
# XLS-R-based CTC model with 5-gram language model from Open Subtitles

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 [CGN dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/), as well as the [MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL](https://commonvoice.mozilla.org) dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.04057
- Cer: 0.01222

## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. 

To improve accuracy, a beam-search decoder based on `pyctcdecode` is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.

## Intended uses & limitations

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

## Training and evaluation data

The model was:

0. initialized with [the 2B parameter model from Facebook](facebook/wav2vec2-xls-r-2b-22-to-16).
1. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).
2. trained `1` epoch (36000 iterations of batch size 32) on [the `cgn` dataset](https://taalmaterialen.ivdnt.org/download/tstc-corpus-gesproken-nederlands/).
3. trained `5` epochs (6000 iterations of batch size 32) on [the `cv8/nl` dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0).

### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
{"language": ["nl"], "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "nl_BE", "nl_NL", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "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": 4.06, "name": "Test WER"}, {"type": "cer", "value": 1.22, "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": 17.77, "name": "Test WER"}, {"type": "cer", "value": 9.77, "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": 16.32, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition hf-asr-leaderboard model_for_talk mozilla-foundation/common_voice_8_0 nl nl_BE nl_NL robust-speech-event dataset:mozilla-foundation/common_voice_8_0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #nl_BE #nl_NL #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us 


# XLS-R-based CTC model with 5-gram language model from Open Subtitles

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.04057
- Cer: 0.01222

## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. 

To improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.

## Intended uses & limitations

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

## Training and evaluation data

The model was:

0. initialized with the 2B parameter model from Facebook.
1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.
2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.
3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.

### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
# XLS-R-based CTC model with 5-gram language model from Open Subtitles

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.04057
- Cer: 0.01222 ## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. 

To improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus. ## Intended uses & limitations

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

The model was:

0. initialized with the 2B parameter model from Facebook.
1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.
2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.
3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset. ### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #nl_BE #nl_NL #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us 
 # XLS-R-based CTC model with 5-gram language model from Open Subtitles

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.04057
- Cer: 0.01222 ## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. 

To improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus. ## Intended uses & limitations

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

The model was:

0. initialized with the 2B parameter model from Facebook.
1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.
2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.
3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset. ### Framework versions

- Transformers 4.16.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #nl_BE #nl_NL #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #model-index #endpoints_compatible #region-us 
# XLS-R-based CTC model with 5-gram language model from Open Subtitles

This model is a version of facebook/wav2vec2-xls-r-2b-22-to-16 fine-tuned mainly on the CGN dataset, as well as the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset (see details below), on which a large 5-gram language model is added based on the Open Subtitles Dutch corpus. This model achieves the following results on the evaluation set (of Common Voice 8.0):
- Wer: 0.04057
- Cer: 0.01222## Model description

The model takes 16kHz sound input, and uses a Wav2Vec2ForCTC decoder with 48 letters to output the letter-transcription probabilities per frame. 

To improve accuracy, a beam-search decoder based on 'pyctcdecode' is then used; it reranks the most promising alignments based on a 5-gram language model trained on the Open Subtitles Dutch corpus.## Intended uses & limitations

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

The model was:

0. initialized with the 2B parameter model from Facebook.
1. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.
2. trained '1' epoch (36000 iterations of batch size 32) on the 'cgn' dataset.
3. trained '5' epochs (6000 iterations of batch size 32) on the 'cv8/nl' dataset.