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---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- en
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-1B - English
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: en
metrics:
- name: Test WER
type: wer
value: 21.05
- name: Test CER
type: cer
value: 8.44
- name: Test WER (+LM)
type: wer
value: 17.31
- name: Test CER (+LM)
type: cer
value: 7.77
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: en
metrics:
- name: Dev WER
type: wer
value: 20.53
- name: Dev CER
type: cer
value: 9.31
- name: Dev WER (+LM)
type: wer
value: 17.70
- name: Dev CER (+LM)
type: cer
value: 8.93
---
# XLS-R-1B-ENGLISH
Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on English using the [Common Voice 8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
## Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-english --dataset mozilla-foundation/common_voice_8_0 --config en --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-english --dataset speech-recognition-community-v2/dev_data --config en --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
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