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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

{"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-en-in-lm", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 

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 (URL 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



### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1