://gist.github.com/KenoLeon/13dfb803a21a08cf224b2e6df0feed80). Before inference, train further on your own data. The training data has a lot of quantitative finance-related jargon and a lot of urban slang. Note that it doesn't hash out F words, so NSFW.
Training and evaluation data
Facebook base large dataset further fine-tuned on thirty-two hours of personal recordings. It has a male voice with an Indian English accent. The recording is done on the omnidirectional microphone with a lot of background noise.
Training procedure
I downloaded my Reddit and Twitter data and started recording each clip not exceeding 13 seconds. When I got enough sample size of 6 hrs I fine-tuned the model with approximately 19% WER. Afterwards, I kept adding the data and kept fine-tuning it. It is now trained on thirty hours of data. (Now the idea is to fine-tune every two-three months only on unrecognized words)
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1589 | 10.0 | 1210 | 0.0754 | 0.1088 |
| 0.1369 | 20.0 | 2420 | 0.0527 | 0.0991 |
| 0.1208 | 30.0 | 3630 | 0.0478 | 0.0951 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1