

# XLS-R-300m-ET

This is a XLS-R-300M model [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) finetuned on around 800 hours of diverse Estonian data.

## Model description
This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech. It consists of only the CTC-based end-to-end model, no language model is currently provided.

## Intended uses & limitations

This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.

## How to use


TODO

#### Limitations and bias

Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:
  * Speech containing technical and other domain-specific terms
  * Children's speech
  * Non-native speech
  * Speech recorded under very noisy conditions or with a microphone far from the speaker
  * Very spontaneous and overlapping speech

## Training data
Acoustic training data:

| Type                  | Amount (h) |
|-----------------------|:------:|
| Broadcast speech      |   591  |
| Spontaneous speech    |   53   |
| Elderly speech corpus |   53   |
| Talks, lectures       |   49   |
| Parliament speeches   |   31   |
| *Total*               |   *761*  |


## Training procedure

Finetuned using Fairseq.

## Evaluation results

### WER

|Dataset | WER |
|---|---|
| jutusaated.devset | 7.9 |
| jutusaated.testset | 6.1 |
| Common Voice 6.1 | 12.5 |
| Common Voice 8.0 | 13.4 |

{"language": "et", "license": "cc-by-4.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "model-index": [{"name": "xls-r-300m-et", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice", "type": "common_voice", "args": "et"}, "metrics": [{"type": "wer", "value": 12.520395591222401, "name": "Test WER"}, {"type": "cer", "value": 2.70911524386249, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "et"}, "metrics": [{"type": "wer", "value": 13.38447882323104, "name": "Test WER"}, {"type": "cer", "value": 2.9816686199500255, "name": "Test CER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio hf-asr-leaderboard et license:cc-by-4.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #et #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 

XLS-R-300m-ET
=============


This is a XLS-R-300M model facebook/wav2vec2-xls-r-300m finetuned on around 800 hours of diverse Estonian data.


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


This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech. It consists of only the CTC-based end-to-end model, no language model is currently provided.


Intended uses & limitations
---------------------------


This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.


How to use
----------


TODO


#### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



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


Finetuned using Fairseq.


Evaluation results
------------------


### WER




#### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



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


Finetuned using Fairseq.


Evaluation results
------------------ ### WER
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #et #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 
 #### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



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


Finetuned using Fairseq.


Evaluation results
------------------ ### WER
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #et #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 
#### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



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


Finetuned using Fairseq.


Evaluation results
------------------### WER