---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-saha-yakut
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: sah
      split: test
      args: sah
    metrics:
    - name: Wer
      type: wer
      value: 0.5216510903426791
---

<!-- 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-large-xls-r-300m-saha-yakut

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6404
- Wer: 0.5217

## 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: 0.0002
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| No log        | 3.4483  | 150  | 3.1675          | 1.0    |
| No log        | 6.8966  | 300  | 1.1392          | 0.8509 |
| 3.5746        | 10.3448 | 450  | 0.6103          | 0.5812 |
| 3.5746        | 13.7931 | 600  | 0.6152          | 0.5565 |
| 3.5746        | 17.2414 | 750  | 0.6420          | 0.5382 |
| 0.2138        | 20.6897 | 900  | 0.6344          | 0.5245 |
| 0.2138        | 24.1379 | 1050 | 0.6543          | 0.5303 |
| 0.1148        | 27.5862 | 1200 | 0.6404          | 0.5217 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1