
# XLS-R-300M Uyghur CV7

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_7_0 - UG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1772
- Wer: 0.2589

## 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 alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), with punctuation removed.

## 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 combination of `train` and `dev` of common voice official splits were used as training data. The official `test` split was used as validation data as well as for final evaluation.

## Training procedure

The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV7 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3043        | 2.73  | 500   | 3.2415          | 1.0    |
| 3.0482        | 5.46  | 1000  | 2.9591          | 1.0    |
| 1.4767        | 8.2   | 1500  | 0.4779          | 0.5777 |
| 1.3152        | 10.93 | 2000  | 0.3697          | 0.4938 |
| 1.2246        | 13.66 | 2500  | 0.3084          | 0.4459 |
| 1.1781        | 16.39 | 3000  | 0.2842          | 0.4154 |
| 1.1351        | 19.13 | 3500  | 0.2615          | 0.3929 |
| 1.1052        | 21.86 | 4000  | 0.2462          | 0.3747 |
| 1.0711        | 24.59 | 4500  | 0.2366          | 0.3652 |
| 1.035         | 27.32 | 5000  | 0.2268          | 0.3557 |
| 1.0277        | 30.05 | 5500  | 0.2243          | 0.3450 |
| 1.002         | 32.79 | 6000  | 0.2204          | 0.3389 |
| 0.9837        | 35.52 | 6500  | 0.2156          | 0.3349 |
| 0.9773        | 38.25 | 7000  | 0.2127          | 0.3289 |
| 0.9807        | 40.98 | 7500  | 0.2142          | 0.3274 |
| 0.9582        | 43.72 | 8000  | 0.2004          | 0.3142 |
| 0.9548        | 46.45 | 8500  | 0.2022          | 0.3050 |
| 0.9251        | 49.18 | 9000  | 0.2019          | 0.3035 |
| 0.9103        | 51.91 | 9500  | 0.1964          | 0.3021 |
| 0.915         | 54.64 | 10000 | 0.1970          | 0.3032 |
| 0.8962        | 57.38 | 10500 | 0.2007          | 0.3046 |
| 0.8729        | 60.11 | 11000 | 0.1967          | 0.2942 |
| 0.8744        | 62.84 | 11500 | 0.1952          | 0.2885 |
| 0.874         | 65.57 | 12000 | 0.1894          | 0.2895 |
| 0.8457        | 68.31 | 12500 | 0.1895          | 0.2828 |
| 0.8519        | 71.04 | 13000 | 0.1912          | 0.2875 |
| 0.8301        | 73.77 | 13500 | 0.1878          | 0.2760 |
| 0.8226        | 76.5  | 14000 | 0.1808          | 0.2701 |
| 0.8071        | 79.23 | 14500 | 0.1849          | 0.2741 |
| 0.7999        | 81.97 | 15000 | 0.1808          | 0.2717 |
| 0.7947        | 84.7  | 15500 | 0.1821          | 0.2716 |
| 0.7783        | 87.43 | 16000 | 0.1824          | 0.2661 |
| 0.7729        | 90.16 | 16500 | 0.1773          | 0.2639 |
| 0.7759        | 92.9  | 17000 | 0.1767          | 0.2629 |
| 0.7713        | 95.63 | 17500 | 0.1780          | 0.2621 |
| 0.7628        | 98.36 | 18000 | 0.1773          | 0.2594 |


### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0

{"language": ["ug"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ug", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "XLS-R-300M Uyghur CV7", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "ug"}, "metrics": [{"type": "wer", "value": 25.845, "name": "Test WER"}, {"type": "cer", "value": 4.795, "name": "Test CER"}]}]}]}
transformers pytorch tensorboard safetensors wav2vec2 automatic-speech-recognition mozilla-foundation/common_voice_7_0 generated_from_trainer ug robust-speech-event hf-asr-leaderboard dataset:mozilla-foundation/common_voice_7_0 base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ug #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us 

XLS-R-300M Uyghur CV7
=====================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - UG dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1772
* Wer: 0.2589


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


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


The model vocabulary consists of the alphabetic characters of the Perso-Arabic script for the Uyghur language, with punctuation removed.


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 combination of 'train' and 'dev' of common voice official splits were used as training data. The official 'test' split was used as validation data as well as for final evaluation.


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


The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV7 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs).


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ug #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ug #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0