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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-large-en-in-lm |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-large-en-in-lm |
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This model is a fine-tuned version of [crossdelenna/wav2vec2-large-en-in-lm](https://huggingface.co/crossdelenna/wav2vec2-large-en-in-lm) |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0478 |
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- Wer: 0.0951 |
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## Model description |
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Wav2vec2 Automatic speech recognition for Indian English accent using the language model. |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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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. |
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(Now the idea is to fine-tune every two-three months only on unrecognized words) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.1589 | 10.0 | 1210 | 0.0754 | 0.1088 | |
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| 0.1369 | 20.0 | 2420 | 0.0527 | 0.0991 | |
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| 0.1208 | 30.0 | 3630 | 0.0478 | 0.0951 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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