
# XLS-R-300M Uzbek CV8

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ dataset.
It achieves the following results on the validation set:
- Loss: 0.3063
- Wer: 0.3852
- Cer: 0.0777

## Model description

For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)

The model vocabulary consists of the [Modern Latin alphabet for Uzbek](https://en.wikipedia.org/wiki/Uzbek_alphabet), with punctuation removed. 
Note that the characters <‘> and <’> do not count as punctuation, as <‘> modifies \<o\> and \<g\>, and <’> indicates the glottal stop or a long vowel.

The decoder uses a kenlm language model built on common_voice text.

## Intended uses & limitations

This model is expected to be of some utility for low-fidelity use cases such as:
- Draft video captions
- Indexing of recorded broadcasts

The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.

## Training and evaluation data

The 50% of the `train` common voice official split was used as training data. The 50% of the official `dev` split was used as validation data, and the full `test` set was used for final evaluation of the model without LM, while the model with LM was evaluated only on 500 examples from the `test` set.

The kenlm language model was compiled from the target sentences of the train + other dataset splits.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.1401        | 3.25  | 500   | 3.1146          | 1.0    | 1.0    |
| 2.7484        | 6.49  | 1000  | 2.2842          | 1.0065 | 0.7069 |
| 1.0899        | 9.74  | 1500  | 0.5414          | 0.6125 | 0.1351 |
| 0.9465        | 12.99 | 2000  | 0.4566          | 0.5635 | 0.1223 |
| 0.8771        | 16.23 | 2500  | 0.4212          | 0.5366 | 0.1161 |
| 0.8346        | 19.48 | 3000  | 0.3994          | 0.5144 | 0.1102 |
| 0.8127        | 22.73 | 3500  | 0.3819          | 0.4944 | 0.1051 |
| 0.7833        | 25.97 | 4000  | 0.3705          | 0.4798 | 0.1011 |
| 0.7603        | 29.22 | 4500  | 0.3661          | 0.4704 | 0.0992 |
| 0.7424        | 32.47 | 5000  | 0.3529          | 0.4577 | 0.0957 |
| 0.7251        | 35.71 | 5500  | 0.3410          | 0.4473 | 0.0928 |
| 0.7106        | 38.96 | 6000  | 0.3401          | 0.4428 | 0.0919 |
| 0.7027        | 42.21 | 6500  | 0.3355          | 0.4353 | 0.0905 |
| 0.6927        | 45.45 | 7000  | 0.3308          | 0.4296 | 0.0885 |
| 0.6828        | 48.7  | 7500  | 0.3246          | 0.4204 | 0.0863 |
| 0.6706        | 51.95 | 8000  | 0.3250          | 0.4233 | 0.0868 |
| 0.6629        | 55.19 | 8500  | 0.3264          | 0.4159 | 0.0849 |
| 0.6556        | 58.44 | 9000  | 0.3213          | 0.4100 | 0.0835 |
| 0.6484        | 61.69 | 9500  | 0.3182          | 0.4124 | 0.0837 |
| 0.6407        | 64.93 | 10000 | 0.3171          | 0.4050 | 0.0825 |
| 0.6375        | 68.18 | 10500 | 0.3150          | 0.4039 | 0.0822 |
| 0.6363        | 71.43 | 11000 | 0.3129          | 0.3991 | 0.0810 |
| 0.6307        | 74.67 | 11500 | 0.3114          | 0.3986 | 0.0807 |
| 0.6232        | 77.92 | 12000 | 0.3103          | 0.3895 | 0.0790 |
| 0.6216        | 81.17 | 12500 | 0.3086          | 0.3891 | 0.0790 |
| 0.6174        | 84.41 | 13000 | 0.3082          | 0.3881 | 0.0785 |
| 0.6196        | 87.66 | 13500 | 0.3059          | 0.3875 | 0.0782 |
| 0.6174        | 90.91 | 14000 | 0.3084          | 0.3862 | 0.0780 |
| 0.6169        | 94.16 | 14500 | 0.3070          | 0.3860 | 0.0779 |
| 0.6166        | 97.4  | 15000 | 0.3066          | 0.3855 | 0.0778 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0

{"language": ["uz"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "XLS-R-300M Uzbek CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "uz"}, "metrics": [{"type": "wer", "value": 15.065, "name": "Test WER (with LM)"}, {"type": "cer", "value": 3.077, "name": "Test CER (with LM)"}, {"type": "wer", "value": 32.88, "name": "Test WER (no LM)"}, {"type": "cer", "value": 6.53, "name": "Test CER (no LM)"}]}]}]}
transformers pytorch tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_8_0 robust-speech-event uz dataset:mozilla-foundation/common_voice_8_0 base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uz #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

XLS-R-300M Uzbek CV8
====================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - UZ dataset.
It achieves the following results on the validation set:


* Loss: 0.3063
* Wer: 0.3852
* Cer: 0.0777


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


For a description of the model architecture, see facebook/wav2vec2-xls-r-300m


The model vocabulary consists of the Modern Latin alphabet for Uzbek, with punctuation removed.
Note that the characters <‘> and <’> do not count as punctuation, as <‘> modifies <o> and <g>, and <’> indicates the glottal stop or a long vowel.


The decoder uses a kenlm language model built on common\_voice text.


Intended uses & limitations
---------------------------


This model is expected to be of some utility for low-fidelity use cases such as:


* Draft video captions
* Indexing of recorded broadcasts


The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.


Training and evaluation data
----------------------------


The 50% of the 'train' common voice official split was used as training data. The 50% of the official 'dev' split was used as validation data, and the full 'test' set was used for final evaluation of the model without LM, while the model with LM was evaluated only on 500 examples from the 'test' set.


The kenlm language model was compiled from the target sentences of the train + other dataset splits.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uz #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #uz #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* 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 4.16.2
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0