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image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearc... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["coco"], "widget": [{"src": "http://images.cocodataset.org/val2017/000000039769.jpg", "example_title": "Cats"}, {"src": "http://images.cocodataset.org/val2017/000000039770.jpg", "example_title": "Castle"}]} | facebook/maskformer-swin-large-coco | null | [
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|
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model card ... | [
"# MaskFormer\n\nMaskFormer model trained on COCO panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a mod... | [
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image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (small-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresea... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | facebook/maskformer-swin-small-ade | null | [
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|
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (small-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model car... | [
"# MaskFormer\n\nMaskFormer model trained on ADE20k semantic segmentation (small-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a m... | [
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image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (small-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearc... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["coco"], "widget": [{"src": "http://images.cocodataset.org/val2017/000000039769.jpg", "example_title": "Cats"}, {"src": "http://images.cocodataset.org/val2017/000000039770.jpg", "example_title": "Castle"}]} | facebook/maskformer-swin-small-coco | null | [
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|
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (small-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model card ... | [
"# MaskFormer\n\nMaskFormer model trained on COCO panoptic segmentation (small-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a mod... | [
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image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresear... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg", "example_title": "House"}, {"src": "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade... | facebook/maskformer-swin-tiny-ade | null | [
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|
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model card... | [
"# MaskFormer\n\nMaskFormer model trained on ADE20k semantic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a mo... | [
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"# MaskFormer\n\nMaskFormer model trained on ADE20k semantic segmentation (tiny-sized version, Swin backbone). It was introd... |
image-segmentation | transformers |
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch... | {"license": "other", "tags": ["vision", "image-segmentation"], "datasets": ["coco"], "widget": [{"src": "http://images.cocodataset.org/val2017/000000039769.jpg", "example_title": "Cats"}, {"src": "http://images.cocodataset.org/val2017/000000039770.jpg", "example_title": "Castle"}]} | facebook/maskformer-swin-tiny-coco | null | [
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|
# MaskFormer
MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository.
Disclaimer: The team releasing MaskFormer did not write a model card f... | [
"# MaskFormer\n\nMaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. \n\nDisclaimer: The team releasing MaskFormer did not write a mode... | [
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translation | transformers |
# mBART-50 many to many multilingual machine translation
This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-many-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Mult... | {"language": ["multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "u... | facebook/mbart-large-50-many-to-many-mmt | null | [
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# mBART-50 many to many multilingual machine translation
This model is a fine-tuned checkpoint of mBART-large-50. 'mbart-large-50-many-to-many-mmt' is fine-tuned for multilingual machine translation. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper.
The mo... | [
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text2text-generation | transformers |
# mBART-50 many to one multilingual machine translation
This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-many-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multi... | {"language": ["multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "u... | facebook/mbart-large-50-many-to-one-mmt | null | [
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# mBART-50 many to one multilingual machine translation
This model is a fine-tuned checkpoint of mBART-large-50. 'mbart-large-50-many-to-many-mmt' is fine-tuned for multilingual machine translation. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper.
The model... | [
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text2text-generation | transformers |
# mBART-50 one to many multilingual machine translation
This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-one-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multil... | {"language": ["multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "u... | facebook/mbart-large-50-one-to-many-mmt | null | [
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# mBART-50 one to many multilingual machine translation
This model is a fine-tuned checkpoint of mBART-large-50. 'mbart-large-50-one-to-many-mmt' is fine-tuned for multilingual machine translation. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper.
The mode... | [
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text2text-generation | transformers |
# mBART-50
mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper.
## Model description
mBART-50 is a m... | {"language": ["multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "u... | facebook/mbart-large-50 | null | [
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# mBART-50
mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper.
## Model description
mBART-50 is a multilingual Sequence-to-Sequence mod... | [
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translation | transformers | #### mbart-large-cc25
Pretrained (not finetuned) multilingual mbart model.
Original Languages
```
export langs=ar_AR,cs_CZ,de_DE,en_XX,es_XX,et_EE,fi_FI,fr_XX,gu_IN,hi_IN,it_IT,ja_XX,kk_KZ,ko_KR,lt_LT,lv_LV,my_MM,ne_NP,nl_XX,ro_RO,ru_RU,si_LK,tr_TR,vi_VN,zh_CN
```
Original Code: https://github.com/pytorch/fairseq/tre... | {"language": ["en", "ar", "cs", "de", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "multilingual"], "tags": ["translation"]} | facebook/mbart-large-cc25 | null | [
"transformers",
"pytorch",
"tf",
"mbart",
"text2text-generation",
"translation",
"en",
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"zh",
"multilingual",
"autotrain_co... | null | 2022-03-02T23:29:05+00:00 | [] | [
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"my",
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"ro",
"ru",
"si",
"tr",
"vi",
"zh",
"multilingual"
] | TAGS
#transformers #pytorch #tf #mbart #text2text-generation #translation #en #ar #cs #de #et #fi #fr #gu #hi #it #ja #kk #ko #lt #lv #my #ne #nl #ro #ru #si #tr #vi #zh #multilingual #autotrain_compatible #endpoints_compatible #has_space #region-us
| #### mbart-large-cc25
Pretrained (not finetuned) multilingual mbart model.
Original Languages
Original Code: URL
Docs: URL
Finetuning Code: examples/seq2seq/URL (as of Aug 20, 2020)
Can also be finetuned for summarization. | [
"#### mbart-large-cc25\n\nPretrained (not finetuned) multilingual mbart model.\nOriginal Languages\n\n\nOriginal Code: URL\nDocs: URL\nFinetuning Code: examples/seq2seq/URL (as of Aug 20, 2020)\n\nCan also be finetuned for summarization."
] | [
"TAGS\n#transformers #pytorch #tf #mbart #text2text-generation #translation #en #ar #cs #de #et #fi #fr #gu #hi #it #ja #kk #ko #lt #lv #my #ne #nl #ro #ru #si #tr #vi #zh #multilingual #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### mbart-large-cc25\n\nPretrained (not finetuned) mult... |
translation | transformers | ### mbart-large-en-ro
This is mbart-large-cc25, finetuned on wmt_en_ro.
It scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in `romanian_postprocessing.md`
Original Code: https://github.com/pytorch/fairseq/tree/master/examples/mbart
Docs: https://huggingface.co/transformers/mas... | {"language": ["en", "ro"], "license": "mit", "tags": ["translation"]} | facebook/mbart-large-en-ro | null | [
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"tf",
"safetensors",
"mbart",
"translation",
"en",
"ro",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tf #safetensors #mbart #translation #en #ro #license-mit #endpoints_compatible #has_space #region-us
| ### mbart-large-en-ro
This is mbart-large-cc25, finetuned on wmt_en_ro.
It scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in 'romanian_postprocessing.md'
Original Code: URL
Docs: URL
Finetuning Code: examples/seq2seq/URL (as of Aug 20, 2020)
| [
"### mbart-large-en-ro\nThis is mbart-large-cc25, finetuned on wmt_en_ro.\n\nIt scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in 'romanian_postprocessing.md'\n\nOriginal Code: URL\n\nDocs: URL\n\nFinetuning Code: examples/seq2seq/URL (as of Aug 20, 2020)"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #mbart #translation #en #ro #license-mit #endpoints_compatible #has_space #region-us \n",
"### mbart-large-en-ro\nThis is mbart-large-cc25, finetuned on wmt_en_ro.\n\nIt scores BLEU 28.1 without post processing and BLEU 38 with postprocessing. Instructions in 'romani... |
fill-mask | transformers |
# Muppet: Massive Multi-task Representations with Pre-Finetuning
# RoBERTa base model
This is a Massive Multi-task Pre-finetuned version of Roberta base. It was introduced in
[this paper](https://arxiv.org/abs/2101.11038). The model improves over roberta-base in a wide range of GLUE, QA tasks (details can be found in... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | facebook/muppet-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2101.11038",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.11038"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2101.11038 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Muppet: Massive Multi-task Representations with Pre-Finetuning
==============================================================
RoBERTa base model
==================
This is a Massive Multi-task Pre-finetuned version of Roberta base. It was introduced in
this paper. The model improves over roberta-base in a wide rang... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2101.11038 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
fill-mask | transformers |
# Muppet: Massive Multi-task Representations with Pre-Finetuning
# RoBERTa large model
This is a Massive Multi-task Pre-finetuned version of Roberta large. It was introduced in
[this paper](https://arxiv.org/abs/2101.11038). The model improves over roberta-base in a wide range of GLUE, QA tasks (details can be f... | {"language": "en", "license": "mit", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | facebook/muppet-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2101.11038",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.11038"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2101.11038 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Muppet: Massive Multi-task Representations with Pre-Finetuning
==============================================================
RoBERTa large model
===================
This is a Massive Multi-task Pre-finetuned version of Roberta large. It was introduced in
this paper. The model improves over roberta-base in a wide r... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2101.11038 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
null | transformers | ## RAG
This is a non-finetuned version of the RAG-Sequence model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
Rag consits of a *question encoder*, *retriever* and a *generator*. The r... | {"license": "apache-2.0", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/rag-sequence-base | null | [
"transformers",
"pytorch",
"rag",
"arxiv:2005.11401",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.11401"
] | [] | TAGS
#transformers #pytorch #rag #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #region-us
| ## RAG
This is a non-finetuned version of the RAG-Sequence model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
Rag consits of a *question encoder*, *retriever* and a *generator*. The retriever should be a 'RagRetriever' inst... | [
"## RAG\n\nThis is a non-finetuned version of the RAG-Sequence model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.\n\nRag consits of a *question encoder*, *retriever* and a *generator*. The retriever should be a 'RagRetri... | [
"TAGS\n#transformers #pytorch #rag #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## RAG\n\nThis is a non-finetuned version of the RAG-Sequence model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Ethan Perez, Aleksandara Piktus ... |
null | transformers | ## RAG
This is the RAG-Sequence Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
The model is a *uncased* model, which means that capital letters are simply converted to lower-case ... | {"language": "en", "license": "apache-2.0", "datasets": ["wiki_dpr"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/rag-sequence-nq | null | [
"transformers",
"pytorch",
"tf",
"rag",
"en",
"dataset:wiki_dpr",
"arxiv:2005.11401",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.11401"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #rag #en #dataset-wiki_dpr #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #region-us
| ## RAG
This is the RAG-Sequence Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
The model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.
The model consits of a *questi... | [
"## RAG\n\nThis is the RAG-Sequence Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.\n\nThe model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.\n\nThe model consits ... | [
"TAGS\n#transformers #pytorch #tf #rag #en #dataset-wiki_dpr #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## RAG\n\nThis is the RAG-Sequence Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Ethan Perez, Aleksandara Piktus e... |
null | transformers | ## RAG
This is a non-finetuned version of the RAG-Token model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
Rag consits of a *question encoder*, *retriever* and a *generator*. The retr... | {"language": "en", "license": "apache-2.0", "datasets": ["wiki_dpr"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/rag-token-base | null | [
"transformers",
"pytorch",
"rag",
"en",
"dataset:wiki_dpr",
"arxiv:2005.11401",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.11401"
] | [
"en"
] | TAGS
#transformers #pytorch #rag #en #dataset-wiki_dpr #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| ## RAG
This is a non-finetuned version of the RAG-Token model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
Rag consits of a *question encoder*, *retriever* and a *generator*. The retriever should be a 'RagRetriever' instanc... | [
"## RAG\n\nThis is a non-finetuned version of the RAG-Token model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.\n\nRag consits of a *question encoder*, *retriever* and a *generator*. The retriever should be a 'RagRetrieve... | [
"TAGS\n#transformers #pytorch #rag #en #dataset-wiki_dpr #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## RAG\n\nThis is a non-finetuned version of the RAG-Token model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Et... |
null | transformers | ## RAG
This is the RAG-Token Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf)
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
The model is a *uncased* model, which means that capital letters are simply converted to lower-case let... | {"language": "en", "license": "apache-2.0", "datasets": ["wiki_dpr"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | facebook/rag-token-nq | null | [
"transformers",
"pytorch",
"tf",
"rag",
"en",
"dataset:wiki_dpr",
"arxiv:2005.11401",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2005.11401"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #rag #en #dataset-wiki_dpr #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| ## RAG
This is the RAG-Token Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.
The model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.
The model consists of a *question... | [
"## RAG\n\nThis is the RAG-Token Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Ethan Perez, Aleksandara Piktus et al.\n\nThe model is a *uncased* model, which means that capital letters are simply converted to lower-case letters.\n\nThe model consists of... | [
"TAGS\n#transformers #pytorch #tf #rag #en #dataset-wiki_dpr #arxiv-2005.11401 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"## RAG\n\nThis is the RAG-Token Model of the the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks \nby Patrick Lewis, Ethan Perez, Aleksandara ... |
automatic-speech-recognition | transformers |
# S2T-LARGE-LIBRISPEECH-ASR
`s2t-large-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/sp... | {"language": "en", "license": "mit", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "hubert-large-ls960-ft", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Lib... | facebook/s2t-large-librispeech-asr | null | [
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"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #model-index #endpoints_compatible #has_space #region-us
| S2T-LARGE-LIBRISPEECH-ASR
=========================
's2t-large-librispeech-asr' is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in this paper and released in
this repository
Model description
-----------------
S2T is an end-to-end sequence-to-... | [
"### How to use\n\n\nAs this a standard sequence to sequence transformer model, you can use the 'generate' method to generate the\ntranscripts by passing the speech features to the model.\n\n\n*Note: The 'Speech2TextProcessor' object uses torchaudio to extract the\nfilter bank features. Make sure to install the 'to... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nAs this a standard sequence to sequence transf... |
automatic-speech-recognition | transformers |
# S2T-MEDIUM-LIBRISPEECH-ASR
`s2t-medium-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": "en", "license": "mit", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librisp... | facebook/s2t-medium-librispeech-asr | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"speech_to_text",
"automatic-speech-recognition",
"audio",
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"dataset:librispeech_asr",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #speech_to_text #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #has_space #region-us
| S2T-MEDIUM-LIBRISPEECH-ASR
==========================
's2t-medium-librispeech-asr' is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in this paper and released in
this repository
Model description
-----------------
S2T is an end-to-end sequence-... | [
"### How to use\n\n\nAs this a standard sequence to sequence transformer model, you can use the 'generate' method to generate the\ntranscripts by passing the speech features to the model.\n\n\n*Note: The 'Speech2TextProcessor' object uses torchaudio to extract the\nfilter bank features. Make sure to install the 'to... | [
"TAGS\n#transformers #pytorch #tf #safetensors #speech_to_text #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nAs this a standard sequence to sequence transformer model, you can... |
automatic-speech-recognition | transformers |
# S2T-MEDIUM-MUSTC-MULTILINGUAL-ST
`s2t-medium-mustc-multilingual-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Multilingual Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fair... | {"language": ["en", "de", "nl", "es", "fr", "it", "pt", "ro", "ru"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition"} | facebook/s2t-medium-mustc-multilingual-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"de",
"nl",
"es",
"fr",
"it",
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"ro",
"ru",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us... | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"de",
"nl",
"es",
"fr",
"it",
"pt",
"ro",
"ru"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #de #nl #es #fr #it #pt #ro #ru #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
| S2T-MEDIUM-MUSTC-MULTILINGUAL-ST
================================
's2t-medium-mustc-multilingual-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Multilingual Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
Model description
-----------------
... | [
"### How to use\n\n\nAs this a standard sequence to sequence transformer model, you can use the 'generate' method to generate the\ntranscripts by passing the speech features to the model.\n\n\nFor multilingual speech translation models, 'eos\\_token\\_id' is used as the 'decoder\\_start\\_token\\_id' and\nthe targe... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #de #nl #es #fr #it #pt #ro #ru #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"### How to use\n\n\nAs this a standard sequence to sequence transfor... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-CA-EN-ST
`s2t-small-covost2-ca-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["ca", "en"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-ca-en-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"ca",
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"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"ca",
"en"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #ca #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-CA-EN-ST
's2t-small-covost2-ca-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-CA-EN-ST\n\n's2t-small-covost2-ca-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #ca #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-CA-EN-ST\n\n's2t-small-covost2-ca-en-st' is a Speech to ... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-DE-EN-ST
`s2t-small-covost2-de-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["de", "en"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-de-en-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"de",
"en",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"de",
"en"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #de #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-DE-EN-ST
's2t-small-covost2-de-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-DE-EN-ST\n\n's2t-small-covost2-de-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #de #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-DE-EN-ST\n\n's2t-small-covost2-de-en-st' is a Speech to ... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-EN-CA-ST
`s2t-small-covost2-en-ca-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["en", "ca"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-en-ca-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"ca",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"en",
"ca"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #ca #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-EN-CA-ST
's2t-small-covost2-en-ca-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-EN-CA-ST\n\n's2t-small-covost2-en-ca-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #ca #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-EN-CA-ST\n\n's2t-small-covost2-en-ca-st' is a Speech to ... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-EN-DE-ST
`s2t-small-covost2-en-de-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["en", "de"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-en-de-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"de",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"en",
"de"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #de #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-EN-DE-ST
's2t-small-covost2-en-de-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-EN-DE-ST\n\n's2t-small-covost2-en-de-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #de #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-EN-DE-ST\n\n's2t-small-covost2-en-de-st' is a Speech to ... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-EN-ET-ST
`s2t-small-covost2-en-et-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["en", "et"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-en-et-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"et",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"en",
"et"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #et #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-EN-ET-ST
's2t-small-covost2-en-et-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-EN-ET-ST\n\n's2t-small-covost2-en-et-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #et #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-EN-ET-ST\n\n's2t-small-covost2-en-et-st' is a Speech to ... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-EN-FA-ST
`s2t-small-covost2-en-fa-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["en", "fa"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-en-fa-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"fa",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"en",
"fa"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #fa #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-EN-FA-ST
's2t-small-covost2-en-fa-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-EN-FA-ST\n\n's2t-small-covost2-en-fa-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #fa #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-EN-FA-ST\n\n's2t-small-covost2-en-fa-st' is a Speech to ... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-ES-EN-ST
`s2t-small-covost2-es-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["es", "en"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-es-en-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"es",
"en",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"es",
"en"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #es #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-ES-EN-ST
's2t-small-covost2-es-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-ES-EN-ST\n\n's2t-small-covost2-es-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #es #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-ES-EN-ST\n\n's2t-small-covost2-es-en-st' is a Speech to ... |
automatic-speech-recognition | transformers |
# S2T-SMALL-COVOST2-FR-EN-ST
`s2t-small-covost2-fr-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/... | {"language": ["fr", "en"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["covost2"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"ex... | facebook/s2t-small-covost2-fr-en-st | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"fr",
"en",
"dataset:covost2",
"arxiv:2010.05171",
"arxiv:1912.06670",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1912.06670",
"1904.08779"
] | [
"fr",
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #speech_to_text #automatic-speech-recognition #audio #speech-translation #fr #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-COVOST2-FR-EN-ST
's2t-small-covost2-fr-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed fo... | [
"# S2T-SMALL-COVOST2-FR-EN-ST\n\n's2t-small-covost2-fr-en-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model... | [
"TAGS\n#transformers #pytorch #tf #safetensors #speech_to_text #automatic-speech-recognition #audio #speech-translation #fr #en #dataset-covost2 #arxiv-2010.05171 #arxiv-1912.06670 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-COVOST2-FR-EN-ST\n\n's2t-small-covost2-fr-en-st' is... |
automatic-speech-recognition | transformers |
# S2T-SMALL-LIBRISPEECH-ASR
`s2t-small-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/sp... | {"language": "en", "license": "mit", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.fl... | facebook/s2t-small-librispeech-asr | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"speech_to_text",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"re... | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #speech_to_text #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #model-index #endpoints_compatible #has_space #region-us
| S2T-SMALL-LIBRISPEECH-ASR
=========================
's2t-small-librispeech-asr' is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in this paper and released in
this repository
Model description
-----------------
S2T is an end-to-end sequence-to-... | [
"### How to use\n\n\nAs this a standard sequence to sequence transformer model, you can use the 'generate' method to generate the\ntranscripts by passing the speech features to the model.\n\n\n*Note: The 'Speech2TextProcessor' object uses torchaudio to extract the\nfilter bank features. Make sure to install the 'to... | [
"TAGS\n#transformers #pytorch #tf #safetensors #speech_to_text #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nAs this a standard sequen... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-DE-ST
`s2t-small-mustc-en-de-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "de"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-de-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"de",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"de"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #de #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-DE-ST
's2t-small-mustc-en-de-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-DE-ST\n\n's2t-small-mustc-en-de-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #de #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-DE-ST\n\n's2t-small-mustc-en-de-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-ES-ST
`s2t-small-mustc-en-es-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "es"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-es-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"es",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"es"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #es #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-ES-ST
's2t-small-mustc-en-es-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-ES-ST\n\n's2t-small-mustc-en-es-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #es #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-ES-ST\n\n's2t-small-mustc-en-es-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-FR-ST
`s2t-small-mustc-en-fr-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "fr"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-fr-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"fr",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"fr"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #fr #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-FR-ST
's2t-small-mustc-en-fr-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-FR-ST\n\n's2t-small-mustc-en-fr-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #fr #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-FR-ST\n\n's2t-small-mustc-en-fr-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-IT-ST
`s2t-small-mustc-en-it-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "it"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-it-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"it",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"it"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #it #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-IT-ST
's2t-small-mustc-en-it-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-IT-ST\n\n's2t-small-mustc-en-it-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #it #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-IT-ST\n\n's2t-small-mustc-en-it-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-NL-ST
`s2t-small-mustc-en-nl-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "nl"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-nl-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"nl",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"nl"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #nl #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-NL-ST
's2t-small-mustc-en-nl-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-NL-ST\n\n's2t-small-mustc-en-nl-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #nl #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-NL-ST\n\n's2t-small-mustc-en-nl-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-PT-ST
`s2t-small-mustc-en-pt-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "pt"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-pt-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"pt",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"pt"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #pt #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-PT-ST
's2t-small-mustc-en-pt-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-PT-ST\n\n's2t-small-mustc-en-pt-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #pt #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-PT-ST\n\n's2t-small-mustc-en-pt-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-RO-ST
`s2t-small-mustc-en-ro-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "ro"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-ro-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"ro",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"ro"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #ro #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-RO-ST
's2t-small-mustc-en-ro-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-RO-ST\n\n's2t-small-mustc-en-ro-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #ro #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-RO-ST\n\n's2t-small-mustc-en-ro-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T-SMALL-MUSTC-EN-RU-ST
`s2t-small-mustc-en-ru-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/spee... | {"language": ["en", "ru"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition"], "datasets": ["mustc"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"exam... | facebook/s2t-small-mustc-en-ru-st | null | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"speech-translation",
"en",
"ru",
"dataset:mustc",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.05171",
"1904.08779"
] | [
"en",
"ru"
] | TAGS
#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #ru #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us
|
# S2T-SMALL-MUSTC-EN-RU-ST
's2t-small-mustc-en-ru-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in this paper and released in
this repository
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for en... | [
"# S2T-SMALL-MUSTC-EN-RU-ST\n\n's2t-small-mustc-en-ru-st' is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).\nThe S2T model was proposed in this paper and released in\nthis repository",
"## Model description\n\nS2T is a transformer-based seq2seq (encoder-decoder) model des... | [
"TAGS\n#transformers #pytorch #tf #speech_to_text #automatic-speech-recognition #audio #speech-translation #en #ru #dataset-mustc #arxiv-2010.05171 #arxiv-1904.08779 #license-mit #endpoints_compatible #region-us \n",
"# S2T-SMALL-MUSTC-EN-RU-ST\n\n's2t-small-mustc-en-ru-st' is a Speech to Text Transformer (S2T) m... |
automatic-speech-recognition | transformers |
# S2T2-Wav2Vec2-CoVoST2-EN-AR-ST
`s2t-wav2vec2-large-en-ar` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in
[Fa... | {"language": ["en", "ar"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition", "speech2text2"], "datasets": ["covost2", "librispeech_asr"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Common Voice 1", "src": "https://cdn-media.huggingface.co/speech... | facebook/s2t-wav2vec2-large-en-ar | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"audio",
"speech-translation",
"speech2text2",
"en",
"ar",
"dataset:covost2",
"dataset:librispeech_asr",
"arxiv:2104.06678",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06678"
] | [
"en",
"ar"
] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #ar #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #has_space #region-us
|
# S2T2-Wav2Vec2-CoVoST2-EN-AR-ST
's2t-wav2vec2-large-en-ar' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in
Fairseq.
## Model description
S2T2 is a ... | [
"# S2T2-Wav2Vec2-CoVoST2-EN-AR-ST\n\n's2t-wav2vec2-large-en-ar' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).\nThe S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in\nFairseq.",
"## Model description\n... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #ar #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #has_space #region-us \n",
"# S2T2-Wav2Vec2-CoVoST2-EN-AR-ST\n\n's2t-wav2vec2-large-en... |
automatic-speech-recognition | transformers |
# S2T2-Wav2Vec2-CoVoST2-EN-CA-ST
`s2t-wav2vec2-large-en-ca` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in
[Fa... | {"language": ["en", "ca"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition", "speech2text2"], "datasets": ["covost2", "librispeech_asr"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Common Voice 1", "src": "https://cdn-media.huggingface.co/speech... | facebook/s2t-wav2vec2-large-en-ca | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"audio",
"speech-translation",
"speech2text2",
"en",
"ca",
"dataset:covost2",
"dataset:librispeech_asr",
"arxiv:2104.06678",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06678"
] | [
"en",
"ca"
] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #ca #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #region-us
|
# S2T2-Wav2Vec2-CoVoST2-EN-CA-ST
's2t-wav2vec2-large-en-ca' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in
Fairseq.
## Model description
S2T2 is a ... | [
"# S2T2-Wav2Vec2-CoVoST2-EN-CA-ST\n\n's2t-wav2vec2-large-en-ca' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).\nThe S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in\nFairseq.",
"## Model description\n... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #ca #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #region-us \n",
"# S2T2-Wav2Vec2-CoVoST2-EN-CA-ST\n\n's2t-wav2vec2-large-en-ca' is a S... |
automatic-speech-recognition | transformers |
# S2T2-Wav2Vec2-CoVoST2-EN-DE-ST
`s2t-wav2vec2-large-en-de` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in
[Fa... | {"language": ["en", "de"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition", "speech2text2"], "datasets": ["covost2", "librispeech_asr"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Common Voice 1", "src": "https://cdn-media.huggingface.co/speech... | facebook/s2t-wav2vec2-large-en-de | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"audio",
"speech-translation",
"speech2text2",
"en",
"de",
"dataset:covost2",
"dataset:librispeech_asr",
"arxiv:2104.06678",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06678"
] | [
"en",
"de"
] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #de #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #has_space #region-us
|
# S2T2-Wav2Vec2-CoVoST2-EN-DE-ST
's2t-wav2vec2-large-en-de' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in
Fairseq.
## Model description
S2T2 is a ... | [
"# S2T2-Wav2Vec2-CoVoST2-EN-DE-ST\n\n's2t-wav2vec2-large-en-de' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).\nThe S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in\nFairseq.",
"## Model description\n... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #de #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #has_space #region-us \n",
"# S2T2-Wav2Vec2-CoVoST2-EN-DE-ST\n\n's2t-wav2vec2-large-en... |
automatic-speech-recognition | transformers |
# S2T2-Wav2Vec2-CoVoST2-EN-TR-ST
`s2t-wav2vec2-large-en-tr` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in
[Fa... | {"language": ["en", "tr"], "license": "mit", "tags": ["audio", "speech-translation", "automatic-speech-recognition", "speech2text2"], "datasets": ["covost2", "librispeech_asr"], "pipeline_tag": "automatic-speech-recognition", "widget": [{"example_title": "Common Voice 1", "src": "https://cdn-media.huggingface.co/speech... | facebook/s2t-wav2vec2-large-en-tr | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"audio",
"speech-translation",
"speech2text2",
"en",
"tr",
"dataset:covost2",
"dataset:librispeech_asr",
"arxiv:2104.06678",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.06678"
] | [
"en",
"tr"
] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #tr #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #has_space #region-us
|
# S2T2-Wav2Vec2-CoVoST2-EN-TR-ST
's2t-wav2vec2-large-en-tr' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).
The S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in
Fairseq.
## Model description
S2T2 is a ... | [
"# S2T2-Wav2Vec2-CoVoST2-EN-TR-ST\n\n's2t-wav2vec2-large-en-tr' is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST).\nThe S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in\nFairseq.",
"## Model description\n... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #audio #speech-translation #speech2text2 #en #tr #dataset-covost2 #dataset-librispeech_asr #arxiv-2104.06678 #license-mit #endpoints_compatible #has_space #region-us \n",
"# S2T2-Wav2Vec2-CoVoST2-EN-TR-ST\n\n's2t-wav2vec2-large-en... |
text-to-speech | fairseq | # tts_transformer-ar-cv7
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- Arabic
- Single-speaker male voice
- Trained on [Common Voice v7](https://commonvo... | {"language": "ar", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["common_voice"], "task": "text-to-speech", "widget": [{"text": "\u0645\u0631\u062d\u0628\u064b\u0627 \u060c \u0647\u0630\u0627 \u0627\u062e\u062a\u0628\u0627\u0631 \u062a\u0634\u063a\u064a\u0644.", "example_title"... | facebook/tts_transformer-ar-cv7 | null | [
"fairseq",
"audio",
"text-to-speech",
"ar",
"dataset:common_voice",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"ar"
] | TAGS
#fairseq #audio #text-to-speech #ar #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-ar-cv7
Transformer text-to-speech model from fairseq S^2 (paper/code):
- Arabic
- Single-speaker male voice
- Trained on Common Voice v7
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-ar-cv7\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Arabic\n- Single-speaker male voice\n- Trained on Common Voice v7",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #ar #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-ar-cv7\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Arabic\n- Single-speaker male voice\n- Trained on Common Voice v7",
"## Usage\n\n\n\nSee... |
text-to-speech | fairseq | # tts_transformer-en-200_speaker-cv4
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- English
- 200 male/female voices (random speaker when using the widget... | {"language": "en", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech", "multi-speaker"], "datasets": ["common_voice"], "task": "text-to-speech", "widget": [{"text": "Hello, this is a test run.", "example_title": "Hello, this is a test run."}]} | facebook/tts_transformer-en-200_speaker-cv4 | null | [
"fairseq",
"audio",
"text-to-speech",
"multi-speaker",
"en",
"dataset:common_voice",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #multi-speaker #en #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-en-200_speaker-cv4
Transformer text-to-speech model from fairseq S^2 (paper/code):
- English
- 200 male/female voices (random speaker when using the widget)
- Trained on Common Voice v4
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-en-200_speaker-cv4\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- English\n- 200 male/female voices (random speaker when using the widget)\n- Trained on Common Voice v4",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #multi-speaker #en #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-en-200_speaker-cv4\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- English\n- 200 male/female voices (random speaker when using th... |
text-to-speech | fairseq | # tts_transformer-en-ljspeech
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- English
- Single-speaker female voice
- Trained on [LJSpeech](https://keithit... | {"language": "en", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["ljspeech"], "task": "text-to-speech", "widget": [{"text": "Hello, this is a test run.", "example_title": "Hello, this is a test run."}]} | facebook/tts_transformer-en-ljspeech | null | [
"fairseq",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"en"
] | TAGS
#fairseq #audio #text-to-speech #en #dataset-ljspeech #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-en-ljspeech
Transformer text-to-speech model from fairseq S^2 (paper/code):
- English
- Single-speaker female voice
- Trained on LJSpeech
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-en-ljspeech\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- English\n- Single-speaker female voice\n- Trained on LJSpeech",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #en #dataset-ljspeech #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-en-ljspeech\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- English\n- Single-speaker female voice\n- Trained on LJSpeech",
"## Usage\n\n\n\nSee al... |
text-to-speech | fairseq | # tts_transformer-es-css10
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- Spanish
- Single-speaker male voice
- Trained on [CSS10](https://github.com/Kyub... | {"language": "es", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["css10"], "task": "text-to-speech", "widget": [{"text": "Hola, esta es una prueba.", "example_title": "Hello, this is a test run."}]} | facebook/tts_transformer-es-css10 | null | [
"fairseq",
"audio",
"text-to-speech",
"es",
"dataset:css10",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"es"
] | TAGS
#fairseq #audio #text-to-speech #es #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-es-css10
Transformer text-to-speech model from fairseq S^2 (paper/code):
- Spanish
- Single-speaker male voice
- Trained on CSS10
## Usage
Dependencies
See also fairseq S^2 example.
| [
"# tts_transformer-es-css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Spanish\n- Single-speaker male voice\n- Trained on CSS10",
"## Usage\nDependencies\n\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #es #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-es-css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Spanish\n- Single-speaker male voice\n- Trained on CSS10",
"## Usage\nDependencies\n\n\n\n\nS... |
text-to-speech | fairseq | # tts_transformer-fr-cv7_css10
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- French
- Single-speaker male voice
- Pre-trained on [Common Voice v7](https:... | {"language": "fr", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["common_voice", "css10"], "task": "text-to-speech", "widget": [{"text": "Bonjour, ceci est un test.", "example_title": "Hello, this is a test run."}]} | facebook/tts_transformer-fr-cv7_css10 | null | [
"fairseq",
"audio",
"text-to-speech",
"fr",
"dataset:common_voice",
"dataset:css10",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"fr"
] | TAGS
#fairseq #audio #text-to-speech #fr #dataset-common_voice #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-fr-cv7_css10
Transformer text-to-speech model from fairseq S^2 (paper/code):
- French
- Single-speaker male voice
- Pre-trained on Common Voice v7, fine-tuned on CSS10
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-fr-cv7_css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- French\n- Single-speaker male voice\n- Pre-trained on Common Voice v7, fine-tuned on CSS10",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #fr #dataset-common_voice #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-fr-cv7_css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- French\n- Single-speaker male voice\n- Pre-trained on Common Voice v7... |
text-to-speech | fairseq | # tts_transformer-ru-cv7_css10
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- Russian
- Single-speaker male voice
- Pre-trained on [Common Voice v7](https... | {"language": "ru", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["common_voice", "css10"], "task": "text-to-speech", "widget": [{"text": "\u0417\u0434\u0440\u0430\u0432\u0441\u0442\u0432\u0443\u0439\u0442\u0435, \u044d\u0442\u043e \u043f\u0440\u043e\u0431\u043d\u044b\u0439 \u04... | facebook/tts_transformer-ru-cv7_css10 | null | [
"fairseq",
"audio",
"text-to-speech",
"ru",
"dataset:common_voice",
"dataset:css10",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"ru"
] | TAGS
#fairseq #audio #text-to-speech #ru #dataset-common_voice #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-ru-cv7_css10
Transformer text-to-speech model from fairseq S^2 (paper/code):
- Russian
- Single-speaker male voice
- Pre-trained on Common Voice v7, fine-tuned on CSS10
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-ru-cv7_css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Russian\n- Single-speaker male voice\n- Pre-trained on Common Voice v7, fine-tuned on CSS10",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #ru #dataset-common_voice #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-ru-cv7_css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Russian\n- Single-speaker male voice\n- Pre-trained on Common Voice v... |
text-to-speech | fairseq | # tts_transformer-tr-cv7
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- Turkish
- Single-speaker male voice
- Trained on [Common Voice v7](https://commonv... | {"language": "tr", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["common_voice"], "task": "text-to-speech", "widget": [{"text": "Merhaba, bu bir deneme \u00e7al\u0131\u015fmas\u0131d\u0131r.", "example_title": "Hello, this is a test run."}]} | facebook/tts_transformer-tr-cv7 | null | [
"fairseq",
"audio",
"text-to-speech",
"tr",
"dataset:common_voice",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"tr"
] | TAGS
#fairseq #audio #text-to-speech #tr #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-tr-cv7
Transformer text-to-speech model from fairseq S^2 (paper/code):
- Turkish
- Single-speaker male voice
- Trained on Common Voice v7
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-tr-cv7\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Turkish\n- Single-speaker male voice\n- Trained on Common Voice v7",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #tr #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-tr-cv7\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Turkish\n- Single-speaker male voice\n- Trained on Common Voice v7",
"## Usage\n\n\n\nSe... |
text-to-speech | fairseq | # tts_transformer-vi-cv7
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- Vietnamese
- Single-speaker male voice
- Trained on [Common Voice v7](https://comm... | {"language": "vi", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["common_voice"], "task": "text-to-speech", "widget": [{"text": "Xin ch\u00e0o, \u0111\u00e2y l\u00e0 m\u1ed9t cu\u1ed9c ch\u1ea1y th\u1eed nghi\u1ec7m.", "example_title": "Hello, this is a test run."}]} | facebook/tts_transformer-vi-cv7 | null | [
"fairseq",
"audio",
"text-to-speech",
"vi",
"dataset:common_voice",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"vi"
] | TAGS
#fairseq #audio #text-to-speech #vi #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-vi-cv7
Transformer text-to-speech model from fairseq S^2 (paper/code):
- Vietnamese
- Single-speaker male voice
- Trained on Common Voice v7
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-vi-cv7\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Vietnamese\n- Single-speaker male voice\n- Trained on Common Voice v7",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #vi #dataset-common_voice #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-vi-cv7\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Vietnamese\n- Single-speaker male voice\n- Trained on Common Voice v7",
"## Usage\n\n\n\... |
text-to-speech | fairseq | # tts_transformer-zh-cv7_css10
[Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)):
- Simplified Chinese
- Single-speaker female voice
- Pre-trained on [Common Vo... | {"language": "zh", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["common_voice", "css10"], "task": "text-to-speech", "widget": [{"text": "\u60a8\u597d\uff0c\u8fd9\u662f\u8bd5\u8fd0\u884c\u3002", "example_title": "Hello, this is a test run."}]} | facebook/tts_transformer-zh-cv7_css10 | null | [
"fairseq",
"audio",
"text-to-speech",
"zh",
"dataset:common_voice",
"dataset:css10",
"arxiv:1809.08895",
"arxiv:2109.06912",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1809.08895",
"2109.06912"
] | [
"zh"
] | TAGS
#fairseq #audio #text-to-speech #zh #dataset-common_voice #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us
| # tts_transformer-zh-cv7_css10
Transformer text-to-speech model from fairseq S^2 (paper/code):
- Simplified Chinese
- Single-speaker female voice
- Pre-trained on Common Voice v7, fine-tuned on CSS10
## Usage
See also fairseq S^2 example.
| [
"# tts_transformer-zh-cv7_css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Simplified Chinese\n- Single-speaker female voice\n- Pre-trained on Common Voice v7, fine-tuned on CSS10",
"## Usage\n\n\n\nSee also fairseq S^2 example."
] | [
"TAGS\n#fairseq #audio #text-to-speech #zh #dataset-common_voice #dataset-css10 #arxiv-1809.08895 #arxiv-2109.06912 #has_space #region-us \n",
"# tts_transformer-zh-cv7_css10\n\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Simplified Chinese\n- Single-speaker female voice\n- Pre-trained on C... |
null | transformers |
# Vision Transformer (base-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girs... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-1k"]} | facebook/vit-mae-base | null | [
"transformers",
"pytorch",
"tf",
"vit_mae",
"pretraining",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.06377",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.06377"
] | [] | TAGS
#transformers #pytorch #tf #vit_mae #pretraining #vision #dataset-imagenet-1k #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in this repo... | [
"# Vision Transformer (base-sized model) pre-trained with MAE\n\nVision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in thi... | [
"TAGS\n#transformers #pytorch #tf #vit_mae #pretraining #vision #dataset-imagenet-1k #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model) pre-trained with MAE\n\nVision Transformer (ViT) model pre-trained using the MAE method. It was intro... |
null | transformers |
# Vision Transformer (huge-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girs... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-1k"]} | facebook/vit-mae-huge | null | [
"transformers",
"pytorch",
"tf",
"vit_mae",
"pretraining",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.06377",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.06377"
] | [] | TAGS
#transformers #pytorch #tf #vit_mae #pretraining #vision #dataset-imagenet-1k #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #region-us
|
# Vision Transformer (huge-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in this repo... | [
"# Vision Transformer (huge-sized model) pre-trained with MAE\n\nVision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in thi... | [
"TAGS\n#transformers #pytorch #tf #vit_mae #pretraining #vision #dataset-imagenet-1k #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Vision Transformer (huge-sized model) pre-trained with MAE\n\nVision Transformer (ViT) model pre-trained using the MAE method. It was introduced in th... |
null | transformers |
# Vision Transformer (large-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Gir... | {"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-1k"]} | facebook/vit-mae-large | null | [
"transformers",
"pytorch",
"tf",
"vit_mae",
"pretraining",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.06377",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.06377"
] | [] | TAGS
#transformers #pytorch #tf #vit_mae #pretraining #vision #dataset-imagenet-1k #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision Transformer (large-sized model) pre-trained with MAE
Vision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in this rep... | [
"# Vision Transformer (large-sized model) pre-trained with MAE\n\nVision Transformer (ViT) model pre-trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in th... | [
"TAGS\n#transformers #pytorch #tf #vit_mae #pretraining #vision #dataset-imagenet-1k #arxiv-2111.06377 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (large-sized model) pre-trained with MAE\n\nVision Transformer (ViT) model pre-trained using the MAE method. It was intr... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-100h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Pa... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-base-100h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Wav2Vec2-Base-100h
==================
Facebook's Wav2Vec2
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Au... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 100k unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Note**: This model does not have a tokenizer as it was pretrained on ... | {"language": "multilingual", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-100k-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"multilingual",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli
Facebook's Wav2Vec2 base model pretrained on the 100k unlabeled subset of VoxPopuli corpus.
Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labe... | [
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the 100k unlabeled subset of VoxPopuli corpus.\n\nNote: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the 100k unlabeled subset of VoxPopuli corpus.... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in cs (refer to Table 1 o... | {"language": "cs", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-cs | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"cs",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"cs"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #cs #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in cs (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in cs (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #cs #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in de (refer to Table 1 o... | {"language": "de", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-de | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"de",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #de #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in de (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in de (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #de #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in en (refer to Table 1 o... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-en | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"en",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #en #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in en (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in en (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #en #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in es (refer to Table 1 o... | {"language": "es", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-es | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"es",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #es #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in es (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in es (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #es #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | null |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in et (refer to Table 1 o... | {"language": "et", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-et | null | [
"audio",
"automatic-speech-recognition",
"voxpopuli",
"et",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"et"
] | TAGS
#audio #automatic-speech-recognition #voxpopuli #et #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in et (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in et (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#audio #automatic-speech-recognition #voxpopuli #et #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in et (refer to Table 1 of pap... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in fi (refer to Table 1 o... | {"language": "fi", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-fi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"fi",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #fi #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in fi (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in fi (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #fi #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in fr (refer to Table 1 o... | {"language": "fr", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-fr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"fr",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #fr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in fr (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in fr (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #fr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in hr (refer to Table 1 o... | {"language": "hr", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-hr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"hr",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"hr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #hr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in hr (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in hr (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #hr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in hu (refer to Table 1 o... | {"language": "hu", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-hu | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"hu",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"hu"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #hu #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in hu (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in hu (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #hu #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in it (refer to Table 1 o... | {"language": "it", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-it | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"it",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"it"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #it #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in it (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in it (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #it #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | null |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in lt (refer to Table 1 o... | {"language": "lt", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-lt | null | [
"audio",
"automatic-speech-recognition",
"voxpopuli",
"lt",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"lt"
] | TAGS
#audio #automatic-speech-recognition #voxpopuli #lt #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in lt (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in lt (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#audio #automatic-speech-recognition #voxpopuli #lt #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in lt (refer to Table 1 of pap... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in nl (refer to Table 1 o... | {"language": "nl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-nl | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"nl",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #nl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in nl (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in nl (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #nl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in pl (refer to Table 1 o... | {"language": "pl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-pl | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"pl",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"pl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #pl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in pl (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in pl (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #pl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in ro (refer to Table 1 o... | {"language": "ro", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-ro | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"ro",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"ro"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #ro #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in ro (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in ro (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #ro #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in sk (refer to Table 1 o... | {"language": "sk", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-sk | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"sk",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"sk"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #sk #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in sk (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in sk (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #sk #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli-Finetuned
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in sl (refer to Table 1 o... | {"language": "sl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli-ft-sl | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"voxpopuli",
"sl",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"sl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #sl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli-Finetuned
Facebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in sl (refer to Table 1 of paper for more information).
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Su... | [
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned on the transcribed data in sl (refer to Table 1 of paper for more information).\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #voxpopuli #sl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli-Finetuned\n\nFacebook's Wav2Vec2 base model pretrained on the 10K unlabeled subset of VoxPopuli corpus and fine-tuned... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10k unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Rep... | {"language": "multilingual", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-10k-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"multilingual",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli
Facebook's Wav2Vec2 base model pretrained on the 10k unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chait... | [
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the 10k unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, Ann... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #multilingual #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the 10k unlabeled subset of VoxPopuli corpus.\... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-960h
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Pa... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "sr... | facebook/wav2vec2-base-960h | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Base-960h
==================
Facebook's Wav2Vec2
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Au... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2006.11477 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **bg** on **17.6k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "bg", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-bg-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"bg",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"bg"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #bg #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in bg on 17.6k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in bg on 17.6k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #bg #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in bg on 17.6k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **cs** on **18.7k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "cs", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-cs-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
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"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"cs",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"cs"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #cs #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in cs on 18.7k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in cs on 18.7k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #cs #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in cs on 18.7k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **da** on **13.6k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "da", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-da-voxpopuli-v2 | null | [
"transformers",
"pytorch",
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"pretraining",
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"automatic-speech-recognition",
"voxpopuli-v2",
"da",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #da #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in da on 13.6k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in da on 13.6k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #da #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in da on 13.6k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **de** on **23.2k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "de", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-de-voxpopuli-v2 | null | [
"transformers",
"pytorch",
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"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"de",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"de"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #de #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in de on 23.2k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in de on 23.2k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #de #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in de on 23.2k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **el** on **17.7k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "el", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-el-voxpopuli-v2 | null | [
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"automatic-speech-recognition",
"voxpopuli-v2",
"el",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #el #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in el on 17.7k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in el on 17.7k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #el #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in el on 17.7k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **en** on **24.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "en", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-en-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"en",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #en #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in en on 24.1k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in en on 24.1k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #en #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in en on 24.1k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **es** on **21.4k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "es", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-es-voxpopuli-v2 | null | [
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"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #es #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in es on 21.4k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in es on 21.4k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #es #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in es on 21.4k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the es unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Repr... | {"language": "es", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-es-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
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"automatic-speech-recognition",
"voxpopuli",
"es",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #es #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli
Facebook's Wav2Vec2 base model pretrained on the es unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaita... | [
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the es unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #es #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the es unlabeled subset of VoxPopuli corpus.\n\nPaper: *... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **et** on **10.6k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "et", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-et-voxpopuli-v2 | null | [
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"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"et"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #et #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in et on 10.6k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in et on 10.6k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #et #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in et on 10.6k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **fi** on **14.2k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "fi", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-fi-voxpopuli-v2 | null | [
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"pretraining",
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"automatic-speech-recognition",
"voxpopuli-v2",
"fi",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #fi #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in fi on 14.2k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in fi on 14.2k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #fi #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in fi on 14.2k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **fr** on **22.8k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "fr", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-fr-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"fr",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #fr #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in fr on 22.8k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in fr on 22.8k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #fr #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in fr on 22.8k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the fr unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Repr... | {"language": "fr", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-fr-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"fr",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #fr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli
Facebook's Wav2Vec2 base model pretrained on the fr unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaita... | [
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the fr unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #fr #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the fr unlabeled subset of VoxPopuli corpus.\n\nPaper: *... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **hr** on **8.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech au... | {"language": "hr", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-hr-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"hr",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"hr"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #hr #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in hr on 8.1k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it w... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in hr on 8.1k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeniz... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #hr #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in hr on 8.1k unlabeled datat of the VoxPopuli corp... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **hu** on **17.7k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "hu", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-hu-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"hu",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"hu"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #hu #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in hu on 17.7k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in hu on 17.7k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #hu #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in hu on 17.7k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **it** on **21.9k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "it", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-it-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"it",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"it"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #it #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in it on 21.9k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in it on 21.9k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #it #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in it on 21.9k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the it unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Repr... | {"language": "it", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-it-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"it",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"it"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #it #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli
Facebook's Wav2Vec2 base model pretrained on the it unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaita... | [
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the it unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #it #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the it unlabeled subset of VoxPopuli corpus.\n\nPaper: *... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **lt** on **14.4k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "lt", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-lt-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"lt",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"lt"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #lt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in lt on 14.4k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in lt on 14.4k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #lt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in lt on 14.4k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **lv** on **13.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "lv", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-lv-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"lv",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"lv"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #lv #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in lv on 13.1k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in lv on 13.1k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #lv #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in lv on 13.1k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **mt** on **9.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech au... | {"language": "mt", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-mt-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"mt",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"mt"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #mt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in mt on 9.1k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it w... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in mt on 9.1k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeniz... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #mt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in mt on 9.1k unlabeled datat of the VoxPopuli corp... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **nl** on **19.0k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "nl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-nl-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"nl",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #nl #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in nl on 19.0k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in nl on 19.0k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #nl #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in nl on 19.0k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Base-VoxPopuli
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the nl unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Repr... | {"language": "nl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli"]} | facebook/wav2vec2-base-nl-voxpopuli | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli",
"nl",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #nl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Base-VoxPopuli
Facebook's Wav2Vec2 base model pretrained on the nl unlabeled subset of VoxPopuli corpus.
Paper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation*
Authors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaita... | [
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the nl unlabeled subset of VoxPopuli corpus.\n\nPaper: *VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation\nLearning, Semi-Supervised Learning and Interpretation*\n\nAuthors: *Changhan Wang, Morgane Riviere, Ann Lee, Anne... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli #nl #arxiv-2101.00390 #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Base-VoxPopuli\n\nFacebook's Wav2Vec2 base model pretrained on the nl unlabeled subset of VoxPopuli corpus.\n\nPaper: *... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **pl** on **21.2k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "pl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-pl-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"pl",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"pl"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #pl #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in pl on 21.2k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in pl on 21.2k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #pl #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in pl on 21.2k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **pt** on **17.5k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "pt", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-pt-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"pt",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #pt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in pt on 17.5k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in pt on 17.5k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #pt #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in pt on 17.5k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **ro** on **17.9k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "ro", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-ro-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"ro",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"ro"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #ro #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in ro on 17.9k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in ro on 17.9k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #ro #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in ro on 17.9k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **sk** on **12.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "sk", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-sk-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"sk",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"sk"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #sk #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in sk on 12.1k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in sk on 12.1k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #sk #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in sk on 12.1k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **sl** on **11.3k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "sl", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-sl-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"sl",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"sl"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #sl #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in sl on 11.3k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in sl on 11.3k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #sl #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in sl on 11.3k unlabeled datat of the VoxPopuli cor... |
automatic-speech-recognition | transformers |
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **sv** on **16.3k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech a... | {"language": "sv", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "voxpopuli-v2"], "datasets": ["voxpopuli"], "inference": false} | facebook/wav2vec2-base-sv-voxpopuli-v2 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"sv",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00390"
] | [
"sv"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #sv #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us
|
# Wav2Vec2-base-VoxPopuli-V2
Facebook's Wav2Vec2 base model pretrained only in sv on 16.3k unlabeled datat of the VoxPopuli corpus.
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it ... | [
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in sv on 16.3k unlabeled datat of the VoxPopuli corpus.\n\nThe model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokeni... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxpopuli-v2 #sv #dataset-voxpopuli #arxiv-2101.00390 #license-cc-by-nc-4.0 #region-us \n",
"# Wav2Vec2-base-VoxPopuli-V2\n\nFacebook's Wav2Vec2 base model pretrained only in sv on 16.3k unlabeled datat of the VoxPopuli cor... |
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