
# XLS-R-based CTC model with 5-gram language model from Common Voice

This model is a version of [facebook/wav2vec2-xls-r-2b-22-to-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16) fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.0669
- Cer: 0.0197

## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. 

To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus.

## Intended uses & limitations

This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).

## Training and evaluation data

0. The model was initialized with [the 2B parameter model from Facebook](facebook/wav2vec2-xls-r-2b-22-to-16).
1. The model was then trained `2000` iterations (batch size 32) on [the `dutch` configuration of the `multilingual_librispeech` dataset](https://huggingface.co/datasets/multilingual_librispeech/).
1. The model was then trained `2000` iterations (batch size 32) on [the `nl` configuration of the `common_voice_8_0` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
2. The model was then trained `6000` iterations (batch size 32) on [the `cgn` dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/).
3. The model was then trained `6000` iterations (batch size 32) on [the `nl` configuation of the `common_voice_8_0` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).

### Framework versions

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

{"language": ["nl"], "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event", "vl"], "datasets": ["mozilla-foundation/common_voice_8_0", "multilingual_librispeech"], "model-index": [{"name": "xls-r-nl-v1-cv8-lm", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "nl"}, "metrics": [{"type": "wer", "value": 6.69, "name": "Test WER"}, {"type": "cer", "value": 1.97, "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": "nl"}, "metrics": [{"type": "wer", "value": 20.79, "name": "Test WER"}, {"type": "cer", "value": 10.72, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "nl"}, "metrics": [{"type": "wer", "value": 19.71, "name": "Test WER"}]}]}]}
transformers pytorch safetensors wav2vec2 automatic-speech-recognition hf-asr-leaderboard model_for_talk mozilla-foundation/common_voice_8_0 nl robust-speech-event vl dataset:mozilla-foundation/common_voice_8_0 dataset:multilingual_librispeech model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #vl #dataset-mozilla-foundation/common_voice_8_0 #dataset-multilingual_librispeech #model-index #endpoints_compatible #region-us 


# XLS-R-based CTC model with 5-gram language model from Common Voice

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.0669
- Cer: 0.0197

## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. 

To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus.

## Intended uses & limitations

This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).

## Training and evaluation data

0. The model was initialized with the 2B parameter model from Facebook.
1. The model was then trained '2000' iterations (batch size 32) on the 'dutch' configuration of the 'multilingual_librispeech' dataset.
1. The model was then trained '2000' iterations (batch size 32) on the 'nl' configuration of the 'common_voice_8_0' dataset.
2. The model was then trained '6000' iterations (batch size 32) on the 'cgn' dataset.
3. The model was then trained '6000' iterations (batch size 32) on the 'nl' configuation of the 'common_voice_8_0' dataset.

### Framework versions

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

# XLS-R-based CTC model with 5-gram language model from Common Voice

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.0669
- Cer: 0.0197 ## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. 

To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus. ## Intended uses & limitations

This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation). ## Training and evaluation data

0. The model was initialized with the 2B parameter model from Facebook.
1. The model was then trained '2000' iterations (batch size 32) on the 'dutch' configuration of the 'multilingual_librispeech' dataset.
1. The model was then trained '2000' iterations (batch size 32) on the 'nl' configuration of the 'common_voice_8_0' dataset.
2. The model was then trained '6000' iterations (batch size 32) on the 'cgn' dataset.
3. The model was then trained '6000' iterations (batch size 32) on the 'nl' configuation of the 'common_voice_8_0' dataset. ### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #vl #dataset-mozilla-foundation/common_voice_8_0 #dataset-multilingual_librispeech #model-index #endpoints_compatible #region-us 
 # XLS-R-based CTC model with 5-gram language model from Common Voice

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.0669
- Cer: 0.0197 ## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. 

To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus. ## Intended uses & limitations

This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation). ## Training and evaluation data

0. The model was initialized with the 2B parameter model from Facebook.
1. The model was then trained '2000' iterations (batch size 32) on the 'dutch' configuration of the 'multilingual_librispeech' dataset.
1. The model was then trained '2000' iterations (batch size 32) on the 'nl' configuration of the 'common_voice_8_0' dataset.
2. The model was then trained '6000' iterations (batch size 32) on the 'cgn' dataset.
3. The model was then trained '6000' iterations (batch size 32) on the 'nl' configuation of the 'common_voice_8_0' dataset. ### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #vl #dataset-mozilla-foundation/common_voice_8_0 #dataset-multilingual_librispeech #model-index #endpoints_compatible #region-us 
# XLS-R-based CTC model with 5-gram language model from Common Voice

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a small 5-gram language model is added based on the Common Voice training corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.0669
- Cer: 0.0197## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the final result. 

To improve accuracy, a beam decoder is used; the beams are scored based on 5-gram language model trained on the Common Voice 8 corpus.## Intended uses & limitations

This model can be used to transcribe Dutch or Flemish spoken dutch to text (without punctuation).