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# wav2vec2-large-xls-r-300m-hsb-v3

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 - HSB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6549
- Wer: 0.4827

### Evaluation Commands

1. To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs

2. To evaluate on speech-recognition-community-v2/dev_data

Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev_data!

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.00045
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.8951        | 3.23  | 100  | 3.6396          | 1.0    |
| 3.314         | 6.45  | 200  | 3.2331          | 1.0    |
| 3.1931        | 9.68  | 300  | 3.0947          | 0.9906 |
| 1.7079        | 12.9  | 400  | 0.8865          | 0.8499 |
| 0.6859        | 16.13 | 500  | 0.7994          | 0.7529 |
| 0.4804        | 19.35 | 600  | 0.7783          | 0.7069 |
| 0.3506        | 22.58 | 700  | 0.6904          | 0.6321 |
| 0.2695        | 25.81 | 800  | 0.6519          | 0.5926 |
| 0.222         | 29.03 | 900  | 0.7041          | 0.5720 |
| 0.1828        | 32.26 | 1000 | 0.6608          | 0.5513 |
| 0.1474        | 35.48 | 1100 | 0.7129          | 0.5319 |
| 0.1269        | 38.71 | 1200 | 0.6664          | 0.5056 |
| 0.1077        | 41.94 | 1300 | 0.6712          | 0.4942 |
| 0.0934        | 45.16 | 1400 | 0.6467          | 0.4879 |
| 0.0819        | 48.39 | 1500 | 0.6549          | 0.4827 |


### Framework versions

- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0

{"language": ["hsb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hsb", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hsb-v3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": 0.4763681592039801, "name": "Test WER"}, {"type": "cer", "value": 0.11194945177476305, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hsb"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition mozilla-foundation/common_voice_8_0 generated_from_trainer hsb robust-speech-event model_for_talk hf-asr-leaderboard dataset:mozilla-foundation/common_voice_8_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hsb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-hsb-v3
================================


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


* Loss: 0.6549
* Wer: 0.4827


### Evaluation Commands


1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split


python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common\_voice\_8\_0 --config hsb --split test --log\_outputs


2. To evaluate on speech-recognition-community-v2/dev\_data


Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev\_data!


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00045
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* 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: 500
* num\_epochs: 50
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0



### Evaluation Commands


1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split


python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common\_voice\_8\_0 --config hsb --split test --log\_outputs


2. To evaluate on speech-recognition-community-v2/dev\_data


Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev\_data! ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00045
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* 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: 500
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hsb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Evaluation Commands


1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split


python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common\_voice\_8\_0 --config hsb --split test --log\_outputs


2. To evaluate on speech-recognition-community-v2/dev\_data


Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev\_data! ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00045
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* 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: 500
* num\_epochs: 50
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hsb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Evaluation Commands


1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split


python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common\_voice\_8\_0 --config hsb --split test --log\_outputs


2. To evaluate on speech-recognition-community-v2/dev\_data


Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev\_data!### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00045
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* 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: 500
* num\_epochs: 50
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0