
# Ukrainian STT model (with Language Model)

🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk

⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UK dataset.

It achieves the following results on the evaluation set without the language model:

- Loss: 0.1875
- Wer: 0.2033
- Cer: 0.0384


## Model description

On 100 test example the model shows the following results:

Without LM:

- WER: 0.1862
- CER: 0.0277

With LM:

- WER: 0.1218
- CER: 0.0190


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.2815        | 7.93  | 500  | 0.3536          | 0.4753 | 0.1009 |
| 1.0869        | 15.86 | 1000 | 0.2317          | 0.3111 | 0.0614 |
| 0.9984        | 23.8  | 1500 | 0.2022          | 0.2676 | 0.0521 |
| 0.975         | 31.74 | 2000 | 0.1948          | 0.2469 | 0.0487 |
| 0.9306        | 39.67 | 2500 | 0.1916          | 0.2377 | 0.0464 |
| 0.8868        | 47.61 | 3000 | 0.1903          | 0.2257 | 0.0439 |
| 0.8424        | 55.55 | 3500 | 0.1786          | 0.2206 | 0.0423 |
| 0.8126        | 63.49 | 4000 | 0.1849          | 0.2160 | 0.0416 |
| 0.7901        | 71.42 | 4500 | 0.1869          | 0.2138 | 0.0413 |
| 0.7671        | 79.36 | 5000 | 0.1855          | 0.2075 | 0.0394 |
| 0.7467        | 87.3  | 5500 | 0.1884          | 0.2049 | 0.0389 |
| 0.731         | 95.24 | 6000 | 0.1877          | 0.2060 | 0.0387 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0

#### Evaluation Commands

1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`

```bash
python eval.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --dataset mozilla-foundation/common_voice_7_0 --config uk --split test
```

### Eval results on Common Voice 7 "test" (WER):

| Without LM | With LM (run `./eval.py`) |
|---|---|
| 21.52 | 14.62 |

{"language": ["uk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "uk"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-uk-with-lm", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "uk"}, "metrics": [{"type": "wer", "value": 14.62, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "uk"}, "metrics": [{"type": "wer", "value": 48.72, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "uk"}, "metrics": [{"type": "wer", "value": 40.66, "name": "Test WER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_7_0 robust-speech-event uk dataset:mozilla-foundation/common_voice_7_0 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #uk #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

Ukrainian STT model (with Language Model)
=========================================


🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech\_recognition\_uk


⭐ See other Ukrainian models - URL


This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - UK dataset.


It achieves the following results on the evaluation set without the language model:


* Loss: 0.1875
* Wer: 0.2033
* Cer: 0.0384


Model description
-----------------


On 100 test example the model shows the following results:


Without LM:


* WER: 0.1862
* CER: 0.0277


With LM:


* WER: 0.1218
* CER: 0.0190


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 20
* total\_train\_batch\_size: 160
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.1.dev0
* Tokenizers 0.11.0


#### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test'


### Eval results on Common Voice 7 "test" (WER):




### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 20
* total\_train\_batch\_size: 160
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.1.dev0
* Tokenizers 0.11.0 #### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test' ### Eval results on Common Voice 7 "test" (WER):
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #uk #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 20
* total\_train\_batch\_size: 160
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.1.dev0
* Tokenizers 0.11.0 #### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test' ### Eval results on Common Voice 7 "test" (WER):
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #uk #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 20
* total\_train\_batch\_size: 160
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.1.dev0
* Tokenizers 0.11.0#### Evaluation Commands


1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test'### Eval results on Common Voice 7 "test" (WER):