readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-en-in-lm
    results: []
  ---
  
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  # wav2vec2-large-en-in-lm
  
  This model is a fine-tuned version of [crossdelenna/wav2vec2-large-en-in-lm](https://huggingface.co/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
  