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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | spacy | ### Details: https://spacy.io/models/pl#pl_core_news_lg
Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), tagger, senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `pl_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["pl"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/pl_core_news_lg | null | [
"spacy",
"token-classification",
"pl",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), tagger, senter, ner.
### Label Scheme
View label scheme (1726 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nPolish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), tagger, senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1726 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nPolish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), tagger, senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1726 labels for 4 co... |
token-classification | spacy | ### Details: https://spacy.io/models/pl#pl_core_news_md
Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), tagger, senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `pl_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["pl"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/pl_core_news_md | null | [
"spacy",
"token-classification",
"pl",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), tagger, senter, ner.
### Label Scheme
View label scheme (1726 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nPolish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), tagger, senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1726 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nPolish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), tagger, senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1726 labels for 4 co... |
token-classification | spacy | ### Details: https://spacy.io/models/pl#pl_core_news_sm
Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), tagger, senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `pl_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.... | {"language": ["pl"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | spacy/pl_core_news_sm | null | [
"spacy",
"token-classification",
"pl",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us
| ### Details: URL
Polish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), tagger, senter, ner.
### Label Scheme
View label scheme (1726 labels for 4 components)
### Accuracy
| [
"### Details: URL\n\n\nPolish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), tagger, senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1726 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pl #license-gpl-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nPolish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), tagger, senter, ner.",
"### Label Scheme\n\n\n\nView label scheme (1726 labels for 4 co... |
token-classification | spacy | ### Details: https://spacy.io/models/pt#pt_core_news_lg
Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `pt_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `... | {"language": ["pt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/pt_core_news_lg | null | [
"spacy",
"token-classification",
"pt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (590 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nPortuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (590 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nPortuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (... |
token-classification | spacy | ### Details: https://spacy.io/models/pt#pt_core_news_md
Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `pt_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `... | {"language": ["pt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/pt_core_news_md | null | [
"spacy",
"token-classification",
"pt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (590 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nPortuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (590 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nPortuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (... |
token-classification | spacy | ### Details: https://spacy.io/models/pt#pt_core_news_sm
Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `pt_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `... | {"language": ["pt"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/pt_core_news_sm | null | [
"spacy",
"token-classification",
"pt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (590 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nPortuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (590 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #pt #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nPortuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (... |
token-classification | spacy | ### Details: https://spacy.io/models/ro#ro_core_news_lg
Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `ro_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<... | {"language": ["ro"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ro_core_news_lg | null | [
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (540 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nRomanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (540 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nRomanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (540 label... |
token-classification | spacy | ### Details: https://spacy.io/models/ro#ro_core_news_md
Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `ro_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<... | {"language": ["ro"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ro_core_news_md | null | [
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (540 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nRomanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (540 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nRomanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (540 label... |
token-classification | spacy | ### Details: https://spacy.io/models/ro#ro_core_news_sm
Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `ro_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<... | {"language": ["ro"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | spacy/ro_core_news_sm | null | [
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ro"
] | TAGS
#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us
| ### Details: URL
Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\_lemmatizer), senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (540 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nRomanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (540 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ro #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Details: URL\n\n\nRomanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable\\_lemmatizer), senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (540 label... |
token-classification | spacy | ### Details: https://spacy.io/models/ru#ru_core_news_lg
Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ru_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["ru"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/ru_core_news_lg | null | [
"spacy",
"token-classification",
"ru",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#spacy #token-classification #ru #license-mit #model-index #region-us
| ### Details: URL
Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (900 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nRussian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (900 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ru #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nRussian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (900 labels for 3 components)",
"###... |
token-classification | spacy | ### Details: https://spacy.io/models/ru#ru_core_news_md
Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ru_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["ru"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/ru_core_news_md | null | [
"spacy",
"token-classification",
"ru",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#spacy #token-classification #ru #license-mit #model-index #region-us
| ### Details: URL
Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (900 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nRussian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (900 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ru #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nRussian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (900 labels for 3 components)",
"###... |
token-classification | spacy | ### Details: https://spacy.io/models/ru#ru_core_news_sm
Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ru_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Defa... | {"language": ["ru"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/ru_core_news_sm | null | [
"spacy",
"token-classification",
"ru",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#spacy #token-classification #ru #license-mit #model-index #region-us
| ### Details: URL
Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\_ruler, lemmatizer.
### Label Scheme
View label scheme (900 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nRussian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (900 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ru #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nRussian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute\\_ruler, lemmatizer.",
"### Label Scheme\n\n\n\nView label scheme (900 labels for 3 components)",
"###... |
token-classification | spacy | ### Details: https://spacy.io/models/xx#xx_ent_wiki_sm
Multi-language pipeline optimized for CPU. Components: ner.
| Feature | Description |
| --- | --- |
| **Name** | `xx_ent_wiki_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `ner` |
| **Components** | `ner` |
| **Vectors*... | {"language": ["multilingual"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/xx_ent_wiki_sm | null | [
"spacy",
"token-classification",
"multilingual",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#spacy #token-classification #multilingual #license-mit #model-index #region-us
| ### Details: URL
Multi-language pipeline optimized for CPU. Components: ner.
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
| [
"### Details: URL\n\n\nMulti-language pipeline optimized for CPU. Components: ner.",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #multilingual #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nMulti-language pipeline optimized for CPU. Components: ner.",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
null | spacy | ### Details: https://spacy.io/models/xx#xx_sent_ud_sm
Multi-language pipeline optimized for CPU. Components: senter.
| Feature | Description |
| --- | --- |
| **Name** | `xx_sent_ud_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `senter` |
| **Components** | `senter` |
| **V... | {"language": ["multilingual"], "license": "cc-by-sa-3.0", "tags": ["spacy"]} | spacy/xx_sent_ud_sm | null | [
"spacy",
"multilingual",
"license:cc-by-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#spacy #multilingual #license-cc-by-sa-3.0 #model-index #region-us
| ### Details: URL
Multi-language pipeline optimized for CPU. Components: senter.
### Label Scheme
### Accuracy
| [
"### Details: URL\n\n\nMulti-language pipeline optimized for CPU. Components: senter.",
"### Label Scheme",
"### Accuracy"
] | [
"TAGS\n#spacy #multilingual #license-cc-by-sa-3.0 #model-index #region-us \n",
"### Details: URL\n\n\nMulti-language pipeline optimized for CPU. Components: senter.",
"### Label Scheme",
"### Accuracy"
] |
token-classification | spacy | ### Details: https://spacy.io/models/zh#zh_core_web_lg
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `zh_core_web_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok... | {"language": ["zh"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/zh_core_web_lg | null | [
"spacy",
"token-classification",
"zh",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#spacy #token-classification #zh #license-mit #model-index #region-us
| ### Details: URL
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (100 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nChinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (100 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #zh #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nChinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (100 labels for 3 components)",
"### Accuracy"
] |
token-classification | spacy | ### Details: https://spacy.io/models/zh#zh_core_web_md
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `zh_core_web_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok... | {"language": ["zh"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/zh_core_web_md | null | [
"spacy",
"token-classification",
"zh",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#spacy #token-classification #zh #license-mit #model-index #region-us
| ### Details: URL
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (100 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nChinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (100 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #zh #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nChinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (100 labels for 3 components)",
"### Accuracy"
] |
token-classification | spacy | ### Details: https://spacy.io/models/zh#zh_core_web_sm
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `zh_core_web_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok... | {"language": ["zh"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/zh_core_web_sm | null | [
"spacy",
"token-classification",
"zh",
"license:mit",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#spacy #token-classification #zh #license-mit #model-index #has_space #region-us
| ### Details: URL
Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\_ruler.
### Label Scheme
View label scheme (100 labels for 3 components)
### Accuracy
| [
"### Details: URL\n\n\nChinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (100 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #zh #license-mit #model-index #has_space #region-us \n",
"### Details: URL\n\n\nChinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (100 labels for 3 components)",
"### Accurac... |
token-classification | spacy | ### Details: https://spacy.io/models/zh#zh_core_web_trf
Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab_size=21128)). Components: transformer, tagger, parser, ner, attribute_ruler.
| Feature | Description |
| ---... | {"language": ["zh"], "license": "mit", "tags": ["spacy", "token-classification"]} | spacy/zh_core_web_trf | null | [
"spacy",
"token-classification",
"zh",
"license:mit",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#spacy #token-classification #zh #license-mit #model-index #region-us
| ### Details: URL
Chinese transformer pipeline (Transformer(name='bert-base-chinese', piece\_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab\_size=21128)). Components: transformer, tagger, parser, ner, attribute\_ruler.
### Label Scheme
View label scheme (99 labels for 3 component... | [
"### Details: URL\n\n\nChinese transformer pipeline (Transformer(name='bert-base-chinese', piece\\_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab\\_size=21128)). Components: transformer, tagger, parser, ner, attribute\\_ruler.",
"### Label Scheme\n\n\n\nView label scheme (99 label... | [
"TAGS\n#spacy #token-classification #zh #license-mit #model-index #region-us \n",
"### Details: URL\n\n\nChinese transformer pipeline (Transformer(name='bert-base-chinese', piece\\_encoder='bert-wordpiece', stride=152, type='bert', width=768, window=208, vocab\\_size=21128)). Components: transformer, tagger, pars... |
text-generation | transformers |
Import it using pipeline
from transformers import pipeline
text_generation = pipeline('text-generation' , model='sparki/kinkyfurs-gpt2')
Then use it
prefix_text = input()
text_generation(prefix_text, max_length=50, num_beams=5,no_repeat_ngram_size=2,early_stopping=True)
| {"language": "en", "license": "mit"} | sparki/kinkyfurs-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Import it using pipeline
from transformers import pipeline
text_generation = pipeline('text-generation' , model='sparki/kinkyfurs-gpt2')
Then use it
prefix_text = input()
text_generation(prefix_text, max_length=50, num_beams=5,no_repeat_ngram_size=2,early_stopping=True)
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | This generates Greek and Korean names | {} | sparkles/generating-greek-korean-names | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This generates Greek and Korean names | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | spasis/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0569
* Precision: 0.9215
* Recall: 0.9423
* F1: 0.9318
* Accuracy: 0.9850
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-bart-large-no-adapter-frozen-enc | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 18.7898
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and e... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-bert-large-no-adapter-frozen-enc | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 11.7664
* Wer: 2.0133
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training an... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-bert-large-no-adapter | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 6.9251
* Wer: 1.7858
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-bert-large | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 6.9670
* Wer: 1.9878
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-gpt2-medium-no-adapter-frozen-enc | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 6.5541
* Wer: 1.9877
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-gpt2-medium | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5264
* Wer: 1.7073
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-gpt2-no-adapter | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1277
* Wer: 1.0334
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-roberta-large-no-adapter-frozen-enc | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 20.5959
* Wer: 1.0008
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training an... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | speech-seq2seq/wav2vec2-2-roberta-large | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 12.2365
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and e... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* trai... |
null | speechbrain |
# SI-SNR Estimator introduced for the REAL-M dataset
This repository provides the Separator models to be able to train a blind SI-SNR estimator with the recipe provided in the SpeechBrain repository to complement the REAL-M real-life speech separation dataset.
The SI-SNR estimator is trained with a recordings from... | {"language": "en", "license": "apache-2.0", "tags": ["Source Separation", "Speech Separation", "Audio Source Separation", "SepFormer", "DPRNN", "Convtasnet", "speechbrain"], "datasets": ["REAL-M", "WHAMR!", "Libri2Mix"], "metrics": ["SI-SNRi"]} | speechbrain/REAL-M-sisnr-estimator-training | null | [
"speechbrain",
"Source Separation",
"Speech Separation",
"Audio Source Separation",
"SepFormer",
"DPRNN",
"Convtasnet",
"en",
"arxiv:2110.10812",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.10812",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #SepFormer #DPRNN #Convtasnet #en #arxiv-2110.10812 #arxiv-2106.04624 #license-apache-2.0 #region-us
|
# SI-SNR Estimator introduced for the REAL-M dataset
This repository provides the Separator models to be able to train a blind SI-SNR estimator with the recipe provided in the SpeechBrain repository to complement the REAL-M real-life speech separation dataset.
The SI-SNR estimator is trained with a recordings from... | [
"# SI-SNR Estimator introduced for the REAL-M dataset\n\nThis repository provides the Separator models to be able to train a blind SI-SNR estimator with the recipe provided in the SpeechBrain repository to complement the REAL-M real-life speech separation dataset. \n\nThe SI-SNR estimator is trained with a recordin... | [
"TAGS\n#speechbrain #Source Separation #Speech Separation #Audio Source Separation #SepFormer #DPRNN #Convtasnet #en #arxiv-2110.10812 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"# SI-SNR Estimator introduced for the REAL-M dataset\n\nThis repository provides the Separator models to be able to train a ... |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Neural SI-SNR Estimator
The Neural SI-SNR Estimator predicts the scale-invariant signal-to-noise ratio (S... | {"language": "en", "license": "apache-2.0", "tags": ["audio-source-separation", "Source Separation", "Speech Separation", "WHAM!", "REAL-M", "SepFormer", "Transformer", "pytorch", "speechbrain"], "datasets": ["REAL-M", "WHAMR!"], "metrics": ["SI-SNRi"]} | speechbrain/REAL-M-sisnr-estimator | null | [
"speechbrain",
"audio-source-separation",
"Source Separation",
"Speech Separation",
"WHAM!",
"REAL-M",
"SepFormer",
"Transformer",
"pytorch",
"en",
"arxiv:2110.10812",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.10812",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-source-separation #Source Separation #Speech Separation #WHAM! #REAL-M #SepFormer #Transformer #pytorch #en #arxiv-2110.10812 #arxiv-2106.04624 #license-apache-2.0 #region-us
|
Neural SI-SNR Estimator
=======================
The Neural SI-SNR Estimator predicts the scale-invariant signal-to-noise ratio (SI-SNR) from the separated signals and the original mixture.
The performance estimation is blind (i.e., no targets signals are needed). This model allows a performance estimation o... | [
"### Minimal example for SI-SNR estimation",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe model was trained with SpeechBrain (fc2eabb7).\n\n\nTo train it from scratch follows these steps:\n\n... | [
"TAGS\n#speechbrain #audio-source-separation #Source Separation #Speech Separation #WHAM! #REAL-M #SepFormer #Transformer #pytorch #en #arxiv-2110.10812 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Minimal example for SI-SNR estimation",
"### Inference on GPU\n\n\nTo perform inference on the GPU, ... |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# End-to-end SLU model for SLURP
This repository provides all the necessary tools to perform Speech to inten... | {"language": "en", "license": "apache-2.0", "tags": ["Spoken language understanding", "speechbrain", "wav2vec2", "hubert", "pytorch"], "datasets": ["SLURP"], "metrics": ["Accuracy"]} | speechbrain/SLU-direct-SLURP-hubert-enc | null | [
"speechbrain",
"Spoken language understanding",
"wav2vec2",
"hubert",
"pytorch",
"en",
"dataset:SLURP",
"arxiv:2011.13205",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2011.13205",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Spoken language understanding #wav2vec2 #hubert #pytorch #en #dataset-SLURP #arxiv-2011.13205 #arxiv-2106.04624 #license-apache-2.0 #region-us
|
End-to-end SLU model for SLURP
==============================
This repository provides all the necessary tools to perform Speech to intent and slot label with a fine-tuned hubert encoder + decoder using SpeechBrain (in E2E trend).
It is trained on SLUR training data.
For a better experience, we encourage yo... | [
"### Perform SLU E2E decoding\n\n\nAn external 'py\\_module\\_file=custom\\_interface.py' is used as an external Predictor class into this HF repos. We use 'foreign\\_class' function from 'speechbrain.pretrained.interfaces' that allow you to load you custom model.\n\n\nThe system is trained with recordings sampled ... | [
"TAGS\n#speechbrain #Spoken language understanding #wav2vec2 #hubert #pytorch #en #dataset-SLURP #arxiv-2011.13205 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Perform SLU E2E decoding\n\n\nAn external 'py\\_module\\_file=custom\\_interface.py' is used as an external Predictor class into this HF rep... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Conformer for KsponSpeech (with Transformer LM)
This repository provides all the necessary tools to perfor... | {"language": ["ko"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "Conformer", "pytorch", "speechbrain"], "datasets": ["ksponspeech"], "metrics": ["wer", "cer"]} | speechbrain/asr-conformer-transformerlm-ksponspeech | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Conformer",
"pytorch",
"ko",
"dataset:ksponspeech",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"ko"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #Conformer #pytorch #ko #dataset-ksponspeech #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
Conformer for KsponSpeech (with Transformer LM)
===============================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on KsponSpeech (Kr) within
SpeechBrain. For a better experience, we encourage you to lea... | [
"### Transcribing your own audio files (in Korean)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook on using the pret... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #Conformer #pytorch #ko #dataset-ksponspeech #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Transcribing your own audio files (in Korean)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"devi... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CRDNN with CTC/Attention trained on CommonVoice 7.0 German (No LM)
This repository provides all the necessa... | {"language": "de", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "pytorch", "speechbrain"], "datasets": ["common_voice"], "metrics": ["wer", "cer"]} | speechbrain/asr-crdnn-commonvoice-de | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"de",
"dataset:common_voice",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"de"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #de #dataset-common_voice #arxiv-2106.04624 #license-apache-2.0 #region-us
|
CRDNN with CTC/Attention trained on CommonVoice 7.0 German (No LM)
==================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (German Language) within
SpeechBrai... | [
"### Transcribing your own audio files (in German)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out how... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #de #dataset-common_voice #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Transcribing your own audio files (in German)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when ... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CRDNN with CTC/Attention trained on CommonVoice French (No LM)
This repository provides all the necessary ... | {"language": "fr", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "pytorch", "speechbrain"], "datasets": ["common_voice"], "metrics": ["wer", "cer"]} | speechbrain/asr-crdnn-commonvoice-fr | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"fr",
"dataset:common_voice",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"fr"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #fr #dataset-common_voice #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
CRDNN with CTC/Attention trained on CommonVoice French (No LM)
==============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (French Language) within
SpeechBrain. For a... | [
"### Transcribing your own audio files (in French)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out how... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #fr #dataset-common_voice #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Transcribing your own audio files (in French)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cud... |
automatic-speech-recognition | speechbrain | <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CRDNN with CTC/Attention trained on CommonVoice Italian (No LM)
This repository provides all the necessary ... | {"language": "it", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "pytorch", "speechbrain"], "datasets": ["common_voice"], "metrics": ["wer", "cer"]} | speechbrain/asr-crdnn-commonvoice-it | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"it",
"dataset:common_voice",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"it"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #it #dataset-common_voice #arxiv-2106.04624 #license-apache-2.0 #region-us
|
CRDNN with CTC/Attention trained on CommonVoice Italian (No LM)
===============================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (IT) within
SpeechBrain. For a better exp... | [
"### Transcribing your own audio files (in Italian)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #it #dataset-common_voice #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Transcribing your own audio files (in Italian)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CRDNN with CTC/Attention and RNNLM trained on LibriSpeech
This repository provides all the necessary tools... | {"language": "en", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "pytorch", "speechbrain"], "datasets": ["librispeech"], "metrics": ["wer", "cer"]} | speechbrain/asr-crdnn-rnnlm-librispeech | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
CRDNN with CTC/Attention and RNNLM trained on LibriSpeech
=========================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on LibriSpeech (EN) within
SpeechBrain. For a better experience we e... | [
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #pytorch #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cud... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CRDNN with CTC/Attention and RNNLM trained on LibriSpeech
This repository provides all the necessary tools... | {"language": "en", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "Tranformer", "pytorch", "speechbrain"], "datasets": ["librispeech"], "metrics": ["wer", "cer"]} | speechbrain/asr-crdnn-transformerlm-librispeech | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Tranformer",
"pytorch",
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #Tranformer #pytorch #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #region-us
|
CRDNN with CTC/Attention and RNNLM trained on LibriSpeech
=========================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on LibriSpeech (EN) within
SpeechBrain. For a better experience, we ... | [
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #Tranformer #pytorch #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cu... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Transformer for AISHELL (Mandarin Chinese)
This repository provides all the necessary tools to perform aut... | {"language": "en", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "Transformers", "pytorch", "speechbrain"], "datasets": ["aishell"], "metrics": ["wer", "cer"]} | speechbrain/asr-transformer-aishell | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformers",
"pytorch",
"en",
"dataset:aishell",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #Transformers #pytorch #en #dataset-aishell #arxiv-2106.04624 #license-apache-2.0 #region-us
|
Transformer for AISHELL (Mandarin Chinese)
==========================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on AISHELL (Mandarin Chinese)
within SpeechBrain. For a better experience, we encourage you to lea... | [
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #Transformers #pytorch #en #dataset-aishell #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Transformer for LibriSpeech (with Transformer LM)
This repository provides all the necessary tools to perf... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "Transformer", "pytorch", "speechbrain", "hf-asr-leaderboard"], "datasets": ["librispeech"], "metrics": ["wer", "cer"], "model-index": [{"name": "Transformer+TransformerLM by SpeechBrain", "results": [{"task": {"t... | speechbrain/asr-transformer-transformerlm-librispeech | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformer",
"pytorch",
"hf-asr-leaderboard",
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"license:apache-2.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #Transformer #pytorch #hf-asr-leaderboard #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #model-index #has_space #region-us
|
Transformer for LibriSpeech (with Transformer LM)
=================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on LibriSpeech (EN) within
SpeechBrain. For a better experience, we encourage you to... | [
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #Transformer #pytorch #hf-asr-leaderboard #en #dataset-librispeech #arxiv-2106.04624 #license-apache-2.0 #model-index #has_space #region-us \n",
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC trained on CommonVoice English (No LM)
This repository provides all the necessary too... | {"language": "en", "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-commonvoice-en | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"en",
"dataset:commonvoice",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #en #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC trained on CommonVoice English (No LM)
===========================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (English Language) within
SpeechBrain. For a bett... | [
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #automatic-speech-recognition #en #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"de... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on CommonVoice French (No LM)
This repository provides all the nece... | {"language": ["fr"], "license": "apache-2.0", "tags": ["CTC", "pytorch", "speechbrain", "Transformer", "hf-asr-leaderboard"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "asr-wav2vec2-commonvoice-fr", "results": [{"task": {"type": "aut... | speechbrain/asr-wav2vec2-commonvoice-fr | null | [
"speechbrain",
"wav2vec2",
"CTC",
"pytorch",
"Transformer",
"hf-asr-leaderboard",
"automatic-speech-recognition",
"fr",
"dataset:commonvoice",
"license:apache-2.0",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#speechbrain #wav2vec2 #CTC #pytorch #Transformer #hf-asr-leaderboard #automatic-speech-recognition #fr #dataset-commonvoice #license-apache-2.0 #model-index #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on CommonVoice French (No LM)
====================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (French Language) within
Speech... | [
"### Transcribing your own audio files (in French)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe model was trained with SpeechBrain.\nTo train it from scratch follow these steps:\n\n\n1. Clo... | [
"TAGS\n#speechbrain #wav2vec2 #CTC #pytorch #Transformer #hf-asr-leaderboard #automatic-speech-recognition #fr #dataset-commonvoice #license-apache-2.0 #model-index #has_space #region-us \n",
"### Transcribing your own audio files (in French)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'ru... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on CommonVoice Italian (No LM)
This repository provides all the nec... | {"language": "en", "license": "apache-2.0", "tags": ["CTC", "Attention", "pytorch", "speechbrain", "Transformer"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-commonvoice-it | null | [
"speechbrain",
"wav2vec2",
"CTC",
"Attention",
"pytorch",
"Transformer",
"automatic-speech-recognition",
"en",
"dataset:commonvoice",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #wav2vec2 #CTC #Attention #pytorch #Transformer #automatic-speech-recognition #en #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #region-us
|
wav2vec 2.0 with CTC/Attention trained on CommonVoice Italian (No LM)
=====================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (Italian Language) within
Spe... | [
"### Transcribing your own audio files (in Italian)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #wav2vec2 #CTC #Attention #pytorch #Transformer #automatic-speech-recognition #en #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Transcribing your own audio files (in Italian)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"de... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC/Attention trained on CommonVoice Kinyarwanda (No LM)
This repository provides all the... | {"language": "rw", "license": "apache-2.0", "tags": ["CTC", "Attention", "pytorch", "speechbrain", "Transformer", "hf-asr-leaderboard"], "datasets": ["commonvoice"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"} | speechbrain/asr-wav2vec2-commonvoice-rw | null | [
"speechbrain",
"wav2vec2",
"CTC",
"Attention",
"pytorch",
"Transformer",
"hf-asr-leaderboard",
"automatic-speech-recognition",
"rw",
"dataset:commonvoice",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"rw"
] | TAGS
#speechbrain #wav2vec2 #CTC #Attention #pytorch #Transformer #hf-asr-leaderboard #automatic-speech-recognition #rw #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
wav2vec 2.0 with CTC/Attention trained on CommonVoice Kinyarwanda (No LM)
=========================================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (Kinyarwanda Language... | [
"### Transcribing your own audio files (in Kinyarwanda)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure ou... | [
"TAGS\n#speechbrain #wav2vec2 #CTC #Attention #pytorch #Transformer #hf-asr-leaderboard #automatic-speech-recognition #rw #dataset-commonvoice #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Transcribing your own audio files (in Kinyarwanda)",
"### Inference on GPU\n\n\nTo perform inferenc... |
automatic-speech-recognition | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Transformer for AISHELL + wav2vec2 (Mandarin Chinese)
This repository provides all the necessary tools to ... | {"language": "en", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "CTC", "Attention", "Transformers", "wav2vec2", "pytorch", "speechbrain"], "datasets": ["aishell"], "metrics": ["wer", "cer"]} | speechbrain/asr-wav2vec2-transformer-aishell | null | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformers",
"wav2vec2",
"pytorch",
"en",
"dataset:aishell",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #automatic-speech-recognition #CTC #Attention #Transformers #wav2vec2 #pytorch #en #dataset-aishell #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
Transformer for AISHELL + wav2vec2 (Mandarin Chinese)
=====================================================
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on AISHELL +wav2vec2 (Mandarin Chinese)
within SpeechBrain. For a better exp... | [
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.\n\n\nParallel Inference on a Batch\n-----------------------------\n\n\nPlease, see this Colab notebook to figure out ho... | [
"TAGS\n#speechbrain #automatic-speech-recognition #CTC #Attention #Transformers #wav2vec2 #pytorch #en #dataset-aishell #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Transcribing your own audio files (in English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_op... |
audio-classification | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Emotion Recognition with wav2vec2 base on IEMOCAP
This repository provides all the necessary tools to perf... | {"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "speechbrain", "Emotion", "Recognition", "wav2vec2", "pytorch"], "datasets": ["iemocap"], "metrics": ["Accuracy"], "inference": false} | speechbrain/emotion-recognition-wav2vec2-IEMOCAP | null | [
"speechbrain",
"audio-classification",
"Emotion",
"Recognition",
"wav2vec2",
"pytorch",
"en",
"dataset:iemocap",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-classification #Emotion #Recognition #wav2vec2 #pytorch #en #dataset-iemocap #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
Emotion Recognition with wav2vec2 base on IEMOCAP
=================================================
This repository provides all the necessary tools to perform emotion recognition with a fine-tuned wav2vec2 (base) model using SpeechBrain.
It is trained on IEMOCAP training data.
For a better experience, we e... | [
"### Perform Emotion recognition\n\n\nAn external 'py\\_module\\_file=URL' is used as an external Predictor class into this HF repos. We use 'foreign\\_class' function from 'speechbrain.pretrained.interfaces' that allow you to load you custom model.\n\n\nThe prediction tensor will contain a tuple of (embedding, id\... | [
"TAGS\n#speechbrain #audio-classification #Emotion #Recognition #wav2vec2 #pytorch #en #dataset-iemocap #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform Emotion recognition\n\n\nAn external 'py\\_module\\_file=URL' is used as an external Predictor class into this HF repos. We use 'for... |
audio-classification | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Command Recognition with xvector embeddings on Google Speech Commands
This repository provides all the nec... | {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Commands", "Keywords", "Keyword Spotting", "pytorch", "xvectors", "TDNN", "Command Recognition", "audio-classification"], "datasets": ["GoogleSpeechCommands"], "metrics": ["Accuracy"], "widget": [{"example_title": "Speech Commands \"down... | speechbrain/google_speech_command_xvector | null | [
"speechbrain",
"embeddings",
"Commands",
"Keywords",
"Keyword Spotting",
"pytorch",
"xvectors",
"TDNN",
"Command Recognition",
"audio-classification",
"en",
"dataset:GoogleSpeechCommands",
"arxiv:1804.03209",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.03209",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #embeddings #Commands #Keywords #Keyword Spotting #pytorch #xvectors #TDNN #Command Recognition #audio-classification #en #dataset-GoogleSpeechCommands #arxiv-1804.03209 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
Command Recognition with xvector embeddings on Google Speech Commands
=====================================================================
This repository provides all the necessary tools to perform command recognition with SpeechBrain using a model pretrained on Google Speech Commands.
You can download the ... | [
"### Perform Command Recognition",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe model was trained with SpeechBrain (b7ff9dc4).\nTo train it from scratch follows these steps:\n\n\n1. Clone Spe... | [
"TAGS\n#speechbrain #embeddings #Commands #Keywords #Keyword Spotting #pytorch #xvectors #TDNN #Command Recognition #audio-classification #en #dataset-GoogleSpeechCommands #arxiv-1804.03209 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform Command Recognition",
"### Inference on GPU\... |
audio-classification | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Language Identification from Speech Recordings with ECAPA embeddings on CommonLanguage
This repository pr... | {"language": ["ar", "eu", "br", "ca", "cv", "cs", "dv", "nl", "en", "eo", "et", "fr", "fy", "ka", "de", "el", "cnh", "id", "ia", "it", "ja", "kab", "rw", "ky", "lv", "mt", "mn", "fa", "pl", "pt", "ro", "rm", "ru", "sah", "sl", "es", "sv", "ta", "tt", "tr", "uk", "cy"], "license": "apache-2.0", "tags": ["audio-classific... | speechbrain/lang-id-commonlanguage_ecapa | null | [
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"CommonLanguage",
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"eu",
"br",
"ca",
"cv",
"cs",
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"eo",
"et",
"fr",
"fy",
"ka",
"de",
"el",
"cnh",
"id",
"ia",
"it",
"ja... | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
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"pt",
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"... | TAGS
#speechbrain #audio-classification #embeddings #Language #Identification #pytorch #ECAPA-TDNN #TDNN #CommonLanguage #ar #eu #br #ca #cv #cs #dv #nl #en #eo #et #fr #fy #ka #de #el #cnh #id #ia #it #ja #kab #rw #ky #lv #mt #mn #fa #pl #pt #ro #rm #ru #sah #sl #es #sv #ta #tt #tr #uk #cy #dataset-Urbansound8k #arxiv... |
Language Identification from Speech Recordings with ECAPA embeddings on CommonLanguage
======================================================================================
This repository provides all the necessary tools to perform language identification from speech recordings with SpeechBrain.
The system ... | [
"### Perform Language Identification from Speech Recordings",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe model was trained with SpeechBrain (a02f860e).\nTo train it from scratch follow thes... | [
"TAGS\n#speechbrain #audio-classification #embeddings #Language #Identification #pytorch #ECAPA-TDNN #TDNN #CommonLanguage #ar #eu #br #ca #cv #cs #dv #nl #en #eo #et #fr #fy #ka #de #el #cnh #id #ia #it #ja #kab #rw #ky #lv #mt #mn #fa #pl #pt #ro #rm #ru #sah #sl #es #sv #ta #tt #tr #uk #cy #dataset-Urbansound8k ... |
audio-classification | speechbrain |
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses
more fully connecte... | {"language": ["multilingual", "ab", "af", "am", "ar", "as", "az", "ba", "be", "bg", "bi", "bo", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fo", "fr", "gl", "gn", "gu", "gv", "ha", "haw", "hi", "hr", "ht", "hu", "hy", "ia", "id", "is", "it", "he", "ja", "jv", "ka", ... | speechbrain/lang-id-voxlingua107-ecapa | null | [
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"multilingual",
"ab",
"af",
"am",
"ar",
"as",
"az",
"ba",
"be",
"bg",
"bi",
"bo",
"br",
"bs",
"ca",
"ceb",
"cs",
"cy",
"da",
"de... | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"multilingual",
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"fa",
"fi",
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"fr",
"gl",
"gn",
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"gv",
"ha",
"haw",
"hi",
"hr",
... | TAGS
#speechbrain #audio-classification #embeddings #Language #Identification #pytorch #ECAPA-TDNN #TDNN #VoxLingua107 #multilingual #ab #af #am #ar #as #az #ba #be #bg #bi #bo #br #bs #ca #ceb #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fo #fr #gl #gn #gu #gv #ha #haw #hi #hr #ht #hu #hy #ia #id #is #it #he #ja #... |
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses
more fully connecte... | [
"# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model",
"## Model description\n\nThis is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.\nThe model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses\nmore fu... | [
"TAGS\n#speechbrain #audio-classification #embeddings #Language #Identification #pytorch #ECAPA-TDNN #TDNN #VoxLingua107 #multilingual #ab #af #am #ar #as #az #ba #be #bg #bi #bo #br #bs #ca #ceb #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fo #fr #gl #gn #gu #gv #ha #haw #hi #hr #ht #hu #hy #ia #id #is #it #he... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# MetricGAN-trained model for Enhancement
This repository provides all the necessary tools to perform enhanc... | {"language": "en", "license": "apache-2.0", "tags": ["audio-to-audio", "speech-enhancement", "PyTorch", "speechbrain"], "datasets": ["Voicebank", "DEMAND"], "metrics": ["PESQ", "STOI"]} | speechbrain/metricgan-plus-voicebank | null | [
"speechbrain",
"audio-to-audio",
"speech-enhancement",
"PyTorch",
"en",
"dataset:Voicebank",
"dataset:DEMAND",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-to-audio #speech-enhancement #PyTorch #en #dataset-Voicebank #dataset-DEMAND #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
MetricGAN-trained model for Enhancement
=======================================
This repository provides all the necessary tools to perform enhancement with
SpeechBrain. For a better experience we encourage you to learn more about
SpeechBrain. The model performance is:
Install SpeechBrain
-----------------... | [
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe model was trained with SpeechBrain (d0accc8).\nTo train it from scratch follows these steps:\n\n\n1. Clone SpeechBrain:\n2. Install it:\n3. Run Trai... | [
"TAGS\n#speechbrain #audio-to-audio #speech-enhancement #PyTorch #en #dataset-Voicebank #dataset-DEMAND #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# ResNet-like model
This repository provides all the necessary tools to perform enhancement and
robust ASR t... | {"language": "en", "license": "apache-2.0", "tags": ["Robust ASR", "audio-to-audio", "speech-enhancement", "PyTorch", "speechbrain"], "datasets": ["Voicebank", "DEMAND"], "metrics": ["WER", "PESQ", "COVL"]} | speechbrain/mtl-mimic-voicebank | null | [
"speechbrain",
"Robust ASR",
"audio-to-audio",
"speech-enhancement",
"PyTorch",
"en",
"dataset:Voicebank",
"dataset:DEMAND",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Robust ASR #audio-to-audio #speech-enhancement #PyTorch #en #dataset-Voicebank #dataset-DEMAND #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
ResNet-like model
=================
This repository provides all the necessary tools to perform enhancement and
robust ASR training (EN) within
SpeechBrain. For a better experience we encourage you to learn more about
SpeechBrain. The model performance is:
Works with SpeechBrain v0.5.12
Pipeline descript... | [
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe model was trained with SpeechBrain (150e1890).\nTo train it from scratch follows these steps:\n\n\n1. Clone SpeechBrain:\n2. Install it:\n3. Run Tra... | [
"TAGS\n#speechbrain #Robust ASR #audio-to-audio #speech-enhancement #PyTorch #en #dataset-Voicebank #dataset-DEMAND #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' ... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WHAM! for speech enhancement (8k sampling frequency)
This repository provides all the ... | {"language": "en", "license": "apache-2.0", "tags": ["audio-to-audio", "Speech Enhancement", "WHAM!", "SepFormer", "Transformer", "pytorch", "speechbrain"], "datasets": ["WHAM!"], "metrics": ["SI-SNR", "PESQ"]} | speechbrain/sepformer-wham-enhancement | null | [
"speechbrain",
"audio-to-audio",
"Speech Enhancement",
"WHAM!",
"SepFormer",
"Transformer",
"pytorch",
"en",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-to-audio #Speech Enhancement #WHAM! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
SepFormer trained on WHAM! for speech enhancement (8k sampling frequency)
=========================================================================
This repository provides all the necessary tools to perform speech enhancement (denoising) with a SepFormer model, implemented with SpeechBrain, and pretrained on... | [
"### Perform speech enhancement on your own audio file",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe training script is currently being worked on an ongoing pull-request.\n\n\nWe will update... | [
"TAGS\n#speechbrain #audio-to-audio #Speech Enhancement #WHAM! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform speech enhancement on your own audio file",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_o... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WHAM!
This repository provides all the necessary tools to perform audio source separat... | {"language": "en", "license": "apache-2.0", "tags": ["audio-to-audio", "audio-source-separation", "Source Separation", "Speech Separation", "Audio Source Separation", "WHAM!", "SepFormer", "Transformer", "speechbrain"], "datasets": ["WHAM!"], "metrics": ["SI-SNRi", "SDRi"]} | speechbrain/sepformer-wham | null | [
"speechbrain",
"audio-to-audio",
"audio-source-separation",
"Source Separation",
"Speech Separation",
"Audio Source Separation",
"WHAM!",
"SepFormer",
"Transformer",
"en",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-to-audio #audio-source-separation #Source Separation #Speech Separation #Audio Source Separation #WHAM! #SepFormer #Transformer #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
SepFormer trained on WHAM!
==========================
This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAM! dataset, which is basically a version of WSJ0-Mix dataset with environmental noise. For a bett... | [
"### Perform source separation on your own audio file\n\n\nThe system expects input recordings sampled at 8kHz (single channel).\nIf your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'ru... | [
"TAGS\n#speechbrain #audio-to-audio #audio-source-separation #Source Separation #Speech Separation #Audio Source Separation #WHAM! #SepFormer #Transformer #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform source separation on your own audio file\n\n\nThe system ex... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WHAMR! for speech enhancement (8k sampling frequency)
This repository provides all the... | {"language": "en", "license": "apache-2.0", "tags": ["audio-to-audio", "Speech Enhancement", "WHAMR!", "SepFormer", "Transformer", "pytorch", "speechbrain"], "datasets": ["WHAMR!"], "metrics": ["SI-SNR", "PESQ"]} | speechbrain/sepformer-whamr-enhancement | null | [
"speechbrain",
"audio-to-audio",
"Speech Enhancement",
"WHAMR!",
"SepFormer",
"Transformer",
"pytorch",
"en",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-to-audio #Speech Enhancement #WHAMR! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
SepFormer trained on WHAMR! for speech enhancement (8k sampling frequency)
==========================================================================
This repository provides all the necessary tools to perform speech enhancement (denoising + dereverberation) with a SepFormer model, implemented with SpeechBrai... | [
"### Perform speech enhancement on your own audio file",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Training\n\n\nThe training script is currently being worked on an ongoing pull-request.\n\n\nWe will update... | [
"TAGS\n#speechbrain #audio-to-audio #Speech Enhancement #WHAMR! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform speech enhancement on your own audio file",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WHAMR!
This repository provides all the necessary tools to perform audio source separa... | {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "Source Separation", "Speech Separation", "Audio Source Separation", "WHAM!", "SepFormer", "Transformer", "audio-to-audio", "audio-source-separation"], "datasets": ["WHAMR!"], "metrics": ["SI-SNRi", "SDRi"]} | speechbrain/sepformer-whamr | null | [
"speechbrain",
"Source Separation",
"Speech Separation",
"Audio Source Separation",
"WHAM!",
"SepFormer",
"Transformer",
"audio-to-audio",
"audio-source-separation",
"en",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WHAM! #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
SepFormer trained on WHAMR!
===========================
This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAMR! dataset, which is basically a version of WSJ0-Mix dataset with environmental noise and reve... | [
"### Perform source separation on your own audio file\n\n\nThe system expects input recordings sampled at 8kHz (single channel).\nIf your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'ru... | [
"TAGS\n#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WHAM! #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform source separation on your own audio file\n\n\nThe system ex... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WHAMR! (16k sampling frequency)
This repository provides all the necessary tools to pe... | {"language": "en", "license": "apache-2.0", "tags": ["audio-to-audio", "audio-source-separation", "Source Separation", "Speech Separation", "WHAM!", "SepFormer", "Transformer", "pytorch", "speechbrain"], "datasets": ["WHAMR!"], "metrics": ["SI-SNRi", "SDRi"]} | speechbrain/sepformer-whamr16k | null | [
"speechbrain",
"audio-to-audio",
"audio-source-separation",
"Source Separation",
"Speech Separation",
"WHAM!",
"SepFormer",
"Transformer",
"pytorch",
"en",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #audio-to-audio #audio-source-separation #Source Separation #Speech Separation #WHAM! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #region-us
|
SepFormer trained on WHAMR! (16k sampling frequency)
====================================================
This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAMR! dataset with 16k sampling frequency, whic... | [
"### Perform source separation on your own audio file\n\n\nThe system expects input recordings sampled at 16kHz (single channel).\nIf your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'r... | [
"TAGS\n#speechbrain #audio-to-audio #audio-source-separation #Source Separation #Speech Separation #WHAM! #SepFormer #Transformer #pytorch #en #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Perform source separation on your own audio file\n\n\nThe system expects input recordings samp... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WSJ0-2Mix
This repository provides all the necessary tools to perform audio source se... | {"language": "en", "license": "apache-2.0", "tags": ["Source Separation", "Speech Separation", "Audio Source Separation", "WSJ02Mix", "SepFormer", "Transformer", "audio-to-audio", "audio-source-separation", "speechbrain"], "datasets": ["WSJ0-2Mix"], "metrics": ["SI-SNRi", "SDRi"]} | speechbrain/sepformer-wsj02mix | null | [
"speechbrain",
"Source Separation",
"Speech Separation",
"Audio Source Separation",
"WSJ02Mix",
"SepFormer",
"Transformer",
"audio-to-audio",
"audio-source-separation",
"en",
"dataset:WSJ0-2Mix",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ02Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-2Mix #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
SepFormer trained on WSJ0-2Mix
==============================
This repository provides all the necessary tools to perform audio source separation with a SepFormer
model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about
SpeechBrain.... | [
"### Perform source separation on your own audio file\n\n\nThe system expects input recordings sampled at 8kHz (single channel).\nIf your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'ru... | [
"TAGS\n#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ02Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-2Mix #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Perform source separation on your own audio f... |
audio-to-audio | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# SepFormer trained on WSJ0-3Mix
This repository provides all the necessary tools to perform audio source se... | {"language": "en", "license": "apache-2.0", "tags": ["Source Separation", "Speech Separation", "Audio Source Separation", "WSJ0-3Mix", "SepFormer", "Transformer", "audio-to-audio", "audio-source-separation", "speechbrain"], "datasets": ["WSJ0-3Mix"], "metrics": ["SI-SNRi", "SDRi"]} | speechbrain/sepformer-wsj03mix | null | [
"speechbrain",
"Source Separation",
"Speech Separation",
"Audio Source Separation",
"WSJ0-3Mix",
"SepFormer",
"Transformer",
"audio-to-audio",
"audio-source-separation",
"en",
"dataset:WSJ0-3Mix",
"arxiv:2010.13154",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.13154",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ0-3Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-3Mix #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #region-us
|
SepFormer trained on WSJ0-3Mix
==============================
This repository provides all the necessary tools to perform audio source separation with a SepFormer
model, implemented with SpeechBrain, and pretrained on WSJ0-3Mix dataset. For a better experience we encourage you to learn more about
SpeechBrain.... | [
"### Perform source separation on your own audio file\n\n\nThe system expects input recordings sampled at 8kHz (single channel).\nIf your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'ru... | [
"TAGS\n#speechbrain #Source Separation #Speech Separation #Audio Source Separation #WSJ0-3Mix #SepFormer #Transformer #audio-to-audio #audio-source-separation #en #dataset-WSJ0-3Mix #arxiv-2010.13154 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Perform source separation on your own audio file\n\n\nT... |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Fluent Speech Commands
The dataset contains real recordings that define a simple spoken language understand... | {"language": "en", "license": "cc0-1.0", "tags": ["speechbrain", "Spoken language understanding"], "datasets": ["FluentSpeechCommands"], "metrics": ["Accuracy"]} | speechbrain/slu-direct-fluent-speech-commands-librispeech-asr | null | [
"speechbrain",
"Spoken language understanding",
"en",
"dataset:FluentSpeechCommands",
"arxiv:1904.03670",
"arxiv:2106.04624",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.03670",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Spoken language understanding #en #dataset-FluentSpeechCommands #arxiv-1904.03670 #arxiv-2106.04624 #license-cc0-1.0 #region-us
|
<iframe src="URL frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Fluent Speech Commands
The dataset contains real recordings that define a simple spoken language understanding task. You can download it from here.
The Fluent Speech Commands dataset contains 30,043 utterance... | [
"# Fluent Speech Commands\nThe dataset contains real recordings that define a simple spoken language understanding task. You can download it from here. \nThe Fluent Speech Commands dataset contains 30,043 utterances from 97 speakers. It is recorded as 16 kHz single-channel .wav files each containing a single uttera... | [
"TAGS\n#speechbrain #Spoken language understanding #en #dataset-FluentSpeechCommands #arxiv-1904.03670 #arxiv-2106.04624 #license-cc0-1.0 #region-us \n",
"# Fluent Speech Commands\nThe dataset contains real recordings that define a simple spoken language understanding task. You can download it from here. \nThe Fl... |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# End-to-end SLU model for Timers and Such
Attention-based RNN sequence-to-sequence model for [Timers and S... | {"language": "en", "license": "cc0-1.0", "tags": ["Spoken language understanding", "speechbrain"], "datasets": ["Timers-and-Such"], "metrics": ["Accuracy"]} | speechbrain/slu-timers-and-such-direct-librispeech-asr | null | [
"speechbrain",
"Spoken language understanding",
"en",
"dataset:Timers-and-Such",
"arxiv:2104.01604",
"arxiv:2106.04624",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.01604",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #Spoken language understanding #en #dataset-Timers-and-Such #arxiv-2104.01604 #arxiv-2106.04624 #license-cc0-1.0 #region-us
|
<iframe src="URL frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# End-to-end SLU model for Timers and Such
Attention-based RNN sequence-to-sequence model for Timers and Such trained on the 'train-real' subset. This model checkpoint achieves 86.7% accuracy on 'test-real'.
... | [
"# End-to-end SLU model for Timers and Such\n\nAttention-based RNN sequence-to-sequence model for Timers and Such trained on the 'train-real' subset. This model checkpoint achieves 86.7% accuracy on 'test-real'.\n\nThe model uses an ASR model trained on LibriSpeech ('speechbrain/asr-crdnn-rnnlm-librispeech') to ext... | [
"TAGS\n#speechbrain #Spoken language understanding #en #dataset-Timers-and-Such #arxiv-2104.01604 #arxiv-2106.04624 #license-cc0-1.0 #region-us \n",
"# End-to-end SLU model for Timers and Such\n\nAttention-based RNN sequence-to-sequence model for Timers and Such trained on the 'train-real' subset. This model chec... |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on Voxceleb
This repository provides all the necessary too... | {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "ECAPA", "TDNN"], "datasets": ["voxceleb"], "metrics": ["EER"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCe... | speechbrain/spkrec-ecapa-voxceleb | null | [
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"ECAPA",
"TDNN",
"en",
"dataset:voxceleb",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #embeddings #Speaker #Verification #Identification #pytorch #ECAPA #TDNN #en #dataset-voxceleb #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
Speaker Verification with ECAPA-TDNN embeddings on Voxceleb
===========================================================
This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
The system can be used to extract speaker embeddings as... | [
"### Compute your speaker embeddings\n\n\nThe system is trained with recordings sampled at 16kHz (single channel).\nThe code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify\\_file* if needed. Make sure your input tensor is compliant with the expected sampli... | [
"TAGS\n#speechbrain #embeddings #Speaker #Verification #Identification #pytorch #ECAPA #TDNN #en #dataset-voxceleb #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Compute your speaker embeddings\n\n\nThe system is trained with recordings sampled at 16kHz (single channel).\nThe code will auto... |
audio-classification | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with xvector embeddings on Voxceleb
This repository provides all the necessary tools ... | {"language": "en", "license": "apache-2.0", "tags": ["embeddings", "Speaker", "Verification", "Identification", "pytorch", "xvectors", "TDNN", "speechbrain", "audio-classification"], "datasets": ["voxceleb"], "metrics": ["EER", "min_dct"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-med... | speechbrain/spkrec-xvect-voxceleb | null | [
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"xvectors",
"TDNN",
"audio-classification",
"en",
"dataset:voxceleb",
"arxiv:2106.04624",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #embeddings #Speaker #Verification #Identification #pytorch #xvectors #TDNN #audio-classification #en #dataset-voxceleb #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us
|
Speaker Verification with xvector embeddings on Voxceleb
========================================================
This repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain.
The system is trained on Voxceleb 1+ Voxceleb2 training data.
For a... | [
"### Compute your speaker embeddings\n\n\nThe system is trained with recordings sampled at 16kHz (single channel).\nThe code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify\\_file* if needed. Make sure your input tensor is compliant with the expected sampli... | [
"TAGS\n#speechbrain #embeddings #Speaker #Verification #Identification #pytorch #xvectors #TDNN #audio-classification #en #dataset-voxceleb #arxiv-2106.04624 #license-apache-2.0 #has_space #region-us \n",
"### Compute your speaker embeddings\n\n\nThe system is trained with recordings sampled at 16kHz (single chan... |
audio-classification | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Sound Recognition with ECAPA embeddings on UrbanSoudnd8k
This repository provides all the necessary tools ... | {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Sound", "Keywords", "Keyword Spotting", "pytorch", "ECAPA-TDNN", "TDNN", "Command Recognition", "audio-classification"], "datasets": ["Urbansound8k"], "metrics": ["Accuracy"]} | speechbrain/urbansound8k_ecapa | null | [
"speechbrain",
"embeddings",
"Sound",
"Keywords",
"Keyword Spotting",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"Command Recognition",
"audio-classification",
"en",
"dataset:Urbansound8k",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #embeddings #Sound #Keywords #Keyword Spotting #pytorch #ECAPA-TDNN #TDNN #Command Recognition #audio-classification #en #dataset-Urbansound8k #arxiv-2106.04624 #license-apache-2.0 #region-us
|
Sound Recognition with ECAPA embeddings on UrbanSoudnd8k
========================================================
This repository provides all the necessary tools to perform sound recognition with SpeechBrain using a model pretrained on UrbanSound8k.
You can download the dataset here
The provided system can r... | [
"### Perform Sound Recognition\n\n\nThe system is trained with recordings sampled at 16kHz (single channel).\nThe code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify\\_file* if needed. Make sure your input tensor is compliant with the expected sampling rat... | [
"TAGS\n#speechbrain #embeddings #Sound #Keywords #Keyword Spotting #pytorch #ECAPA-TDNN #TDNN #Command Recognition #audio-classification #en #dataset-Urbansound8k #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Perform Sound Recognition\n\n\nThe system is trained with recordings sampled at 16kHz (singl... |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Voice Activity Detection with a (small) CRDNN model trained on Libriparty
This repository provides all the... | {"language": "en", "tags": ["speechbrain", "VAD", "SAD", "Voice Activity Detection", "Speech Activity Detection", "Speaker Diarization", "pytorch", "CRDNN", "LibriSpeech", "LibryParty"], "datasets": ["Urbansound8k"], "metrics": ["Accuracy"]} | speechbrain/vad-crdnn-libriparty | null | [
"speechbrain",
"VAD",
"SAD",
"Voice Activity Detection",
"Speech Activity Detection",
"Speaker Diarization",
"pytorch",
"CRDNN",
"LibriSpeech",
"LibryParty",
"en",
"dataset:Urbansound8k",
"arxiv:2106.04624",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #VAD #SAD #Voice Activity Detection #Speech Activity Detection #Speaker Diarization #pytorch #CRDNN #LibriSpeech #LibryParty #en #dataset-Urbansound8k #arxiv-2106.04624 #has_space #region-us
|
Voice Activity Detection with a (small) CRDNN model trained on Libriparty
=========================================================================
This repository provides all the necessary tools to perform voice activity detection with SpeechBrain using a model pretrained on Libriparty.
The pre-trained sy... | [
"### Perform Voice Activity Detection\n\n\nThe output is a tensor that contains the beginning/end second of each\ndetected speech segment. You can save the boundaries on a file with:\n\n\nSometimes it is useful to jointly visualize the VAD output with the input signal itself. This is helpful to quickly figure out i... | [
"TAGS\n#speechbrain #VAD #SAD #Voice Activity Detection #Speech Activity Detection #Speaker Diarization #pytorch #CRDNN #LibriSpeech #LibryParty #en #dataset-Urbansound8k #arxiv-2106.04624 #has_space #region-us \n",
"### Perform Voice Activity Detection\n\n\nThe output is a tensor that contains the beginning/end ... |
text-classification | transformers | # Text classifier using DistilBERT to determine Partisanship
## This is one of many single-class partisanship models
label_0 refers to "left" while label_1 refers to "other".
This model was trained on 40,000 articles.
### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens... | {} | spencerh/leftpartisan | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Text classifier using DistilBERT to determine Partisanship
## This is one of many single-class partisanship models
label_0 refers to "left" while label_1 refers to "other".
This model was trained on 40,000 articles.
### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens... | [
"# Text classifier using DistilBERT to determine Partisanship",
"## This is one of many single-class partisanship models\n\nlabel_0 refers to \"left\" while label_1 refers to \"other\". \n\nThis model was trained on 40,000 articles.",
"### Best Practices\nThis model was optimized for 512 token-length text. Any... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Text classifier using DistilBERT to determine Partisanship",
"## This is one of many single-class partisanship models\n\nlabel_0 refers to \"left\" while label_1 refers to \"other\".... |
text-classification | transformers | # Text classifier using DistilBERT to determine Partisanship
## This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/centerpartisan)
label_0 refers to "other" while label_1 refers to "right" (right as in right-leaning).
This was trained with 40,000 arti... | {} | spencerh/rightpartisan | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Text classifier using DistilBERT to determine Partisanship
## This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/centerpartisan)
label_0 refers to "other" while label_1 refers to "right" (right as in right-leaning).
This was trained with 40,000 arti... | [
"# Text classifier using DistilBERT to determine Partisanship",
"## This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/centerpartisan)\n\n\nlabel_0 refers to \"other\" while label_1 refers to \"right\" (right as in right-leaning).\n\nThis was traine... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Text classifier using DistilBERT to determine Partisanship",
"## This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/c... |
fill-mask | transformers | # DistilBERT Yelp Review Sentiment
This model is used for sentiment analysis on english yelp reviews.
It is a DistilBERT model trained on 1 million reviews from the yelp open dataset.
It is a regression model, with outputs in the range of ~-2 to ~2. With -2 being 1 star and 2 being 5 stars.
It was trained using t... | {} | spentaur/yelp | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT Yelp Review Sentiment
This model is used for sentiment analysis on english yelp reviews.
It is a DistilBERT model trained on 1 million reviews from the yelp open dataset.
It is a regression model, with outputs in the range of ~-2 to ~2. With -2 being 1 star and 2 being 5 stars.
It was trained using t... | [
"# DistilBERT Yelp Review Sentiment\nThis model is used for sentiment analysis on english yelp reviews. \nIt is a DistilBERT model trained on 1 million reviews from the yelp open dataset. \nIt is a regression model, with outputs in the range of ~-2 to ~2. With -2 being 1 star and 2 being 5 stars. \nIt was traine... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT Yelp Review Sentiment\nThis model is used for sentiment analysis on english yelp reviews. \nIt is a DistilBERT model trained on 1 million reviews from the yelp open dataset. \nIt is a regres... |
text-generation | transformers |
#Sherlock DialoGPT Model | {"tags": ["conversational"]} | spockinese/DialoGPT-small-sherlock | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Sherlock DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | # Kek model
---
A customized DialoGPT model designed for personal use. Usage is the same with DialoGPT.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("spuun/kek")
model = AutoModelForCausalLM.from_pretrained("spuun/kek")
# Let's chat for 5... | {} | spuun/kek | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Kek model
---
A customized DialoGPT model designed for personal use. Usage is the same with DialoGPT.
| [
"# Kek model\n---\nA customized DialoGPT model designed for personal use. Usage is the same with DialoGPT."
] | [
"TAGS\n#region-us \n",
"# Kek model\n---\nA customized DialoGPT model designed for personal use. Usage is the same with DialoGPT."
] |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 601516964
- CO2 Emissions (in grams): 3.3930796843275846
## Validation Metrics
- Loss: 1.9823806285858154
- Rouge1: 42.8783
- Rouge2: 7.4603
- RougeL: 42.8492
- RougeLsum: 43.0556
- Gen Len: 2.8952
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-AUS-to-US"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.3930796843275846} | spy24/autonlp-AUS-to-US-601516964 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:spy24/autonlp-data-AUS-to-US",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-spy24/autonlp-data-AUS-to-US #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 601516964
- CO2 Emissions (in grams): 3.3930796843275846
## Validation Metrics
- Loss: 1.9823806285858154
- Rouge1: 42.8783
- Rouge2: 7.4603
- RougeL: 42.8492
- RougeLsum: 43.0556
- Gen Len: 2.8952
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 601516964\n- CO2 Emissions (in grams): 3.3930796843275846",
"## Validation Metrics\n\n- Loss: 1.9823806285858154\n- Rouge1: 42.8783\n- Rouge2: 7.4603\n- RougeL: 42.8492\n- RougeLsum: 43.0556\n- Gen Len: 2.8952",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-spy24/autonlp-data-AUS-to-US #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 601516964\n- CO2 Emissio... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606817121
- CO2 Emissions (in grams): 1.1512164322839105
## Validation Metrics
- Loss: 2.0312094688415527
- Rouge1: 34.8844
- Rouge2: 5.2023
- RougeL: 34.6339
- RougeLsum: 34.8555
- Gen Len: 3.1792
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-AUS-to-US2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.1512164322839105} | spy24/autonlp-AUS-to-US2-606817121 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:spy24/autonlp-data-AUS-to-US2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-spy24/autonlp-data-AUS-to-US2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606817121
- CO2 Emissions (in grams): 1.1512164322839105
## Validation Metrics
- Loss: 2.0312094688415527
- Rouge1: 34.8844
- Rouge2: 5.2023
- RougeL: 34.6339
- RougeLsum: 34.8555
- Gen Len: 3.1792
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 606817121\n- CO2 Emissions (in grams): 1.1512164322839105",
"## Validation Metrics\n\n- Loss: 2.0312094688415527\n- Rouge1: 34.8844\n- Rouge2: 5.2023\n- RougeL: 34.6339\n- RougeLsum: 34.8555\n- Gen Len: 3.1792",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-spy24/autonlp-data-AUS-to-US2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 606817121\n- CO2 Emissi... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 600416931
- CO2 Emissions (in grams): 1.113131499202784
## Validation Metrics
- Loss: 1.8278849124908447
- Rouge1: 45.7945
- Rouge2: 8.5245
- RougeL: 45.8031
- RougeLsum: 45.9067
- Gen Len: 3.0622
## Usage
You can use cURL to access this mode... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-UK-to-US"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.113131499202784} | spy24/autonlp-UK-to-US-600416931 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:spy24/autonlp-data-UK-to-US",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-spy24/autonlp-data-UK-to-US #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 600416931
- CO2 Emissions (in grams): 1.113131499202784
## Validation Metrics
- Loss: 1.8278849124908447
- Rouge1: 45.7945
- Rouge2: 8.5245
- RougeL: 45.8031
- RougeLsum: 45.9067
- Gen Len: 3.0622
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 600416931\n- CO2 Emissions (in grams): 1.113131499202784",
"## Validation Metrics\n\n- Loss: 1.8278849124908447\n- Rouge1: 45.7945\n- Rouge2: 8.5245\n- RougeL: 45.8031\n- RougeLsum: 45.9067\n- Gen Len: 3.0622",
"## Usage\n\nYou can use... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 600416931\n- CO2 Emission... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606917136
- CO2 Emissions (in grams): 1.2956300881026077
## Validation Metrics
- Loss: 2.2489309310913086
- Rouge1: 31.0639
- Rouge2: 2.2447
- RougeL: 31.1492
- RougeLsum: 31.1753
- Gen Len: 3.4798
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-US-to-AUS3"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.2956300881026077} | spy24/autonlp-US-to-AUS3-606917136 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:spy24/autonlp-data-US-to-AUS3",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-spy24/autonlp-data-US-to-AUS3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606917136
- CO2 Emissions (in grams): 1.2956300881026077
## Validation Metrics
- Loss: 2.2489309310913086
- Rouge1: 31.0639
- Rouge2: 2.2447
- RougeL: 31.1492
- RougeLsum: 31.1753
- Gen Len: 3.4798
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 606917136\n- CO2 Emissions (in grams): 1.2956300881026077",
"## Validation Metrics\n\n- Loss: 2.2489309310913086\n- Rouge1: 31.0639\n- Rouge2: 2.2447\n- RougeL: 31.1492\n- RougeLsum: 31.1753\n- Gen Len: 3.4798",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-spy24/autonlp-data-US-to-AUS3 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 606917136\n- CO2 Emissi... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 604417040
- CO2 Emissions (in grams): 3.3271667948644614
## Validation Metrics
- Loss: 1.919085144996643
- Rouge1: 39.2808
- Rouge2: 4.905
- RougeL: 39.113
- RougeLsum: 39.1463
- Gen Len: 3.4611
## Usage
You can use cURL to access this model:... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-US-to-UK"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.3271667948644614} | spy24/autonlp-US-to-UK-604417040 | null | [
"transformers",
"pytorch",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 604417040
- CO2 Emissions (in grams): 3.3271667948644614
## Validation Metrics
- Loss: 1.919085144996643
- Rouge1: 39.2808
- Rouge2: 4.905
- RougeL: 39.113
- RougeLsum: 39.1463
- Gen Len: 3.4611
## Usage
You can use cURL to access this model:... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 604417040\n- CO2 Emission... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606317091
- CO2 Emissions (in grams): 1.1913570653422176
## Validation Metrics
- Loss: 1.9264822006225586
- Rouge1: 44.2035
- Rouge2: 6.134
- RougeL: 43.9114
- RougeLsum: 44.0231
- Gen Len: 3.6134
## Usage
You can use cURL to access this mode... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-US-to-UK2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.1913570653422176} | spy24/autonlp-US-to-UK2-606317091 | null | [
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"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606317091
- CO2 Emissions (in grams): 1.1913570653422176
## Validation Metrics
- Loss: 1.9264822006225586
- Rouge1: 44.2035
- Rouge2: 6.134
- RougeL: 43.9114
- RougeLsum: 44.0231
- Gen Len: 3.6134
## Usage
You can use cURL to access this mode... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 606317091\n- CO2 Emissio... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 607117159
- CO2 Emissions (in grams): 1.4276876566788055
## Validation Metrics
- Loss: 1.5177973508834839
- Rouge1: 46.134
- Rouge2: 10.578
- RougeL: 45.8856
- RougeLsum: 46.0088
- Gen Len: 3.7283
## Usage
You can use cURL to access this mode... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-US_to_AUS"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.4276876566788055} | spy24/autonlp-US_to_AUS-607117159 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 607117159
- CO2 Emissions (in grams): 1.4276876566788055
## Validation Metrics
- Loss: 1.5177973508834839
- Rouge1: 46.134
- Rouge2: 10.578
- RougeL: 45.8856
- RougeLsum: 46.0088
- Gen Len: 3.7283
## Usage
You can use cURL to access this mode... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 607117159\n- CO2 Emissions (in grams): 1.4276876566788055",
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 607117159\n- CO2 Emissio... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 607217177
- CO2 Emissions (in grams): 193.70003779879124
## Validation Metrics
- Loss: 1.2881609201431274
- Rouge1: 48.3375
- Rouge2: 25.9756
- RougeL: 42.2748
- RougeLsum: 42.2797
- Gen Len: 18.4359
## Usage
You can use cURL to access this m... | {"language": "unk", "tags": "autonlp", "datasets": ["spy24/autonlp-data-paraphrasing"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 193.70003779879124} | spy24/autonlp-paraphrasing-607217177 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 607217177
- CO2 Emissions (in grams): 193.70003779879124
## Validation Metrics
- Loss: 1.2881609201431274
- Rouge1: 48.3375
- Rouge2: 25.9756
- RougeL: 42.2748
- RougeLsum: 42.2797
- Gen Len: 18.4359
## Usage
You can use cURL to access this m... | [
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"## Usage\n\nYou can ... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 607217177\n- CO2 Emis... |
null | transformers | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-mnli-headless`, has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the [Multi-Genre Natural Language ... | {} | squeezebert/squeezebert-mnli-headless | null | [
"transformers",
"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11316",
"1904.00962"
] | [] | TAGS
#transformers #pytorch #squeezebert #arxiv-2006.11316 #arxiv-1904.00962 #endpoints_compatible #region-us
| language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, 'squeezebert-mnli-headless', has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the Multi-Genre Natural Language I... | [
"# SqueezeBERT pretrained model\n\nThis model, 'squeezebert-mnli-headless', has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the Multi-Genre Natural Language Inference (MNLI) dataset. This is a \"headless\" model with ... | [
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null | transformers | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-mnli`, has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the [Multi-Genre Natural Language Inference... | {} | squeezebert/squeezebert-mnli | null | [
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"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11316",
"1904.00962"
] | [] | TAGS
#transformers #pytorch #squeezebert #arxiv-2006.11316 #arxiv-1904.00962 #endpoints_compatible #region-us
| language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, 'squeezebert-mnli', has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the Multi-Genre Natural Language Inference ... | [
"# SqueezeBERT pretrained model\n\nThis model, 'squeezebert-mnli', has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the Multi-Genre Natural Language Inference (MNLI) dataset.\nSqueezeBERT was introduced in this paper. ... | [
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null | transformers | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arx... | {} | squeezebert/squeezebert-uncased | null | [
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"pytorch",
"squeezebert",
"arxiv:2006.11316",
"arxiv:1904.00962",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
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] | [] | TAGS
#transformers #pytorch #squeezebert #arxiv-2006.11316 #arxiv-1904.00962 #endpoints_compatible #has_space #region-us
| language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, 'squeezebert-uncased', is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in this paper. This model i... | [
"# SqueezeBERT pretrained model\n\nThis model, 'squeezebert-uncased', is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.\nSqueezeBERT was introduced in this paper. This model is case-insensitive. The model architecture is similar to B... | [
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fill-mask | transformers |
# BertHarmon
Research done at Johns Hopkins University by Michael DeLeo
Contact: mdeleo2@jh.edu
## Introduction
BertHarmon is a BERT model trained for the task of Chess.
## Sample Usage
```python
from transformers import pipeline
task = pipeline('fill-mask', ... | {"thumbnail": "https://en.memesrandom.com/wp-content/uploads/2020/11/juega-ajedrez.jpeg", "widget": [{"text": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1 White <MOVE_SEP> [MASK]"}, {"example_title": "Empty Board"}, {"text": "6Q1/5k2/3P4/1R3p2/P4P2/7Q/6RK/8 b - - 2 60 Black <MOVE_SEP> [MASK]"}, {"example_t... | squish/BertHarmon | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BertHarmon
Research done at Johns Hopkins University by Michael DeLeo
Contact: mdeleo2@URL
!iu-13
## Introduction
BertHarmon is a BERT model trained for the task of Chess.
!IMG_0145
## Sample Usage
The base string consists of the FEN_position followed by the player color and a move seperator. Finally wit... | [
"# BertHarmon\n\nResearch done at Johns Hopkins University by Michael DeLeo\n\nContact: mdeleo2@URL\n\n!iu-13",
"## Introduction\n\nBertHarmon is a BERT model trained for the task of Chess.\n\n!IMG_0145",
"## Sample Usage\n\n\n\nThe base string consists of the FEN_position followed by the player color and a mov... | [
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"## Introduction\n\nBertHarmon is a BERT model trained for the task of Chess.\n\n!... |
fill-mask | transformers |
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | sramasamy8/testModel | null | [
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"pytorch",
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"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT base model (uncased)
=========================
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team rel... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to... |
image-classification | transformers |
# pollution
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpic... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | sreeramajay/pollution | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# pollution
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### air pollution
!air pollution
#### land pollution
!land pollution
#### water pollution
!water pollution | [
"# pollution\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### air pollution\n\n!air pollution",
"#### land pollution\n\n!land pollution",
"#### water po... | [
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"# pollution\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any... |
text-generation | transformers |
# Joker DialoGPT Model | {"tags": ["conversational"]} | sreyanghosh/DialoGPT-medium-joker | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Joker DialoGPT Model | [
"# Joker DialoGPT Model"
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"# Joker DialoGPT Model"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | srirachasenpai/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | srosy/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1582
* Accuracy: 0.939
* F1: 0.9392
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | srosy/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0590
* Precision: 0.9266
* Recall: 0.9381
* F1: 0.9323
* Accuracy: 0.9844
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
text-generation | transformers |
# Breaking Bad DialoGPT Model | {"tags": ["conversational"]} | srv/DialoGPT-medium-Breaking_Bad | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Breaking Bad DialoGPT Model | [
"# Breaking Bad DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Breaking Bad DialoGPT Model"
] |
text-generation | transformers |
#Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | ssam/DialoGPT-small-RickmfSanchez | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick Sanchez DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | # finetune-wav2vec2-large-xlsr-bengali
***
## Usage
*** | {"language": "Bengali", "license": "apache-2.0", "tags": ["bn", "audio", "automatic-speech-recognition", "speech"], "datasets": ["custom"], "metrics": ["wer"], "model-index": [{"name": "finetune-wav2vec2-large-xlsr-bengali", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "d... | sshasnain/finetune-wav2vec2-large-xlsr-bengali | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bn",
"audio",
"speech",
"dataset:custom",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Bengali"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #bn #audio #speech #dataset-custom #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # finetune-wav2vec2-large-xlsr-bengali
*
## Usage
* | [
"# finetune-wav2vec2-large-xlsr-bengali\n*",
"## Usage\n*"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #bn #audio #speech #dataset-custom #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# finetune-wav2vec2-large-xlsr-bengali\n*",
"## Usage\n*"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-bangla-command-synthetic
This model is a fine-tuned version of [sshasnain/wav2vec2-xls-r-300m-bangla-command... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-300m-bangla-command-synthetic", "results": []}]} | sshasnain/wav2vec2-xls-r-300m-bangla-command-synthetic | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-bangla-command-synthetic
This model is a fine-tuned version of sshasnain/wav2vec2-xls-r-300m-bangla-command on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0254
- eval_wer: 0.4311
- eval_runtime: 2.5036
- eval_samples_per_second: 76.689
- eval_steps... | [
"# wav2vec2-xls-r-300m-bangla-command-synthetic\n\nThis model is a fine-tuned version of sshasnain/wav2vec2-xls-r-300m-bangla-command on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0254\n- eval_wer: 0.4311\n- eval_runtime: 2.5036\n- eval_samples_per_second: 76.689\n- ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-bangla-command-synthetic\n\nThis model is a fine-tuned version of sshasnain/wav2vec2-xls-r-300m-bangla-command on the None dat... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic", "results": []}]} | sshasnain/wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic
=============================================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0068
* Wer: 0.4111
Model descriptio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
automatic-speech-recognition | transformers | # wav2vec2-xls-r-300m-bangla-command
***
## Usage
Commands
'৫ টা কলম দেন'
'চেয়ারটা কোথায় রেখেছেন'
'ডানের বালতিটার প্রাইজ কেমন'
'দশ কেজি আলু কত'
'বাজুসের ল্যাপটপটা এসেছে'
'বাসার জন্য দরজা আছে'
'ম্যাম মোবাইলটা কি আছে'
'হ্যালো শ্যাম্পুর দাম বল' | {"language": "Bengali", "license": "apache-2.0", "tags": ["bn", "audio", "automatic-speech-recognition", "speech"], "datasets": ["custom"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xls-r-300m-bangla-command", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dat... | sshasnain/wav2vec2-xls-r-300m-bangla-command | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"bn",
"audio",
"speech",
"dataset:custom",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"Bengali"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #bn #audio #speech #dataset-custom #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # wav2vec2-xls-r-300m-bangla-command
*
## Usage
Commands
'৫ টা কলম দেন'
'চেয়ারটা কোথায় রেখেছেন'
'ডানের বালতিটার প্রাইজ কেমন'
'দশ কেজি আলু কত'
'বাজুসের ল্যাপটপটা এসেছে'
'বাসার জন্য দরজা আছে'
'ম্যাম মোবাইলটা কি আছে'
'হ্যালো শ্যাম্পুর দাম বল' | [
"# wav2vec2-xls-r-300m-bangla-command\n*",
"## Usage\nCommands\n'৫ টা কলম দেন' \n\n'চেয়ারটা কোথায় রেখেছেন' \n\n'ডানের বালতিটার প্রাইজ কেমন'\n\n'দশ কেজি আলু কত'\n\n'বাজুসের ল্যাপটপটা এসেছে' \n\n'বাসার জন্য দরজা আছে' \n\n'ম্যাম মোবাইলটা কি আছে' \n\n'হ্যালো শ্যাম্পুর দাম বল'"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #bn #audio #speech #dataset-custom #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-bangla-command\n*",
"## Usage\nCommands\n'৫ টা কলম দেন' \n\n'চেয়ারটা কোথায় রেখেছেন' \n\n'ডানের বালতিটার প্রাইজ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-timit-trainer
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xls-r-timit-trainer", "results": []}]} | sshasnain/wav2vec2-xls-r-timit-trainer | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-timit-trainer
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1064
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
translation | transformers |
# Blenderbot-3B
## Model description
+ [Paper](https://arxiv.org/abs/1907.06616).
+ [Original PARLAI Code]
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-r... | {"language": ["en"], "license": "apache-2.0", "tags": ["translation", "facebook", "convAI"], "datasets": ["blended_skill_talk"], "metrics": ["perplexity"]} | sshleifer/bb3b-tok | null | [
"transformers",
"blenderbot",
"text2text-generation",
"translation",
"facebook",
"convAI",
"en",
"dataset:blended_skill_talk",
"arxiv:1907.06616",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.06616"
] | [
"en"
] | TAGS
#transformers #blenderbot #text2text-generation #translation #facebook #convAI #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Blenderbot-3B
=============
Model description
-----------------
* Paper.
* [Original PARLAI Code]
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* wmt19-en-ru
* wmt19-ru-en
* wmt19-en-de
* wmt19-de-en
Intended uses & limitations
---------------------------
#### H... | [
"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, content gets truncated\n\n\nTraining data\n-------------\n\n\nPretrained weights were left identical to the original model released by fairseq. For more details, ... | [
"TAGS\n#transformers #blenderbot #text2text-generation #translation #facebook #convAI #en #dataset-blended_skill_talk #arxiv-1907.06616 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use",
"#### Limitations and bias\n\n\n* The original (and this ported model) doesn'... |
summarization | transformers |
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for DistilBART models
| Model Name | MM Params |... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | sshleifer/distilbart-cnn-12-3 | null | [
"transformers",
"pytorch",
"jax",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Metrics for DistilBART models
| [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Metrics for DistilBART models"
] | [
"TAGS\n#transformers #pytorch #jax #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BAR... |
summarization | transformers |
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for DistilBART models
| Model Name | MM Params |... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | sshleifer/distilbart-cnn-12-6 | null | [
"transformers",
"pytorch",
"jax",
"rust",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Metrics for DistilBART models
| [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Metrics for DistilBART models"
] | [
"TAGS\n#transformers #pytorch #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrai... |
summarization | transformers |
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for DistilBART models
| Model Name | MM Params |... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | sshleifer/distilbart-cnn-6-6 | null | [
"transformers",
"pytorch",
"jax",
"rust",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Metrics for DistilBART models
| [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Metrics for DistilBART models"
] | [
"TAGS\n#transformers #pytorch #jax #rust #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrai... |
summarization | transformers |
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transformers/model_doc/bart.html?#transformers.BartForConditionalGeneration) for more information.
### Metrics for DistilBART models
| Model Name | MM Params |... | {"language": "en", "license": "apache-2.0", "tags": ["summarization"], "datasets": ["cnn_dailymail", "xsum"], "thumbnail": "https://huggingface.co/front/thumbnails/distilbart_medium.png"} | sshleifer/distilbart-xsum-1-1 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Usage
This checkpoint should be loaded into 'BartForConditionalGeneration.from\_pretrained'. See the BART docs for more information.
### Metrics for DistilBART models
| [
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretrained'. See the BART docs for more information.",
"### Metrics for DistilBART models"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bart #text2text-generation #summarization #en #dataset-cnn_dailymail #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Usage\n\n\nThis checkpoint should be loaded into 'BartForConditionalGeneration.from\\_pretraine... |
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