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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers | ## daT5-large
A smaller version of [Google's mt5-large](https://huggingface.co/google/mt5-base) model, where the original model is reduced to only include Danish embeddings.
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emillykkejensen/daT5-large")... | {"language": ["da"], "license": "apache-2.0"} | emillykkejensen/daT5-large | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #da #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## daT5-large
A smaller version of Google's mt5-large model, where the original model is reduced to only include Danish embeddings.
## How to use
## Further reading
Gist showing (in Danish) how the embeddings are extracted (for mt5-base)
Article explaining how to do it by David Dale
## Also check out
daT5-base | [
"## daT5-large\nA smaller version of Google's mt5-large model, where the original model is reduced to only include Danish embeddings.",
"## How to use",
"## Further reading\n\nGist showing (in Danish) how the embeddings are extracted (for mt5-base)\n\nArticle explaining how to do it by David Dale",
"## Also c... | [
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"## daT5-large\nA smaller version of Google's mt5-large model, where the original model is reduced to only include Danish embeddings.",
"## How ... |
fill-mask | transformers |
# ClinicalBERT - Bio + Clinical BERT Model
The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC no... | {"language": "en", "license": "mit", "tags": ["fill-mask"]} | emilyalsentzer/Bio_ClinicalBERT | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.03323",
"1901.08746"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #en #arxiv-1904.03323 #arxiv-1901.08746 #license-mit #endpoints_compatible #has_space #region-us
|
# ClinicalBERT - Bio + Clinical BERT Model
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base ('cased_L-12_H-768_A-12') or BioBERT ('BioBERT-Base v1.0 + PubMed 200K + PMC 270K') & trained on either all MIMIC notes or only discharge summaries.
T... | [
"# ClinicalBERT - Bio + Clinical BERT Model\n\nThe Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base ('cased_L-12_H-768_A-12') or BioBERT ('BioBERT-Base v1.0 + PubMed 200K + PMC 270K') & trained on either all MIMIC notes or only discharge summarie... | [
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"# ClinicalBERT - Bio + Clinical BERT Model\n\nThe Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized w... |
fill-mask | transformers |
# ClinicalBERT - Bio + Discharge Summary BERT Model
The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either al... | {"language": "en", "license": "mit", "tags": ["fill-mask"]} | emilyalsentzer/Bio_Discharge_Summary_BERT | null | [
"transformers",
"pytorch",
"jax",
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"en",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.03323",
"1901.08746"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #en #arxiv-1904.03323 #arxiv-1901.08746 #license-mit #endpoints_compatible #has_space #region-us
|
# ClinicalBERT - Bio + Discharge Summary BERT Model
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base ('cased_L-12_H-768_A-12') or BioBERT ('BioBERT-Base v1.0 + PubMed 200K + PMC 270K') & trained on either all MIMIC notes or only discharge summ... | [
"# ClinicalBERT - Bio + Discharge Summary BERT Model\n\nThe Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base ('cased_L-12_H-768_A-12') or BioBERT ('BioBERT-Base v1.0 + PubMed 200K + PMC 270K') & trained on either all MIMIC notes or only discharge... | [
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"# ClinicalBERT - Bio + Discharge Summary BERT Model\n\nThe Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initiali... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `eml914/streaming_transformer_asr_librispeech`
This model was trained by Emiru Tsunoo using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 12eb132418a1f69548f7998e53273cd05d989ed9
pip install -e .
cd egs2/l... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | eml914/streaming_transformer_asr_librispeech | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'eml914/streaming\_transformer\_asr\_librispeech'
This model was trained by Emiru Tsunoo using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Nov 17 18:18:46 JST 2021'
* python version: '3.8.11 (def... | [
"### 'eml914/streaming\\_transformer\\_asr\\_librispeech'\n\n\nThis model was trained by Emiru Tsunoo using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Nov 17 18:18:46 JST 2021'\n* python version: '3.8.11 (default, Aug 3 ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'eml914/streaming\\_transformer\\_asr\\_librispeech'\n\n\nThis model was trained by Emiru Tsunoo using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESU... |
summarization | transformers |
# arxiv27k-t5-abst-title-gen/
This model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6002
- Rouge1: 32.8
- Rouge2: 21.9
- Rougel: 34.8
-
## Model description
Model has been trained with a colab-pro notebook in 4 hours.... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "arxiv27k-t5-abst-title-gen/", "results": []}]} | emre/arxiv27k-t5-abst-title-gen | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# arxiv27k-t5-abst-title-gen/
This model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6002
- Rouge1: 32.8
- Rouge2: 21.9
- Rougel: 34.8
-
## Model description
Model has been trained with a colab-pro notebook in 4 hours.... | [
"# arxiv27k-t5-abst-title-gen/\n\nThis model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.6002\n- Rouge1: 32.8\n- Rouge2: 21.9\n- Rougel: 34.8\n-",
"## Model description\n\nModel has been trained with a colab-pro not... | [
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"# arxiv27k-t5-abst-title-gen/\n\nThis model is a fine-tuned version of mt5-small on the arxiv-abs... |
question-answering | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | emre/distilbert-base-uncased-finetuned-squad | null | [
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"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1620
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
question-answering | transformers | # Turkish SQuAD Model : Question Answering
Fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset
* Loodos-BERT-base: https://huggingface.co/loodos/bert-base-turkish-uncased
* TQuAD dataset: https://github.com/TQuad/turkish-nlp-qa-dataset
# Training Code
```
!python3 Turkish-QA.py ... | {"language": "tr", "tags": ["question-answering", "loodos-bert-base", "TQuAD", "tr"], "datasets": ["TQuAD"]} | emre/distilbert-tr-q-a | null | [
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"bert",
"question-answering",
"loodos-bert-base",
"TQuAD",
"tr",
"dataset:TQuAD",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #question-answering #loodos-bert-base #TQuAD #tr #dataset-TQuAD #endpoints_compatible #has_space #region-us
| # Turkish SQuAD Model : Question Answering
Fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset
* Loodos-BERT-base: URL
* TQuAD dataset: URL
# Training Code
# Example Usage
> Load Model
> Apply the model
| [
"# Turkish SQuAD Model : Question Answering\n\nFine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset\n* Loodos-BERT-base: URL\n* TQuAD dataset: URL",
"# Training Code",
"# Example Usage\n\n> Load Model\n\n\n> Apply the model"
] | [
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"# Turkish SQuAD Model : Question Answering\n\nFine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset\n* Loodos-BERT-base: URL\n* TQ... |
null | transformers |
# jurisprudence-textgen-gpt-2
Pretrained model on Turkish language using a causal language modeling (CLM) objective.
## Model description of Original GPT-2
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only... | {"language": "tr", "license": "mit"} | emre/jurisprudence-textgen-gpt-2 | null | [
"transformers",
"tf",
"gpt2",
"tr",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #tf #gpt2 #tr #license-mit #endpoints_compatible #text-generation-inference #region-us
|
# jurisprudence-textgen-gpt-2
Pretrained model on Turkish language using a causal language modeling (CLM) objective.
## Model description of Original GPT-2
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only... | [
"# jurisprudence-textgen-gpt-2\n\nPretrained model on Turkish language using a causal language modeling (CLM) objective.",
"## Model description of Original GPT-2\nGPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw ... | [
"TAGS\n#transformers #tf #gpt2 #tr #license-mit #endpoints_compatible #text-generation-inference #region-us \n",
"# jurisprudence-textgen-gpt-2\n\nPretrained model on Turkish language using a causal language modeling (CLM) objective.",
"## Model description of Original GPT-2\nGPT-2 is a transformers model pretr... |
automatic-speech-recognition | transformers |
# wav2vec-tr-lite-AG
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor ... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]} | emre/wav2vec-tr-lite-AG | null | [
"transformers",
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"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec-tr-lite-AG
==================
Usage
-----
The model can be used directly (without a language model) as follows:
'''python
import torch
import torchaudio
from datasets import load\_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test\_dataset = load\_dataset("common\_voice", "tr", spli... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00005\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00005\n* train\\_batch... |
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-large-xls-r-300m-tr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": "tr", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": ... | emre/wav2vec2-large-xls-r-300m-tr | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"tr",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-300m",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_com... | wav2vec2-large-xls-r-300m-tr
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2224
* Wer: 0.2869
Model description
-----------------
More ... | [
"### 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_8_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoin... |
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-large-xlsr-53-W2V2-TATAR-SMALL
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingf... | {"language": "tt", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "tt"], "datasets": ["common_voice"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "wav2vec2-large-xlsr-53-W2V2-TATAR-SM... | emre/wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
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"tt",
"dataset:common_voice",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"e... | null | 2022-03-02T23:29:05+00:00 | [] | [
"tt"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tt #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL
=======================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4714
* Wer: 0.5316
Model description
-----------------
More infor... | [
"### 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 #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tt #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Traini... |
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-large-xlsr-53-W2V2-TR-MED
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-W2V2-TR-MED", "results": []}]} | emre/wav2vec2-large-xlsr-53-W2V2-TR-MED | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-W2V2-TR-MED
==================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4467
* Wer: 0.4598
Model description
-----------------
More information nee... | [
"### 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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
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-large-xlsr-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-demo-colab", "results": []}]} | emre/wav2vec2-large-xlsr-53-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-demo-colab
=================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3966
* Wer: 0.4834
Model description
-----------------
More information neede... | [
"### 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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
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-large-xlsr-53-sah-CV8
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fa... | {"language": "sah", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-sah-CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {... | emre/wav2vec2-large-xlsr-53-sah-CV8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"sah",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sah"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-sah-CV8
==============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5089
* Wer: 0.5606
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #robust-speech-event #hf-asr-leaderboard #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-Br-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0573
- Wer: 0.6675
## Model description
More information needed
#... | {"language": "br", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Br-small", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"na... | emre/wav2vec2-xls-r-300m-Br-small | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"br"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-Br-small
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0573
* Wer: 0.6675
Model description
-----------------
More information needed
Intended ... | [
"### 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 #robust-speech-event #hf-asr-leaderboard #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were... |
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-Russian-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"language": ["ru"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Russian-small", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset... | emre/wav2vec2-xls-r-300m-Russian-small | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ru",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-Russian-small
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3514
* Wer: 0.4838
Model description
-----------------
More information needed
... | [
"### 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 #hf-asr-leaderboard #robust-speech-event #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were... |
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-Tr-med-CommonVoice8-Tr-med-CommonVoice8
This model is a fine-tuned version of [emre/wav2vec2-xls-r-300m-Tr-m... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8", "results": []}]} | emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"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 #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8
===========================================================
This model is a fine-tuned version of emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2708
* Wer: 0.50... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #dataset-common_voice #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* t... |
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-Tr-med-CommonVoice8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"language": "tr", "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Tr-med-CommonVoice8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dat... | emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-Tr-med-CommonVoice8
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2556
* Wer: 0.4914
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #hf-asr-leaderboard #robust-speech-event #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were... |
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-Turkish-Tr-med
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Turkish-Tr-med", "results": []}]} | emre/wav2vec2-xls-r-300m-Turkish-Tr-med | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-Turkish-Tr-med
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4727
* Wer: 0.4677
Model description
-----------------
More information needed... | [
"### 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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
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-Turkish-Tr-small-CommonVoice8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8", "results": []}]} | emre/wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8
=================================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4813
* Wer: 0.7207
Model description
-------------... | [
"### 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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
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-Turkish-Tr-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Turkish-Tr-small", "results": []}]} | emre/wav2vec2-xls-r-300m-Turkish-Tr-small | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-Turkish-Tr-small
====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4375
* Wer: 0.5050
Model description
-----------------
More information ne... | [
"### 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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
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-W2V2-XLSR-300M-YAKUT-SMALL
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hugg... | {"language": "sah", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition... | emre/wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"sah",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sah"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL
==============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9068
* Wer: 0.7900
Model description
-----------------
... | [
"### 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 #robust-speech-event #hf-asr-leaderboard #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-ab-CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2105
- Wer: 0.5474
## Model description
More information needed
## ... | {"language": "ab", "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-ab-CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "datase... | emre/wav2vec2-xls-r-300m-ab-CV8 | null | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
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"robust-speech-event",
"ab",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ab"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ab #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-ab-CV8
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2105
* Wer: 0.5474
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #hf-asr-leaderboard #robust-speech-event #ab #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were... |
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-as-CV8-v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebo... | {"language": "as", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-as-CV8-v1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dat... | emre/wav2vec2-xls-r-300m-as-CV8-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"as",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"as"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #as #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-as-CV8-v1
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# wav2vec2-xls-r-300m-as-CV8-v1\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #as #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-xls-r-300m-as-CV8-v1\n\nThis model is a fine-tuned versio... |
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-bas-CV8-v2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceb... | {"language": "bas", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "bas", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-bas-CV8-v2", "results": [{"task": {"type... | emre/wav2vec2-xls-r-300m-bas-CV8-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
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"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"bas"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #bas #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-bas-CV8-v2
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6121
* Wer: 0.5697
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #common_voice #generated_from_trainer #bas #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\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-gl-CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/... | {"language": "gl", "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-gl-CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name... | emre/wav2vec2-xls-r-300m-gl-CV8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"gl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gl"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #gl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-gl-CV8
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2151
* Wer: 0.2080
---
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #hf-asr-leaderboard #robust-speech-event #gl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were... |
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-hy-AM-CV8-v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-hy-AM-CV8-v1", "results": []}]} | emre/wav2vec2-xls-r-300m-hy-AM-CV8-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-hy-AM-CV8-v1
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9145
* Wer: 0.9598
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learnin... |
zero-shot-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-base-multilingual-cased_allnli_tr
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.... | {"language": ["tr"], "license": "mit", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok... | emrecan/bert-base-multilingual-cased-allnli_tr | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-multilingual-cased\_allnli\_tr
========================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6144
* Accuracy: 0.7662
Model description
-----------------
More informatio... | [
"### 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 #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
zero-shot-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-base-turkish-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/d... | {"language": ["tr"], "license": "mit", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok... | emrecan/bert-base-turkish-cased-allnli_tr | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #bert #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| bert-base-turkish-cased\_allnli\_tr
===================================
This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5771
* Accuracy: 0.7978
Model description
-----------------
More information needed
... | [
"### 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 #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\... |
sentence-similarity | sentence-transformers |
# emrecan/bert-base-turkish-cased-mean-nli-stsb-tr
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine translated versions of [NLI](... | {"language": ["tr"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["nli_tr", "emrecan/stsb-mt-turkish"], "pipeline_tag": "sentence-similarity", "widget": {"source_sentence": "Bu \u00e7ok mutlu bir ki\u015fi", "sentences": ["Bu mutlu... | emrecan/bert-base-turkish-cased-mean-nli-stsb-tr | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"tr",
"dataset:nli_tr",
"dataset:emrecan/stsb-mt-turkish",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #tr #dataset-nli_tr #dataset-emrecan/stsb-mt-turkish #license-apache-2.0 #endpoints_compatible #has_space #region-us
| emrecan/bert-base-turkish-cased-mean-nli-stsb-tr
================================================
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine transla... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #tr #dataset-nli_tr #dataset-emrecan/stsb-mt-turkish #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
zero-shot-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. -->
# convbert-base-turkish-mc4-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/convbert-base-turkish-mc4-cased](https://... | {"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \... | emrecan/convbert-base-turkish-mc4-cased-allnli_tr | null | [
"transformers",
"pytorch",
"convbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #convbert #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| convbert-base-turkish-mc4-cased\_allnli\_tr
===========================================
This model is a fine-tuned version of dbmdz/convbert-base-turkish-mc4-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5541
* Accuracy: 0.8111
Model description
-----------------
... | [
"### 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 #convbert #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
zero-shot-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-turkish-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://hugg... | {"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \... | emrecan/distilbert-base-turkish-cased-allnli_tr | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #distilbert #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| distilbert-base-turkish-cased\_allnli\_tr
=========================================
This model is a fine-tuned version of dbmdz/distilbert-base-turkish-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6481
* Accuracy: 0.7381
Model description
-----------------
More i... | [
"### 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 #distilbert #text-classification #zero-shot-classification #nli #tr #dataset-nli_tr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
question-answering | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | en/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1453
Model description
-----------------
More information needed
Intended uses ... | [
"### 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 #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_s... |
feature-extraction | transformers |
# Model description
The model was created for selective question answering in Polish. I.e. it is used to find passages containing the answers to the given question.
It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture. The architecture requires two separate models.
The question part ha... | {"language": "pl", "datasets": ["enelpol/czywiesz"]} | enelpol/czywiesz-context | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"pl",
"dataset:enelpol/czywiesz",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #bert #feature-extraction #pl #dataset-enelpol/czywiesz #endpoints_compatible #region-us
|
# Model description
The model was created for selective question answering in Polish. I.e. it is used to find passages containing the answers to the given question.
It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture. The architecture requires two separate models.
The question part ha... | [
"# Model description\n\nThe model was created for selective question answering in Polish. I.e. it is used to find passages containing the answers to the given question.\n\nIt is used to encode the contexts (aka passages) in the DPR bi-encoder architecture. The architecture requires two separate models.\nThe questio... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #pl #dataset-enelpol/czywiesz #endpoints_compatible #region-us \n",
"# Model description\n\nThe model was created for selective question answering in Polish. I.e. it is used to find passages containing the answers to the given question.\n\nIt is used to enco... |
feature-extraction | transformers |
## Model description
This is the question encoder for the Polish DPR question answering model. The full model consists of two encoders.
Please read [context encoder documentation](https://huggingface.co/enelpol/czywiesz-context) to get the details of the model. | {"language": "pl", "datasets": ["enelpol/czywiesz"], "task_categories": ["question_answering"], "task_ids": ["open-domain-qa"], "multilinguality": ["monolingual"], "size_categories": ["1k<n<10K"]} | enelpol/czywiesz-question | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"pl",
"dataset:enelpol/czywiesz",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #bert #feature-extraction #pl #dataset-enelpol/czywiesz #endpoints_compatible #region-us
|
## Model description
This is the question encoder for the Polish DPR question answering model. The full model consists of two encoders.
Please read context encoder documentation to get the details of the model. | [
"## Model description\n\nThis is the question encoder for the Polish DPR question answering model. The full model consists of two encoders.\nPlease read context encoder documentation to get the details of the model."
] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #pl #dataset-enelpol/czywiesz #endpoints_compatible #region-us \n",
"## Model description\n\nThis is the question encoder for the Polish DPR question answering model. The full model consists of two encoders.\nPlease read context encoder documentation to get ... |
text2text-generation | transformers | Trained with prefix `ocr: `. | {} | enelpol/poleval2021-task3 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Trained with prefix 'ocr: '. | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | This is fine-tuned model on Bhagvad Gita and creates text based on prompts.
Example of usage:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("epsil/bhagvad_gita")
model = AutoModelForCausalLM.from_pretrained("epsil/bhagvad_gita")
```
Input
```
from transfo... | {} | epsil/bhagvad_gita | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is fine-tuned model on Bhagvad Gita and creates text based on prompts.
Example of usage:
Input
Output
> Created by Saurabh Mishra
> Made with <span style="color: #e25555;">♥</span> in India
| [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
# Persian-t5-formality-transfer
This is a formality style transfer model for the Persian language to convert colloquial text into a formal one. It is based on [the monolingual T5 model for Persian.](https://huggingface.co/Ahmad/parsT5-base) and [Persian T5 paraphraser](https://huggingface.co/erfan226/persian-t5-parap... | {"language": "fa", "tags": ["Style transfer", "Formality style transfer"], "widget": [{"text": "\u0645\u0646 \u0628\u0627 \u062f\u0648\u0633\u062a\u0627\u0645 \u0645\u06cc\u0631\u0645 \u0628\u0627\u0632\u06cc."}, {"text": "\u0645\u0646 \u0628\u0647 \u062e\u0648\u0646\u0647 \u062f\u0648\u0633\u062a\u0645 \u0631\u0641\u0... | erfan226/persian-t5-formality-transfer | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Style transfer",
"Formality style transfer",
"fa",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #Style transfer #Formality style transfer #fa #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Persian-t5-formality-transfer
This is a formality style transfer model for the Persian language to convert colloquial text into a formal one. It is based on the monolingual T5 model for Persian. and Persian T5 paraphraser
Note: This model is still in development and therefore its outputs might not be very good. Ho... | [
"# Persian-t5-formality-transfer\n\nThis is a formality style transfer model for the Persian language to convert colloquial text into a formal one. It is based on the monolingual T5 model for Persian. and Persian T5 paraphraser\n\nNote: This model is still in development and therefore its outputs might not be very ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #Style transfer #Formality style transfer #fa #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Persian-t5-formality-transfer\n\nThis is a formality style transfer model for the Persian language to convert c... |
text2text-generation | transformers |
# Persian-t5-paraphraser
This is a paraphrasing model for the Persian language. It is based on [the monolingual T5 model for Persian.](https://huggingface.co/Ahmad/parsT5-base)
## Usage
```python
>>> pip install transformers
>>> from transformers import (T5ForConditionalGeneration, AutoTokenizer, pipeline)
>>> imp... | {"language": "fa", "tags": ["paraphrasing"], "datasets": ["tapaco"], "widget": [{"text": "\u0627\u06cc\u0646 \u06cc\u06a9 \u0645\u0642\u0627\u0644\u0647\u0654 \u062e\u0631\u062f \u0622\u0644\u0645\u0627\u0646 \u0627\u0633\u062a. \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0627 \u06af\u0633\u062a\u063... | erfan226/persian-t5-paraphraser | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"paraphrasing",
"fa",
"dataset:tapaco",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #paraphrasing #fa #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Persian-t5-paraphraser
This is a paraphrasing model for the Persian language. It is based on the monolingual T5 model for Persian.
## Usage
## Training data
This model was trained on the Persian subset of the Tapaco dataset. It should be noted that this model was trained on a very small dataset and therefore th... | [
"# Persian-t5-paraphraser\n\nThis is a paraphrasing model for the Persian language. It is based on the monolingual T5 model for Persian.",
"## Usage",
"## Training data\nThis model was trained on the Persian subset of the Tapaco dataset. It should be noted that this model was trained on a very small dataset and... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #paraphrasing #fa #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Persian-t5-paraphraser\n\nThis is a paraphrasing model for the Persian language. It is based on the monolingual T5 model for Persian."... |
question-answering | 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-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | ericRosello/bert-base-uncased-finetuned-squad-frozen-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0178
Model description
-----------------
Base model weights were frozen leaving only to finetune th... | [
"### 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 #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_size: 1... |
question-answering | 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-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-uncased-finetuned-squad", "results": []}]} | ericRosello/bert-base-uncased-finetuned-squad-frozen-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4571
Model description
-----------------
Most base model weights were frozen leaving only to finetu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_size: 2... |
question-answering | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | ericRosello/distilbert-base-uncased-finetuned-squad-frozen-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 4.3629
Model description
-----------------
Base model weights were frozen leaving o... | [
"### 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 #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_s... |
question-answering | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | ericRosello/distilbert-base-uncased-finetuned-squad-frozen-v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2104
Model description
-----------------
Most base model weights were frozen leav... | [
"### 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 #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_s... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | ericklasco/DialoGPT-small-erickHarryPotter | 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"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# Rick | {"tags": ["conversational"]} | ericzhou/DialoGPT-Medium-Rick | 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 | [
"# Rick"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick"
] |
text-generation | transformers |
# rick | {"tags": ["conversational"]} | ericzhou/DialoGPT-Medium-Rick_v2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# rick | [
"# rick"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# rick"
] |
text-generation | transformers |
# elon | {"tags": ["conversational"]} | ericzhou/DialoGPT-medium-elon | 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
|
# elon | [
"# elon"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# elon"
] |
text-generation | transformers | # GPT2 Keyword Based Lecture Generator
## Model description
GPT2 fine-tuned on the TED Talks Dataset (published under the Creative Commons BY-NC-ND license).
## Intended uses
Used to generate spoken-word lectures.
### How to use
Input text:
<BOS> title <|SEP|> Some keywords <|SEP|>
Keyword Format: "M... | {} | erikinfo/gpt2TEDlectures | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT2 Keyword Based Lecture Generator
## Model description
GPT2 fine-tuned on the TED Talks Dataset (published under the Creative Commons BY-NC-ND license).
## Intended uses
Used to generate spoken-word lectures.
### How to use
Input text:
<BOS> title <|SEP|> Some keywords <|SEP|>
Keyword Format: "M... | [
"# GPT2 Keyword Based Lecture Generator",
"## Model description\n\nGPT2 fine-tuned on the TED Talks Dataset (published under the Creative Commons BY-NC-ND license).",
"## Intended uses\n\nUsed to generate spoken-word lectures.",
"### How to use\n\nInput text: \n\n <BOS> title <|SEP|> Some keywords <|... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2 Keyword Based Lecture Generator",
"## Model description\n\nGPT2 fine-tuned on the TED Talks Dataset (published under the Creative Commons BY-NC-ND license).",
... |
text-classification | transformers | # Classifying Text into DB07 Codes
This model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) fine-tuned to classify Danish descriptions of activities into [Dansk Branchekode DB07](https://www.dst.dk/en/Statistik/dokumentation/nomenklaturer/dansk-branchekode-db07) codes.
## Data
Approximately 2.5 mill... | {} | erst/xlm-roberta-base-finetuned-db07 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Classifying Text into DB07 Codes
This model is xlm-roberta-base fine-tuned to classify Danish descriptions of activities into Dansk Branchekode DB07 codes.
## Data
Approximately 2.5 million business names and descriptions of activities from Norwegian and Danish businesses were used to fine-tune the model. The Norw... | [
"# Classifying Text into DB07 Codes\n\nThis model is xlm-roberta-base fine-tuned to classify Danish descriptions of activities into Dansk Branchekode DB07 codes.",
"## Data\nApproximately 2.5 million business names and descriptions of activities from Norwegian and Danish businesses were used to fine-tune the mode... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Classifying Text into DB07 Codes\n\nThis model is xlm-roberta-base fine-tuned to classify Danish descriptions of activities into Dansk Branchekode DB07 codes.",
"## Data\nApproximately ... |
text-classification | transformers | # Classifying Text into NACE Codes
This model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) fine-tuned to classify descriptions of activities into [NACE Rev. 2](https://ec.europa.eu/eurostat/web/nace-rev2) codes.
## Data
The data used to fine-tune the model consist of 2.5 million descriptions of act... | {} | erst/xlm-roberta-base-finetuned-nace | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Classifying Text into NACE Codes
This model is xlm-roberta-base fine-tuned to classify descriptions of activities into NACE Rev. 2 codes.
## Data
The data used to fine-tune the model consist of 2.5 million descriptions of activities from Norwegian and Danish businesses. To improve the model's multilingual performa... | [
"# Classifying Text into NACE Codes\n\nThis model is xlm-roberta-base fine-tuned to classify descriptions of activities into NACE Rev. 2 codes.",
"## Data\nThe data used to fine-tune the model consist of 2.5 million descriptions of activities from Norwegian and Danish businesses. To improve the model's multilingu... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Classifying Text into NACE Codes\n\nThis model is xlm-roberta-base fine-tuned to classify descriptions of activities into NACE Rev. 2 codes.",
"## Data\nThe data used to fine-tune the m... |
text2text-generation | 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. -->
# t5-cocktails_recipe-base
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "t5-base", "model-index": [{"name": "t5-cocktails_recipe-base", "results": []}]} | erwanlc/t5-cocktails_recipe-base | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# t5-cocktails_recipe-base
This model is a fine-tuned version of t5-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The ... | [
"# t5-cocktails_recipe-base\n\nThis model is a fine-tuned version of t5-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### ... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# t5-cocktails_recipe-base\n\nThis model is a fine-tuned version of t5-base on an ... |
text2text-generation | 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. -->
# t5-cocktails_recipe-small
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "t5-base", "model-index": [{"name": "t5-cocktails_recipe-small", "results": []}]} | erwanlc/t5-cocktails_recipe-small | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-cocktails_recipe-small
This model is a fine-tuned version of t5-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The... | [
"# t5-cocktails_recipe-small\n\nThis model is a fine-tuned version of t5-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-cocktails_recipe-small\n\nThis model is a fine-tuned version of t5-base on ... |
text2text-generation | 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. -->
# t5-coktails_recipe-base
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/t5-v1_1-base", "model-index": [{"name": "t5-coktails_recipe-base", "results": []}]} | erwanlc/t5-coktails_recipe-base | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/t5-v1_1-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-coktails_recipe-base
This model is a fine-tuned version of google/t5-v1_1-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# t5-coktails_recipe-base\n\nThis model is a fine-tuned version of google/t5-v1_1-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-coktails_recipe-base\n\nThis model is a fine-tuned version of google/t5-v1_1... |
text2text-generation | 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. -->
# t5-coktails_recipe-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-coktails_recipe-small", "results": []}]} | erwanlc/t5-coktails_recipe-small | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-coktails_recipe-small
This model is a fine-tuned version of t5-small on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The... | [
"# t5-coktails_recipe-small\n\nThis model is a fine-tuned version of t5-small on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-coktails_recipe-small\n\nThis model is a fine-tuned version of t5-small on an unknown dataset.",
"## Model... |
image-classification | fastai |
## Pet breeds classification model
Finetuned model on The Oxford-IIIT Pet Dataset. It was introduced in
[this paper](https://www.robots.ox.ac.uk/~vgg/publications/2012/parkhi12a/) and first released in
[this webpage](https://www.robots.ox.ac.uk/~vgg/data/pets/).
The pretrained model was trained on the ImageNet datas... | {"library_name": "fastai", "tags": ["image-classification", "fastai"], "datasets": ["Oxford-IIIT Pet Dataset", "ImageNet"]} | espejelomar/fastai-pet-breeds-classification | null | [
"fastai",
"image-classification",
"arxiv:1512.03385",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385"
] | [] | TAGS
#fastai #image-classification #arxiv-1512.03385 #has_space #region-us
|
## Pet breeds classification model
Finetuned model on The Oxford-IIIT Pet Dataset. It was introduced in
this paper and first released in
this webpage.
The pretrained model was trained on the ImageNet dataset, a dataset that has 100,000+ images across 200 different classes. It was introduced in this paper and availab... | [
"## Pet breeds classification model\n\nFinetuned model on The Oxford-IIIT Pet Dataset. It was introduced in\nthis paper and first released in\nthis webpage.\n\nThe pretrained model was trained on the ImageNet dataset, a dataset that has 100,000+ images across 200 different classes. It was introduced in this paper a... | [
"TAGS\n#fastai #image-classification #arxiv-1512.03385 #has_space #region-us \n",
"## Pet breeds classification model\n\nFinetuned model on The Oxford-IIIT Pet Dataset. It was introduced in\nthis paper and first released in\nthis webpage.\n\nThe pretrained model was trained on the ImageNet dataset, a dataset that... |
audio-to-audio | espnet | ## Example ESPnet2 ENH model
### `Chenda_Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave`
♻️ Imported from https://zenodo.org/record/4498562/
This model was trained by Chenda Li using wsj0_2mix/enh1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming s... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-enhancement", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | espnet/Chenda_Li_wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"speech-enhancement",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ENH model
### 'Chenda_Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave'
️ Imported from URL
This model was trained by Chenda Li using wsj0_2mix/enh1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ENH model",
"### 'Chenda_Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by Chenda Li using wsj0_2mix/enh1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ENH model",
"### 'Chenda_Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by Chenda Li using wsj0_2... |
audio-to-audio | espnet | ## Example ESPnet2 ENH model
### `Chenda_Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave`
♻️ Imported from https://zenodo.org/record/4498554/
This model was trained by Chenda Li using wsj0_2mix/enh1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
`... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-enhancement", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | espnet/Chenda_Li_wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"speech-enhancement",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ENH model
### 'Chenda_Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave'
️ Imported from URL
This model was trained by Chenda Li using wsj0_2mix/enh1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ENH model",
"### 'Chenda_Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by Chenda Li using wsj0_2mix/enh1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ENH model",
"### 'Chenda_Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by Chenda Li using wsj0_2mix/e... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR model
### `Dan_Berrebbi_aishell4_asr`
This model was trained by dan_berrebbi using aishell4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout da1a26652f7d5a019cc24ad1e0e6e844f2b57e1b
pip install -e .
cd egs2/aishell4/asr1
./run.sh --ski... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell4"]} | espnet/Dan_Berrebbi_aishell4_asr | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell4",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell4 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'Dan\_Berrebbi\_aishell4\_asr'
This model was trained by dan\_berrebbi using aishell4 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Sep 21 09:36:01 EDT 2021'
* python version: '3.7.11 (default, Jul 27 2021, 14... | [
"### 'Dan\\_Berrebbi\\_aishell4\\_asr'\n\n\nThis model was trained by dan\\_berrebbi using aishell4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Sep 21 09:36:01 EDT 2021'\n* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GC... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell4 #license-cc-by-4.0 #region-us \n",
"### 'Dan\\_Berrebbi\\_aishell4\\_asr'\n\n\nThis model was trained by dan\\_berrebbi using aishell4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n---------... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `Emiru_Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4604023/
This model was trained by Emiru Tsunoo using aishell/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell"]} | espnet/Emiru_Tsunoo_aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'Emiru_Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.URL'
️ Imported from URL
This model was trained by Emiru Tsunoo using aishell/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Emiru_Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Emiru Tsunoo using aishell/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Emiru_Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Emiru Tsunoo us... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `Hoon_Chung/jsut_asr_train_asr_conformer8_raw_char_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4292742/
This model was trained by Hoon Chung using jsut/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
... | {"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["jsut"]} | espnet/Hoon_Chung_jsut_asr_train_asr_conformer8_raw_char_sp_valid.acc.ave | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"ja",
"dataset:jsut",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"ja"
] | TAGS
#espnet #audio #automatic-speech-recognition #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'Hoon_Chung/jsut_asr_train_asr_conformer8_raw_char_sp_valid.URL'
️ Imported from URL
This model was trained by Hoon Chung using jsut/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Hoon_Chung/jsut_asr_train_asr_conformer8_raw_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Hoon Chung using jsut/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #ja #dataset-jsut #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Hoon_Chung/jsut_asr_train_asr_conformer8_raw_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Hoon Chung using jsut/asr1 recipe in ... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `Hoon_Chung/zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4014588/
This model was trained by Hoon Chung using zeroth_korean/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```pytho... | {"language": "kr", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["zeroth_korean"]} | espnet/Hoon_Chung_zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.acc.ave | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"kr",
"dataset:zeroth_korean",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"kr"
] | TAGS
#espnet #audio #automatic-speech-recognition #kr #dataset-zeroth_korean #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'Hoon_Chung/zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.URL'
️ Imported from URL
This model was trained by Hoon Chung using zeroth_korean/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Hoon_Chung/zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.URL'\n️ Imported from URL\n\nThis model was trained by Hoon Chung using zeroth_korean/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #kr #dataset-zeroth_korean #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Hoon_Chung/zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.URL'\n️ Imported from URL\n\nThis model was trained by Hoon Chung using zero... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_Hubert_transformer`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/)
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espne... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["sinhala"]} | espnet/Karthik_DSTC2_asr_train_asr_Hubert_transformer | null | [
"espnet",
"tensorboard",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'espnet/Karthik_DSTC2_asr_train_asr_Hubert_transformer'
This model was trained by Karthik using DSTC2/asr1 recipe in espnet
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_Hubert_transformer'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_Hubert_transformer'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet",
"#... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_transformer`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
au... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["sinhala"]} | espnet/Karthik_DSTC2_asr_train_asr_transformer | null | [
"espnet",
"tensorboard",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'espnet/Karthik_DSTC2_asr_train_asr_transformer'
This model was trained by Karthik using DSTC2/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_transformer'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #tensorboard #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_DSTC2_asr_train_asr_transformer'\n\nThis model was trained by Karthik using DSTC2/asr1 recipe in espnet.",
"### Dem... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `espnet/Karthik_sinhala_asr_train_asr_transformer`
This model was trained by Karthik using sinhala/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["sinhala"]} | espnet/Karthik_sinhala_asr_train_asr_transformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:sinhala",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'espnet/Karthik_sinhala_asr_train_asr_transformer'
This model was trained by Karthik using sinhala/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_sinhala_asr_train_asr_transformer'\n\nThis model was trained by Karthik using sinhala/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-sinhala #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'espnet/Karthik_sinhala_asr_train_asr_transformer'\n\nThis model was trained by Karthik using sinhala/asr1 recipe in espnet.",
"### Demo: How to... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `Shinji_Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4304245/
This model was trained by Shinji Watanabe using laborotv/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPne... | {"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["laborotv"]} | espnet/Shinji_Watanabe_laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"ja",
"dataset:laborotv",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"ja"
] | TAGS
#espnet #audio #automatic-speech-recognition #ja #dataset-laborotv #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'Shinji_Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using laborotv/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using laborotv/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #ja #dataset-laborotv #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `Shinji_Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best`
♻️ Imported from https://zenodo.org/record/4030677/
This model was trained by Shinji Watanabe using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESP... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/Shinji_Watanabe_librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'Shinji_Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanab... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `Shinji Watanabe/open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4630406/
This model was trained by Shinji Watanabe using gigaspeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
#... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["gigaspeech"]} | espnet/Shinji_Watanabe_open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:gigaspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-gigaspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'Shinji Watanabe/open_li52_asr_train_asr_raw_bpe7000_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using gigaspeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'Shinji Watanabe/open_li52_asr_train_asr_raw_bpe7000_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using gigaspeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-gigaspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'Shinji Watanabe/open_li52_asr_train_asr_raw_bpe7000_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using gigaspe... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4585546/
This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["spgispeech"]} | espnet/Shinji_Watanabe_spgispeech_asr_train_asr_conformer6_n_fft512_hop_lengt-truncated-f1ac86 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:spgispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-spgispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using spgispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arX... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-spgispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.URL'\n️ Imported from URL\n\nThis model was train... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4585558/
This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
#... | {"language": "en_unnorm", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["spgispeech"]} | espnet/Shinji_Watanabe_spgispeech_asr_train_asr_conformer6_n_fft512_hop_lengt-truncated-a013d0 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:spgispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en_unnorm"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-spgispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using spgispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.URL'\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using spgispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-spgispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji_Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.URL'\n️ Imported from URL\n\nThis model was tr... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw`
This model was trained by Wangyou Zhang using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/chime4/enh1
./run.sh --skip_data_prep fal... | {"license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["chime4"]} | espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:chime4",
"arxiv:1804.00015",
"arxiv:2011.03706",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015",
"2011.03706"
] | [] | TAGS
#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us
|
## ESPnet2 ENH model
### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw'
This model was trained by Wangyou Zhang using chime4 recipe in espnet.
### Demo: How to use in ESPnet2
## ENH config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ENH config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor ar... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: How t... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer`
This model was trained by Yushi Ueda using iemocap recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout dfa2868243a897c2a6c34b7407eaea5e4b5508a5
pip install -e .
c... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iemocap"]} | espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:iemocap",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/YushiUeda\_iemocap\_sentiment\_asr\_train\_asr\_conformer'
This model was trained by Yushi Ueda using iemocap recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Feb 17 11:25:22 EST 2022'
* python version: '... | [
"### 'espnet/YushiUeda\\_iemocap\\_sentiment\\_asr\\_train\\_asr\\_conformer'\n\n\nThis model was trained by Yushi Ueda using iemocap recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Feb 17 11:25:22 EST 2022'\n* python version: '3.7.11 (d... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_iemocap\\_sentiment\\_asr\\_train\\_asr\\_conformer'\n\n\nThis model was trained by Yushi Ueda using iemocap recipe in espnet.",
"### Demo: How to use in ESPnet2... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_hubert`
This model was trained by Yushi Ueda using iemocap recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout dfa2868243a897c2a6c34b7407eaea5e4b5508a5
pip install... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iemocap"]} | espnet/YushiUeda_iemocap_sentiment_asr_train_asr_conformer_hubert | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:iemocap",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/YushiUeda\_iemocap\_sentiment\_asr\_train\_asr\_conformer\_hubert'
This model was trained by Yushi Ueda using iemocap recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sat Feb 12 23:11:32 EST 2022'
* python ve... | [
"### 'espnet/YushiUeda\\_iemocap\\_sentiment\\_asr\\_train\\_asr\\_conformer\\_hubert'\n\n\nThis model was trained by Yushi Ueda using iemocap recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sat Feb 12 23:11:32 EST 2022'\n* python version: '... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-iemocap #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_iemocap\\_sentiment\\_asr\\_train\\_asr\\_conformer\\_hubert'\n\n\nThis model was trained by Yushi Ueda using iemocap recipe in espnet.",
"### Demo: How to use i... |
null | espnet |
## ESPnet2 DIAR model
### `espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best`
This model was trained by YushiUeda using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 650472b45a67612eaac09c7fbd61dc25f8ff2405... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["mini_librispeech"]} | espnet/YushiUeda_mini_librispeech_diar_train_diar_raw_valid.acc.best | null | [
"espnet",
"audio",
"diarization",
"dataset:mini_librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #diarization #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 DIAR model
------------------
### 'espnet/YushiUeda\_mini\_librispeech\_diar\_train\_diar\_raw\_valid.URL'
This model was trained by YushiUeda using mini\_librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Jan 4 16:43:34 EST 2022'
* p... | [
"### 'espnet/YushiUeda\\_mini\\_librispeech\\_diar\\_train\\_diar\\_raw\\_valid.URL'\n\n\nThis model was trained by YushiUeda using mini\\_librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Jan 4 16:43:34 EST 2022'\n* python ver... | [
"TAGS\n#espnet #audio #diarization #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/YushiUeda\\_mini\\_librispeech\\_diar\\_train\\_diar\\_raw\\_valid.URL'\n\n\nThis model was trained by YushiUeda using mini\\_librispeech recipe in espnet.",
"### Demo: How to use in ES... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.acc.best`
♻️ Imported from https://zenodo.org/record/5154341/
This model was trained by Yushi Ueda using ksponspeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: Ho... | {"language": "kr", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["ksponspeech"]} | espnet/Yushi_Ueda_ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256-truncated-eb42e5 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"kr",
"dataset:ksponspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"kr"
] | TAGS
#espnet #audio #automatic-speech-recognition #kr #dataset-ksponspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.URL'
️ Imported from URL
This model was trained by Yushi Ueda using ksponspeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using ksponspeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #kr #dataset-ksponspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.URL'\n️ Imported from URL\n\nThis model was train... |
null | espnet | ## ESPnet2 DIAR pretrained model
### `Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best`
♻️ Imported from https://zenodo.org/record/5264020/
This model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speaker-diarization"], "datasets": ["mini_librispeech"]} | espnet/Yushi_Ueda_mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best | null | [
"espnet",
"audio",
"speaker-diarization",
"en",
"dataset:mini_librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #speaker-diarization #en #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 DIAR pretrained model
### 'Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.URL'
️ Imported from URL
This model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 DIAR pretrained model",
"### 'Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using mini_librispeech/diar1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #speaker-diarization #en #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 DIAR pretrained model",
"### 'Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.URL'\n️ Imported from URL\n\nThis model was trained by Yushi Ueda using mini_l... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `akreal/espnet2_swbd_da_hubert_conformer`
This model was trained by Pavel Denisov using swbd_da recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 08c6efbc6299c972301236625f9abafe087c9f9c
pip install -e .
cd egs2/swbd_da/a... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["swbd_da"]} | espnet/akreal_swbd_da_hubert_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:swbd_da",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-swbd_da #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'akreal/espnet2\_swbd\_da\_hubert\_conformer'
This model was trained by Pavel Denisov using swbd\_da recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Jan 20 19:31:21 CET 2022'
* python version: '3.8.12 (default, ... | [
"### 'akreal/espnet2\\_swbd\\_da\\_hubert\\_conformer'\n\n\nThis model was trained by Pavel Denisov using swbd\\_da recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Jan 20 19:31:21 CET 2022'\n* python version: '3.8.12 (default, Aug 30 202... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-swbd_da #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'akreal/espnet2\\_swbd\\_da\\_hubert\\_conformer'\n\n\nThis model was trained by Pavel Denisov using swbd\\_da recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n===... |
audio-to-audio | espnet |
# ESPnet2 ENH pretrained model
## `anogkongda/librimix_enh_train_raw_valid.si_snr.ave`
♻️ Imported from <https://zenodo.org/record/4480771#.YN70WJozZH4>
This model was trained by anogkongda using librimix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["librimix"], "inference": false} | espnet/anogkongda-librimix_enh_train_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"audio-source-separation",
"audio-to-audio",
"en",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
# ESPnet2 ENH pretrained model
## 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'
️ Imported from <URL
This model was trained by anogkongda using librimix recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
### Training config
See full config in 'URL'
| [
"# ESPnet2 ENH pretrained model",
"## 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n\n️ Imported from <URL\nThis model was trained by anogkongda using librimix recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"### Training config\n\nSee full config in 'URL... | [
"TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ENH pretrained model",
"## 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n\n️ Imported from <URL\nThis model was trained by anogkongda using librimix recipe... |
audio-to-audio | espnet | ## Example ESPnet2 ENH model
### `anogkongda/librimix_enh_train_raw_valid.si_snr.ave`
♻️ Imported from https://zenodo.org/record/4480771/
This model was trained by anogkongda using librimix/enh1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citi... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-enhancement", "audio-to-audio"], "datasets": ["librimix"]} | espnet/anogkongda_librimix_enh_train_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"speech-enhancement",
"audio-to-audio",
"en",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ENH model
### 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'
️ Imported from URL
This model was trained by anogkongda using librimix/enh1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ENH model",
"### 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by anogkongda using librimix/enh1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #speech-enhancement #audio-to-audio #en #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ENH model",
"### 'anogkongda/librimix_enh_train_raw_valid.si_snr.ave'\n️ Imported from URL\n\nThis model was trained by anogkongda using librimix/enh1 recipe i... |
null | espnet |
## ESPnet2 ST model
### `espnet/brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix`
This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 77fce65312877a132bbae01917ad26b74... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-translation"], "datasets": ["iwslt22_dialect"]} | espnet/brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix | null | [
"espnet",
"audio",
"speech-translation",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ST model
----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_st\_transformer\_fisherlike\_4gpu\_bbins16m\_fix'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Feb 8 13:29:21 ES... | [
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_st\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 13:29:21 EST 202... | [
"TAGS\n#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_st\\_transformer\\_fisherlike\\_4gpu\\_bbins16m\\_fix'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### ... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug`
This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 77fce65312877a132bbae... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iwslt22_dialect"]} | espnet/brianyan918_iwslt22_dialect_train_asr_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_train\_asr\_conformer\_ctc0.3\_lr2e-3\_warmup15k\_newspecaug'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Feb... | [
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Feb 2 05:... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_asr\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect reci... |
null | espnet |
## ESPnet2 ST model
### `espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug`
This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 77fce65312877a132bbae01... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speech-translation"], "datasets": ["iwslt22_dialect"]} | espnet/brianyan918_iwslt22_dialect_train_st_conformer_ctc0.3_lr2e-3_warmup15k_newspecaug | null | [
"espnet",
"audio",
"speech-translation",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ST model
----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_train\_st\_conformer\_ctc0.3\_lr2e-3\_warmup15k\_newspecaug'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Feb 8 ... | [
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 12:5... | [
"TAGS\n#espnet #audio #speech-translation #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_train\\_st\\_conformer\\_ctc0.3\\_lr2e-3\\_warmup15k\\_newspecaug'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espne... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/brianyan918_iwslt22_dialect_transformer_fisherlike`
This model was trained by Brian Yan using iwslt22_dialect recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 77fce65312877a132bbae01917ad26b74f6e2e14
pip install ... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["iwslt22_dialect"]} | espnet/brianyan918_iwslt22_dialect_transformer_fisherlike | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:iwslt22_dialect",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/brianyan918\_iwslt22\_dialect\_transformer\_fisherlike'
This model was trained by Brian Yan using iwslt22\_dialect recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Jan 31 10:15:38 EST 2022'
* python versi... | [
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_transformer\\_fisherlike'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Jan 31 10:15:38 EST 2022'\n* python version: '3.8.1... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-iwslt22_dialect #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/brianyan918\\_iwslt22\\_dialect\\_transformer\\_fisherlike'\n\n\nThis model was trained by Brian Yan using iwslt22\\_dialect recipe in espnet.",
"### Demo: How to use in... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp`
♻️ Imported from https://huggingface.co/
This model was trained by byan using librispeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPne... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/byan_librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_ac-truncated-68a97b | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp'
️ Imported from URL
This model was trained by byan using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp'\n️ Imported from URL\n\nThis model was trained by byan using librispeech/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp'\n️ Imported from URL\n\nThis model was ... |
audio-to-audio | espnet |
# ESPnet2 ENH pretrained model
## `Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en`
♻️ Imported from <https://zenodo.org/record/4498562#.YOAOApozZH4>.
This model was trained by Chenda Li using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```tex... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["wsj0_2mix"], "inference": false} | espnet/chenda-li-wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"audio-source-separation",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us
|
# ESPnet2 ENH pretrained model
## 'Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en'
️ Imported from <URL
This model was trained by Chenda Li using wsj0_2mix recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Training config
See full config in 'U... | [
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
"### Training config\... | [
"TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using... |
audio-to-audio | espnet |
# ESPnet2 ENH pretrained model
## `Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en`
♻️ Imported from <https://zenodo.org/record/4498554#.YOAOEpozZH4>.
This model was trained by Chenda Li using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["wsj0_2mix"], "inference": false} | espnet/chenda-li-wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave | null | [
"espnet",
"audio",
"audio-source-separation",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us
|
# ESPnet2 ENH pretrained model
## 'Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en'
️ Imported from <URL
This model was trained by Chenda Li using wsj0_2mix recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Training config
See full config in 'URL'
... | [
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0_2mix recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
"### Training config\n\nSe... | [
"TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-wsj0_2mix #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ENH pretrained model",
"## 'Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Chenda Li using wsj0... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer`
This model was trained by ftshijt using puebla_nahuatl recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/puebla_nahuatl/asr1
./run.sh --skip_data_prep false... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["puebla_nahuatl"]} | espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:puebla_nahuatl",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-puebla_nahuatl #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/ftshijt\_espnet2\_asr\_puebla\_nahuatl\_transfer'
This model was trained by ftshijt using puebla\_nahuatl recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Nov 7 18:16:55 EST 2021'
* python version: '3.9.7... | [
"### 'espnet/ftshijt\\_espnet2\\_asr\\_puebla\\_nahuatl\\_transfer'\n\n\nThis model was trained by ftshijt using puebla\\_nahuatl recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 18:16:55 EST 2021'\n* python version: '3.9.7 (default... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-puebla_nahuatl #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/ftshijt\\_espnet2\\_asr\\_puebla\\_nahuatl\\_transfer'\n\n\nThis model was trained by ftshijt using puebla\\_nahuatl recipe in espnet.",
"### Demo: How to use in ESPnet2\... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_totonac_transformer`
This model was trained by ftshijt using totonac recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/totonac/asr1
./run.sh --skip_data_prep false --skip_train true... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["totonac"]} | espnet/ftshijt_espnet2_asr_totonac_transformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:totonac",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-totonac #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/ftshijt\_espnet2\_asr\_totonac\_transformer'
This model was trained by ftshijt using totonac recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Nov 7 09:22:09 EST 2021'
* python version: '3.9.7 (default, Se... | [
"### 'espnet/ftshijt\\_espnet2\\_asr\\_totonac\\_transformer'\n\n\nThis model was trained by ftshijt using totonac recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Nov 7 09:22:09 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021, ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-totonac #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/ftshijt\\_espnet2\\_asr\\_totonac\\_transformer'\n\n\nThis model was trained by ftshijt using totonac recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer`
This model was trained by ftshijt using yolo_mixtec recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/yolo_mixtec/asr1
./run.sh --skip_data_prep false --ski... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["yolo_mixtec"]} | espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:yolo_mixtec",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-yolo_mixtec #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/ftshijt\_espnet2\_asr\_yolo\_mixtec\_transformer'
This model was trained by ftshijt using yolo\_mixtec recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Nov 10 02:59:39 EST 2021'
* python version: '3.9.7 (... | [
"### 'espnet/ftshijt\\_espnet2\\_asr\\_yolo\\_mixtec\\_transformer'\n\n\nThis model was trained by ftshijt using yolo\\_mixtec recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Nov 10 02:59:39 EST 2021'\n* python version: '3.9.7 (default, ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-yolo_mixtec #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/ftshijt\\_espnet2\\_asr\\_yolo\\_mixtec\\_transformer'\n\n\nThis model was trained by ftshijt using yolo\\_mixtec recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nR... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `ftshijt/mls_asr_transformer_valid.acc.best`
♻️ Imported from https://zenodo.org/record/4458452/
This model was trained by ftshijt using mls/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```Bib... | {"language": "es", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mls"]} | espnet/ftshijt_mls_asr_transformer_valid.acc.best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"es",
"dataset:mls",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"es"
] | TAGS
#espnet #audio #automatic-speech-recognition #es #dataset-mls #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'ftshijt/mls_asr_transformer_valid.URL'
️ Imported from URL
This model was trained by ftshijt using mls/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'ftshijt/mls_asr_transformer_valid.URL'\n️ Imported from URL\n\nThis model was trained by ftshijt using mls/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #es #dataset-mls #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'ftshijt/mls_asr_transformer_valid.URL'\n️ Imported from URL\n\nThis model was trained by ftshijt using mls/asr1 recipe in espnet.",
"### Demo: How to ... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `jv_openslr35`
♻️ Imported from https://zenodo.org/record/5090139/
This model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inprocee... | {"language": "jv", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["jv_openslr35"]} | espnet/jv_openslr35 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"jv",
"dataset:jv_openslr35",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"jv"
] | TAGS
#espnet #audio #automatic-speech-recognition #jv #dataset-jv_openslr35 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'jv_openslr35'
️ Imported from URL
This model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'jv_openslr35'\n️ Imported from URL\n\nThis model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #jv #dataset-jv_openslr35 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'jv_openslr35'\n️ Imported from URL\n\nThis model was trained by jv_openslr35 using jv_openslr35/asr1 recipe in espnet.",
"### Demo: How to... |
automatic-speech-recognition | espnet |
# ESPnet2 ASR pretrained model
## `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
♻️ Imported from <https://zenodo.org/record/3957940#.YN7zwJozZH4>
This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# comin... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mini-an4"]} | espnet/kamo-naoyuki-mini_an4_asr_train_raw_bpe_valid.acc.best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:mini-an4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-mini-an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
# ESPnet2 ASR pretrained model
## 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'
️ Imported from <URL
This model was trained by kan-bayashi using jsut/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
### Training config
See full config in 'URL'
| [
"# ESPnet2 ASR pretrained model",
"## 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n\n️ Imported from <URL\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"### Training config\n\nSee full config in 'UR... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-mini-an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ASR pretrained model",
"## 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n\n️ Imported from <URL\nThis model was trained by kan-bayashi using jsut/tts1 recipe in espnet... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/aishell_conformer`
♻️ Imported from https://zenodo.org/record/4105763/
This model was trained by kamo-naoyuki using aishell/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["aishell"]} | espnet/kamo-naoyuki_aishell_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:aishell",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/aishell_conformer'
️ Imported from URL
This model was trained by kamo-naoyuki using aishell/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/aishell_conformer'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using aishell/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-aishell #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/aishell_conformer'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using aishell/asr1 recipe in espnet.",
"### Demo: H... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4414883/
This model was trained by kamo-naoyuki using chime4/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
#... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["chime4"]} | espnet/kamo-naoyuki_chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.acc.ave | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:chime4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using chime4/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using chime4/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-chime4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using chime4/... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4415021/
This model was ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["dirha_wsj"]} | espnet/kamo-naoyuki_dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scp-truncated-2fd1f8 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:dirha_wsj",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-dirha_wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using dirha_... | [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-dirha_wsj #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_spee... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4430974/
This model was trained by kamo-naoyuki using hkust/asr1 recipe in [espnet](https://github.com/espnet/espnet/)... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["hkust"]} | espnet/kamo-naoyuki_hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20-truncated-934e17 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:hkust",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-hkust #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using hkust/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using hkust/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-hkust #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.URL'\n️ Imported from URL\n\nThis mo... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4543003/
This model was trained ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend-truncated-55c091 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/as... | [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using li... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_b... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4543018/
This model was trained ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend-truncated-b76af5 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/as... | [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using li... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_b... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4541452/
This model was trained by kamo-naoyuki using librispeech/asr... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer5_raw_bpe5000_schedule-truncated-c8e5f9 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.
### Demo: How to... | [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_ac... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.ave`
♻️ Imported from https://zenodo.org/record/4604066/
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in [es... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/kamo-naoyuki_librispeech_asr_train_asr_conformer6_n_fft512_hop_length2-truncated-a63357 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.
### Demo: How to use in ESPnet2... | [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using librispeech/asr1 recipe in espnet.",
"### Demo: How ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_va... |
automatic-speech-recognition | espnet | ## Example ESPnet2 ASR model
### `kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3957940/
This model was trained by kamo-naoyuki using mini_an4/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
##... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["mini_an4"]} | espnet/kamo-naoyuki_mini_an4_asr_train_raw_bpe_valid.acc.best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:mini_an4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-mini_an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 ASR model
### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'
️ Imported from URL
This model was trained by kamo-naoyuki using mini_an4/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using mini_an4/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-mini_an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.URL'\n️ Imported from URL\n\nThis model was trained by kamo-naoyuki using mini_an4/asr1 recipe in esp... |
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