---
language:
- hy-AM
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-1b-hy-cv
  results:
  - task: 
      type: automatic-speech-recognition
      name: Speech Recognition
    dataset:
      type: mozilla-foundation/common_voice_8_0
      name: Common Voice hy-AM
      args: hy-AM
    metrics:
      - type: wer
        value: 10.92896174863388
        name: WER LM
      - type: cer
        value: 2.3773394031360646
        name: CER LM
  - task: 
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Robust Speech Event - Dev Data
      type: speech-recognition-community-v2/dev_data
      args: hy
    metrics:
       - name: Test WER
         type: wer
         value: 19.942816297355254
       - name: Test CER
         type: cer
         value: 7.332618465282714
---


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Wav2Vec2-XLS-R-1b-hy

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_3/ - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1827
- Wer: 0.2389
- Cer: 0.0427
- Wer LM: 0.1093
- Cer LM: 0.0238

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 842
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 4.0311        | 3.51  | 200  | 0.7943          | 0.8981 | 0.2374 |
| 1.4388        | 7.02  | 400  | 0.2546          | 0.3821 | 0.0658 |
| 1.0949        | 10.53 | 600  | 0.2201          | 0.3216 | 0.0573 |
| 1.0279        | 14.04 | 800  | 0.2250          | 0.3271 | 0.0583 |
| 0.9923        | 17.54 | 1000 | 0.2074          | 0.3111 | 0.0543 |
| 0.972         | 21.05 | 1200 | 0.2165          | 0.2955 | 0.0536 |
| 0.9587        | 24.56 | 1400 | 0.2064          | 0.3017 | 0.0535 |
| 0.9421        | 28.07 | 1600 | 0.2062          | 0.2884 | 0.0519 |
| 0.9189        | 31.58 | 1800 | 0.2014          | 0.2822 | 0.0507 |
| 0.8919        | 35.09 | 2000 | 0.1952          | 0.2689 | 0.0488 |
| 0.8615        | 38.6  | 2200 | 0.2020          | 0.2685 | 0.0480 |
| 0.834         | 42.11 | 2400 | 0.2001          | 0.2654 | 0.0467 |
| 0.8056        | 45.61 | 2600 | 0.1935          | 0.2498 | 0.0448 |
| 0.7888        | 49.12 | 2800 | 0.1892          | 0.2451 | 0.0446 |
| 0.761         | 52.63 | 3000 | 0.1884          | 0.2432 | 0.0441 |
| 0.742         | 56.14 | 3200 | 0.1827          | 0.2389 | 0.0427 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0