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@@ -50,14 +50,14 @@ The performance of the model is the following:
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  | Release | Test CER | Test WER | GPUs |
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  |:-------------:|:--------------:|:--------------:| :--------:|
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- | 16-08-22 | 2.40 | 9.54 | 1xRTXA6000 48GB |
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  ## Pipeline description
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  This ASR system is composed of 2 different but linked blocks:
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  - Tokenizer (char) that transforms words into chars and trained with
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  the train transcriptions (train.tsv) of CommonVoice (ES).
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- - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-large-xlsr-53-spanish](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-spanish)) is combined with two DNN layers and finetuned on CommonVoice DE.
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  The obtained final acoustic representation is given to the CTC decoder.
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  The system is trained with recordings sampled at 16kHz (single channel).
@@ -121,6 +121,25 @@ The SpeechBrain team does not provide any warranty on the performance achieved b
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  Please, cite SpeechBrain if you use it for your research or business.
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  ```bibtex
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  @misc{speechbrain,
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  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
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  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
 
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  | Release | Test CER | Test WER | GPUs |
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  |:-------------:|:--------------:|:--------------:| :--------:|
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+ | 16-08-22 | - | 7.83 | 3xRTX2080Ti 12GB |
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  ## Pipeline description
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  This ASR system is composed of 2 different but linked blocks:
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  - Tokenizer (char) that transforms words into chars and trained with
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  the train transcriptions (train.tsv) of CommonVoice (ES).
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+ - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-large-xlsr-53-spanish](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-spanish)) is combined with two DNN layers and finetuned on CommonVoice ES.
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  The obtained final acoustic representation is given to the CTC decoder.
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  The system is trained with recordings sampled at 16kHz (single channel).
 
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  Please, cite SpeechBrain if you use it for your research or business.
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  ```bibtex
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+
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+ @article{lopez2022tid,
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+ title={TID Spanish ASR system for the Albayzin 2022 Speech-to-Text Transcription Challenge},
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+ author={L{\'o}pez, Fernando and Luque, Jordi},
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+ journal={Proc. IberSPEECH 2022},
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+ pages={271--275},
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+ year={2022}
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+ }
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+
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+ @misc{https://doi.org/10.48550/arxiv.2210.15226,
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+ doi = {10.48550/ARXIV.2210.15226},
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+ url = {https://arxiv.org/abs/2210.15226},
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+ author = {López, Fernando and Luque, Jordi},
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+ title = {Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptation},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+
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  @misc{speechbrain,
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  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
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  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},